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Discussion in ' Tech Talk ' started by MojitoLady , Apr 23, 2022 .

MojitoLady Expand Collapse Member

Hello All RV Life Friends! My first time posting and asking a question. I need a good reliable wifi/internet while traveling. The free wifi at campgrounds is not secure and just too weak (although, I very much appreciate it's even provided and free at that) and I need my phone too much to use a hotspot. There are so many options out there but to narrow it down I've been reading the reviews on a service called Travel Data. Similar to Nomad, however, TD's customer service seems much better and their plans are much more affordable. But before I jump in, I wanted to reach out to my RV Life Friends for any feedback or suggestions. I am not looking to buy multiple sim cards, that is not feasible for me. I need reliable wifi for downloading files and streaming and doesn't manage or throttle. Thanks in advance for your help!  

Rollin Ollens Expand Collapse Member

I have been using a mobile wifi device for a number of years now. I began with AT&T but we discovered that their service was very poor in many areas even though their map appeared to cover more area that others. We traveled mostly in the west and many places we visited had no or very poor reception. We switched to Verizon and, although not as good as we expected, it was still far better than AT&T. We paid a few dollars per month more but at least we had a service that was more reliable. We are retired so "great" service is not critical. We have friends that operate a business while on the move and they have a satellite service. They are very pleased but I'm, sure that type of service will be pricey. If you are primarily going to be east of the Mississippi I think any of the big four will be able to cover your needs. FYI, Our experience does not cover east of the Big Muddy but I am sure others will be chipping in with their experience. I'm including a link that discusses coverage for the big four if it will help you. https://www.letstalk.com/cellphones/guides/best-coverage-map/#att Good luck. Let us know which way you go. Darrell  

Rollin Ollens said: ↑ I have been using a mobile wifi device for a number of years now. I began with AT&T but we discovered that their service was very poor in many areas even though their map appeared to cover more area that others. We traveled mostly in the west and many places we visited had no or very poor reception. We switched to Verizon and, although not as good as we expected, it was still far better than AT&T. We paid a few dollars per month more but at least we had a service that was more reliable. We are retired so "great" service is not critical. We have friends that operate a business while on the move and they have a satellite service. They are very pleased but I'm, sure that type of service will be pricey. If you are primarily going to be east of the Mississippi I think any of the big four will be able to cover your needs. FYI, Our experience does not cover east of the Big Muddy but I am sure others will be chipping in with their experience. I'm including a link that discusses coverage for the big four if it will help you. https://www.letstalk.com/cellphones/guides/best-coverage-map/#att Good luck. Let us know which way you go. Darrell Click to expand...

Fitzjohnfan Expand Collapse Member

If you are a member of FMCA, you might want to look into their Sprint/Verizon MiFi. I'm not saying its good or bad, but lots of the members there have gotten it and received various results  

NYDutch Expand Collapse Member

We travel mostly in the east now, and we've found having cell service with both AT&T and Verizon has worked well for us everywhere we've been. One or the other service has always been good, and many times both are acceptable. We use separate hotspots rather than our phones. Our AT&T unlimited data only service plan is no longer available, although we're grandfathered in at $23.50/mo. Our Verizon service is through their Visible division's unlimited talk, text, and data service at $25/mo with "Party Pay". We use it only for data, and the maximum speed is throttled to 5 Mbps which we find works reasonably well for moderate resolution streaming.  

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Travel Analytics: Data Sources, Use Cases, and Real-Life Examples

• 11 min read
• Data Science ,   Travel
• 19 Sep, 2023
• No comments Share

In the continuously changing travel industry, data-driven insights are revolutionizing decision-making processes. Beyond its fundamental role in elevating customer experience, travel analytics has a valuable impact on revenue growth and cultivating a competitive edge. This article explores the influence of analytics on marketing strategies, revenue management, guest personalization, and other aspects of the travel industry. We will examine key data sources, real-world use cases, and how to approach analytics in travel.

Internal vs. external travel data sources

At the core of effective travel analytics lies the fundamental requirement for diverse and robust data from internal and external sources.

Internal travel data sources

Internal data refers to the proprietary information generated and collected by an organization as a result of its day-to-day operations and interactions with clients. It provides a comprehensive overview of a company's performance, highlighting both successes and areas needing improvement. By harnessing internal data sources, travel businesses can develop personalized services that cater to their customers' ever-evolving needs, as well as optimize their operations, enhance marketing strategies, and improve resource allocation, ultimately driving growth and profitability. Company’s booking website and mobile app allow you to track and collect a wealth of data, from web traffic information to user behavioral metrics (session duration, navigation paths, etc.), geolocation, profiles, and feedback. Internal systems — like a property management system (PMS) in hospitality, a passenger service system (PSS) used by airlines, or an OTA back office — are the backbone and the most relevant source of transaction details, booking histories, and inventory data for a travel business. Learn more about the functions of internal systems with our dedicated articles about hotel property management systems and passenger service systems . Customer relationship management (CRM) systems contain customer profiles with demographic information like age, gender, location, travel preferences, contact information, and communication history. They record and track customer and company interactions, spanning various communication channels like emails, chats, and phone calls. Loyalty program data can also be part of CRM records with information on membership tiers or rewards earned.

External travel data sources and providers

External data encompasses all types of information and datasets created outside a company and existing beyond its direct control, ownership, or management. Analyzing external data helps travel businesses stay competitive, adapt to shifting market conditions, and make well-informed choices regarding pricing, promotion, and overall strategies. Other travel booking platforms are the main source of information on competitors' offerings.   By monitoring and comparing them, you can identify popular services, destinations, and accommodations. This approach culminates in inventory optimization and competitive pricing strategies. Social media and travel blogs contain valuable information about travelers' experiences, recommendations, preferences, and popular upcoming events. These online platforms include customer reviews and user comments, covering everything from current trends to destination-specific information. Economic indicators cover a wide range of statistics that reflect the economic state of a region, country, or industry. They can be indicators of inflation, exchange rates, unemployment, and consumer spending. For the travel industry, this data is essential for understanding economic conditions that can impact travel patterns and consumer behavior. Travel data providers offer a diverse range of ready-to-use data for hotels, travel agencies, and other stakeholders . For instance, a notable expert in this domain, OAG , has a suite of air-related datasets, including historical flight data spanning 20 years. Businesses selling vacation rentals can take advantage of data collection from AirDNA , Mashvisor , and others. Read our article to find a more detailed exploration of short-term and vacation rental data . Other external data sources that can be helpful when building travel analytics solutions are weather forecasts and reports, and information on events that take place in a particular location.

The importance of the events economy in the travel industry.

4 travel analytics use cases

4 travel analytics use cases.

1. Revenue management

Revenue management is a set of practices for maximizing revenue by setting optimal prices based on demand. Many factors should be considered to find the right balance between rates and current seat availability, room occupancy levels, etc. That’s where modern analytical methods can help you out. Demand forecasting with machine learning allows companies to spot the slightest shifts in consumer behavior and predict a probable future demand for a product or service. For instance, businesses can prepare for more customers when flight bookings intensify for a specific destination. They can adjust prices and create appealing travel packages. Read our dedicated article to learn more about demand forecasting methods . Price prediction empowers companies to maintain a competitive edge in the market by establishing optimal rates. Learn about our experience in building machine learning models for flight price prediction and hotel price prediction . Occupancy rate prediction is crucial for making data-based decisions on pricing strategy, planned maintenance, and staff scheduling. Accurate forecasts allow revenue managers to quickly identify periods of increased or decreased interest, for example, months in which rooms will be sold at a higher or lower price. To gain further insights on this topic, take a look at our article that discusses occupancy rate prediction using machine learning . Dynamic pricing is the process of setting the price of a product or service in response to current demand, market trends, and other factors. Demand forecasting, price prediction, and occupancy rate prediction can be part of a dynamic pricing approach, helping revenue managers maximize profits in ever-changing conditions.

2. Sentiment analysis

With the widespread adoption of social media, travelers often share their experiences and recommendations, providing businesses with information about customers’ perceptions and preferences. This data can be used to run sentiment analysis. Sentiment analysis employs natural language processing (NLP) , statistics, and machine learning to decipher emotions, opinions, and judgments in text by extracting subjective information. Also known as opinion mining, it categorizes sentiments as positive, negative, or neutral. This type of text analysis can pinpoint areas for adjustment and assist in making improvements in service quality and customer experience, ultimately enhancing the brand's reputation. Read our article about sentiment analysis in hotel reviews to learn how we generate instant feedback snapshots, enabling travelers to swiftly compare various options and make the optimal choice.

3. Personalizing customer experience

Over 70 percent of customers expect the companies they engage with to consider their unique needs and desires. By analyzing factors like age, goals, status, region, values, and interests, businesses can facilitate effective personalization strategies for the various consumer types. Recommender systems and trip planning tools are two popular examples of how analytics enable this process in travel. Recommender systems use machine learning algorithms to make real-time personalized offers that meet the needs of a particular traveler. They can also aid in sustainable travel by suggesting ways to reduce carbon emissions and mitigate over-tourism, offering places with fewer visitors. For a deeper understanding, read our article that covers recommender systems or watch our video about the main types of recommender systems.

Main types of recommender systems.

Trip planning  is another example of how analytics drive personalization in travel. It can be viewed as an advanced recommender system that generates an itinerary and recommends the best order of places to visit based on a traveler’s preferences, behavior, and other data. While some platforms go no further than proposing accommodations or ticket booking, more advanced services offer comprehensive trip arrangements, saving both time and money.

4. Risk management

In the travel industry, challenges can arise from various unexpected events. It’s important to surveil such situations and spot potential disruptions. Fraud detection driven by analytics helps pinpoint subtle and hidden events in user behavior that can signal possible risks, such as fake bookings and chargebacks. To counter these risks, besides analytical tools, implement travel fraud prevention measures, including additional verification steps and raising the company staff's awareness of the fraud risk. Read our article on ML-assisted fraud detection or watch this video:

Fraud detection: Fighting financial crime with machine learning.

Guest screening is another application of travel analytics, involving the processing of guest information, booking details, and historical behavior patterns. In this examination, each reservation is assigned a risk score — a quantitative representation of the potential risk associated with that particular reservation. It takes into account factors such as the likelihood of property damage, the risk of non-payment, and the potential for illegal activities. Guest screening equips hoteliers with the insights needed to make informed decisions regarding reservation acceptance. Identifying potential issues before guests arrive enables you to create a secure environment and mitigate risks that can impact the property's safety, reputation, and financial stability. Flight arrival and departure time prediction is a powerful method for mitigating delay scenarios. Machine learning algorithms consider various factors like weather, arrival gates, aircraft type, and more, enabling airlines and ground handlers to better allocate gates, swap aircraft or crews on particular flights as needed, and thus minimize tarmac wait times. Predictive maintenance utilizes machine learning to process data from IoT sensors and detect early signs of equipment failures. Due to this analytical method, airlines, car rental companies, and other businesses can address small problems before they develop into costly damages. For example, in aviation, predictive maintenance reduces the need to pull the aircraft out of service, helps optimize repair schedules, prevents unexpected faults, and insures against flight delays.

Real-life examples of successful travel analytics implementation

Here, we're sharing real-life examples of notable brands leveraging analytics to achieve impressive results in customer engagement, cost reduction, and operational efficiency.

Marriott: Bringing more personalization to the loyalty program

Marriott International, headquartered in Bethesda, Maryland, is a renowned American multinational company that operates, franchises, and licenses a diverse portfolio of lodging options, including hotels, residential properties, and timeshares. Marriott began as an A&W Root Beer franchise in Washington, DC, in 1927. Thirty years later, the company expanded into the lodging industry, marking a significant milestone. To drive analytics, Mariotte partnered with IBM. After migrating to a modern data stack , Marriott deployed Db2 Warehouse on Cloud , which allowed IBM data scientists to map comprehensive customer data across all Marriott brands and build a travel experience platform that now powers Marriott Bonvoy, the industry's largest loyalty program with over 140 million members. Each time customers redeem their loyalty points, the platform gains more data to better understand those travelers and market to them in a more personalized way.

American Airlines: Optimizing operations and reducing fuel consumption

American Airlines, founded almost 100 years ago, has a deep-rooted legacy of pioneering achievements, launching the first major carrier loyalty program , and introducing airport lounges. Headquartered in Fort Worth, Texas, it is the largest airline globally in terms of scheduled passengers carried and revenue passenger miles, a metric used to measure the total miles traveled by paying passengers. After transitioning its data platform to Microsoft Azure, American Airlines got a specific focus on optimizing operational efficiency. At the heart of its analytical efforts is an intelligent gating program that leverages real-time data on routes and runways and automatically assigns the nearest available gate to arriving aircraft. This enabled significant reductions in taxi time and fuel consumption, saving up to 10 hours of taxi time per day and thousands of gallons of jet fuel per year. American Airlines also developed the Hub Efficiency Analytics Tool (HEAT), an ML-driven program implemented to minimize the impact of weather-related flight delays. It analyzes various data points, including weather conditions, customer connections, air traffic, and gate availability. HEAT's proficiency in dynamically adjusting departure and arrival times has decreased flight cancellations during adverse weather conditions. All told, American Airlines' strategic embrace of AI, machine learning, and data analytics has led to notable improvements in operational efficiency, customer experience, and cost reductions.

Hilton: Personalization with data-driven smart suggestions

Hilton Worldwide Holdings Inc. is a renowned American multinational hospitality company headquartered in McLean, Virginia. From its inception in 1919, Hilton's goal was to operate the finest hotel in Texas. Commitment to excellence and innovation has positioned it as one of the world's most respected brands that has grown to operate in over 100 countries. Recognizing the growing importance of data in hospitality, the company is constantly investing in analytics to stay ahead in an ever-evolving market. For example, Hilton implemented in-page data capture technology by Celebrus , to accurately capture the smallest details of customer behavior on hotel sites. It allows for a better understanding of visitors and enhances their experience. Hilton also benefits from OnQ CRM, a proprietary system developed to handle large amounts of diverse customer data collected through various communication channels. With predictive analytics, the company can use these large volumes of data to forecast customer retention and better meet guest demands. Incorporating advanced data analytics and innovative technologies, Hilton has not only solidified its position as one of the global hospitality leaders but has also demonstrated its commitment to an exceptional guest experience.

Agoda: Enhancing user journey with OpenText Analytics

Agoda, founded in 2005, has revolutionized travel by simplifying the search and booking process. This Singapore-based OTA operates globally and is considered one of Asia's leading travel-tech companies. With millions of registered customers and over 6,000 employees across 31 markets, Agoda offers access to over 2 million properties worldwide and a range of travel-related services. Every day, the company's website serves millions of customers, which makes a frictionless booking journey and overall usability a major competitive factor. “Customers will stick with us if we provide high-quality products and a streamlined experience, and accommodation owners will offer optimized pricing if we give them the best marketplace," Idan Zalzberg, VP Data at Agoda, explains . To stay ahead of rivals, Agoda employed Vertica Analytics Platform (now owned by OpenText.) It enabled the OTA to analyze complex user behavior patterns in seconds and constantly improve the website based on insights from large volumes of data.

How to approach travel data analytics

The process of unlocking valuable insights from data involves the following key steps. Assemble a data science team. Building or hiring a skilled data science team with expertise in the travel domain is a fundamental step to take before the process even begins. Pay attention to the collaboration and communication skills of the team members since they must interact a lot with other stakeholders and be on the same page with them. Formulate key questions. Clearly define the problem your organization is going to solve with the help of analytics. Establish measurable goals and KPIs to gauge the success of your efforts. Articulating the anticipated results is critical for ensuring that all stakeholders have a shared understanding of the project's purpose and direction. Define data sources and collection methods. The next crucial phase is to gather the necessary data for analysis. The choice of data sources and collection methods should align with business objectives to ensure that the analysis is based on accurate and relevant information. Define the timeframe for data collection and create a clear plan with information on team assignments, relevant data sources, and key requirements. Choose analytical tools. Depending on the nature of the data and your business objectives, a variety of techniques may be employed, including machine learning, deep learning , natural language processing (NLP), and computer vision . Discuss the most appropriate methods with your data science team. Communicate and visualize the results. This step ensures that stakeholders understand and can use the analytics results to make informed decisions. Engage with stakeholders in a way that resonates with their level of expertise and interest, maintaining clarity and transparency. To enhance effective storytelling, utilize BI tools like Power BI or Tableau, or Python libraries like Matplotlib and Seaborn. Read our article on data visualization libraries and tools to learn more. Consider implementing data governance. An effective data governance strategy ensures the accuracy, availability, and security of data, making it compliant with data regulations, such as the GDPR in the EU and CCPA in California. Data governance policies safeguard the company’s reputation and enhance both partner and customer trust. These steps are crucial to fully leverage the power of data in your travel operations and ensure that your business initiatives are geared towards making informed, customer-centric decisions.

Comparing online travel review platforms as destination image information agents

• Original Research
• Open access
• Published: 22 April 2021
• Volume 23 , pages 159–187, ( 2021 )

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• Xinxin Guo   ORCID: orcid.org/0000-0002-7904-3040 1 ,
• Juho Pesonen 1 &
• Raija Komppula 1  

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Online travel reviews have been extensively used as an important data source in tourism research. Typically, data for online travel review research is collected only from one platform. However, drawing definite conclusions based on single platform analyses may thus produce biases and lead to erroneous conclusions and decisions. Therefore, this research verifies whether or not there are discrepancies and commonalities between different travel review platforms. In this study, five native Chinese travel review platforms were selected: Ctrip; Qyer; Mafengwo; Tuniu; and Qunar. Using a mixed content analysis method, the destination image of Finland was extracted from 10,197 travel reviews in Simplified Chinese as the destination image is a popular topic in online review research. Results show Finland’s destination image representation varies between Chinese travel review platforms. This discrepancy is especially prominent in the dimension of functional and mixed functional-psychological destination attributes. Significant theoretical contributions and managerial implications for the analysis of online travel reviews and destination image research are discussed.

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1 Introduction

Today, online travel reviews (OTRs) have a huge influence on the tourist decision-making process, because they are often used when tourists compare various options and make travel-related decisions. OTRs are also an indicator of a destination’s post-visit destination image (DI) because tourists write reviews of their experiences based on the image they have after the trip (González-Rodríguez et al. 2016 ; Park et al. 2007 ). OTRs are therefore gaining increasing attention in tourism research and destination marketing. Meanwhile, DI is increasingly analyzed using online textual data instead of other data collection methods such as interviews (Lu and Stepchenkova 2015 ). New analysis methods based on big data allow us to gain in-depth knowledge from this vast social media data ocean (Fazzolari and Petrocchi 2018 ).

