Download to read offline
Expanding typologies of tourists spatio-temporal activities using the sequence alignment method
The document presents research using sequence alignment methods (SAM) to analyze GPS tracking data from visitors to Ueno Zoo in Tokyo, Japan. 113 visitors were tracked and their movements divided into sequences of 30 zones within the zoo. SAM was used to cluster visitors into groups based on similarities in their movement sequences. Several clusters emerged that represented typical routes through the zoo. However, limitations were identified in the preliminary analysis due to potential biases from the sample tracking data. The researchers propose future work to quantitatively evaluate the clustering results and examine relationships between clusters and visitor attribute data.
More Related Content
Expanding typologies of tourists spatio-temporal activities using the sequence alignment method
- 1. ENTER 2016 ResearchTrack Slide Number 1 Expanding Typologies of Tourists’ Spatio-temporal Activities Using the Sequence Alignment Method Junya Kawase and Fumiko Ito Department of Urban System Science Tokyo Metropolitan University, Japan j.kawase0922@gmail.com
- 2. ENTER 2016 ResearchTrack Slide Number 2 Introduction • Activities have TWO consecutive aspects – Space – Time • GPS devices are useful for activity surveys – When and where the subjects have passed – Where the subjects have been staying
- 3. ENTER 2016 ResearchTrack Slide Number 3 Introduction • We often make groups by using subjects’ attribute data: – Age – Gender – Accompanying person • Is above-mentioned grouping always appropriate method ?
- 4. ENTER 2016 ResearchTrack Slide Number 4 Introduction • We have to divide the subjects according to their REAL activities • What is important INDEX of tourists activities? – Combination of sites tourists visited – Order of visits to the sites – Time they spend in each sites
- 5. ENTER 2016 ResearchTrack Slide Number 5 Challenge • A quantitative method is required in order to clarify and compare characteristics of tourists activities.
- 6. ENTER 2016 ResearchTrack Slide Number 6 Sequence Alignment Method • SAM is: – the basic tool of bioinformatics – a method of comparing sequences of characters and measuring similarity and difference of them
- 7. ENTER 2016 ResearchTrack Slide Number 7 Levenshtein Distance A> AA B B C C C A> AA B B B C C A> AA B C C C B – To count is the minimal number of edit operation required to change one sequence of characters into the other C 1 edit operation 1 edit operation
- 8. ENTER 2016 ResearchTrack Slide Number 8 Making Sequences of Characters Study Area >AAABBCCCDDEE >AAAACCCDDDE >AAAAAAADDDEE A BC D E A BC D E A A A B B E CCCD D E Analysis by Using SAM
- 9. ENTER 2016 ResearchTrack Slide Number 9 Previous studies • Wilson (1998): illustrated SAM use in the analysis of daily activity patterns derived from time-use diaries.
- 10. ENTER 2016 ResearchTrack Slide Number 10 Previous studies • Shoval and Issacson (2007): conducted GPS-tracking activity analysis of tourists visiting the Old City of Akko(Israel) and obtained a taxonomic guide tree from which they derived clusters of typical patters by applying SAM. • Shoval et al.(2015) conducted typologies of tourists visiting Hong Kong.
- 11. ENTER 2016 ResearchTrack Slide Number 11 Preliminary Analysis • Techniques of SAM for typologies of spatio- temporal activities are not confirmed sufficiently • As a first step, To follow previous studies
- 12. ENTER 2016 ResearchTrack Slide Number 12 Study Area • Ueno Zoo (Tokyo) has: – 14.2 ha site area – 2 entrance gates – 3 exit gates – East Garden – West Garden – bridge – small monorail line – footfall of 3.6 million / year East Garden West Garden
- 13. ENTER 2016 ResearchTrack Slide Number 13 Data Collection by GPS Loggers • To distribute GPS loggers at the Main Gate • To collect loggers at the 3 exit gates • We obtained 113 valid sets in the day Main Gate
- 14. ENTER 2016 ResearchTrack Slide Number 14 Zoning Zoo Site Eh Eg Ei Eb Ef Ee EaEc Ed El Ej Ek Em Es En Ba Wh Wa Ma Wb Wc Wj Ws We Wi Wv Wk Wd Wf Wg • To divide the zoo site into 30 zones • Each of the zones is assigned a code with two alphabets
- 15. ENTER 2016 ResearchTrack Slide Number 15 • To convert the subjects’ locations into codes once every minute Eh Eg Ei Eb Ef Ee EaEc Ed El Ej Ek Em Es En Ba Wh Wa Ma Wb Wc Wj Ws We Wi Wv Wk Wd Wf Wg Zoning the Zoo Site
- 16. ENTER 2016 ResearchTrack Slide Number 16 Application • ClustalTXY (Wilson, 2008)
- 17. ENTER 2016 ResearchTrack Slide Number 17 Preliminary Analysis 6300102 6300010 6300146 6300046 6300045 6300112 6300040 6300038 6300149 6300070 6300145 6300027 6300134 6300079 6300086 6300109 6300083 6300082 6300126 6300113 6300138 6300085 6300081 6300093 6300098 6300095 6300104 6300136 6300018 6300091 6300033 6300064 6300073 6300031 6300124 6300063 6300110 6300030 6300078 6300060 6300107 6300053 6300062 6300039 6300101 6300094 6300037 6300065 6300071 6300105 6300076 6300114 6300139 6300130 6300131 6300077 6300099 6300058 6300052 6300009 6300120 6300050 6300068 6300142 6300119 6300008 6300092 6300115 6300103 6300067 6300151 6300024 6300006 6300026 6300108 6300020 6300011 6300127 6300015 6300090 6300097 6300096 6300047 6300005 6300048 6300117 6300029 6300075 6300028 6300116 6300003 6300004 6300066 6300042 6300137 6300049 6300100 6300041 6300002 6300016 6300025 6300140 6300150 6300007 6300087 6300084 6300141 6300143 6300051 6300072 6300106 6300129 6300133 AA DDCCBB EE FF • Whether stayed for tens of minutes somewhere or not • Which zone they stayed for tens of minutes Clusters don’t have typical routes.
