Tourism Service Portfolio
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The document proposes a concept called a "Tourism Service Portfolio" (TSP) to facilitate data exchange for developing smart tourism services. The TSP would list required smart tourism services for a destination and the corresponding regional data needed to provide each service. It aims to help data owners recognize what data is needed and encourage trading of data between owners and service providers. Developing a TSP requires determining service priorities through stakeholder consensus. The concept is presented as a way to address challenges in collecting the large amounts of data required from various private owners for intelligent tourism applications.
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Tourism Service Portfolio
- 1. ENTER 2017 Research Track Slide Number 1 Tourism Service Portfolio Hidekazu Kasahara, Masaaki Iiyama, Michihiko Minoh Kyoto University Enter2017
- 2. ENTER 2017 Research Track Slide Number 2 Summary of Proposal • For developing the smart tourism in destination, how to collect the data is key. (Characteristics of Ai technology) • Smart service providers and data owners are not always the same. (Exception is data giant) • Most of data is owned by various data owners (data ownership). • The data owners do not know the need for their data (Recognition gap). • So, by making the list of required data, we will facilitate the data exchange among data owners and service providers. • The list is called as “tourism service portfolio.”
- 3. ENTER 2017 Research Track Slide Number 3 Summary of Proposal Usage is Limited to Members in Closed Market Usage is Not Limited Tourist Service Portfolio (TSP) TSP Priorities DataTSP Priorities Data Open Access RDClosed Access RD Private Sector Public Sector Provided via API Dynamic Data Static/Statistic Data GPS Traj. SNS Post Transaction Biological camera Weather Population Road Map Disaster Regional Data Owners TSP shows the needs of data to data owners
- 4. ENTER 2017 Research Track Slide Number 4 Contents • Summary • Background • Research Objectives • Methodology • Previous Researches • Definition of Smart Tourism • Tourism Service Portfolio • Conclusions Enter2017
- 5. ENTER 2017 Research Track Slide Number 5 Background • Tokyo Olympic in 2020 • Governmental Policy (MIC/ METI/ JTA) – Tourism services using IoT/ Bigdata/ Artificial intelligence technology – This can be called “smart tourism services.” • However, no standard concept for developing smart tourism services in the destination. – What’s smart tourism services – How and who provides. – What kind of data are needed. Enter2017
- 6. ENTER 2017 Research Track Slide Number 6 Research Objectives • Propose new standard concept for developing smart tourism services in the destination from the viewpoint of informatics. – What’s smart tourism – What’s the most important problem – How to solve the problem Enter2017
- 7. ENTER 2017 Research Track Slide Number 7 Methodology • Closed discussions with tourism community – Japanese government (MIC, METI) – Kyoto city local government – Venture companies in Kyoto – Privacy/security experts • Open discussion in symposium – IT companies • Yahoo!, NAVITIME, NEC – Researchers • University, Think Tank • Refer previous researches Enter2017
- 8. ENTER 2017 Research Track Slide Number 8Enter2017 Traditional Tourism Mainframe Flight Booking e Tourism Internet Web-based technology Room Reservation Web Guide and Map Smart Tourism Intelligent information processing (AI) Internet of Things Big Data processing Smart phone, Sensors Real-time Recommendation Evacuation Support Traffic Congestion Avoidance Resource Optimization What’s smart tourism? Personalize Real time
- 9. ENTER 2017 Research Track Slide Number 9 Previous Research • “Tourism supported by integrated efforts at a destination to collect and aggregate/harness data derived from physical infrastructure, social connections, government/organizational sources and human bodies/minds in combination with the use of advanced technologies to transform that data into on-site experiences and business value- propositions with a clear focus on efficiency, sustainability and experience enrichment.“ (Gretzel et al. 2015) Enter2017 Data Technology Service Coexistence of Tourists and Inhabitants From the viewpoint of informatics ….
- 10. ENTER 2017 Research Track Slide Number 10 Difficulty of Data Collection • Intelligent information processing requires vast amount of data. • Various data owners collect data independently. (Data ownership) • Smart service providers and data owners are not always the same. • Data monopoly by Data Giant （Google, Apple, Facebook , Amazon) • It is difficult for new service providers to collect the data and develop smart tourism services, especially in a region. Enter2017 Data Techn ology Servic e How to collect data? Technical and social problem.