Earlier studies involving OTRs have relied on a single data source (Xiang et al. 2017 ). In using a single data source for OTR research, researchers ignore platform-specific biases such as differences in platform design, user base, platform-specific behavior, and storage strategy (Pfeffer 2014 ). Using a single platform is also a potential source of sampling bias that potentially complicates the interpretation of the research findings (Tufekci 2014 ). This study aims to explore whether or not platform-specific biases in OTRs should be accounted for in tourism research and practice, and if so how. Moreover, earlier studies have mainly used statistical analyses, natural language processing techniques, or algorithms to explore the length of the review, frequently words, topics, and review sentiment (Xiang et al. 2017 ; Zhang and Cole 2016 ), or have analyzed the functional features of different websites (Pai et al. 2014 ).

Additionally, research on OTR platforms in tourism studies is still based largely on the Western context (Xiang et al. 2017 ). With the rapid growth of Chinese outbound tourists in recent years, scholars are increasingly focusing on China and other Asian countries. The exploration of Chinese social media platforms has become an important research venue (Sotiriadis and Sotiriadis 2017 ). However, cultural and language barriers mean research on Chinese OTR platforms is rarely published in English. Data from OTR platforms may, therefore, provide a new approach to destination image research among Chinese tourists.

To address these gaps in the previous research, this study makes an in-depth comparison of various native Chinese OTR platforms to identify their potential differences and universal attributes. The differences are analyzed by comparing the DI between platforms as the DI concept is an important topic in OTR analysis (Marine-Roig and Ferrer-Rosell 2018 ). Additionally, the aim was to explore the reasons for discrepancies and commonalities in the representation of the DI. An instrumental case study approach (Mills et al. 2013 ) was used in this study as we are interested in the differences of online platforms in representing DI instead of the case itself. Since the research team is familiar with Finland and tourism in the country, it was chosen as the case destination. Finland is also a relatively new destination for Chinese tourists, but growing fast before COVID-19 pandemic. This development aspect makes Finland an interesting case to study the phenomenon. However, we acknowledge that the context is secondary for this research compared to the phenomenon itself and the destination could have been virtually any another destination. The data was collected from five Chinese OTR platforms and analyzed using a mixed content analysis approach focusing on data referring to Finland as a tourist destination. A qualitative content analysis was used to formulate a DI coding manual (for use in the analysis) from part of the samples. A quantitative content analysis was then conducted to objectively extract Finland’s DI from the other OTRs’ data, based on the coding manual.

The structure of the paper is as follows: Chapter 2 presents previous social media studies of OTR platforms. Chapter 3 introduces the theoretical background of the DI framework. The methodology and results are presented in Chapters 4 and 5. A theoretical discussion and practical implications based on the results are presented in Chapter 6. The final chapter includes a conclusion, a discussion of the study’s limitations, and suggestions for future study.

2 Social media analytics on online travel review platforms

In 2018, the number of Chinese outbound tourists exceeded 149 million (iResearch 2019 ). The increasing outbound travel has also led to the increasing use of online travel review platforms in China. With the development of information technologies, China’s tourism information services now cover the pre-travel, on-travel, and post-travel processes (Pan et al. 2019 ). OTR platforms are especially prominent: 51.4% of outbound tourists obtain travel recommendations and information from Chinese OTR platforms (iResearch 2019 ). Besides, 71.6% of Chinese outbound tourists share travel experiences on Chinese social media, and 39.9% of tourists share travel experiences on OTR platforms (iResearch 2019 ). All the evidence indicates that OTR platforms are very important in any attempt to understand outbound Chinese tourists.

There is a vast amount of online information on OTR platforms, commonly known as “big data”. When researchers conduct DI studies based on shared online travel experiences, OTRs are regarded as a form of electronic word-of-mouth communication (eWOM) (Marine-Roig 2017 ). Although online reviews may be seen as unsolicited and unbiased online information that reflects the realistic tourist perception of the destination (Marine-Roig 2017 ), the OTR content given by different tourist segments has different focuses (Van der Zee and Bertocchi 2018 ). Nowadays, the application of OTRs in tourism research has received increasing attention, and researchers usually collect data from a single Western OTR platform, especially TripAdvisor, Yelp, or Expedia (Xiang et al. 2017 ).

Many researchers have adopted a single OTR platform approach in tourism studies (Xiang et al. 2017 ). The platform-specific biases of different OTR platforms mean that multi-platform data sources may be more valid in researching tourism phenomena. These biases are not only reflected in the platform design itself (Pai et al. 2014 ), but in the posting behavior of tourists and managers (Pfeffer 2014 ). The research already demonstrates that the major Western OTR platforms differ regarding the cost of reviewing (Chevalier et al. 2018 ; Zhuang et al. 2018 ), the review content posting behavior in terms of the number of reviews, the review length, customer preference, and sentiment, for example (Proserpio and Zervas 2017 ; Wang and Chaudhry 2018 ; Xiang et al. 2017 ).

Researchers are thus well aware of the differences between major Western OTR platforms. However, although we often use OTR platforms for destination image analytics (Marine-Roig and Ferrer-Rosell 2018 ), how the OTR platform itself affects DI analytics remains unknown. It is imperative to understand how the DI differs between different platforms or whether there is a difference at all. We, therefore, compare Chinese OTR platforms and analyze the results of online DI analytics from major Chinese OTR platforms.

3 The framework of the destination image

The usual definition of DI is the sum of a person’s beliefs, ideas and impressions of a destination (Crompton 1979 ). It is formed in a process in which personal, sociocultural, and information technology factors (Beerli and Martin 2004 ; Josiassen et al. 2015 ; Kislali et al. 2016 ; San Martín and Del Bosque 2008 ), as well as stimulus factors (e.g., information sources, previous experience of the destination) affect the formation of the image (Gartner 1993 ). According to Gartner ( 1993 ), destination information can be regarded as a continuum of various image formation agents, ranging from traditional forms of the induced agent to autonomous and organic image agents. “Induced agents” refers to the information provided by commercial destination actors representing the supply-side view of DI as the projected image (Mak 2017 ). “Autonomous image agents” refers to information sources which are not under the control of the destination organizations, referring to news, movies, and documentaries, for example (Gartner 1993 ). “Organic image agents” refers to information sources based on a visit to the area (Gartner 1993 ).

With the development of information technology, induced and organic image formation agents are not necessarily mutually exclusive but may complement each other (Selby and Morgan 1996 ). One view is that the Internet can be seen as an induced information agent in the image formation process (Beerli and Martin 2004 ). The opposing view is that the previous point is outdated in the modern online environment, and the different online travel platforms (such as official tourism websites, travel blog platforms, or travel review platforms) on the Internet can be classified as induced, autonomous, or organic information agents (Llodrà-Riera et al. 2015 ). Online destination information can, therefore, be regarded as an agent of induced or organic image formation, both of which play a significant role in the image formation process (Llodrà-Riera et al. 2015 ). Besides, when tourists obtain destination information from different online travel platforms, there may be a discrepancy between the destination images based on official tourism website content (induced), travel blog platform content (autonomous), and travel review platform content (organic) (Mak 2017 ; Marine-Roig and Ferrer-Rosell 2018 ). Perceptions of official tourism website content (induced) and travel blog platform content (autonomous) differ less from each other (Marine-Roig and Ferrer-Rosell 2018 ).

Due to the OTRs’ source credibility and information quality, travel review platforms as organic information agents are more unbiased and trustworthy than the induced information agents of official tourism websites (Filieri et al. 2015 ). The assessment of DI formation based on OTR data is, therefore, becoming increasingly popular. In particular, understanding DI based on different OTR platform content may assist in exploring whether there is a DI discrepancy between different organic information agents. Mak ( 2017 ) used the term online destination image to depict “the online representation of the collective beliefs, knowledge, ideas, feelings and overall impressions of a destination.”

There are two main approaches to defining DI construction. One considers DI as a multidimensional construct with two main components: the cognitive image and the affective image of destinations (Baloglu and McCleary 1999 ). These two images respectively represent a tourist’s knowledge of the destination and their emotions based on their destination knowledge (Baloglu and McCleary 1999 ; Gartner 1993 ). The other most-cited construction is considering DI as a person’s overall evaluation of the destination, which includes attribute-based and holistic components (Echtner and Ritchie 1991 , 1993 ). Each component can be further subdivided into functional-psychological; or common-unique characteristics (Echtner and Ritchie 1991 , 1993 ). The attribute-holistic continuum illustrates whether the representation of DI is from the perspective of an individual attribute or a holistic aggregate. The functional-psychological continuum refers to functional (directly observable or measurable) or psychological (less tangible, difficult to measure) attributes. The common-unique continuum also refers to common characteristics, attributes, and impressions according to which destinations are commonly compared, or it refers to unique or destination-specific features (Echtner and Ritchie 1991 , 1993 ). By introducing a three-dimensional DI framework, Echtner and Ritchie ( 1993 ) developed a 35-item destination attribute scale, ranging from more functional attributes (including tourist sites/activities, national parks, and historic sites) and mixed destination attributes (including crowdedness, cleanliness, and political stability) to more psychological destination attributes (including hospitality, place atmosphere, and reputation).

Subsequently, some studies have proposed various scales to determine the destination attributes and measure the DI (Beerli and Martin 2004 ; Choi et al. 2007 ; Enright and Newton 2005 ; Gallarza et al. 2002 ; Marine-Roig 2017 ; Rodrigues et al. 2015 ; Vinyals-Mirabent 2019 ). In this study, we have combined Echtner and Ritchie’s ( 1993 ) functional-psychological attribute scales and Beerli and Martín’s ( 2004 ) attribute classification as an adapted framework (see Appendix 1 ) for data analysis. Echtner and Ritchie’s ( 1993 ) study placed 35-item destination attributes into a functional-psychological scale, which does not cover all the universal attributes in the destination. Therefore, another often cited destination attribute study by Beerli and Martín ( 2004 ) was applied for the adapted framework. Beerli and Martín’s ( 2004 ) study classified destination attributes into nine dimensions, but they did not distinguish the functional or psychological features of these attributes. For this reason we developed an adapted attribute framework which combines the advantages of Echtner and Ritchie’s ( 1993 ) and Beerli and Martín’s ( 2004 ) studies.

In order to build the adapted attribute framework, the first step was to place Echtner and Ritchie’s ( 1993 ) 35 identified destination attributes into Beerli and Martín’s ( 2004 ) destination attribute classifications. Then, according to the functional-psychological definition of the attribute given in Echtner and Ritchie’s ( 1993 ) study, the functional and psychological feature of the attribute classification were determined. For example, tourist sites, tourist activities, sports activities, national parks, and tourist entertainment were regarded as functional destination attributes in Echtner and Ritchie’s study ( 1993 ). In Beerli and Martín’s ( 2004 ) study, these attributes were classified as a tourism leisure dimension. Therefore, the tourism leisure dimension was considered a functional attribute after some research group discussion. Moreover, in the tourism leisure dimension, architecture and buildings, which were not covered in Echtner and Ritchie’s ( 1993 ) study but were identified in Beerli and Martín’s ( 2004 ) study, were also considered as a functional destination attribute. The adapted DI framework comprehensively illustrates the destination attributes from continuous functional to psychological characteristics in nine dimensions. The tourism leisure and recreation, natural resources, and tourism infrastructure dimensions are more related to the functional level. On the other hand, the dimensions of culture, history, art, general infrastructure, and natural environment belong to the mixed functional-psychological level. The abstract psychological attributes include politics and economics, the social environment, and the atmosphere of the place in question.

As socio-demographic and sociocultural factors (Beerli and Martin 2004 ; Josiassen et al. 2015 ; Kislali et al. 2016 ; San Martín and Del Bosque 2008 ) play an important role in the image formation process. It can be assumed that tourists with different cultural backgrounds may perceive the same destination attribute differently (Nakayama and Wan 2019 ). As most of the academic research on destination image analytics has been conducted Using western platforms, a short review of the literature focusing on Chinese tourists’ perceived images of Western destinations may highlight the dimensions of the image the Chinese tourists’ highlight. Chinese tourists retain different preferences for domestic and Western destinations (Li and Stepchenkova 2012 ; Wang and Hsu 2010 ). In domestic travel, the service quality attribute is the most important factor in shaping the DI (Wang and Hsu 2010 ). However, most Chinese tourists visiting Western countries share travel experiences concerning natural resources and local cultures (Huang and Gross 2010 ; Li and Stepchenkova 2012 ; Sun et al., 2015 ). Chinese tourists are also willing to discuss political and economic issues affecting Western destinations (Li and Stepchenkova 2012 ). To confirm judgements about Western destinations, Chinese outbound tourists tend to compare differences between a Western destination’s social systems and China’s (Huang and Gross 2010 ). Additionally, Chinese cultural norms play an important role in the process of perception formation and the interpretation of Western destinations (Sun et al. 2015 ). These cultural norms include the desire for harmony and respect for the authorities. The different cultural backgrounds of Chinese and Western tourists mean there may be significant differences in perceptions of the same destination (Tang et al. 2009 ). According to Kim and Morrison ( 2005 ), Chinese outbound tourists are more likely to change their perception of destinations in a short period than Western tourists.

4 Methodology

Adopting the mixed qualitative and quantitative content analysis approach, this study compared the representation of the image of Finland in different Chinese OTRs, to interpret the commonalities and discrepancies between various platforms. China has become one of the largest source markets in international tourism (UNWTO 2018 ), and this growth has also been witnessed in Finland. Between 2011 and 2018, the number of Chinese tourists visiting Finland increased by 323% (Statistics Finland 2019 ). In 2012, the Finnish national tourism office (VisitFinland) and the Finnish airline company Finnair established digital marketing strategies on Weibo, in China. Although the data reveals that the Nordic countries have great potential in the Chinese market, gaps and deficiencies remain in DI research in the Nordic countries (Andersen et al. 2018 ). Today, China has the largest market of Internet users, accounting for 21% of the worldwide total (Meeker 2019 ). With an increasing number of Chinese tourists sharing travel experiences online, the massive amount of information they generate provides researchers with a way of studying the DI of a European destination from the perspective of Chinese tourists. Even though the study uses Finland and China as examples, the results can be generalized to other market combinations.

In China, travel websites with review functions can be classified in two main categories: travel vertical platforms, such as Mafengwo, Qyer; and online tour agents (OTA), such as Ctrip, Tuniu, and Qunar (Graff and Parulis-Cook 2019 , p. 53). Vertical travel websites rely heavily on user-created content, and provide tourists with generated travel information and related travel-specific services (Graff and Parulis-Cook 2019 ; Kizmaz 2018 ). Chinese OTA websites provide many travel-related services including visa arrangements, tax refunds, and financial services, as well as travel information. Many Chinese OTA websites now also have a review function for users to share their comments about destinations. Before entering the detailed introduction of the research method, a flowchart (Fig.  1 ) summarizing the key information of the research process is shown below.

The flowchart of the research process

The flowchart shows the four main parts of the research process, namely data collection, data pre-processing, qualitative content analysis, and quantitative content analysis. The following chapters will introduce each of these steps in detail.

4.1 Data collection and pre-processing

Baidu is the largest online search engine in China. Its information center ( http://site.baidu.com/ ) displays 23 popular Chinese tourism websites. Manually typing “芬兰” (Finland) into the search engine of each tourism information website resulted in six websites with Chinese OTRs for Finland. These OTRs were on Qyer, Ctrip, Mafengwo, Tuniu, Qunar, and Maotuying (the Chinese version of TripAdvisor). On Maotuying, the OTRs in Chinese are translated from other languages. This platform was therefore excluded from the study. Table 1 displays background information about these five platforms. Figure  2 shows the format of the OTRs on different platforms. OTRs are basically comprised of four components: linguistic features; semantic features; sentiment; and reviewer information (Xiang et al. 2017 ). The differences in travel platform design mean that not all these features can be found on each platform (Xiang et al. 2017 ). For example, except for Tuniu, tourists can attach photos to OTRs on the other four platforms. On Qyer, Mafengwo, Tuniu, and Qunar tourists can comment on others’ OTRs. However, all the platforms contain basic review features (textual review content, ratings, and the release time) and reviewer’s information (nickname, profile photo).

The format of the OTRs on Qyer, Ctrip, Mafengwo, Tuniu, and Qunar

In Fig.  2 , the red box indicates the content of the OTRs. The black circle indicates the reply function.

Data collection was conducted using a web crawler, Octoparse, which was used to extract the required data information from the hypertext markup language on the travel review webpages. In this study, we collected only the textual review content, release date, and reviewer’s nickname. The collection process took place between early October and the end of December 2018. A total of 10,389 OTRs related to Finland were crawled by the setting crawling process. The textual OTR content includes descriptions of attractions, hotels, restaurants, entertainment activities, and others. Furthermore, only Simplified Chinese OTRs were considered in this study. After deleting 192 English and Traditional Chinese OTRs, a total of 10,197 Simplified Chinese OTRs were applied for further analysis. As Table 2 shows, Qyer had the largest number (3570) of OTRs, followed by Ctrip and Qunar. Mafengwo and Tuniu had a nearly equal number of OTRs and are the smallest platforms.

4.2 Data analysis

The content analysis approach is commonly adopted to analyze textual messages (Stepchenkova and Mills 2010 ). It can be used to compress many words into categories based on explicit coding rules (Harwood and Garry 2004 ). Most of the existing literature used either the qualitative or quantitative content analyses to study the perceived destination from OTRs, and the quantitative approach seems to be the mainstream choice (Marine-Roig and Ferrer-Rosell 2018 ; Qi et al. 2018 ; Zhang and Cole 2016 ). Applying the computerized quantitative content analysis approach to OTR-based image studies includes two basic steps, data pre-processing and attribute identification (Marine-Roig and Clavé 2016 ; Xiang et al. 2017 ). Data pre-processing generally involves some operations, including tokenization (means breaking the review text into words, phrases, or other meaningful elements), and removing stop words (e.g. a, the, so, or other words do not contribute to the meanings of the text) (Xiang et al. 2017 ). Attribute identification in a quantitative content analysis aims to detect the frequency, density and weight of keywords or key phrases in the content by computer program, and then aggregate keywords or key phrases into destination attribute categories (Marine-Roig and Clavé 2016 ). Because a quantitative content analysis often focuses on searching for keywords, adopting a quantitative computerized approach alone often results in ignoring valuable contextual information embedded in the OTR data (Stepchenkova et al. 2009 ; Zhang and Cole 2016 ).

In contrast to the quantitative content analysis approach, the qualitative content analysis approach is the subjective interpretation of textual content, and used to manually extract the DI from a small number of tourists’ narrative descriptions (Sun et al. 2015 ; Tegegne et al. 2018 ). Using the qualitative content analysis approach could extract the valuable contextual information embedded in the textual content. The systematic classification process of encoding the destination attributes and identifying attribute categories is the core of the qualitative content analysis approach in DI studies (Lian and Yu 2017 ). In addition, inter-coder reliability must be carefully considered, which means that different coders need to produce the same encoding results in the same way (Lian and Yu 2017 ). Although a qualitative content analysis focuses on valuable contextual information embedded in the text content, manual coding is quite time-consuming to apply for large-scale text analysis. In order to solve the two problems of extracting valuable information embedded in review content, and processing large amounts of OTR data, thus, a novel approach combining both qualitative and quantitative methods was applied in this study.