- 18. ENTER 2016 ResearchTrack Slide Number 18 Limitation of Preliminary Analysis Group (i): East Garden → West Garden (46 subjects) Group (ii): East Garden → West Garden → East Garden (54 subjects) Group(iii): other route (13 subjects)
- 19. ENTER 2016 ResearchTrack Slide Number 19 Limitation of Preliminary Analysis 6300102 6300010 6300146 6300046 6300045 6300112 6300040 6300038 6300149 6300070 6300145 6300027 6300134 6300079 6300086 6300109 6300083 6300082 6300126 6300113 6300138 6300085 6300081 6300093 6300098 6300095 6300104 6300136 6300018 6300091 6300033 6300064 6300073 6300031 6300124 6300063 6300110 6300030 6300078 6300060 6300107 6300053 6300062 6300039 6300101 6300094 6300037 6300065 6300071 6300105 6300076 6300114 6300139 6300130 6300131 6300077 6300099 6300058 6300052 6300009 6300120 6300050 6300068 6300142 6300119 6300008 6300092 6300115 6300103 6300067 6300151 6300024 6300006 6300026 6300108 6300020 6300011 6300127 6300015 6300090 6300097 6300096 6300047 6300005 6300048 6300117 6300029 6300075 6300028 6300116 6300003 6300004 6300066 6300042 6300137 6300049 6300100 6300041 6300002 6300016 6300025 6300140 6300150 6300007 6300087 6300084 6300141 6300143 6300051 6300072 6300106 6300129 6300133 AA DDCCBB EE FF Group (i) Group (ii) Group (iii)
- 20. ENTER 2016 ResearchTrack Slide Number 20 Main Analysis • To conduct typologies by SAM for: – Group (i) – Group (ii) • Group (iii) excluded from this analysis
- 21. ENTER 2016 ResearchTrack Slide Number 21 Main Analysis 6300079Agn3 6300085Aen1 6300109Adn1 6300027Aen1 6300134Abn1 6300129Fcn3 6300072Fgm1 6300106Fgn1 6300095Ago3 6300104Aaq1 6300077Dgr3 6300099Dgp1 6300130Deo1 6300131Ddm1 6300008Dgr3 6300003Eap3 6300097Dam1 6300020Deq1 6300137Edp3 6300076Ddo1 6300009Dfm1 6300120Deo1 6300103Dgn1 6300119Dco1 6300115Dcr1 6300138Ado1 6300067Dcq1 6300151Ddm3 6300024Dan1 6300086Agn3 6300047Dao1 6300004Eap3 6300107Cao1 6300094Ccq3 6300037Cco1 6300073Bcq1 6300143Edp1 6300064Baq1 6300110Bap1 6300063Bbq3 6300124Bgo1 6300127Ddp1 6300018Bgq1 6300117Dgq1 6300060Baq1 6300093Aap1 77 66 44 33 22 1155 For Group (i)
- 22. ENTER 2016 ResearchTrack Slide Number 22 Time-Space Path Map • Time-space path maps represent subjects’ movement by lines that increase in height by one meter for every elapsed minutes.
- 23. ENTER 2016 ResearchTrack Slide Number 23 Kernel Density Map • Kernel density estimation maps represent the hot spots from their GPS logs.