- 11. ENTER 2017 Research Track Slide Number 11 Regional Data (RD) Enter2017 with Global Attribute Dynamic Data Static Data Statistical Data Statistically Integrated • The dynamic data show real-time and individual condition of an object that changes in short time, and are available accordingly in short time, less than 2-3 hours in general. • The static data show individual status of an object that does not change in long time, and the object is mainly environmental object. The average temporal range is generally long. • The statistical data are integrations of dynamic data like a number of tourists who visit a destination. ex. GPS
- 12. ENTER 2017 Research Track Slide Number 12 Dynamic Data Open Access RDClosed Access RD Private Sector Public Sector Provided via API Dynamic Data Static/Statistic Data Usage is Not Limited GPS Traj. SNS Post Transaction Biological Camera Weather Population Road Map Disaster Regional Data Owners How to collect dynamic data owned by private sector? To share the service provider’s data needs. Usage is Limited Service providers
- 13. ENTER 2017 Research Track Slide Number 13 TSP Usage is Limited to Members in Closed Market Usage is Not Limited Tourist Service Portfolio (TSP) TSP Priorities DataTSP Priorities Data Open Access RDClosed Access RD Private Entities Public Entities Provided via API Dynamic Data Static/Statistic Data GPS Traj. SNS Post Transaction Biological camera Weather Population Road Map Disaster Regional Data Owners TSP shows the needs of data to data owners
- 14. ENTER 2017 Research Track Slide Number 14 Tourism Service Portfolio (TSP) • TSP is defined as the list of smart tourism service (STS) required in the destination and correspondent RD to each required STS. • By using TSP, data holders can recognize the needs for their data and can start trading among service providers. • STS listed in TSP are given priorities in order to indicate the importance of STS based on the degree that the STS improves the satisfactions of tourists and inhabitants. • The priorities are expected to be decided by the consensus of all stakeholders including data owners, STS providers, inhabitants and local governments.
- 15. ENTER 2017 Research Track Slide Number 15 Service User Tech nology Static Data Dynamic Data Current Service Prior ity Offline Map Tourist Map (Toilet, Police box, ATM, Cycle, Parking, Tourist Spot, AED) Event Only Private A Transfer Guide Tourist Time table, Map Yes - SNS post Analysis DMO Statistical Analysis SNS post No A Travel Guide Tourist Tourist Spot Data, Tourist Spot, Tourist Route Yes A Disaster Alert Tourist/ Inhabitant Disaster Data Yes A Route Recommendation Tourist Recomm endation Tourist Spot, Tourist Route Tourist Trajectory, Climate Data No B Spot Recommendation Tourist Recomm endation Tourist Spot, Tourist Route SNS post, Tourist Trajectory, Climate Data No B Congestion Forecast Tourist/ Inhabitant/ DMO Positon Data Analysis Tourist Trajectory, Transportation Trajectory No C Bus Arrival Forecast Tourist/ Inhabitant Positon Data Analysis Map Tourist Trajectory, Bus Trajectory No C Tourism Service Portfolio (TSP) Enter2017
- 16. ENTER 2017 Research Track Slide Number 16 Regional Data Platform Private Sector Data Public Sector Data Data Processing for Services Preprocessing (Incl. Privacy) Regional Round Table for Making TSP Regional Data Platform (RDP) Smart Service Portfolio (STP) Making STP Data Collecting based on STP Smart Service Provider Private Data Holder Public Data Holder RDP collects RD from various data owners, and transforms the collected RD, to the symbol data by using intelligent information processing, distributes the symbol data. Data Data Data (ex. Mobile Carrier, Rail, Retail) (ex. Government) University Government Incubation Support
- 17. ENTER 2017 Research Track Slide Number 17 Smart Tourism Definition • Tourism supported by real-time and personalized tourism services based on a list of required services in a destination with use of intelligent information processing, and regional data (RD) collected in the destination for promoting on-site experiences of tourists and coexistence with inhabitants and tourists. Enter2017
- 18. ENTER 2017 Research Track Slide Number 18 Conclusions • This paper propose new standard concept for developing smart tourism services in the destination from the viewpoint of informatics. • List the issues for collecting data required for smart tourism. • Newly define the smart tourism from the view point of informatics. • Propose the concept of smart tourism portfolio. Enter2017