The qualitative content analysis in this study was conducted first to identify Finland’s destination attributes and build up a coding manual. In this process, the coding of the destination attributes and categorization followed the adapted attribute framework from previous studies (see Appendix 1 ). Basically, the adapted attribute framework ensured the validity of encoding destination attributes and identifying attribute categories. Therefore, two coders randomly chose 10% of the travel reviews to formulate a coding manual of Finland’s destination attributes. All data coding was conducted on Chinese-language texts using the computer-assisted text analysis software NVivo 12 Plus. The data reached a saturation point when adding additional OTRs failed to reveal novel aspects or issues (Papathanassis and Knolle 2011 ). The coding manual was built by using the following steps: (a) an OTR was read carefully and destination attribute were identified based on the context of review content, (b) the identified attribute was verified in the adapted attribute framework, (c) the code was confirmed if the identified attribute existed in the framework, (d) if the identified attribute did not exist in the framework, the coders discussed and decided on the attribute code and its classification. Furthermore, in order to ensure the reliability of the coding manual, a second reviewer with tourism expertise in China and Finland was asked to review the codes.

In the process of formulating the coding manual, several operations were performed on the selected reviews. First, the coders made efforts to unify the spelling of the names of attractions on different review platforms. For example, the description of Kamppi Chapel and the Silent Church pointed to the same attraction, which was coded as “Kamppi Chapel” under the destination attribute code “churches”. Second, universal terms of destination attributes were applied in the cases that Chinese tourists mentioned general infrastructure without giving a specific name. For instance, Chinese tourists gave descriptions of Finland’s libraries without referring to a certain place, thus the general terms “libraries” were applied to these descriptions.

Based on the coding manual from the qualitative content analysis, a computerized quantitative content analysis was applied to the remaining data by using the “automatic coding using existing coding patterns” function in NVivo 12 Plus. The premise of using pattern-based auto-coding is that the coder needs to manually code part of the material. When using the identified codes for automatic coding, NVivo compares each text part (e.g., a sentence or paragraph) with the review content already coded into the destination attribute. If the content of the text paragraph is similar in wording to the content already coded for the destination attribute, the text paragraph will be coded for that identified attribute. In doing so, the quantitative content analysis results can then reveal Finland’s image on the various Chinese OTR platforms. This study further used the post hoc z test to verify whether the differences between Finland’s image on different platforms are significant. The chi-square post hoc z-test using adjusted residuals is applied to detect differences between groups data (Zhang et al. 2017 ). The premises of using the z-test are that the variance is known and the sample size is large enough (sample size ≥), as is in this case (Table 4 ). The test shows the cells in the chi-square table that have significantly lower or higher adjusted residuals on the 95% confidence interval.

5 Results of the destination image analysis

5.1 qualitative analysis results for finland’s destination image categories.

Compared with the adapted DI framework (see Table 3 ), the qualitative content analysis results proved that Finland’s Chinese OTR data-based DI covered all nine destination attribute dimensions, from the functional to the psychological levels. However, within every attribute dimension, while some universal destination attributes did not appear, other new destination attributes were identified in the data.

The following paragraphs demonstrate some of the aspects identified with some quotations from the source data to illustrate the points.

At the functional level, attributes identified in the adapted frameworks such as beaches and the richness of the countryside did not appear in the Chinese OTRs. However, other attributes were identified in the selected OTR samples, for example, the new attributes of city parks (Quotation 1) and payment methods (Quotation 2) were identified at the functional level. This can be seen in the following quotations:

Quotation 1 “ Sibelius Park is located about 1.5 km northwest of Temppeliaukio Church. It was built to commemorate the great Finnish musician Sibelius. The park is full of flowers and green grass, …”—Reviewer (Case number: 5143) from Ctrip.

Quotation 2 “… At the terminal, we bought a round-trip ticket for 5 euros at the self-service ticket vending machine. It seems that only cash is accepted, and no credit card was accepted. ….”—Reviewer (Case number: 3474) from Qyer.

At the mixed functional-psychological level, attributes concerning the development of health services and telecommunications and traffic congestion which were in the adapted framework were missing. However, a few new attributes, such as educational facilities (Quotation 3), and national industry (Quotation 4) were identified at the mixed functional-psychological level. The following quotations illustrate these aspects.

Quotation 3: “The informatization of Finnish libraries is very good. Finns can borrow materials from the public libraries… and return books to another library which is near their home, …”—Reviewer (Case number: 9583) from Mafengwo.

Quotation 4: “… Known for its technology-intensive industries, it has become a leading technology center in the Nordic region, where Nokia's headquarters is located.”—Reviewer (Case number: 5700) from Qyer.

At the psychological level, aspects from the adapted framework including the degree of urbanization, economic development, the opportunity for adventure, the mystic vs prosaic aspect, the luxurious vs impoverished nature of the destination, or fashionable vs outdated elements could not be discerned. This did not mean that Chinese tourists were not aware of Finnish destination attributes at the psychological level. On the contrary, Chinese outbound tourists seem to have an abundant and unique psychological perception of Finland, especially concerning the atmospheric dimensions. The following quotations (Quotation 5, 6) emphasize the “harmonious” atmosphere.

Quotation 5 “…there are a lot of people of different skin color sitting on the steps, sunbathing and chatting, and the whole atmosphere is very harmonious and enjoyable.”—Reviewer (Case number: 9172) from Qyer

Quotation 6 “There are also food stalls in the market, there are fruit sellers and handicraft sellers. This free market is across the road from the presidential palace and other government buildings—what a great harmonious society scene!”—Reviewer (Case number: 10030) from Tuniu.

Table 3 shows Finland’s destination attributes identified in Chinese OTRs. As can be seen most of the attributes appeared at the psychological level including attributes concerning ethnic origins, an ancient and historic atmosphere, an artistic atmosphere, as well as desolate, depressing, harmonious, majestic, fairytale, magical and solemn attributes.

5.2 Quantitative analysis results of Finland’s image on five review platforms

Based on the coding manual created in the qualitative analysis phase, a quantitative content analysis for the remaining 90% of OTR data was conducted. The results presented in Table 4 show that Chinese tourists visiting Finland generally perceived Finland as a leisure destination with various cultural, historic, and artistic elements. The culture, history, and art dimensions had the largest amount of coding references, accounting for 20.32% of the total, followed by the tourism leisure and recreation dimensions, accounting for 19.51%. These two attribute dimensions accounted for a large share of the Finland’s DI at the functional level. The largest dimension at the psychological level was the place atmosphere, with a 19.25% share of mentions. Furthermore, the shares of the dimensions encompassing Finnish natural resources, natural environment, political and economic, and social environment were 5.52, 5.69, 3.53, and 2.35%. The proportions of the latter three dimensions were much lower than the proportions of the first three dimensions.

As can be seen from Table 4 , the results therefore also reveal a discrepancy in Finland’s DI based on different platform OTRs. From the number of destination attribute references, the total number of destination attribute references came to 4961 on Qyer, the largest of the other four platforms, followed by Ctrip, with 3579, Mafengwo, with 2493, Qunar, with 2019, and Tuniu, with 1437 references. Qyer had a greater number of attribute references than the other four platforms concerning the natural resources dimension (320), tourism infrastructure dimension (651), general infrastructure dimension (977), natural environment dimension (257), political and economic dimension (234), social environment dimension (120), and the place atmosphere dimension (889). Ctrip had a greater number of destination attributes references concerning the tourism leisure and recreation dimension (827), and the culture, history, art dimension (823). The remaining three platforms Qunar, Mafengwo and Tuniu did not have the largest number of references for any destination attribute dimension.

The z test showed that the differences between the five travel review platforms were significant. Reviews on Qyer discuss natural resources, tourism infrastructure, general infrastructure, and political and economic situation significantly more than what could be expected. However, there is significantly less information about tourism leisure and recreation, and culture, history and art compared to other platforms. These two destination image dimensions are more prominent on Ctrip, as well as tourism infrastructure. However, Ctrip lacks reviews on general infrastructure, as does Qunar and Mafengwo. Tuniu seems to focus the most on culture, history, and art as well as the atmosphere of the place. The results are interesting also in the destination image dimensions. It seems that the most significant differences are in functional dimensions whereas psychological dimensions are relatively similar between different platforms.

6 Discussion

There is no doubt that big data has had a major impact on tourism research (Li et al. 2018 ). Whereas most previous studies have used a quantitative content analysis approach with a single OTR platform (Lalicic et al. 2021 ; Tseng et al. 2015 ; Xiang et al. 2017 ), we explored the differences and universal attributes of various OTR platforms with a mixed qualitative and quantitative content analysis approach. This approach allowed us to extract valuable contextual information embedded in a large amount of OTR data. Additionally, in applying Simplified Chinese OTR content from multiple Chinese-based platforms, this study interpreted Finland’s image in terms of nine destination attribute dimensions of a functional and psychological destination attribute scale. In this study, we also identified statistically significant differences between the review topics on various Chinese OTR platforms. Users of different platforms discuss different issues in their reviews. These differences can have a significant effect on what kind of conclusions are drawn from destination image studies based on OTRs. We also found that Chinese online travel reviews of Finland focus on functional and mixed psychological-functional destination attributes.

6.1 Differences in destination image between OTR platforms

First of all, this study aimed to explore if and how the destination image differed on various OTR platforms (Table 4 ). The results of this study show that there are indeed many differences between platforms. This is an important observation for researchers as well as tourism managers. OTRs are often analyzed to understand DI (Lalicic et al. 2021 ; Marine-Roig and Clavé 2016 ; Marine-Roig and Ferrer-Rosell 2018 ). The results demonstrated how the conclusions made about DI can differ depending on where the OTR analyzed come from. For example, if only Ctrip or Mafengwo reviews are analyzed the results would show that the Chinese tourists focus on tourism leisure and recreation. However, if only Tuniu reviews were analyzed the results would show that Chinese tourists focus and pay attention mainly to culture, history, and art. Only by including a wide range of sources and data is it possible to form a comprehensive picture of the actual DI.

Previous research has shown that Western OTR platforms (TripAdvisor, Expedia, and Yelp) have discrepancies in their displays of the hotel product, and each platform has its own characteristics (Xiang et al. 2017 ). According to the results of this study, platform-based specific characteristics in the representation of Finland’s image exist based on different Chinese platforms’ OTR content. Tourism studies, especially DI research, should take into account platform-specific biases, and data collected from multiple OTR platform can better reflect DI compared to individual platforms. These platform-specific characteristics are especially prominent in the functional and mixed functional-psychological dimensions.

Earlier studies have also argued that these platform-specific content characteristics were explained by the different user groups on each OTR platform (Xiang et al. 2017 ). However, based on the Chinese OTR platforms, it seems that the differences cannot be directly reflected by their target user groups. For example, both Ctrip and Tuniu focus on middle- and high-income tourists, but the platform-specific characteristics in their OTR content still differ. These differences, therefore, seem to be the result of multiple factors, including not only the user group but their online product, marketing, or market positioning. It is critical to understand these differences when conducting social media analytics research for destination image analyses. Where the data is collected matters. Various platforms’ destination image analyses provide different results. Drawing definite conclusions based on single platform analyses can thus produce biases and lead to incorrect conclusions on what the customers thinkg about the destination (Pfeffer 2014 ).

6.2 OTRs as organic image information agents

Second, this study demonstrates that OTRs, as organic image information agents, can contribute significantly to functional and psychological destination attributes, and are especially prominent at the functional and mixed functional-psychological level. This result is interesting when compared to the earlier literature. Earlier studies have shown that organic image formation agents (e.g., word-of-mouth) affect the perception of psychological destination attributes, whereas induced (advertising) and autonomous (non-promotional) sources contribute to the formation of functional destination attributes (Baloglu 2000 ; Mak 2017 ). In this study, the results show that, overall, the functional attributes and mixed functional-psychological attributes had more descriptions than the psychological attributes in Chinese OTRs. This was especially true for the tourism leisure and recreation attribute dimension; the and culture, history, art dimension. The reason for this may be related to the development of information technology. The application of portable communication devices and the popularization of information networks have made it easy for tourists to share all aspects of their travel experience through social media platforms (Huertas and Marine-Roig 2016 ).

This study also contributes to the tourism literature by revealing the relational nature of DI and its formation by organic image information agents referring to OTR platforms. The results of this study confirm that each destination may have its own destination attribute scale, which is consistent with Beerli and Martín’s ( 2004 ) study. Although, Finland was perceived as a leisure destination with various cultural, historic, and artistic elements, and all nine destination attribute dimensions were recognized in travel reviews, some universal destination attributes such as beaches and the wealth of the countryside did not appear and some new attributes such as city parks and payment methods were identified (Table 3 ). The results expand our knowledge on the topics that tourists pay attention to when they are travelling and what factors can affect the perceived destination image.

Furthermore, according to the results of this study, most Chinese tourists pay less attention to the political attributes in Finland, including safety issues, the political environment, and political stability. However, more than ten years ago, when OTR platforms were rarely used by Chinese outbound tourists, most considered safety one of the most important destination attributes (Kim et al. 2005 ). The development of information technology may explain this: tourists now have more diverse ways of obtaining destination information through social media, leading to a more comprehensive understanding of destinations and thus minimize their risk perception. Tourists assist each other especially through OTR platforms, which may be beneficial for risk reduction (Jacobsen and Munar 2012 ).

6.3 Practical implications

Chinese tourists use organic image information agents, OTRs, as influential destination information sources (iResearch 2019 ). Due to the discrepancy in the representation of DI on different OTR platforms, this study provides at least four implications for all DMOs. Western DMOs could use the knowledge concerning destination image analysis to position themselves in the Chinese market and modify their service design and marketing processes.

First, these Chinese platforms provide not only OTR functions to tourists but also travel products to consumers, including flights, accommodation, and tour guide services. Based on the background of each platform and the DI it represents, DMOs may need to develop suitable product strategies for each OTR platform and different target groups. The most efficient marketing combinations come from marketing and selling the correct tourism service on the correct channel.

Second, DI directly affects the travel intentions of potential tourists (Chaulagain et al. 2019 ). Today, induced and organic sources may complement each other (Selby and Morgan 1996 ). DMOs should focus on the image displayed by OTR content and try to increase interactivity with tourists (Huertas and Marine-Roig 2016 ), especially on opinion leaders’ OTRs. As OTRs become increasingly influential, a DI represented on OTR platforms will increasingly affect the perceived DI of tourists searching for travel-related information (Marine-Roig and Ferrer-Rosell 2018 ). Indubitably, opinion leaders’ OTRs have a great impact on the DI of potential tourists (Jalilvand 2017 ). Their OTRs may enhance DI and make the information recipient feel the destination is attractive. Their comments may also weaken the DI and discourage tourists from visiting a destination. Given that most Chinese OTR platforms offer a comment function (see Fig.  1 ), DMOs could supplement destination information or repair damaged DI through opinion leaders’ OTRs (Chevalier et al. 2018 ).

Third, DMOs should aim to reduce the gap between tourists’ perceived and the destination’s projected images. Tourists will thus have satisfying experiences and perceive a strong destination brand (Marine-Roig and Ferrer-Rosell 2018 ). Understanding tourists’ perceived images are crucial here. In reducing the gap between these two kinds of image, DMOs need to adjust marketing strategies, and the entire destination may even need to be developed in tune with the realities of the general infrastructure and social and political environments (Marine-Roig and Ferrer-Rosell 2018 ).

Fourth, DMOs should pay great attention to the influence of tourists’ cultural backgrounds and demographic characteristics on the perception of the destination. The image formation process is not only affected by destination information sources (induced, autonomous, and organic) but also by sociocultural and socio-demographic characteristics (Josiassen et al. 2015 ; Nakayama and Wan 2019 ; San Martín and Del Bosque 2008 ). Chinese outbound tourists emphasize Chinese cultural norms in the process of image perception and interpretation (Sun et al. 2015 ). Therefore, Chinese tourists seem to have an abundant and unique psychological perception in the place atmosphere dimension (Table 3 ), the “harmonious atmosphere” attribute was especially identified in the review content. In addition, most users of China’s OTR platforms are white-collar workers (Table 1 ), and their average age is around 35 (iResearch 2019 ). Young Chinese outbound tourists’ emphasize local culture and novel travel experiences (Sparks and Pan 2009 ), and consequently Chinese tourists have a strong perception of a destination’s culture, history, and art attributes. These findings prove that understanding the characteristics of tourists is of great help to grasp the reasons behind DI formation.

In addition to the contributions this study has for all destinations, this study also provides three insights for Finnish DMOs. First, to improve Chinese tourists’ awareness of Finland, Finnish DMOs should strengthen their cooperation with OTR platforms like Tuniu, Qunar, and Mafengwo, or find Chinese agents to launch Finnish tourism products on them. Of the nine dimensions of DI analyzed, Chinese tourists seem to lack an awareness of the Finnish social and political environments. Perhaps the Finnish DMOs should consider making more efforts in these areas on Chinese social media to increase Chinese tourists’ familiarity with Finnish destinations. Finally, overall, Chinese tourists perceive Finland as “a cultural, history, art, and leisure destination”, which differs from the Finnish DMOs’ promotion of “a nature destination” (VisitFinland 2019 ). Finnish DMOs, therefore, need to interview Chinese tourists more comprehensively to identify why Chinese tourists do not mention nature-based attributes in their reviews. Are they not interested in nature? Do they fail to access it when they visit? Are they unaware of Finland’s natural attractions? It may be that Chinese tourists prefer to stay in cities and do not venture into nature tourism areas. Thus, the Chinese market might not be the right market to position Finland as a nature destination.

7 Conclusion and limitations

In the new era, big data provides a new stream for tourism research (Lu and Stepchenkova 2015 ). Many earlier studies adopted a single platform as a data source, but they ignored that using single data sources may induce a sampling bias that potentially complicates the interpretation of the research findings. Therefore, in this study, we explored Chinese outbound tourists’ perception of Finland, and compared the discrepancies and commonality of Finland’s image between different OTR platforms. An analysis of Simplified Chinese OTRs proved that different Chinese OTR platforms had a DI discrepancy. The results showed that all nine destination attribute dimensions could be identified in the Chinese perception of the destination. However, when the destination attributes of each dimension were examined more closely, major differences in the destination attributes could be observed on the OTR platforms. In this study, the DI based on different OTR platform content revealed discrepancies at the functional and mixed functional-psychological levels. These differences may be the result of a variety of factors, such as the platform’s target group, market positioning, or other factors. At the psychological destination attribute level, different OTR platforms showed consistency in their representation of the social image, political and economic, and place atmosphere dimensions.

Additionally, rich OTR content could cover all destination attribute dimensions, but each destination is unique when it comes to the scale of the destination attribute. With the development of information technology, OTR platforms have gradually become an important channel for tourists to obtain destination information (Marine-Roig 2017 ). Especially in the introduction to the functional and the mixed functional-psychological destination attributes, OTRs provide a more comprehensive understanding of destinations. At the psychological level of destination attributes, although Chinese OTRs show an abundant and unique psychological perception of the place atmosphere attribute, less attention was paid to the political and social environment, such as safety issues. The reason for this result may be related to the advantages of OTRs, in that tourists can assist each other on OTR platforms to reduce the risk of travel (Jacobsen and Munar 2012 ).

Although this study used mixed methods to conduct a comparative analysis of DI represented by various OTR platforms, it has some limitations that may inspire future research. First, an adapted DI framework was applied in this study, and the Finnish attribute coding manual was defined by tourism researchers proficient in Chinese and Finnish culture. However, there is an inevitable degree of subjectivity during the qualitative content analysis process. In this study, OTRs were crawled from Chinese OTR platforms, ignoring the significance of their Western equivalents. To further understand the discrepancies in the same destination’s representation by different OTR platforms, other Western OTR platforms like TripAdvisor, Yelp, or Google Reviews might also be included in future research. Meanwhile, in this study, the discrepancies in the DI on OTR platforms were discussed based on the basic information of the platforms. In order to explain the results of the discrepancies more accurately, other indicators could be considered in future research, such as the actual market and actual product positioning.

The study’s most important limitation is that the analysis process does not calculate the length of the review text, or split the sentences when analyzing the attributes of the phrases in the text, which is common to computerized methods that clean, debug, and analyze large-scale OTR data. Future research could use detailed computerized methods, such as those proposed by Marine-Roig and Clavé ( 2016 ), to compare whether the computerized analytical results are consistent with the results of this study method. It is also possible to apply the compositional data analysis approach (CoDa) (Coenders and Ferrer-Rosell 2020 ; Lalicic et al. 2021 ) to analyze multiple destinations’ image, thus verifying whether the reasons for the discrepancies between different platforms are the same for different destinations. Besides, although this study has conducted a detailed analysis of each of the destination attributes of the image, due to word limitations, this paper only shows the discrepancies between the various platforms from the perspective of the main DI dimensions. For future research, given the improved performance of big data analysis software, different analysis methods and software could be used to verify the results of this research, such as performing a quantitative analysis using Python. Alternatively, photos could be considered for use as a data source to verify whether discrepancies exist between various OTR platforms.

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Guo, X., Pesonen, J. & Komppula, R. Comparing online travel review platforms as destination image information agents. Inf Technol Tourism 23 , 159–187 (2021). https://doi.org/10.1007/s40558-021-00201-w

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Top Pick Cellular Data Plans for RVers and Cruisers

The best cellular data plans for rvers & cruisers.

Welcome to our central resource page for data plans, highlighting the plans we currently consider the best for phones, hotspots, tablets & routers.

Finding the right data plans for your unique needs is no easy task - using cellular data as a primary way online is not how most carriers have designed their mobile plans.

At the Mobile Internet Resource Center - we strive to provide unbiased mobile internet information for US based RVers, boaters and nomads.

Last Update:

Mar 28, 2024

Shopping for Cellular Data Plans Finding Your Best Data Plan for RVing or Boating!

Today's RVer or boater needs a home internet replacement while traveling. They're working from 'home', remote learning, researching the next stop, banking, keeping entertained and staying in touch with friends & family - all of which require internet access.

Cellular data is the most popular way to keep online while traveling, with Starlink being a common option for places without a cellular signal. A nomad's data plans need to get laptops, computers, smart TVs, gaming systems, and home automation systems online.

Using Cellular Data Resource Collection

Those living on the road for any length of time are not the typical cellular customer just using a bit of data when away from home, they're using it as a home internet replacement while on the go.

Which means seeking out data plans that include a lot of data that can be used as a hotspot from either smartphones, tablets, hotspot devices (Jetpacks, MiFis) or cellular embedded routers (Peplink, Winegard, Mofi, InstyConnect).

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Visible upgrades visible+ plan to include 10 mbps mobile hotspot and more international use, fmca releases t-mobile tech connect+ max cellular plans – unlimited mobile hotspot data for $59.99/mo, t-mobile home and business internet plans price increase to $60/month, changes price lock terms, at&t updates postpaid smartphone plan lineup, increasing prices and mobile hotspot allotments, t-mobile home internet adds sercomm 5g router with antenna ports and an optional external antenna.

We publish an updated video as warranted throughout the year, but plans change all the time. So be sure to check the recent news stories above for any changes, and the grid below is kept constantly up to date.

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The latest travel data.

MONTHLY INSIGHTS March 04, 2024

U.S. Travel has temporarily paused our monthly data newsletter, however, the latest travel data is still available via the U.S. Travel Insights Dashboard . This dashboard is updated each month (member login required).

The U.S. Travel Insights Dashboard , developed in collaboration with Tourism Economics, is supported by more than 20 data sources. The dashboard is the most comprehensive and centralized source for high-frequency intelligence on the U.S. travel industry, tracking industry performance, travel volumes and predictive indicators of recovery including air and lodging forecasts, DMO website traffic, convention and group trends, travel spending and losses, traveler sentiment, among others to measure the health of the industry.

Key Highlights January 2024:

• Travel appetite started the year on a softer note, but overall growth continued. Air passenger growth remained positive, up 6% versus the prior year but lower than the double-digit growth seen through 2023. Foreign visits remained strong, up 24% YoY.
• Hotel room demand continued a trend of slight contraction falling 1% versus the prior year, while short-term rental demand grew 1%, a lower rate than 2023.
• A particular bright spot was that group room demand within the top 25 markets displayed solid growth of 9% relative to the prior year.
• The outlook for the economy remains fairly optimistic due to the strength of the labor market, looser financial conditions and healthy household and nonfinancial corporate balance sheets. This has filtered through to slightly higher consumer sentiment in February.
• Sentiment is also growing for upcoming leisure travel in 2024. The share of travelers reporting having travel plans within the next six months increased to 93% in January from 92% in December, according to Longwoods International’s monthly survey.
• Travel price inflation (TPI) fell slightly in January as a result of falling transportation prices. Sticky services inflation should see relief from decelerating wage growth. However, upside risks stem from rising healthcare costs, supply chain disruptions and slowing labor supply. Source: U.S. Travel Association and Tourism Economics

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Additional monthly insights are available through the full U.S. Travel Monthly Data Report, exclusive to members. Please inquire with  membership  if you are interested in learning about becoming a member of U.S. Travel Association. 

aloSIM Review: eSIM Travel Data Plans Made Easy

The tide is turning.

Old-school plastic SIM cards are being ditched in favour of newer, more flexible technology.

Almost all major new flagship phone launches in the last few years include a common feature… eSIM support. Apple’s done it. So has Google, Samsung, Huawei, Nokia, Oppo, Sony, and Xiaomi.

The question is, are you on board yet?

Leading industry body GSMA has predicted that by 2025 60% of smartphone sales will be eSIM-compatible devices, and between 2-3 billion devices globally will use eSIM. So, there’s no question about where things are heading.

But what does this mean for you when you travel? How can you stay connected to the Internet and avoid painful international roaming data fees?

I’ve taken a close look at the new kid on the block in the travel eSIM industry, aloSIM , to see how they’re shaking things up. And I must admit, I’m impressed.

What Is a Travel eSIM and Why Do I Need One?

The traditional SIM card is a small microchip on a plastic base, inserted into a phone. This gives carriers a way to identify each device on the network, for sending a signal, and track usage and billing. eSIM technology does away with a physical piece of plastic, and replaces it with an integrated chip inside the phone that can’t be removed, but can be modified via software.

If you’re already using an eSIM in your smartphone, you’ll know how convenient it is. You can switch between plans from the same carrier or even different carriers with just a few taps. No need to get off the comfy couch and drive down to a store, or wait for a physical SIM card in the mail. 

When you take your phone on the road or overseas, your “home” plan might not be the best option for you… this is where travel eSIM providers like aloSIM come into the picture.

Is My Device Compatible With eSIMs?

Before we get carried away (no pun intended – well, maybe just a little), the first step is to check if your current smartphone or tablet supports eSIM technology. aloSIM have put together a user-friendly and intuitive step-by-step method for checking if your phone will play nicely. Or if you prefer a classic huge list of phone models, they offer that too . 

Once you’re good to go, proceed to the next step. If your phone isn’t supported, it might be a good time to scroll through Amazon and look for a new phone. Wink wink!

You also need to ensure your phone is not locked to a particular carrier. Here’s a guide on how to check . If you purchased your handset for no (or little) upfront payment, and are still paying it off, it might be locked to prevent you from switching to other carriers. If you purchased your handset outright, then you should be fine.

Who is aloSIM?

So, who is this aloSIM that I’ve mentioned?

Launched in June 2022 by the team at AffinityClick , aloSIM aimed to make the process of finding and buying budget-friendly AND travel-friendly prepaid eSIM data plans as easy as possible. And they’ve nailed it.

AffinityClick, based in Canada and led by CEO Justin Shimoon , has been in the VoIP calling, mobile data, and messaging app business since 2013 (that’s a long time in Internet years ). They are the same group behind the popular telephony app, Hushed , which you might have used before.

So, although aloSIM might seem new, it’s got a rock-solid, experienced team behind it, which explains why it already feels like such a refined product.

aloSIM offers eSIM packages by country or region, with different data quotas and validity lengths to suit most travellers. Currently, over 130 countries are supported, so there’s a good chance they’ve got you covered on your next international vacation or business trip.

How To Buy an eSIM From aloSIM

The easiest way to start is to download their free mobile app ( iOS , Android ). But you can also browse and buy via their website , if you prefer the feel of your laptop or desktop computer and mouse. 

It just takes 3 simple steps to find and buy your travel eSIM:

1. Decide on your destination (country or region). I really like that when you type the country name, it also shows relevant regional eSIM data plans. For example, I entered “France,” and 2 additional regions were shown: “Mediterranean” and “Western Europe”.

2. Choose an eSIM plan based on your anticipated usage and length. If you choose a regional plan, the list of supported countries is displayed. For example, Mediterranean includes France, Spain, Italy, Croatia, Greece, and Turkey.

Not sure which package to choose? No problem! aloSIM have created the most flexible, powerful AND easy-to-use data calculator . Just drag the sliders on the left for each type of online activity, and it will give you a daily, weekly, and monthly estimate for data usage.

3. Install the eSIM in seconds . The installation process is a little different, depending on the device you use. Here are aloSIM’s step-by-step guides for: iPhone/iPad , Samsung Galaxy , Google Pixel , Android phone/tablet , and even Windows computer .

Depending on your device, there are up to 3 ways to install the eSIM – Automatic (through the aloSIM mobile app), scanning a QR code , or “ manually ” which involves copying and pasting a bunch of settings. Most popular smartphones will support automatic or QR methods.

How To Use aloSIM

Like many travel eSIM providers, aloSIM only focuses on providing “ data ” – not voice calls and text. I personally use mobile apps like WhatsApp, Telegram, Skype, Instagram, Facebook Messenger, Zoom, and Google Voice for making voice and video calls, and receiving text messages. So, living without old-school SMS is not a big deal.

But if you still need to receive regular voice calls and SMS while travelling, it’s recommended to keep your “home” eSIM (or SIM) installed and enable voice/text roaming (if your phone supports 2 or more active eSIMs/SIMs). Then just use aloSIM for roaming data overseas. If you’re not sure how to do this, aloSIM has created a handy guide on how to talk with your home carrier to get the information you need.

Once you have purchased and installed your eSIM package from aloSIM, it needs to be “activated”. The activation process does not require a WiFi or 4G connection, so it’s best to do that when you arrive at your destination. 

Once you are online, just use your phone as you normally would – web browsing, social media, streaming videos, and navigating. And keep track of your usage in the aloSIM app by clicking “My eSIMs” at the bottom.

Once your data package expires or runs out, you can top up. Super simple!

What I Like About aloSIM

There’s a lot to like about aloSIM, but for the sake of getting to lunch on time today, I’ll summarise the most important points.

1. The aloSIM website is clean, sharp, and fast-loading. There’s no fluff or distractions, just the info you need, presented in a well-structured, easy-to-follow way. * Cue angelic choir and sunbeam from the sky*  

2. The eSIM data plans have genuinely low prices with flexible allowances and lengths that will suit the majority of travellers – from light to moderate data users, starting at 7 days. Just pay for what you need. For example, 7 days in the United Kingdom (1GB) is only US$5, or 30 days (3GB) is only US$10. Wow! That’s insanely cheap! There’s no need to choose premium “unlimited” plans when you’re not going to use that much.

3. aloSIM live chat – easy access to help is available on every page. A chatbot integrates with the knowledge base to try and proactively provide answers. But if you want to chat with a real live fleshy human, you don’t have to dig around endless confusing menus. Why can’t more companies provide this (I’m looking at you, AirAsia!). 

4. aloSIM supports mobile hotspots , so your tablet or laptop can share the same Internet connection as your phone. This is important when travelling with multiple devices, or with friends and family.

5. It’s easy to see which networks you’ll be roaming on, before making a purchase. Just click the “view” link next to “network” when reviewing your purchase. Most eSIM providers don’t make it easy to find this crucial information because they cut corners with cheap networks. aloSIM tends to use better quality networks, so that means wider coverage and faster speeds for you.

6. aloSIM eSIMs can be topped up. Unlike competing eSIM providers, there’s no need to purchase and install an entirely new eSIM whenever you want to extend the length of your prepaid package. 

7. aloSIM offers a very thorough support area on their website with answers to almost every possible question you may have. This will put your mind at ease if it’s your first time dipping your toes into the world of travel eSIMs. Here’s a list of common questions…

How do I text with a data-only eSIM?

How do I setup iMessage with an eSIM?

How do I know if my eSIM data plan is working?

What’s the difference between eSIM data and a cell plan?

Can I set up a mobile hotspot with aloSIM?

8. aloSIM offer more advanced software and platform features that are not commonly found with other travel eSIM providers, such as auto-activation . These bells and whistles aren’t the main reason for choosing them, but it demonstrates how they really understand the needs of their users and the nature of travel. I have to admit, it feels good to be understood by an eSIM provider.

Tips To Get the Most From aloSIM’s Travel eSIM

aloSIM provides plenty of information during the checkout process and in their support area to answer all your burning questions. But here are the most important things you need to know if it’s your first time using aloSIM.

1. Install your eSIM 1-2 days before departure, but only activate it once you arrive at your destination. Your data package will start ticking once the eSIM is activated. Installation requires a WiFi or data connection, however, activation does not. 

2. The eSIM never expires , but the data package does. So don’t uninstall your eSIM when you’re finished, just archive it in the aloSIM app. 

3. Create an account now (it’s free) and install the mobile app, so that whenever you’re ready to travel, everything is set, and you can quickly purchase your desired eSIM.

4. If you use Android, set up a data usage warning, so you can find out if you’re using up your data too quickly. Here are instructions for the Google Pixel and Samsung Galaxy . And here’s a guide that covers other types of devices.

5. Follow aloSIM on Instagram , Facebook , and Twitter for their latest updates and news.

The Bottom Line

aloSIM learned a thing or two out of the Apple playbook. Even though they’re not the first travel eSIM player, and might appear to be late to the party, they’ve managed to make a big splash because of intense attention to detail, which culminates in a product that shines brighter than the rest.

aloSIM is an easy choice for travellers using their eSIM-compatible phone overseas for the first time. 

But once you’ve tried aloSIM, I’m sure it won’t be your last.

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Online reviews rank No. 3 in consideration among high-value travelers when choosing a travel brand.

Google/Greenberg, U.S., High-Value Traveler Research, Base: Business and leisure travelers, n=1151, June 2018.

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Travel search data for online reviews - think with google, related content, related data.

More than 160 million people have gone into their Google Accounts to review and adjust their privacy settings.

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of experience bookings are happening once travelers arrive at their destination.

Travel search 01

Search interest in the U.S. for “my activity,” where people can manage information saved to their Google Account has increased sixfold since 2016.

Responsible marketer 01

Travelers who book their activities ahead of their trip spend 47% more on lodging and 81% more on transportation than those who wait to book in destination.

Travel search 02

In the 12 weeks leading up to a trip, there are 3X more experiences searches than hotel searches.

Travel search 03

In the 12 weeks leading up to a trip, 8X more experiences searches than air searches

Travel search 04

ORIGINAL RESEARCH article

Big data recommendation research based on travel consumer sentiment analysis.

• School of Business, Jilin Business and Technology College, ChangChun, China

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine (SVM) algorithm based on travel consumer sentiment analysis and builds an Hadoop Distributed File System (HDFS) system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification.

Introduction

In the era of big data, the whole tourism industry is also undergoing a revolution. As the scale of tourism development continues to grow, tourism data information is also exploding. Currently, big data are gaining more attention as it continues to integrate with the tourism industry, opening new doors for innovation in tourism decision making as well. Through mobile Internet technology, tourists can search for travel information, book travel services, and share travel experiences, generating “tourism big data.” In particular, in recent years, more and more travel consumers are making online travel reviews, which are comments on various aspects, such as services and prices ( Boley et al., 2018 ; Buttazzoni et al., 2018 ; Cottrill et al., 2018 ; Luo et al., 2018 ; Prem et al., 2018 ; Yunlong and Yuanchang, 2018 ). The textual keywords are the most important data carriers of this information. By analyzing these textual reviews, it is possible to effectively understand the emotional state of consumers when traveling and their satisfaction with the service.

Online comment data are an important part of tourism big data, which have attracted extensive research because it can directly reflect the real emotions of tourists. The effects of tourism big data are mainly reflected in two aspects ( Yi et al., 2014 ; Cynarski, 2018 ; Mayer and Woltering, 2018 ; Woosnam et al., 2018 ): on the one hand, tourism big data are beneficial to the transformation of tourism industry and have a great impact on the healthy and rapid development of tourism. On the other hand, tourism big data can contribute to the innovation of tourism management tools. “Sentiment analysis” takes textual comments as the object of research and is able to automatically determine the emotional tendencies of the subject of the comment. There are two main branches of sentiment analysis technology ( De Vos, 2019 ; Haynes, 2020 ; Hunold et al., 2020 ; Van and Hieu, 2020 ), namely, sentiment analysis based on machine learning and sentiment analysis based on semantic methods. The former can rely on certain algorithms to achieve intelligent classification of data by computers. The main intelligent classification methods are Bayesian classification, KNN classification methods, decision tree classification, and support vector machine (SVM) classification. There are many other text classification algorithms, such as text classification using neural network approach. Abubakar and Ilkan (2016) pointed out that online reviews of tourist destinations affect tourists’ trust and stimulate their purchase demand. Gao et al. (2016) achieved affective polarity classification of text by building semantic features and binary models for sentiment analysis of microblog comment data. In addition, data mining and applications of sentiment analysis in tourism have been gradually developed. Tu and Tang (2016) pointed out the shortcomings of machine learning-based sentiment analysis methods in analyzing tourism reviews and established a sentiment analysis model based on semantic lexicon, which effectively improved the management efficiency of tourists’ evaluation remarks. Ma et al. (2016) constructed a sentiment analysis model, which is conducive to improving the service quality and image marketing of tourist destinations.