- 19. ENTER 2017 Research Track Slide Number 19 Future Work • Make tourism service portfolio. – Tourist Services Benchmarking among destinations internationally. • Privacy issues. • TAP maker issue. • Data ownership. • Business model. • Etc… Enter2017
- 20. ENTER 2017 Research Track Slide Number 20 • Appedix
- 21. ENTER 2017 Research Track Slide Number 21 Smart Service Requires Regional Data Enter2017 Regional Data (RD) with Global Attribute Dynamic Data Static Data Statistical Data Statistically Integrated • The dynamic data show real-time and individual condition of an object that changes in short time, and are available accordingly in short time, less than 2-3 hours in general. • The static data show individual status of an object that does not change in long time, and the object is mainly environmental object. The average temporal range is generally long. • The statistical data are integrations of dynamic data like a number of tourists who visit a destination. ex. GPS
- 22. ENTER 2017 Research Track Slide Number 22 Regional Data Enter2017 Recently Publicized as Open Data Traditionally Publicized as Open Data Type Data Global Attribute Dynamic Data Tourist location Sales transaction Surveillance camera Transportation status SNS post Climate Transportation (Taxi, Bus, Train, etc) Disaster alert No No No No No No No No Static Data Event Public facility (Toilet, AED, Police, etc) Tourist spot data Time schedule Road network Geographical map No No No / Yes No / Yes Yes Yes Statistical Data Tourist statistics Population statistics Weather statistics Sales statistics Yes Yes Yes Yes
- 23. ENTER 2017 Research Track Slide Number 23 Regional Data Enter2017 Recently Publicized as Open Data Traditionally Publicized as Open Data Type Data Global Attribute Dynamic Data Tourist location Sales transaction Surveillance camera Transportation status SNS post Climate Transportation (Taxi, Bus, Train, etc) Disaster alert No No No No No No No No Static Data Event Public facility (Toilet, AED, Police, etc) Tourist spot data Time schedule Road network Geographical map No No No / Yes No / Yes Yes Yes Statistical Data Tourist statistics Population statistics Weather statistics Sales statistics Yes Yes Yes Yes Difficult to collect
- 24. ENTER 2017 Research Track Slide Number 24 Issues of Data Collecting • Ownership – Data has measured and collected by various owners. – Data holder has motivation to keep the data inside. • Probe car data Car navigation, auto maker • Location data mobile carrier • Surveillance camera Retail, rail • Data Giant （Google, Apple, Facebook , Amazon) – They collect dynamic data via services. – They play leading role in developing smart services. • New smart service providers try collecting RD independently, but can collect too small number of RD to machine learning. • Easy access to RD promotes smart services.
Editor's Notes
- For Short Papers: each presentation is approximately 15 minutes long. It is recommended to use 12 minutes to present and 3 minutes to discuss. Thank you for your introduction. I’m Hidekazu Kasahara from Kyoto University. My major is informatics, mainly tourism informatics, GPS trajectory analysis, partly image analysis. Let me start my presentation about tourism service portfolio. This paper reports the current status of our discussion about the smart tourism in a destination from the view point of informatics. So, this can be considered as a kind of discussion paper. This paper proposes a data-focused concept for developing the smart tourism in a destination. Tourism service portfolio is a key idea of our proposed concept.
- For developing the smart tourism in destination, how to collect the data is key. Smart service providers and data owners are not always the same. However, most of data is owned by various private entities like GPS (data ownership). The data owners do not know the need for their data (Recognition gap). So, by making the list of required data, we will facilitate the data exchange among data owners and service providers. The list is called as “tourism service portfolio.”
- This slide shows the contents
- Tokyo Olympic games will be held in 2020. So, Japanese government intends to promote the number of tourists by new tourism services using IoT, big data, artificial intelligence technology. that can be called “smart tourism services.” However, there is no standard concept for developing smart tourism services in the destination. Without the socially recognized concept, some serious problems could arise.
- So, we try proposing
- We are shaping the standard concept with following discussions and reference studies.