Although online review data have many advantages, its variable quantity and variety of characteristics also bring great trouble to the practical application. In particular, the current analysis and extraction technology are still immature, with low accuracy, slow extraction speed, and other drawbacks. Therefore, the current technology can no longer meet the needs of the market, so this problem needs to be solved. It should be noted that the current technology for processing online review data mainly relies on the concurrent storage and processing technology of massive data. HDFS concurrent storage system and Map-Reduce model have the ability to store and process massive data information at high speed concurrently ( Fujiu et al., 2016 ; Rossini et al., 2016 ; Seera and Taruna, 2018 ; Sengan et al., 2020 ), which maximizes the use of all aspects of computer resources and has strong concurrency and high efficiency. SVM algorithm is a vector-based binary classification algorithm that is suitable for classification and analysis in massive data.

Therefore, this paper proposes an SVM classification solution based on the concurrent processing technology of massive data, and also designs and implements a classifier based on the improved SVM algorithm in the context of big data. The main work of this paper is reflected in the following two points: (1) This paper obtains standardized text data by word separation of travel reviews, so as to exploit the value of travel online reviews by using sentiment analysis technology, and makes an innovative exploration in the application. (2) The key technology for analysis of sentiment vocabulary is classification; therefore, this paper proposes to classify sentiment of travel online review data by machine learning algorithm and designs an improved SVM algorithm. The improved SVM algorithm makes full use of the threshold between SVM and point vector for denoising, which improves the accuracy of the system. Meanwhile, the Map-Reduce model in Hadoop platform is utilized to greatly improve the possibility of parallel processing and reduce the time consumption.

The rest of the paper is organized as follows: In section 2, the text pre-processing of online travel comment data is studied in detail, while section 3 provides the detailed classification model of tourism emotion based on machine learning. Section 4 provides detailed results and discussion. Finally, the paper is concluded in section 5.

Text Pre-Processing of Travel Online Review Data

Comment type information pre-processing.

The research data in this paper are mainly taken from the raw data of online travel platforms and microblogs. To convert these raw data into a language that can be recognized by computers, a systematic pre-processing work has to be performed. This specifically includes denoising, normalizing the representation, and word separation.

1. Denoising is to reduce data interference and make the study more focused. The objects removed by denoising are interfering comments and duplicate comments in the original comment data. Interference information is information that is not related to the research topic, such as “…” Such pure symbols do not reflect the commenter’s expression intention and have little value to the research topic, so this paper chooses to remove them. Repeated comments are multiple copies of the same comment by a certain user, so only one comment by this user is kept as the object of analysis in this paper.

2. The normative representation is mainly to correct and transform the original data so that it can be identified quickly in the data analysis. Since some commenters will adopt personalized expressions or Internet phrases in their comments, even the most common word separation system cannot identify them. Therefore, this paper will choose to replace or modify these data by consent to make the expressions more standardized and uniform.

3. The role of the word separation module is to make the evaluation dimensions correspond to the evaluation words one by one by using the word separation software, forming the initial correspondence-type data, and paving the way for the subsequent vectorization of the data.

Assuming that the vocabulary of the current evaluation dimension consists of i characters, match the first i characters of the current string. It also matches the i characters after the evaluation dimension ( i values are 2–10). If this i character is the evaluation vocabulary, the field is sliced out and corresponds to the previous evaluation dimension, thus forming the initial correspondence data.

Text Vectorization

When the corresponding data are formed, the vectorization process begins. The model can also be represented by the mathematical formula ( Minaee et al., 2021 ):

where D is the set of feature terms, t n is each feature term, and W n is the weight corresponding to each feature term.

The specific process of data vectorization is divided into two parts: data vectorization and error tolerance ( Hartmann et al., 2019 ).

Data Vectorization

The key to data vectorization is the determination of evaluation dimensions and evaluation vocabulary weights, and the frequency of dimension vocabulary occurrence is found to be a very scientific way of description through the preliminary market research, so the frequency value is chosen as the evaluation criterion here ( Kadhim, 2019 ). The introduction of this evaluation dimension in the vector will play a crucial role in the classification results. When the vector is classified, based on the positive and negative values of the third component of the vector it will be possible to prioritize the comments into positive and negative, which will then be subdivided by the SVM algorithm, thus saving a lot of time costs and improving the efficiency of the system. For example, Table 1 shows the matching results of the travel-based sentiment evaluation. The effect after vectoring is shown in Table 2 .

Table 1 . Matching results of tourism-based emotional evaluation.

Table 2 . Vector table.

Error Tolerance

Since the combination of Chinese vocabulary is very different and the computer has limited ability to recognize Chinese text vocabulary, there will be various errors misleading the final result during the data pre-processing. Therefore, in the error-tolerant stage, when there is data in the vector that exceeds the boundary value, this vector is decisively excluded as an error vector to improve the accuracy of the system data.

Machine Learning-Based Travel Sentiment Classification Model

The machine learning-based travel sentiment classification model contains the entire process from data collection to recommendation classification, as shown in Figure 1 .

Figure 1 . Support vector machine (SVM)-based travel sentiment classification model.

SVM Algorithm Improvement

The core of the SVM algorithm is to find the support vector and the optimal hyperplane ( Varatharajan et al., 2018 ; Zeng et al., 2018 ; Hu et al., 2020 ), so the key problem is to find the interval threshold between the vector corresponding to the data points and the support vector. Many linearly indistinguishable scenarios often occur in practice. Therefore, the selection of the kernel function is a key for the linearly indistinguishable problem. Since the data volume of travel online reviews is too large and the dimensionality is relatively low, the selection of polynomial type kernel function is particularly suitable.

For the case of linear indivisibility, the innovation of this paper is to improve the operation method by expanding the point vector in the SVM classifier into individual sub-vectors and then substituting them into the polynomial in turn. The original polynomial kernel function is as:

The SVM classifier decision function when linearly indistinguishable is as:

where x is the vector to be classified, x i is the support vector, and x is an n -dimensional vector in the calculation of this formula.

It is obvious that the total time complexity is as:

It can be seen that n inner product calculations are performed during the operation and the number of times is d .

Therefore, the computational effort of the improved SVM algorithm is not related to the number of support vectors but to the spatial dimensionality of the study context and the order of the kernel function in the polynomial. The use of the improved SVM algorithm is particularly effective for the case of this paper where the number of vectors is large and the dimensionality and order are small and low. When computing the vectors, there is no effect on the selection of the classification results and the calculation of the threshold value because the spreading vectors of the point vectors are discarded. Only the normal vectors need to be substituted into the computation to perform the classification, so that the efficiency of the algorithm is greatly improved. The penalty function in the linearly indistinguishable case is as:

where C is a free parameter, and based on practical experience, C is set to 2 in this study. R is the number of vectors.

Another important issue, the choice of the order d -value has a great impact on the accuracy and time complexity of the kernel function. Regarding the effect of different d -value choices on the recognition rate of the algorithm, it is illustrated here by the experimental results shown in Table 3 .

Table 3 . Experimental results with different d value.

In this paper, the data dimension is 3 and the number of training samples is 3,776 data points. The polynomial type kernel function is used, and the parameter C is 2. From Table 3 , we can see that the data recognition rate is highest when d  = 2, so the d of the polynomial kernel function is 2 in this study.

Big Data Processing Module

When deploying an HDFS storage structure on a Hadoop platform, a Master is used as the host, which is responsible for the NameNode and JobTracker. Each Slave usually has a DateNode for storing data information and its backups, and executes Map tasks and Reduce tasks in conjunction with a local TaskTraker according to the application requirements. The design idea of the Map-Reduce model is shown in Figure 2 .

Figure 2 . Map-Reduce model design ideas.

The Map-Reduce framework treats the input of an application as a set of <key,{list of value} > keys and the <key,{list of value} > keys input to the Map function are raw vector form data, where key represents the evaluation dimension weights and {list of value} corresponds to the “evaluation vocabulary” and “positive/negative meaning,” respectively. When <key,value> is derived from the Map, it comes to the Reduce class, and the Reduce task starts to process the healthy values scattered in many different nodes. The Reduce function saves the classification results of each data node into different classifiers and counts the number and percentage of nodes in each classifier.

Experiment and Result Analysis

Experimental environment and data sources.

The PC environment used for the test experiment is Intel I7 processor, 16G RAM, Windows 10 operating system, Eclipse, and Matlab. The cooperation with an online tourism platform in terms of big data was conducted and the online review data were captured using the big data cloud computing service platform owned by it. In 2019, the total tourism revenue of Hangzhou city was 219.741 billion yuan, an increase of 12.72% year-on-year. This study used this platform to capture online review data of Hangzhou West Lake Scenic Area in 2019. There are two main types of these data: (1) microblog data, such as Sina Weibo and Tencent Weibo; (2) OTA online review data, such as Ctrip.com , GoWhere.com , and Tongcheng.com . The collected raw data are pre-processed, among which the most important work is to manually classify these data, and the obtained division results yield the dataset shown in Table 4 .

Table 4 . Results of the manual division of the West Lake Scenic Area online comments.

Evaluation Indicators

Travel sentiment classification can be summarized as a matter of binary classification. Those that contain negative evaluations are not recommended reviews and those that do not contain negative evaluations are recommended reviews. In general, there are two major dimensions for the evaluation criteria of classification models, one is the accuracy of classification, and the other is the efficiency of classification. In this paper, we will compare the travel sentiment classification models from these two perspectives. The evaluation metrics of classification accuracy use recall, correctness, and precision. Among them, correctness is an evaluation of the degree of classification of correct results in the classification results; precision is the percentage of each correct classification target in its category; and recall is a metric to evaluate the proportion of the category of recall targets. These evaluation metrics are calculated as follows:

Performance Analysis of the Improved SVM Algorithm

To verify the superiority of the improved SVM algorithm with respect to the original algorithm, two aspects of accuracy and time consumption are verified separately.

Figure 3 represents the comparison of the result accuracy of the original SVM and the improved SVM algorithm. It can be seen that the original SVM algorithm is higher at small data size. However, when the data volume grows to more than 600 G, the accuracy gap between the two algorithms gradually decreases. When the data volume grows to more than 1 T, the accuracy rate of the original SVM algorithm starts to decline, while the accuracy rate of the improved SVM algorithm remains around 80%. It can be inferred that the accuracy of the improved SVM algorithm has been well stabilized in the context of big data.

Figure 3 . The accuracy comparison results of the original SVM and improved SVM algorithm.

Figure 4 shows the result accuracy of the improved SVM algorithm in the Hadoop system. It can be seen that after the introduction of Hadoop platform, the result accuracy of the algorithm has improved, especially when dealing with large data. From Figure 4 , we can see that the accuracy rate is maintained at about 80%, which is basically in line with the actual application requirements. A comparison of the time consumption of the improved SVM algorithm and the original SVM algorithm is shown in Figure 5 .

Figure 4 . The accuracy of improvement algorithms results under the background of big data.

Figure 5 . The time-consuming comparison of two algorithms.

From Figure 5 , it is clear that when the amount of data is small, there is no significant advantage of the improved SVM algorithm after decomposing the vectors due to the limited number of both point vectors and support vectors. Therefore, there will not be much difference between the two in terms of time cost. However, when the data size gradually increases, the improved SVM algorithm has a great advantage in the computation of kernel function due to the linear indistinguishability gradually increases, especially when the data size is more than 20 GB, this advantage is especially obvious. Overall, in the context of massive data, the improved SVM algorithm greatly improves the execution efficiency of the algorithm while ensuring a certain accuracy of the results.

Performance Comparison of Different Classification Models

Sentiment analysis models based on SVM, plain Bayes and dictionary rules were continuously trained and optimized according to five major measures for assessing model accuracy. The classification results of different models at the same test set are shown in Table 5 .

Table 5 . Classification effects of different models at the same test set.

The results of the accuracy analysis of these three models are shown in Figure 6 . From the results, it can be seen that the commonality of the three is that the accuracy rate is high, above 83%, and all of them can achieve the accurate management of tourism emotion. However, there are still some differences among the three. Compared with the model based on sentiment dictionary rules, the classification accuracy of the machine learning-based model is more stable, and the improved SVM classifier is generally better than the plain Bayesian classifier. Among them, the classification accuracy of the dictionary model is higher than that of the machine learning classification when the number of test sets is small, but it gradually decreases as the number of test sets increases.

Figure 6 . Sentiment classification accuracy ( Acc ) of each classifier.

In summary, the travel sentiment classification model based on sentiment dictionary has better classification results when dealing with a small amount of text. However, as the number of test sets increases, the improved support vector machine-based travel sentiment classification model is able to achieve more desirable classification results. Therefore, the improved support vector machine-based sentiment analysis method has great superiority in dealing with large amount of travel review data.

In this paper, a typical method of sentiment analysis is used to build a machine learning model based on support vector machines to analyze the sentiment of travel online comments, and the feasibility and effectiveness of the idea are verified by experimental models. In the algorithm study, the SVM support vector machine algorithm is chosen and improved for the case of clear categories and large amount of data. In addition, the improved SVM algorithm is applied using Map-Reduce and classifies large-scale textual keywords, while the HDFS system is used to store and read and write data rows concurrently to improve the efficiency when training large amounts of data. Finally, the efficiency of the proposed method is verified through comparative experiments. The shortcoming of this study is that the data size of the test set used in the experiments is limited and does not reflect well the speedup ratio relationship of the classification process under more processors. The data size of the experiments and the number of nodes in the experimental platform will be increased in the future.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

ZY was responsible for designing the framework of the entire manuscript from topic selection to solution to experimental verification.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: tourism consumption, sentiment analysis, big data analysis, support vector machine, Map-Reduce

Citation: Yuan Z (2022) Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis. Front. Psychol . 13:857292. doi: 10.3389/fpsyg.2022.857292

Received: 19 January 2022; Accepted: 04 February 2022; Published: 28 February 2022.

Reviewed by:

Copyright © 2022 Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhu Yuan, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Travel before, during and after the COVID-19 pandemic: Exploring factors in essential travel using empirical data

a Urban Mobility Institute, the Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University, College of Transportation Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, PR China

Zhiyang Wan

Maopeng sun.

b College of Transportation Engineering, Chang'an University, Xi'an 710064, China

Associated Data

Data will be made available on request.

The COVID-19 pandemic has a significant impact on daily life, leading to quarantines and essential travel restrictions worldwide in an effort to curb the virus's spread. Despite the potential importance of essential travel, research on changes in travel patterns during the pandemic has been limited, and the concept of essential travel has not been fully explored. This paper aims to address this gap by using GPS data from taxis in Xi'an City between January and April 2020 to investigate differences in travel patterns across three periods pre, during, and post the pandemic. Spatial statistical models are used to examine the major supply and demand-oriented factors that affect spatial travel patterns in different periods, and essential and nonessential socioeconomic resources are defined based on types of services. Results indicate that the spatial distribution of travel demand was highly correlated with the location of socioeconomic resources and opportunities, regardless of the period. During the “Emergency Response” period, essential travel was found to be highly associated with facilities and businesses providing essential resources and opportunities, such as essential food provider, general hospital and daily grocery supplies. The findings suggest that local authorities may better identify essential travel destinations by referencing the empirical results, strengthening public transit connections to these locations, and ultimately promoting traffic fairness in the post-pandemic era.

1. Introduction

The COVID-19 pandemic, caused by the infectious coronavirus, has had an unprecedented impact on the world since early 2020 ( Zhang et al., 2020 ). This impact is significant and varied, affecting social operation, environment, economic growth, energy consumption, and other aspects ( Rahman et al., 2021 ). One of the most noticeable manifestations is the changes in travel patterns. Due to restrictions on mobility, many industries, businesses, and households have been greatly affected. Global road transportation and aviation activities have dropped by an average of 50% during COVID-19 compared to 2019 ( IEA, 2020 ). A report by Marchant on the effects of COVID-19 lockdowns on transport showed that the top 10 cities around the world saw reductions in travel by over 80% ( Marchant, 2020 ). In fact, many of the reductions were due to non-essential travel, which governments encouraged people to avoid.

The COVID-19 pandemic has highlighted the importance of understanding essential travel and its impacts on travel behaviors, not only during the pandemic but also in the post-pandemic era ( Chen et al., 2021 ). Defining essential travel is crucial for policymakers to reconsider the most basic needs of citizens, especially during extreme circumstances like the COVID-19 pandemic. Although many countries and regions have advocated for only essential travel, the specific definition varies due to regional and cultural background differences. This concept has in general been defined as travel that is fundamental to daily life ( Chen et al., 2021 ). In this paper, we created an operational definition of essential travel as travel that is still going on during the period of “Emergency Response to Public Health Emergencies” (hereinafter referred to as “Emergency Response”) when non-essential activities were largely constrained by the government to control the spreading of the virus. Under such definition, essential travel includes but not limited to work, buying necessities and food, and seeking healthcare, etc. This study aims to answer several questions regarding essential travel. How did daily travel patterns differ pre, during, and post the “Emergency Response”? What factors influenced people's travel behavior during different periods? How did these factors vary across different regions? The answers to these questions can provide valuable insights into understanding essential travel. He et al. (2021) pointed out that the concept of essential travel is useful for identifying the basic travel needs of different groups, especially socially disadvantaged groups. However, existing research has failed to provide sufficient empirical evidence to justify the definition of essential travel, which is difficult to observe directly during normal times.

In this study, we aim to examine travel patterns during different periods of the COVID-19 pandemic using taxi travel data, and discuss how the results can inform decision-making for public transit services in the post-pandemic world. We employ spatial models to analyze the impact of essential and non-essential points of interest (POIs) on travel during the Pre, During, and Post periods. Our findings suggest that the influence of essential POIs on travel does not show a significant decrease in the During period and even slightly increases, with the coefficient of essential shopping showing a positive significance in this period only. On the other hand, the impact of non-essential POIs on travel decreases in the During period, with non-essential catering and non-essential education becoming non-significant. Furthermore, the spatial spillover effects of essential catering, essential healthcare, and y-lag indicate that in the During period, people may travel more directly to their destinations, and the influence of the surrounding areas on their destinations significantly decrease.

A more comprehensive comprehension of essential travel can enable planners to estimate the demand for essential travel more precisely and make recommendations for enhancing the public transit system to provide people in different locations with more equitable access to essential goods and services. This level of equity could have a significant impact on daily life and various stages of personal development, as it is necessary to have access to various socioeconomic resources and opportunities, such as employment, healthcare, and social connections.

The article will be organized as follows. Section 2 displays a careful literature review on the impact of COVID-19 on travel and the definition of essential travel in existing studies. Details about the research design, including methodologies and data will be presented in Section 3. Results are shown and discussed in Section 4. We will summarize the major findings and policy implications in the final section of the article.

2. Literature review

2.1. individual travel during quarantine.