- As it is said in yesterday’s key note speech, tourism services are changing with advance of information technology. Corresponding to main frame, flight booking. Emergent of the internet and web, online room reservation and web-based guide and map, And now, on-site services during staying in a destination can be realized due to the rapid advances of sensors, smartphones, big data processing, machine learning and Internet of things (IoT). Namely, intelligent information processing technology The direction of change is more personalization and real time. (In the early stage of the mainframe age in 1950s, flight booking systems went online from traditional manual booking system. In early 2000s, the advances of web-based technologies led to emergence of e-Tourism. For instance, tourism guides and map services were globally distributed and web-based room reservation services were opened to consumers. However, these services are mainly used during planning and preparing stage before travel. And now, on-site services during staying in a destination can be realized because rapid advances of sensors, smartphones, big data processing, machine learning and Internet of things (IoT). ) In near future, real-time and personalized smart tourist information services like real time spot recommendation, evacuation support, and traffic congestion avoidance, touristic resource optimization can be realized.
- “Tourism supported by integrated efforts at a destination to collect and aggregate/harness data derived from physical infrastructure, social connections, government/organizational sources and human bodies/minds in combination with the use of advanced technologies to transform that data into on-site experiences and business value-propositions with a clear focus on efficiency, sustainability and experience enrichment.“ Gretzel The definition by Gretzel’s work indicates their concept of the smart tourism. But, from the viewpoint of informatics, it should more focus on data and technology. Because, for smart tourism, data collection and processing over a destination becomes important. And there is some difficulty in collecting enough quantity of data. Please notice that the data required by smart tourism includes sensor data like gps trajectory, surveillance camera movies. In the paper, I pointed out the importance of co-existence with tourists and inhabitants. However, it is not explained now because of time limitation. The definition indicates only concept of the smart tourism and, lacks concrete viewpoints for realizing the smart tourism. In addition, inhabitants live at the destination, therefore, not only tourists but also inhabitants should be taken into consideration for the smart tourism (Gretzel 2015b). In other words, what kind of services are necessary for both of them should be discussed in the destination.
- I explain the difficulty of data collection more in detail. Data Ownership Data have measured and collected by various owners. Data owners have motivation to keep the data inside. Probe car data Car navigation, auto maker Location data mobile carrier Surveillance camera Retail, rail Smart service providers and data holders are not always the same. This is a big issue. The data distribution is difficult among them. And only Data Giant （Google, Apple, Facebook , Amazon) have enough data and can provide the smart services. They collect data via services. They play leading role in developing smart services. New smart service providers try collecting RD independently, but can collect too small number of RD to machine learning. Because data, service and technology are co-related each other, Well how to solve the problem. We call the list of tourism services as a tourism service portfolio (TSP). RD can be categorized into three types: a type of RD that changes in short time is defined as dynamic data like GPS trajectory, statistically processed dynamic data is defined as statistical data like tourist statistics, and the other type of RD that does not change for long time is defined as static data like time schedule of train. From different viewpoint, RD have an attribute whether the RD can be used globally or not, which is defined as the global attribute. The statistical data have the global attribute in general. A part of the static data can have the global attribute. The available temporal range of RD is short, therefore, it is difficult for off-site parties to collect efficiently. The regional parties will be competitive to off-site parties to collect and use RD. Since there are various types of RD, it is impossible to collect all RD even for regional parties. Hence it is reasonable to consider the STS first and then, to prioritize what type of RD to be collected. Based on our interviews with some regional parties including local governments and venture companies, many regional parties that provide STS are so small that they cannot collect necessary RD by themselves.
- This shows category of regional data that are used for the smart services in the region. We categorize the reginal data into three types. Since RD show the status of objects such as humans, things and events in the region, RD have utility values for most of people who stay in the destination. RD can be categorized into three types: dynamic data, static data and statistical data. The dynamic data show real-time and individual condition of an object that changes in short time, and are available accordingly in short time, less than two or three hours in general. The static data show individual status of an object that does not change in long time, and the object is mainly environmental object like road network. The average temporal range of the static data is generally long. The statistical data is an integration of dynamic data like a number of tourists who visit a destination.