The outbreak of the COVID-19 pandemic has a significant impact on travel activities and behaviors. Past studies on infectious diseases have shown that travel restrictions play a crucial role in controlling the initial spread of such diseases ( Aldila et al., 2020 ; Beck and Hensher, 2020 ; Chinazzi et al., 2020 ; de Bruin et al., 2020 ; De Haas et al., 2020 ). Sharkey and Wood (2020) estimated a difference-in-difference model and found that a 1% decrease in non-essential travel would result in a 6.4% reduction in new cases on average. Villas-Boas et al. (2020) reported similar conclusions. As a result, many countries and regions implemented corresponding travel restrictions and advocated only “Essential Travel”. These restrictive policies resulted in a year-on-year decline of more than 50% in global road transportation and air travel during the same period in 2020 ( IEA, 2020 ). Despite the negative impacts of the pandemic, researchers have also discovered some positive effects of the COVID-19 pandemic on individual travel. For instance, the pandemic has resulted in shorter travel times for public transit and a reduction in accidents. The congestion index of cities worldwide has decreased to varying degrees as well ( Clarke, 2020 ; Hurley, 2020 ).

The current body of research has predominantly focused on the changes in travel preferences, patterns, and means brought about by the COVID-19 pandemic. On one hand, due to various travel restrictions and the potential risk of virus transmission during travel, people's willingness to travel has decreased significantly, particularly for non-essential travel. Cui et al. (2021) reported a severe decline in the output of all transport sectors in China. Beck and Hensher (2020) observed the largest drop in outdoor recreational activities in Australia. On the other hand, in order to maintain social distancing during travel, some individuals who previously relied on public transit have shifted to using private cars ( Campisi et al., 2020 ; Labonté-LeMoyne et al., 2020 ; Shakibaei et al., 2021 ; Zhang et al., 2020 ) or non-motorized modes of transportation ( Bergantino et al., 2021 ; Teixeira and Lopes, 2020 ; Zhang and Fricker, 2021 ). Moreover, another study found that pre-existing disparities in travel behavior across socioeconomic status (SES) clusters were exacerbated during the pandemic lockdown ( Kara et al., 2021 ). Meanwhile, recent studies have indicated that individuals are willing to travel more if the safety and health risks of travel can be mitigated and public transit can be restored. However, the recovery has fallen far short of expectations ( Beck and Hensher, 2020 ; Przybylowski et al., 2021 ).

The COVID-19 pandemic and the resulting quarantine measures have presented both challenges and opportunities in the field of transport planning. On the one hand, the restricted mobility caused by quarantine orders has made it difficult for people in certain areas to access essential supplies and resources. For instance, residents in suburban areas have had to rely more on nonmotorized modes of transportation to obtain what they need than their urban counterparts. Policymakers must take appropriate actions to address potential disparities in individuals' travel choices during the pandemic and ensure that everyone has equal access to necessary goods and services. On the other hand, the quarantine measures have provided a valuable opportunity to study the fundamentals of transport services under extreme circumstances. Although travel is a means of accessing socioeconomic resources, some types of travel are more essential to daily life than others. By restricting non-essential travel during the Emergency Response period, the contrasts between essential and non-essential travel have become more apparent and can be studied more closely.

While previous research provided significant contributions to understanding how the pandemic has impacted travel behavior, there remains a need for more detailed and nuanced analysis to fully grasp the implications of these changes. In particular, there has been a lack of research that explores the interconnectivity between travel restrictions, quarantine orders, and the overall decrease in travel demand in relation to how it affects access to essential goods and services. Understanding this connection could prove useful for urban transport planners as they strive to address potential disparities in accessibility among individuals residing in different areas of cities.

2.2. A closer look at essential travel

During the Stay-at-Home policy period, there was a significant decrease in travel demand and activities. People were advised to travel only for essential purposes. However, the definition of essential travel varied across policies, orders, and guidelines, as well as across cultures and countries. In its guidelines, the World Health Organization (WHO) recommended that individuals only undertake essential travel and noted that different countries may have varying definitions of essential travel. The WHO's definition of essential travel included travel in emergency situations and humanitarian activities, such as the travel of vital personnel, returning to one's country of origin, and obtaining essential supplies such as food, medicine, and fuel ( WHO, 2020 ). Table 1 presents the definitions of essential travel adopted by various institutions worldwide during the COVID-19 pandemic ( C.D.C.P, 2022 ; C.D.P.H, 2021 ; E.U, 2020 ; Government, 2020a ; Health, 2021 ; Immigration, 2021 ; University, 2021 ). The definitions of essential travel varied across institutions and the pandemic's different periods. Governments at higher levels were less likely to define essential travel for specific purposes and impose detailed travel restrictions.

A summary of travel policies and definitions of essential travel by different institutions across the world.

It is worth noting that even before the COVID-19 pandemic, researchers had been discussing essential travel. Some studies have examined the characteristics of essential travel and have used this concept to evaluate whether transport planning is adapting to travel demand, ensuring fairness, and optimizing resource allocation efficiency ( Krumdieck et al., 2010 ; Laube et al., 2007 ). These studies defined essential travel as travel that contributes to people's health, work, income, and other basic needs, but relied mainly on small-scale surveys or subjective observations and explanations. Gordon et al. (1988) is one of the few quantitative studies that used the National Personal Transport Research (NPTS) in the 1980s to demonstrate that non-essential personal travel accounted for about 30% of its total travel. Krumdieck et al. (2010) categorized travel purposes into three categories: 50% for essential, 20% for necessary, and 10% for optional. However, empirical evidence is still limited at present.

Since the outbreak of COVID-19, an increasing number of researchers have conducted quantitative studies on essential travel or non-essential travel in these unusual circumstances. Some scholars have evaluated the impact of non-essential travel on disease transmission. The results showed that for every 1% reduction in non-essential travel, there was an average reduction of 6.4% in new cases, but the definition of non-essential travel was uncertain ( Sharkey and Wood, 2020 ). Another group of scholars linked essential travel to socio-economic status (SES). The research showed that COVID-19 exacerbated existing disparities in mobility between socioeconomic classes. The lower and middle socioeconomic groups mainly took long-distance and medium-distance trips for work, while the higher socioeconomic group mainly took short trips for recreational and other non-work purposes ( Kara et al., 2021 ).

There is little quantitative research on essential travel, and a big obstacle lies in the source of data. On one hand, it is difficult to separate the essential travel and non-essential travel in daily situations. On the other hand, it is difficult to obtain the travel data of various modes at the city level. However, as one of the important components of the urban transport system, taxis have the characteristics of 24-h operation and the starting and ending points are completely determined by passengers, and people are more inclined to use taxis than public transit in extreme cases such as pandemic ( Tong et al., 2012 ). In addition, Xie (2018) shows that the statistical law of taxi travel behavior obeys power law distribution, which is consistent with the power law distribution characteristics of residents' travel behavior in city level, further verifying the rationality of using taxi travel to analyze residents' travel. Therefore, this paper intends to use the GPS data of taxi operation to carry out the essential travel-related modeling analysis.

There is a lack of quantitative research on essential travel, and a significant challenge lies in obtaining suitable data sources. On the one hand, it is difficult to differentiate between essential and non-essential travel in everyday situations. On the other hand, it can be challenging to obtain travel data across different modes of transportation at the city level. However, taxis are a crucial component of the urban transport system due to their 24-h operation and the fact that their starting and ending points are entirely determined by passengers. Moreover, during exceptional circumstances such as a pandemic, people tend to prefer using taxis over public transit ( Tong et al., 2012 ). Additionally, Xie (2018) shows that the statistical behavior of taxi travel conforms to a power law distribution, which is consistent with the power law distribution characteristics of residents' travel behavior at the city level. This finding further supports the rationality of using taxi travel data to analyze residents' travel patterns. Therefore, this study aims to utilize GPS data obtained from taxi operations to conduct modeling analysis on essential travel.

The literature on essential travel has been expanded as the COVID-19 pandemic goes on, but it has not adequately shown how such travel differs from other travel in terms of temporal and spatial patterns, and how such travel is made out of purposes to obtain unevenly distributed socioeconomic opportunities. After all, travel is to help people acquire resources at reasonable prices and costs, so essential travel may be conceptually critical in ensuring the minimum level of personal and family development. Therefore, more research is needed to help understand and utilize the concept in the practices of transport planning. As mentioned above, this paper defines that “Essential Travel” refers to the travel that is still going on during the period of “Emergency Response”, so as to model it and quantitatively analyze its characteristics and influencing factors.

The literature on essential travel has grown during the COVID-19 pandemic, but it has not adequately explored how this type of travel differs from other travel in terms of temporal and spatial patterns, or how it is shaped by the pursuit of unevenly distributed socioeconomic opportunities. Travel is intended to help people acquire resources at reasonable prices and costs, and essential travel may play a crucial role in ensuring a minimum level of personal and family development. Therefore, additional research is necessary to better understand and utilize the concept of essential travel in transport planning practices. As stated earlier, this paper defines essential travel as travel that continues during the “Emergency Response” period. The goal of this study is to model and quantitatively analyze the characteristics and influencing factors of essential travel.

3. Research design

3.1. the study area.

This study examines travel behaviors by taxi in the City of Xi ‘an between January and April 2020. Xi'an is one of the largest cities in China and a central city in the Northwest of the country. It has a population of 13 million and its built-up area reached 729 km 2 in 2019. Xi'an had long been a capital city in the ancient China and thus developed a largely monocentric urban form so far. Similar to other large cities, the urban core is highly dense, while population density decreases as distance to the urban core increases ( Qin et al., 2023 ). Fig. 1 displays the spatial distribution of taxi destinations in a workday.

Spatial distribution of taxi travel destinations.

This research focuses on Xi'an, China, for several reasons. Firstly, during the spring of 2020, when most people were making essential travel due to strict quarantine measures, Xi'an provides an excellent opportunity to observe the behaviors of essential travel. Secondly, while the pandemic impact varied among cities in China, most cities adopted similar quarantine guidelines. As a representative city of China, Xi'an can provide empirical evidence regarding the demand and supply of essential travel during this special period. Lastly, Xi'an was chosen for this study because data was collected covering different periods of the “Emergency Response”, and microscopic taxi data made these studies possible. Furthermore, it is important to note the policy of stay-at-home orders were implemented under emergency response status in Xi'an. Based on the investigation of the pandemic and expert judgment, the pandemic prevention and control command department divided different regions of the city into high, medium, and low-risk areas according to the level of epidemic risk. Residents in low-risk areas could choose to make essential travel, while residents in other areas were advised to stay at home to control the transmission of the virus as much as possible. Urban rail transit and ground public transit were temporarily suspended during the period.

The study area is divided into 1 km × 1 km grid cells, and both the dependent and independent variables will be calculated at this level. Based on the previously mentioned details, it is assumed that the travel patterns observed by taxis are representative of overall travel patterns. To protect individuals' privacy, the original GPS data has been preprocessed, and travel tracks have been aggregated at the grid cell level ( Zhou et al., 2022a ). The dataset also includes information on travel start time, ending time, travel distance, and other relevant details.

3.2. The variables setting

The study analyzed travel behaviors during different periods of the pandemic, dividing the collected data into three periods based on the start and end of the “Emergency Responses” in Xi'an: Pre, During, and Post. The “Emergency Response” in Xi'an was implemented on January 25th, 2020 and lifted on February 28th, 2020 ( Government, 2020b ). Fig. 2 shows significant differences in travel volumes across the three periods. In the Pre period, the daily average number of taxi-riding trips remained stable between 480,000 and 550,000, except for holidays when it would fluctuate. The number of taxi-riding trips dropped rapidly in the During period from January 21 due to the potential risks in travel. By January 25, the number of taxi-riding trips had plummeted to about 100,000, representing a decrease of more than 80%, which may have been due to the implementation of the strict Stay-at-Home policy. Over the next 10 days, it continued to drop until February 20th. In the Post period, with the loosening of the policy, the number of taxi-riding trips gradually rebounded. By the end of April, the daily passenger travel had recovered and reached about 400,000 trips a day, representing a recovery rate of about 80% compared to normal days.

Daily change of taxi-riding trips and Cumulative Confirmed Cases in China.

Assuming that the number of taxi-riding trips in each grid cell represents the actual taxi demand, we can consider it as a function of both travel demand and transport supply (see the function below Eq. (1) ). Travel demands depend largely on the availability of socio-economic opportunities and resources such as restaurants, supermarkets, schools, hospitals, etc., in the vicinity of the destination. The density of each category of points of interest (POI) potentially affects an individual's likelihood to make travel decisions. Accessibility to a place is also a crucial factor that influences travel choices. The location of major transport infrastructure can attract people to visit certain places. For instance, places located in close proximity to freeways will have an advantage in people's travel choices.

In the analysis, three independent variables are used: the average daily number of taxi-riding trips in each grid in the Pre, During, and Post periods. Table 2 describes the dependent variables. To compare the model coefficients across different periods, the dependent variables are normalized based on mean values.

Dependent variable.

The selection of independent variables is informed by prior research ( Chung, 1997 ; Gutiérrez et al., 2011 ; Morrall and Bolger, 1996 ; Sung and Oh, 2011 ; Taylor and Fink, 2003 ; Taylor et al., 2009 ). Our focus is primarily on two dimensions of independent variables: travel demand and transport supply, and we choose appropriate forms of values for each variable. The specific variables are presented in Table 3 .

Description of independent variables.

We would like to provide further clarification on the choice of dependent and independent variables. Firstly, we choose workdays as the dependent variables for the three periods, as they can better capture the essential travel. Additionally, as depicted in Fig. 2 , there is hardly any difference in the number of taxi-riding trips between workdays and weekends during the “Emergency Response” period. Secondly, we have not included variables such as bus stop or subway station density or distance as independent variables, as public transit was completely closed during the “Emergency Response” period, leading to different variables for each period. Lastly, while there are many other potential independent variables to consider, such as secondary road density and walking distance, we have not included them due to their strong collinearity, which would impact the modeling and were abandoned in the pre-modeling.

The travel demand variables used in this study serve as a proxy for localized resources and characteristics that attract travel. These variables provide an indication of how different types of socioeconomic opportunities can attract people to travel ( Zhou et al., 2022b ). Point of Interests (POI) data from Gaode Map Open Platform is used to measure these variables. Given the focus of this study on essential travel, we select four categories—catering, shopping, medical services, and science/culture & education—as major independent variables. Additionally, we matched the closest POI data of various types to the drop-off points of taxi passengers in the Pre and During periods, respectively (using the original latitude and longitude information of the drop-off point). Then, we associated their secondary POI classification attributes and grouped all POIs according to the secondary classification standard. The grouping statistical results are shown in Table 4 . In this study, we defined POIs that did not decrease in proportion in the During period as Essential POIs, while the rest were classified as Non-Essential POIs. The specific division results and quantity of Essential and Non-Essential POIs are shown in Fig. 3 .

Group statistics of the nearest POI from the taxi drop-off point.

POI category and quantity.

The land use mix variable is derived from fine-grained land use information provided by the Gaode Map Open Platform. The Herfindahl–Hirschman index (HHI) is widely used to measure industry concentration in economics, and can be used to reflect diversity of land uses ( Palan, 2010 ). The calculation formula of the HHI is shown in Eq. (2) . A small HHI value indicates a greater mixability.

Where X i is the total number of POIs in grid i , and X ij is the total number of POIs of category j in grid i .

Fig. 4 depicts the spatial distribution of catering POIs for both essential and non-essential types. It is evident from the figure that non-essential catering POIs are mainly concentrated in the urban core within the second Ring Road, while essential catering POIs are more widely distributed, covering almost all major urban subcenters ( Giuliano et al., 2019 ). This could be because non-essential catering businesses such as tea houses and dessert houses tend to locate in densely populated areas where there is adequate demand to support their operations. Fig. 5 illustrates the spatial distribution of essential and non-essential types of shopping POIs. Although the distribution of both groups is generally similar, non-essential shopping businesses cluster in a few hotspots that are not shown in the essential shopping business map. For instance, agglomerations of home building materials markets can be easily seen in many cities due to the apparent advantages of agglomerative economies of scale. People tend to visit these agglomerations to purchase materials in bulk, and businesses located there can benefit from premium volumes of potential customers. Both figures demonstrate the different spatial patterns of essential businesses compared to non-essential ones, which could contribute to the wide spatiotemporal variations in travel demand during the COVID pandemic.

Spatial distribution of POIs in essential and non-essential catering businesses.

Spatial distribution of POIs in essential and non-essential shopping businesses.

3.3. Spatial autocorrelation analysis

Because of spatial heterogeneity of socioeconomic opportunities, the spatial patterns of dependent variables and independent variables are both non-stationary across space. Travel by taxi is distributed along the road network, but the grid is artificially generated regardless of the network. Therefore, the travel demand by taxi can be highly subject of spatial autocorrelation: such demand in a grid cell may be highly correlated with its adjacent spatial units. As spatial autocorrelation can be classified into global spatial autocorrelation and local spatial autocorrelation, we examined them by calculating the Global Moran's I and local Moran's I respectively.

Global Moran's I calculated as Eq. (3) , which is a global measure of spatial autocorrelation ( Moran, 1950 ):

Where n is the number of spatial units, i and j are longitudinal and latitudinal indexes, x denotes the variable, X ¯ is the mean of x , and w ij indicates the spatial weight between i and j . Global Moran's I index is a value ranging from −1 to 1. The spatial distribution is more similar to clustering of dissimilar values if Global Moran's Index approaches −1; otherwise, it would be more similar to clustering of similar values. The value tends to be randomly distributed in space if Global Moran's Index approaches 0.

In order to achieve two purposes, Anselin (1995) proposed Local Moran's I, which can be interpreted as indicators of local pockets of nonstationarity or hot spots, and be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers”. The expression is shown as Eq. (4) :

When Local Moran's I index>0, it means that surrounding units have similar values to the unit; When Local Moran's I index<0, it indicates surrounding units have dissimilar values to the unit. And when Local Moran's I index = 0, it implies that surrounding units have no relationship with the unit.

3.4. Regression models

If there are different degrees of spatial correlation, the traditional panel model can not be used in setting model, such as the Ordinary Least-Squares (OLS) regressions ( Elhorst, 2014 ). Because spatial correlation will lead to the correlation of error terms in linear regression model, or will lead to biased estimation results, thus spatial regression model should be chosen. The commonly spatial models contain the spatial lag model (SLM), the spatial lag of X model (SLX) and the spatial Durbin model (SDM).

The SLM model is suitable for the endogenous spatial correlation between dependent variables, focusing on the spatial spillover effect of dependent variables. The SLX model is suitable for the endogeneity of independent variables, and the adjacency matrix W in the model can be parameterized to better adapt to different spatial distributions ( Vega and Elhorst, 2015 ). The SDM model is a general form of spatial model, which can be simplified to the first two under certain conditions, including not only the spatial lag of the dependent variable, but also the spatial lag of the independent variable. Its original form is shown as Eq. (5) .

Where, y is a vector (n*1) of observations of the dependent variable; X is matrix(n*k) of observations of the independent variables; λ , β and θ are vector(k*1) of regression coefficients; W is spatial weight matrix and ε is a vector(n*1) of error terms.

SDM model theory holds that the observed values of dependent variables are influenced not only by the dependent variables in the surrounding areas, but also by the independent variables in the surrounding areas, so the key factors affecting the dependent variables can be observed more comprehensively from the endogenous and exogenous perspectives ( Borst and McCluskey, 2007 ; Elhorst, 2014 ). Therefore, the total marginal effect of independent variables on dependent variables can be further divided into direct effect and indirect effect. Through Taylor expansion, it's easy to verify that I − λW − 1 = I + λW + λ 2 W 2 + λ 3 W 3 + … . Suppose that X contains k explanatory variables and the r − th explanatory variable is X r = x 1 r x 2 r … x nr (n × 1), then we can get Xβ = x 1 … x k β 1 … β k = ∑ r = 1 k β r x r , so Eq. (5) can be rewritten as Eq. (6):

Suppose S r W = β r I − λW − 1 , then Eq. (6) can be expanded into Eq. (7) :

According to Eq. (7) , it can be seen that ∂ y i ∂ x jr = S r W ij . This indicates that the variable x jr in region j may have an impact on the dependent variable in any other region i , which is the spatial autocorrelation effect of the spatial model. Specifically, if j = i , then ∂ y i ∂ x ir = S r W ii , which corresponds to the diagonal element of Eq. (7). This can be understood as the direct effect of the independent variable x ir in region i on the dependent variable y i in the same region. Therefore, the direct effect of the independent variable x on the dependent variable y is the average of all diagonal elements in Eq. (7) , while the indirect effect is the average of all non-diagonal elements ( Chen, 2010 ).

There are two parameter estimation methods for spatial models, one is generalized two-stage least-squares cross-sectional regression method (gs2sls), the other is maximum likelihood estimation method (ML). The main difference between them is that it is more effective when the error obeys the assumption of normal distribution, otherwise it is not as robust as the former, for example, in the presence of heteroscedasticity. The heteroscedasticity of the dependent variable in this paper is proved by using White test, so the parameter estimation method of gs2sls is used in this paper.

4.1. Spatial analysis: overall distribution and autocorrelation

Fig. 6 illustrates the spatial distribution of taxi travel demand in the different periods of the pandemic, revealing significant disparities in demand across the three periods. In the Pre period, taxi travel was primarily concentrated within the beltway, with numerous hotspots in the urban core. However, as a result of the Stay-at-Home policy, almost all hotspots disappeared in the During period, and travel intensity declined precipitously. In the Post period, travel demand had rebounded and had nearly returned to pre-pandemic levels.

Hotspots of taxi destinations in different periods.

Table 5 displays the global Moran's I index for the number of taxi-riding trips in the three periods. The results indicate that the global Moran's I index for all three periods is significantly greater than 0, which suggests that the number of taxi-riding trips is spatially autocorrelated. Furthermore, we calculated the local Moran's I index for the number of taxi-riding trips in each period. The results show that the spatial distribution of the local Moran's I index is similar across all three periods. Specifically, the index is mostly significant within the second Ring Road, characterized by a High-High Cluster. Outside the second Ring Road, the index is generally not significant, with only a few areas exhibiting a Low-High Cluster, such as the airport and the edge of the second Ring Road.

Global Moran's I index of the number of taxi-riding trips in three periods.

4.2. Inferential analysis

4.2.1. model comparison.

The study utilizes both global OLS regression model and spatial regression models to examine the relationship between the number of taxi-riding trips and various independent variables in different pandemic periods. Table 6 provides a comparison of the models to show how the coefficients change when controlling for the spatial autocorrelation of the dependent and independent variables. The results indicate that the Pseudo R-squared of the SLX and SDM models is larger than that of the global OLS model. Additionally, the two indexes of Log-Likelihood and AIC suggest that all three spatial models are superior to the OLS model. These findings suggest that incorporating spatial dimensions is necessary to more accurately estimate relationships of interest.

Results of models.

Note: The dependent variable was transformed by multiplying the value by 1000. E.g.: 1000 essential catering POI is associated with 0.111 taxi trip arrivals in the SDM model.

Table 6 reveals differences among the spatial models as well. SLM (Y-Lag only), SLX (X-Lag only), and SDM (X-Lag & Y-Lag) exhibit a gradual increase in R-squared and Likelihood, and a corresponding decrease in AIC. The results indicate that the SDM model performs the best, implying that controlling the spatial autocorrelation of both dependent and independent variables can effectively help estimate taxi travel demand in different pandemic periods. Compared to the global OLS model, the coefficients of several variables in the SDM model exhibit different statistical significance, while the signs of coefficients remain the same. These findings suggest that the importance of a certain variable in predicting the number of taxi-riding trips may change when the spatial autocorrelations are controlled. Moreover, the results reveal that the number of taxi-riding trips in a certain spatial unit is not only related to the influencing factors of this unit, but also to the influencing factors in adjacent units. Hence, the SDM model can better reflect the degree of influence of different variables on the dependent variable.

4.2.2. Variable association analysis

According to the results in Table 6 , the factors affecting the number of taxi-riding trips in different periods show substantial differences in terms of their coefficients. In the Pre and Post periods, the impacts of the independent variables are similar to a large extent, with some notable differences. Firstly, in the Post period, there is a significant suppression of travel demand for essential healthcare, which may be due to people's fear of potential risks during the pandemic. Secondly, non-essential education strengthens its impact in the Post period, likely because people choose to use taxis as a more private and safer travel option for that purpose. In contrast, the influencing factors of taxi travel demand in the During period are significantly different from those in the other two periods. Firstly, the importance of E_catering, E_shopping, and E_education does not significantly decrease and even slightly increases, as evidenced by the significant positive coefficient of E_shopping in the During period only. This strongly confirms the hypothesis of this study that essential travel is closely associated with basic socio-economic providers, such as local food stores, convenience stores, and important educational facilities. Secondly, the transport supply type variables, such as landuse_mix and Primary_density, are no longer significant in the During period. It is possible that during the Emergency Response periods, when the main purpose of travel is essential, people may not be concerned about building characteristics such as land use patterns and road network accessibility, which led to the disappearance of the effects of these variables.

The SDM model also reveals the spatial spillover effects of variables, which vary across different pandemic periods. Firstly, the spillover effect of E_shopping is negative in the Pre and Post periods, but positive in the During period. This suggests that in the Pre and Post periods, the surrounding areas suppress E_shopping in a region, while in the During period, they drive E_shopping in a region. This may be due to limited supply of necessary goods during the Emergency Response period, leading people to travel to areas with concentrated convenience stores, thus increasing the probability of completing essential shopping. Secondly, in the During period, the spillover effects of E_catering, E_healthcare, NE_education, and Y-lag are no longer significant. This may indicate that, in the case of virus transmission, people directly go to their destination without the need for more contact with nearby facilities. For example, under normal circumstances, a person may not only look for restaurants in the geographic unit where the taxi arrives but also look for drinks in nearby units after dinner. However, during the Emergency Response period, they may only focus on the unit where the target restaurant is located. Thus, spillover effects are greatly compromised in the During period. Based on these findings, the SDM model can better reveal the complex relationships of variables that change over different periods.

4.2.3. Combined spatial effect analysis

Appendix A presents the direct, indirect, and total marginal effects of each independent variable in the SDM model across three different periods. The marginal effects of the independent variables on the dependent variables can be divided into two parts: direct effects from local units and indirect effects from surrounding geographical units. It is important to note that direct impact and indirect impact should be treated independently since they may represent localized impact and sub-regional impact, respectively, in the urban environment. Therefore, when the direct effect is offset by the indirect effect and the total effect may not be significant, it does not necessarily mean that the variables are irrelevant. On the contrary, the influence of the variables varies within and outside geographical units.

The results presented in Appendix A reveal several key findings. Firstly, in the Pre and Post periods, the direct effects of the independent variables on taxi travel demand are notably smaller than the indirect effects (which are an order of magnitude larger). This indicates that the demand for taxi travel in a given grid cell is significantly influenced by the demand from its surrounding region. In contrast, in the During period, only direct effects are observed while indirect effects are not statistically significant. This suggests that the demand for taxi travel during this period was more constrained to the destination, with little spillover effect to the surrounding areas. Secondly, for variables such as essential shopping, healthcare, and essential education, the direct effect is generally positive, but the indirect effect is strongly negative or insignificant. As a result, the total spillover effect is negative or insignificant. This finding highlights the complex spatial relationships among different variables at the regional level, which cannot be adequately captured by a simple OLS model. Hence, a more sophisticated spatial model like SDM is required for accurate analysis. Overall, the results demonstrate the importance of considering both direct and indirect effects when examining the impact of independent variables on taxi travel demand. Moreover, the findings underscore the need for a spatially explicit model that can account for the complex spatial relationships among variables.

5. Discussion and conclusion

The empirical spatial model has demonstrated that taxi travel is influenced by various factors, such as individual travel demands and transport supply. Building on the existing literature, this paper specifically investigates the shifts in taxi travel demand during different pandemic periods and identifies unique travel patterns during the Emergency Response period, which helps redefine essential travel in a more precise manner. Additionally, by examining the travel disparities across different pandemic periods, we can provide more effective optimization strategies for public transit to better serve essential travel in the post-pandemic era.

First, the SDM model proves to be a better method for characterizing the relationship between taxi travel demand and various factors. Given that taxi travel demand is influenced by numerous factors and its spatial distribution is highly uneven, this paper compares the global OLS model to a range of spatial models. The SDM model, which demonstrates superior performance, not only estimates the spatial relationship between taxi travel demand and various factors, but also calculates the spatial spillover effect between local and surrounding regions. As a result, public administration officials may need to take into account neighboring units when predicting travel demand in each geographic unit. For example, in order to estimate travel demand for healthcare services, facilities within a certain distance should be considered to account for spillover effects, although such effects could vary across different periods.

Secondly, this paper provides a clearer definition of essential travel by contrasting travel differences across pandemic periods. The contribution of essential travel purposes to taxi travel demand do not decrease significantly and even slightly increase, while that of non-essential travel purposes dropped significantly. This indicates the critical role of essential travel during the extreme conditions of society-wide quarantine. People prioritize visiting resources and facilities that provide the most vital services, such as convenience stores to obtain daily supplies, rather than purchasing luxury goods in large shopping plaza. These differences have been identified through empirical analysis, leading to a more explicit concept of essential travel.

Thirdly, in the During period, taxi travel demand was found to be highly associated with localized resources, while the impact of surrounding areas was significantly reduced compared to the Pre and Post periods. Therefore, essential travel during this period is closely related to local businesses and facilities that provide essential services. Consequently, addressing travel demand from a spatial perspective during the pandemic requires a context-based approach. While socioeconomic resources can typically be accessed over a wide geographic area during normal times, this is much less feasible during events like the COVID-19 pandemic. Similarly, post-pandemic, the differences in mobility between individuals will also need to be considered when planning for essential resource provision. Ensuring local access to essential resources should be the primary focus of facility planning by public administration in order to enhance the welfare of those in need.

The findings of this study can inform the optimization of public transit services in a hierarchical manner during the post-pandemic period. In order to ensure that all citizens have access to essential resources, it is crucial to accurately identify the destinations of essential travel and efficiently connect individuals to these locations. To assess the adequacy of the current public transit system in meeting this demand, we further analyzed the coverage of major essential travel destinations by bus in a grid cell-based manner ( Table 7 and Fig. 7 ). Table 7 indicates that the bus system can meet 66.32%, 77.22%, and 65.29% of travel demand in the Pre, During, and Post periods, respectively. In the During period, bus services can cover more travel destinations than the other two periods, regardless of grid cell category. These results suggest that the current public transit system has done a relatively good job in matching essential demand as compared to non-essential demand. However, the findings also highlight the need to prioritize improving transit services, particularly by expanding the bus network to grid cells with the highest quartiles of essential travel demand but without a current bus line. For instance, Fig. 7 illustrates that the grid cells with red boundaries in the first two quartiles have high essential travel demand, but no public transit line passes through them. Therefore, these areas should be given priority consideration in the optimization of the public transit system.

Percent of travel demand covered by public transit.

Travel demand and public transport line coverage.

The clarification of the concept of essential travel is a significant contribution of this study. The existing literature lacks a uniform standard for defining essential travel, which is typically associated with travel for food, work, medicine, etc. This paper proposes a novel approach to define essential travel based on local travel demand and transport supply, providing a decision-making basis for optimizing public transit that caters to essential travel in the post-pandemic era. The public administration has been increasingly concerned about the inequity in access to spatial opportunities among socially disadvantaged people. The findings of this research can provide evidence for identifying communities' capability of providing essential services.

At the end of this paper, we want to acknowledge some limitations of research design and data analysis. This paper focuses on the spatial distribution of taxi travel demand in different periods with regard to the pandemic, and mainly discusses its patterns relative to building environment, facility accessibility and population. Some more refined characteristics, such as population age structure, and family income were not included in this study due to the data unavailability. Admittedly, understanding these factors may be of great help to modeling and estimating essential travel demand. In addition, although spatial models are found to be one of the best choices in this study, we expect to see other advanced methods such as machine learning techniques to explore this topic in future research. Finally, due to the limited data, we failed to obtain data on all modes of travel in different periods and had to rely on taxi GPS tracks only. In addition, since the taxi data does not have the attributes of travel purpose, we cannot carry out statistics on whether the travel is essential or not according to the purpose. In the future, more research may be needed to apply the empirical model to other cities and countries, so as to explore essential travel from a more general perspective.

Author contributions

The authors confirm contribution to the paper as follows: Chao Yang: Conceptualization, Supervision, and Project administration; Zhiyang Wan: Data curation, Investigation, Methodology, and Writing-original draft; Quan Yuan: Conceptualization, Funding acquisition, Methodology, Writing-review & editing; Zhou Yang: Methodology, and Visualization; Maopeng Sun: Data curation and Resources.

Acknowledgements

The research is supported by the Shanghai sailing project (Grant ID 20YF1451700), Science and Technology Commission of Shanghai Municipality (Grant ID 23692119000), and Fundamental Research Funds for the Central Universities (Grant ID 2023-4-YB-01).

Appendix A. Spatial effect analysis of SDM

Notes: for significant correlation ‘***’ = 0.01; ‘**’ = 0.05; ‘*’ = 0.1.

Data availability

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DOT to investigate data security and privacy practices of top US airlines

The U.S. Department of Transportation announced its first industry-wide review of data security and privacy policies across the largest U.S. airlines.

The DOT said in a press release Thursday that the review will examine whether U.S. airline giants are properly protecting their customers’ personal information and whether airlines are “unfairly or deceptively monetizing or sharing that data with third parties.”

Letters to airline executives will include questions about how the airlines collect and handle passengers’ personal information, monetize customer data through targeted advertising and how employees and contractors are trained to handle passenger’s information.

Those airlines include Allegiant, Alaska, American, Delta, Frontier, Hawaiian, JetBlue, Southwest, Spirit and United.

The department, which oversees U.S. government policy on all matters related to transportation, said it would investigate and take enforcement action as it discovers evidence of problematic practices.

U.S. Secretary for Transportation Pete Buttigieg said the review aims to “ensure airlines are being good stewards of sensitive passenger data.”

The DOT did not say what specifically prompted the review, but that the action was part of the U.S. government’s “broader push to protect consumer privacy across the economy.”

In recent months, the U.S. Federal Trade Commission — which regulates consumer data privacy matters — has banned data brokers and other companies from sharing users’ sensitive location and browsing data with others, ordered companies hit by data breaches to overhaul their security practices and pledged to strengthen the federal law known as COPPA that prevents companies from obtaining data on children under the age of 13.

The DOT said that the FTC is “also exploring rules to more broadly crack down on the harms stemming from surveillance and lax data security.”

Transportation Secretary Buttigieg said the DOT’s privacy review will be carried with the expertise and partnership of Sen. Ron Wyden, a senior Democrat who sits on the Senate Intelligence Committee.

Wyden has raised alarms about the sharing and sale of sensitive U.S. consumer data to data brokers — companies that collect and resell people’s personal data, like precise location data, often derived from their phones and computers.

In recent months, Wyden has warned that data brokers sell access to Americans’ personal information, which can identify which websites they visit and the places they travel to. Wyden also warned that U.S. intelligence agencies can — and have — purchased commercially available information about Americans from data brokers, which the intelligence community argues that they don’t need to obtain a search warrant for data they can purchase.

In remarks, Wyden said: “Because consumers will often never know that their personal data was misused or sold to shady data brokers, effective privacy regulation cannot depend on consumer complaints to identify corporate abuses.”

We’re in Myanmar right now and it’s SO epic… click here to follow along on Instagram.

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Home » Gear » EPIC AloSim Review – DATA, CALLS AND MORE (2024)

EPIC AloSim Review – DATA, CALLS AND MORE (2024)

It was July 2018, and I’d just arrived in New Delhi, India for the very first time.

After confidently getting on the metro and popping out into the universe of chaos that only those have been there can really comprehend, I made a grave mistake that would end up costing me hundreds of dollars, and a bit of my sanity.

Armed with everything BUT data connection, I hailed a rickshaw, and thus found myself a victim of an elaborate scam… that was only possible because of my lack of internet connection.

Getting a physical SIM isn’t always possible the moment you get off a flight, and roaming charges can be expensive and finicky. But luckily… travel technology has increased leaps and bounds in the years since, and it’s now possible to be connected from the second you arrive in your destination thanks to eSIMS.

A win for backpackers, and a rightful lose for scammers everywhere.

In this post, I’m going to get give you an honest AloSIM review , a new eSIM company that’s about to shake up the market and make your travel days so much easier.

Let’s get into it.

Who The Fuck Are AloSim ?

Okay, okay… I was thinking the same thing. In the wide world of international SIM cards , there are a lot of players these days, and if I had to guess, you probably haven’t heard of AloSim. A relatively new player to the eSIM game, AloSIm hit the ground running in 2022 with one goal – to make the process of buying and using electronic SIMS easy and affordable AF for us budget travelers.

And to be honest…they’ve absolutely nailed it. With connectivity in over 170 countries along with handy regional options, this is one of the best (and perhaps underrated) eSims on the market.

Based in Canada and run by CEO Justin Shimoon, of the existing company Affinity Click, these guys aren’t strangers to the digital communication scene. The same team is behind Hushed, an award-winning venture that’s provided temporary phone numbers to folks for years.

So while it may seem like AloSIM came out of nowhere compared to other top-performing eSIMS, rest assured that they have the industry experience you’d want to see.

All About eSim

But first, you may be wondering what eSIMS are all about … because they certainly weren’t around when I first hit the road as recently as 2017. So to catch those of you (like me) who are used to physical SIM cards, eSIMS are on a mission to revolutionize the data space for digital nomads, vacationers, and backpackers alike.

Instead of the usual jaunt of having to get yourself to the nearest “franchise” store as soon as you arrive in a new country (or resolve yourself to paying those seriously CRAZY roaming charges from your home carrier), eSIMS ensure you’re connected right off the bat. Like, from the second the plane wheels touch down, or for the overlanders among us, the minute we officially cross another border.