- This figure shows the relationships of data owner and service providers Among these, it is very difficult to collect the dynamic data. And these data are mainly owned by private sector entities. So, the issue is hoe to collectthe dynamic data. 観光客が求める観光情報サービスは数多いが，資金や人材といった経営資源に限りがあるので，観光地はどのサービスを提供するかを選択しなければならない．
- 観光客が求める観光情報サービスは数多いが，資金や人材といった経営資源に限りがあるので，観光地はどのサービスを提供するかを選択しなければならない．
- TSP is defined as the list of STS required in the destination and correspondent RD to each required STS. STS listed in TSP are given priorities in order to indicate the importance of STS based on the degree that the STS improves the satisfactions of tourists and inhabitants. Since the priorities are expected to be decided by the consensus of all stakeholders including data owners, STS providers, inhabitants and local governments, TSP shows regional strategic objectives of tourism. Stakeholders can share the objectives via TSP. TSP is used for confining the quantity of collected RD to minimize. The inhabitants should be considered as one of stakeholders because they suffer disadvantages, for example the traffic congestions, due to the increase of tourists. Regarding the inhabitants as a stakeholder of smart tourism, we can split STS into two types: the former one is STS for tourists, and the latter one is STS for inhabitants. Both type of services can be realized by using the RD. In this paper, we focus on describing the STS for tourists. Some of STS can be used for both of tourists and inhabitants. For example, if the tourists avoid the crowded public bus according to an advice from a congestion avoidance assistance service, disadvantages of inhabitants suffered from the bus congestion will decrease. Determining who construct TSP is an issue. The organization that has a responsibility of the destination tourism management like destination management organisation (DMO) is the ideal decision maker. Though making TSP needs highly technical knowledges about STS and intelligent data processing, most of local governments and DMO lack enough ability. Collaboration with academic organisations which can select and filter the potential STSs for TSP will be a solution to construct TSP.
- RDP is defined as a platform which collects RD from various data owners, and transforms the collected RD, mainly the sensor data to the symbol data that have semantic information by using intelligent information processing technology, and distributes the symbol data to STS providers. As the RDP business model, three models are thinkable. The first model is a “governmental budget model”, relying on a governmental budget, the second model is a “corporate model”, obtaining funds by selling services and data by itself, and the third model is a “mixture model”, a mixture of the first and the second models. The business models cover all activities of RDP system, but do not cover activities of each STS provider which uses the symbol data output from RDP. Costs of the RDP system mainly consist of database server maintenance, data collecting and data processing. As revenue sources, we guess two types of revenue sources. As the data sales, RDP can sell the RD for corporate users. As the data processing agency, RDP sells their data processing ability for small companies without the ability. Not small number of companies have RD but cannot process the RD by themselves.
- Considering the all, we defined the smart tourism as follows; We define new definitions, STS, RD and We call the real-time and personalized tourism service as a smart tourism service (STS), the list of services as a tourism service portfolio (TSP). RD can be categorized into three types: a type of RD that changes in short time is defined as dynamic data like GPS trajectory, statistically processed dynamic data is defined as statistical data like tourist statistics, and the other type of RD that does not change for long time is defined as static data like time schedule of train. From different viewpoint, RD have an attribute whether the RD can be used globally or not, which is defined as the global attribute. The statistical data have the global attribute in general. A part of the static data can have the global attribute. The available temporal range of RD is short, therefore, it is difficult for off-site parties to collect efficiently. The regional parties will be competitive to off-site parties to collect and use RD. Since there are various types of RD, it is impossible to collect all RD even for regional parties. Hence it is reasonable to consider the STS first and then, to prioritize what type of RD to be collected. Based on our interviews with some regional parties including local governments and venture companies, many regional parties that provide STS are so small that they cannot collect necessary RD by themselves.
- Since RD show the status of objects such as humans, things and events in the region, RD have utility values for most of people who stay in the destination. RD can be categorized into three types: dynamic data, static data and statistical data. The dynamic data show real-time and individual condition of an object that changes in short time, and are available accordingly in short time, less than two or three hours in general. The static data show individual status of an object that does not change in long time, and the object is mainly environmental object like road network. The average temporal range of the static data is generally long. The statistical data is an integration of dynamic data like a number of tourists who visit a destination.
- This is some examples of regional data.
- This is some examples of regional data.
- The data ownership is an important factor that affects the RD collection by the regional entities. As the number of data owner increases, the cost of RD collection increases. Also, the data owner has motivation to keep the RD inside when the RD include individual identification information, company secret, or competitive information. We call the former issue as a multiple data owner issue, and the latter issue as a secret data issue. In case the multiple data owner issue, it is difficult to collect various RD from various data owners. データの保有者とサービス提供者が異なることが多い点がスマートサービスの特徴