Completely forgoing those tiny (and environmentally unfriendly) pieces of plastic we’re all used to, eSim technology is already inside your phone, and is something you’ll be able to activate and updated digitally. In the case of eSIM providers like AloSim , it quite literally took me 5 minutes to get online.

Unlike physical SIMs that force you to stick to one carrier, these handy technological innovations allow you to switch between networks with a quick tap. Run out of data and need to upgrade? No more rushing to the nearest corner store – with eSIMs, you can stay comfy in your hammock and top up right from there.

But with so many pros, you may be wondering, what’s the catch? And unfortunately, there are few…

One hella’ important thing to know about eSIMs is that they do NOT work with all phones. So before you go on and grab one, do check compatibility! For my fellow iPhone users out there, you’re good to go with an 11 or later. And for Android, it’s a bit more variable, with a much, much longer “in the clear” list. But luckily, aloSIM has put together a fantastic (and user-friendly) tool to determine if your phone is supported .

Another thing to keep in mind with both eSims AND regular Sims is this: your phone MUST be unlocked to use them. If you bought yours through a phone company (particularly in the US), note that it MAY very well be locked. To help you figure out what you’re working with, here’s yet another iconic guide AloSIM put together so you can check .

Another negative of eSIMS is that they’re sometimes more expensive than physical, local data cards depending on the country. Even so, I really think they’re still worth it, particularly at the beginning of your trip so that you’ll be connected from moment one, which can be especially important when landing in scam-prone airports as I unfortunately found out.

AloSim Review

I’ll be honest, I’m not exactly an eSIM veteran over here. As a budget traveler, I’d had it drilled into my head that local Sims are ALWAYS cheaper, and had taken a bit of an octogenarian approach to the technology.

But I finally took the leap recently while on the road in Indonesia, during a time when I truly needed a SIM – my local card had run out, and the small, less-visited island I’m writing this from didn’t have many top-up options.

So trust me when I say I was truly impressed with AloSIM from the jump. Now let’s dive into exactly how this epic eSim really keeps you connected.

How Does It Work?

One of the best things about AloSIM is that it works with hundreds of local carriers around the globe. So as you move about from country to country, it keeps you tethered to the fastest carriers. And while it doesn’t work EVERYWHERE, 170+ nations is pretty close right?

As AloSIM connects to the speediest data options, the cool thing is you can keep your physical SIM card right in its usual space – where you can switch back to it at any time you’d like.

Using the Website/App

One thing I particularly loved about AloSIM’s site is that it’s designed with user experience in mind. Every question you could possibly have is answered, and they make sure you don’t have to rely on Google or YouTube to figure something out.

If you’re feeling it, you can also do it all on their app, which is the easiest way to top up data or modify plans.

…But back to their site!

As soon as you land on the homepage, you’ll be able to pop over to their “shop for eSim” page where you can choose between 170+ countries or 11 specific regions . Each destination has a few different packages to get you started – everything from 1 GB to 30 GB and for a few locales, even more.

Once you’ve made your selection and are ready to get connected, you’ll just need to create an account, and then you can pay and activate your shiny new travel eSIM . I found the QR code they provided super useful – I just scanned it with my phone and then I was able to get online in less than 5 minutes.

The website (or app) is also where you’ll go to top up when the time comes. The one thing to keep in mind is to MAKE SURE that you don’t delete your SIM! You can switch it off at any time, but you’ll need to store it in your phone to use it again.

What Destinations Are Covered?

Get hyped because AloSIM covers almost the entire WORLD! With over 170 countries on their roster, I think it’s better I get into the places you won’t be able to connect.

As an off the beaten path traveler through and through, I was hella’ impressed to see they cover countries like Afghanistan, Pakistan, and Tajikistan. You can even use AloSim while backpacking in Iran, which most eSIMS do not support due to sanctions. Pretty damn inclusive if you ask me.

Cuba, Syria, Yemen, Chad and Libya are a few examples of their exclusions. But most likely – you won’t be heading there anyway. The one country I was surprised to see left off the list is India – but their telecommunication procedures are admittedly wild, to say the least.

Another cool feature is that you can also purchase a regional SIM that will work in more than one country. While they don’t yet have a “whole world” option, you’ll find the following super handy for multi-country trips:

• Asia – China, Hong Kong, Indonesia, Japan, Macau, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, Vietnam, Bangladesh
• Asia 5 Pack – Hong Kong, Malaysia, South Korea, Taiwan, Thailand
• Eastern Europe – Bulgaria, Croatia, Austria, Czech Republic, Hungary, Slovakia, Lithuania, Latvia, Estonia, Greece, Serbia
• Europe – 34 countries
• French Guiana, Martinique and Guadeloupe
• Mediterranean – France, Spain, Italy, Croatia, Greece, Turkey
• North America – Canada, USA, Mexico
• Scandinavia – Denmark, Norway, Sweden, Finland
• South America – Argentina, Bolivia, Brazil, Chile, Peru, Uruguay, Colombia, Ecuador, Paraguay
• UK, Ireland, and Scotland
• Western Europe – France, Italy, Portugal, Spain

What Does AloSim Cost?

Unsurprisingly, the cost of AloSim varies a LOT depending on where in the world you’re going to be using it.

Europe (34 Countries)

• 1 GB $5.00 – 7 Days
• 2 GB $8.00 – 15 Days
• 3 GB $13.00 – 30 Days
• 5 GB $20.00 – 30 Days
• 10 GB $37.00 – 30 Days
• 2 GB $9.50 – 15 Days
• 3 GB $15.00 – 30 Days
• 50 GB $100.00 – 90 Days
• 100 GB $185.00 – 180 Days

North America

• 1 GB $8.00 – 7 Days
• 3 GB $21.00 – 30 Days
• 5 GB $32.50 – 30 Days
• 10 GB $50.00 – 30 Days

Now – that’s priced pretty fairly considering what you get. But if you’re like me and always seeking discounts, I’ve got something you might just like. To get a sweet cut off your AloSIM purchase, you can use our code, THEBROKEBACKPACKER , at checkout to make the price just a little bit more budget-friendly.

Does AloSim Offer Local Numbers?

Nope, they do not.

But, most of the AloSim packages DO offer FREE international phone number that customers can use to call and text all over the world.

But honestly – most travellers don’t even find this that much of an issue since these days we rarely make PHONE calls and generally use WhatsApp anyway. You’ll also find that Facetime Audio, Facebook Messenger calling, and all other social media calling apps work perfectly with nothing but data.

What’s unique about AloSIM compared to other companies though is their sister company – Hushed – is all about providing local phone numbers. So you can always grab one over there, starting at just $2.99 USD!

Pros and Cons of Using AloSim

What’s an AloSim review without some pros and cons? I think you can tell by now I’m firmly in the “get an AloSim camp” but in case I need to convince you further… why don’t you use our code THEBROKEBACKPACKER for the cheapest possible price? 😉

Covers 170+ countries

International phone number

Affordable prices

Hotspot capabilities

You can top up easily without buying a new SIM

Great live chat customer support

No local number

Doesn’t have a global plan option

Might not always be cheaper than physical SIMs

Connection sometimes didn’t work when the same local SIM company had no issues

Alternatives To AloSim

Now, it’s 2024 – the eSim community is large and growing, which means AloSim has got some competitors. But how do they really measure up? Let’s look into a couple:

GigSky eSIM – who’s been operating since 2010 – has quite similar prices to AloSim. Almost identical, really. Where things differ though is how many countries they cover. GigSky boasts coverage in 200 nations, which is pretty wild considering there are officially over 197. Like AloSim, they only offer data services – which means no local numbers. They also have an in-air option, but with an offer of 100 MB only, most airlines’ Wifi packages give far better value.

Another newcomer to the eSim market, YeSim operates in over 120 countries. While their prices are nearly the same as Alo’s, what sets them apart is they give out cheeky local phone numbers! Like I said, you don’t usually NEED a local number, but in some countries, you actually might to be able to sign up for ride-share apps and the like.

FAQ’s About AloSim

Here are a few commonly asked questions about AloSim…

Which is better AloSIM or airalo?

While airalo has wider coverage (and a global plan option), AloSIM has super competitive prices and much better customer support. Their website is also way more user friendly if you ask me.

Is AloSIM legit?

Indeed! While they’re a new player in the eSIM game, their founders have been involved in telecommunications for a minute. The SIM is real and works as it’s supposed to.

Can I use WhatsApp with AloSIM?

Of course! Whatsapp worked super fast with my AloSIM. You can use it for voice and video calls too.

Final Thoughts on AloSim

Coming from a bonafide “local SIM card only” traveler, you can call me impressed by all that AloSIM has to offer. The website is fantastic, the purchase and activation process was seamless, and I even got some work done thanks to its hotspot abilities.

I can’t even count the amount of times I’ve started to have a mini panic attack thinking I’d lost a SIM… a fear that’s completely erased thanks to the magic of eSIM. While you may think that AloSIMs prices aren’t always the cheapest, remember that they give you the HUGE luxury of connectivity from the second you arrive at your destination. And that’s a peace of mind this budget backpacker is willing to pay for.

I’ve hoped I’ve convinced you that this company is worth your purchase, but just as another reminder: you get to start out with a sweet discount. Just pop in the code THEBROKEBACKPACKER at checkout, and you’re good to go!

Happy travels fellow wanderer – and here’s to making them a bit more conveniently connected thanks to AloSIM 🙂

And for transparency’s sake, please know that some of the links in our content are affiliate links . That means that if you book your accommodation, buy your gear, or sort your insurance through our link, we earn a small commission (at no extra cost to you). That said, we only link to the gear we trust and never recommend services we don’t believe are up to scratch. Again, thank you!

Samantha Shea

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Industries Overview

Latest articles, amazon accounted for 40% of ecommerce sales, 4% of retail sales in 2023, tiktok won’t dial back on tiktok shop content, but it may not matter for time spent, ctv, identity consolidation, and more: ad trends that cookie deprecation will—and won’t—change, rebrand lessons from jell-o and its agency partner, two more banks with baas partnerships hit with fdic consent orders, in-store is the most popular way to discover clothing brands and products, how advertising agencies discuss ai in pitch conversations, tiktok can’t count on gen z for big gains in time spent anymore, credit cards are the most popular payment method in the us, youtube shorts introduces gated content feature, as battle for creators intensifies, about emarketer, data drop: 5 charts you need to navigate digital travel in 2024, bookings rebound, surpassing pre-pandemic volume.

This data drop was written with the assistance of AI.

In 2024, travel revenues are set to rise alongside significant growth in both leisure and business digital travel sales, reflecting a trend where over half of internet users now book their travel digitally. With digital ad spending in the travel industry keeping pace with these sales increases, digital content emerges as a crucial resource for prospective travelers.

Travel revenues will grow in 2024

Robust consumer confidence and a rebounding appetite for traditional travel experiences will boost overall market growth. In 2024, revenue growth will increase by single digits in Europe and North America and double digits in Latin America and Asia, according to Skift’s “Global Travel Outlook 2024” report. The airline industry is charting the course for a 12% revenue climb in 2024, with cruises buoying up by 11%.

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Dept. of Transportation to Review How Major U.S. Airlines Handle Passenger’s Private Data — What to Know

The review will include Allegiant Air, Alaska, American, Delta, Frontier, Hawaiian, JetBlue, Southwest, Spirit, and United.

The Department of Transportation (DOT) will conduct a privacy review of major airlines in the United States with a focus on how popular carriers handle and store passenger’s personal information.

The review will examine how the ten largest airlines in the U.S. collect, handle, and maintain the personal data of their customers, including procedures relating to the monetization of that passenger data, according to the DOT . The review will also look at how airlines prevent data breaches as well as any complaints or allegations of airline employees or contractors mishandling personal information.

The review will be handled by the DOT’s Office of Aviation Consumer Protection (OACP) and will include Allegiant Air, Alaska, American, Delta, Frontier, Hawaiian, JetBlue, Southwest, Spirit, and United.

“Airline passengers should have confidence that their personal information is not being shared improperly with third parties or mishandled by employees,” Transportation Sec. Pete Buttigieg said in a statement. “This review of airline practices is the beginning of a new initiative by DOT to ensure airlines are being good stewards of sensitive passenger data.”

The DOT said this is just the first of what will become “periodic reviews of airline privacy practices… to ensure that carriers adequately protect consumers’ personal information and follow the law.”

The Department said it will take action if it finds “evidence of problematic practices,” including launching investigations or issuing guidance.

Airlines have not been spared from widespread privacy breaches , like last year when hackers stole personal information for more than 8,000 pilot applicants from American Airlines and Southwest Airlines, according to CBS News . Or just this week when Reuters reported Spanish airline Air Europa confirmed the personal data of its customers may have been breached.

They also haven’t been immune from using that data to cash in, like when The Wall Street Journal reported last year United Airlines was considering using the passenger information it had to sell targeted ads, potentially on its in-flight entertainment system.

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Inside: the grand hotel, birmingham.

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Tucked away on Birmingham's Colmore Row, overlooking the grounds of St. Phillips Cathedral, sits The Grand Hotel Birmingham , an elegant Grade II-listed hotel dating back to 1879 that has hosted a myriad of notable figures, from Winston Churchill to Charlie Chaplin.

Swan through the imposing iron-bolted double doors, ascending gold-lined marble steps as you tiptoe across cool flagstone tiles flowing into The Grand's rich lobby adorned with antique granite pillars, towering oxidized mirrors, and glass-blown orbital pendant lights hanging from a tray ceiling.

Curve around olive velvet sofas and black marble coffee tables shielded by sprawling devil's ivy planted in mosaic stone pots and you'll glide under a retro arched doorway flowing into a wide reception.

Monochrome film portraits and gothic silver lamps garnish the cream-woven walls with round layered oak tables, an art nouveau black vintage mailbox, and a copper vase filled with yellow Easter lilies, eucalyptus, and twisted twigs twining towards a stained-glass art deco ceiling.

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Whilst a forest green ribbed iron reception desk sits in front of a stain glass geometric wall topped with echeveria in weathered stone pots and mid-century brass lamps.

Mottled grey double-door elevators encased in oak arched frames whiz up to the lavish two-floor Penthouse Suite, set across 1,450 square feet, at a price tag of $2,500 per night.

Wandering past a stretching Bronx oak reception table sat on black titanium legs with potted eucalyptus, you'll climb up a vintage oak staircase with a black round barrel mesh chandelier and a grand portrait collage of floppy disks.

French double doors open into a commodious living room with chevron herringbone wool rugs, golden lava bowls, and orange Murano glass lamps on oak tree stumps beside regency brushed velvet and stitched cotton library armchairs.

The living room in the Penthouse Suite.

Espresso wraparound leather studded chairs surround a grand oak banquet table with dried orange blossom nestled in stone pots and a four-tiered rippled glass chandelier hanging above.

Whilst dahlias and daisies sit in glass bulb vases on stretching Italian marble coffee tables, which make the perfect spot for sipping an americano as you hear the sound of crackling flames from your white stone fireplace.

Cracking log fire

As the moonlight peers in, snuggle up on your golden velvet corner sofa, nestled deep into camel linen-stitched cushions with sweeping panoramic views, or shoot a game of table football with a freshly made Pomegranate martini in hand, prepared at your dark oak liquor cabinet with single malt whiskeys, rose copper cocktail shakers, and potted terrariums.

Before diving into a Tolkien novel as you sink into a peach velvet tub chair with a copper lever arm floor lamp proving to be a sturdy reading companion.

Downstairs, modern pastel Charles Pedone artworks and dark oak cabinets line the walls of the master bedroom with pots of fern, marble geometric vases, glass bubbled tea lights, brass tea strainers, and a vintage bust of Beethoven.

A woven peach and cream sofa sits on licorice oak flooring in front of a magnolia granite fireplace with a mid-century orb chandelier overhead. Whilst Charcoal wool herringbone throws drape over a thin-framed four-poster bed guarded by obsidian bedside hutches faced with rustic metal caged doors.

Stretch out starfish-style and you'll spot bright roses and dahlias in a mint ceramic vase on a black granite coffee table bordered by plush burnt orange sofa chairs.

The bedroom in the Penthouse Suite.

Sliding glass doors glide into a paltial bathroom with geometric zebra floors, twining sword fern in rippled stone pots, a deep soaking tub, cozy cotton dressing gowns, Carrara marble countertops lit up by satin nickel wall lanterns, and a walk-in shower chamber wrapped in cream tiles.

Zigzag honey carpets flow into a dressing room with floor-to-ceiling fitted oak wardrobes and an art nouveau espresso point pouf perched in front of an oak dressing table with a Hollywood vanity mirror.

The lavish bathroom in the Penthouse Suite.

In the cellar of The Grand , down a dove grey stone staircase with a royal wool runner held in place with gold brass baroque rods, you'll stumble across its retro American diner, Isaac's .

Art deco stained glass double doors swing open to reveal this Emerald City suburb wrapped in forest green, with black and white honeycomb mosaic tiles, fresh corn plants in bronze studded pots, monochrome shots of New York City, brass coned wall lights, and a gold glass globe chandelier hanging from a French rosette ornate ceiling.

Little cozy nooks sit behind velvet curtains whilst an oak grand piano with a vintage brass gramophone marks the entrance to a raised dining area plastered in vinyl floral trellis wallpaper with brass crescent floor lamps, olive green Chesterfield leather sofas, and gold pleated wall artworks.

Sip spicy margaritas and cherry bakewell sours at a black storm rippled-marble bar with forest green glossed tiles and chrome dome lamps as you graze on homemade nachos, baby back ribs, and seafood platters with rock oysters, scarlet prawns, dressed crab, and tuna tataki.

Juicy short rib burger at Isaac's.

On the next floor, stride through arched oak doors into The Grand's sumptuous cocktail bar and afternoon tea parlor, Madeleine .

Utopian gold snowflake orb chandeliers dangle from an espresso-coffered ceiling embellished with gold rosettes, whilst copper antique mirrors with dried cherry blossoms adorn the walls.

Madeleine Bar

Danish magenta velvet high-back armchairs with hot pink tassels float around mocha-rippled marble tables and cream leather armless lounge chairs sit on damask orange rugs with navy blue velvet swivel tub chairs and echeveria in weathered stone pots.

Whilst burnt orange marble pillars support its large Georgian windows with laser-cut bronze wall lights decorating espresso oak paneled walls.

Antique red leather stools surround a camel suede bar topped with black marble and Edwardian brass lamps where you can sip a glass of Perrier-Jouet Grand Brut or admire pencil sketched portraits and monochrome film photographs of the old Birmingham Bullring.

Curling up on peach velvet Chesterfield sofas beside an espresso-inset Scandinavian fireplace, nibble on chicken lemon and thyme sandwiches, smoked salmon tarts, and Valrhona gianduja petit gateau with hazelnut financier and soft caramel, complete with an earl grey compote scone with whipped Rodda's clotted cream.

Chic, retro style at its finest.

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