- 24. ENTER 2016 ResearchTrack Slide Number 24 Group (i), Cluster 1 to 4 • Cluster 1,2,3 & 4 have same characteristics – Cluster 1 is the most typical type – Cluster 2,3 & 4 are the derivatives of 1 A Typical Route of Cluster 1
- 25. ENTER 2016 ResearchTrack Slide Number 25 Group (i), Cluster 6 • Cluster 6 stayed for tens of minutes at Shinobazu Pond Terrace in West garden
- 26. ENTER 2016 ResearchTrack Slide Number 26 For Group (i), Cluster 7 • Cluster 7 went around on the north side of East Garden also but their direction is opposite to other clusters. A Typical Route of Cluster 7
- 27. ENTER 2016 ResearchTrack Slide Number 27 Main Analysis 6300038Adr14 6300149Adr32 6300058Ddp12 6300030Bfp12 6300046Adp12 6300145Adr12 6300029Ear34 6300075Edq34 6300028Edp34 6300070Adr32 6300087Ecn12 6300150Ecn32 6300052Dco32 6300068Der32 6300049Ecr32 6300100Ecq14 6300105Cco12 6300092Daq12 6300071Ccn32 6300114Ddn12 6300139Dgo12 6300108Den12 6300006Dan12 6300026Deo12 6300142Dfn32 6300113Agn14 6300039Cco34 6300101Cco32 6300053Cgo12 6300065Cco12 6300062Cco12 6300081Agn32 6300051Fgn14 6300098Agn12 6300133Fgn12 6300136Agp12 6300140Egr12 6300025Efq14 6300033Baq32 6300078Bfq12 6300084Efq12 6300141Egq12 6300090Dap32 6300096Dep12 6300048Daq14 6300016Eaq12 6300005Daq32 6300042Edr32 6300066Edq12 6300031Baq34 6300116Ecq14 6300041Ear32 6300007Egn34 6300083Abp32 1212 1111 88991010 For Group (ii)
- 28. ENTER 2016 ResearchTrack Slide Number 28 Group (ii), Cluster 8 to 10 • Cluster 8,9 and 10 have: – same typical routes as Cluster 1,2,3 & 4 – differences in terms of routes in West Garden Time-space path map of Cluster 1Time-space path map of Cluster 8
- 29. ENTER 2016 ResearchTrack Slide Number 29 Group (ii), Cluster 11 & 12 • Cluster 11 stayed for tens of minutes at East Garden Cafeteria • Cluster 12 contains many routes
- 30. ENTER 2016 ResearchTrack Slide Number 30 Summarization of Results • Preliminary analysis are: – overall pictures of visitors – lacking some spatio-temporal aspects • Main analysis represent: – some typical patterns in their activities – the detailed characteristics of the patterns
- 31. ENTER 2016 ResearchTrack Slide Number 31 Conclusions • The bias of sampling tracking data of tourists have very important influence for results of typologies by using SAM • Samples affect each other as noise
- 32. ENTER 2016 ResearchTrack Slide Number 32 Future Works • A quantitative method is required in order to evaluate clusters of results by using SAM – To adopt validity measures – It is useful to check the bias of sampling and grouping • Relationship between clusters and attribute data of subjects
Editor's Notes
- #2 In this presentation, I will talk about “HOW TO GET TYPICAL PATTERNS of tourists‘ Spatio-temporal activities“. Such a typical pattern is called TYPOLOGY. We are using a method that was based on the Sequence Alignment Method, which is called SAM I will introduce some previous studies using SAM and our new attempts. First, I will explain that “Why we need to understand about Spatio-temporal Activities of tourists?“
- #3 Activities of people have two consecutive aspects about SPACE and TIME. Today, we have a variety of GPS devices. GPS devices are very useful for activity surveys, because we can measure accurate, continuous, worldwide, and three-dimensional position of human subject. So, we can illustrate where the subjects have passed, and we can tell where the subjects have stayed by using GPS logs.
- #4 When conducting such an activity survey, we often make groups by using their attribute data, Age, Gender, Accompanying person and other kind of attribute data. However, is such a grouping always appropriate method? The answer would be NO.
- #5 Sometimes, we have to divide the subjects according to their real activities. However, we’ll be faced with a problem. What is important index of their real activities for grouping? Combination of sites tourists visited? Order of visits to the sites? Time they spend in each sites? We don’t have this answer yet. Because, tourists are very arbitrary in choosing the sites they visit.
- #6 So, a quantitative method is required in order to clarify and compare characteristics of tourists’ activities
- #7 To solve this difficulty, some researchers have applied the sequence alignment method to the typologies of tourists’ activities. The sequence alignment method is the basic tool of bioinformatics. In other words, SAM is a method of comparing sequences of characters and measuring similarity and difference of them.
- #8 Sequence similarity and difference are measured using the concept of Levenshtein distance. Levenshtein distance is defined by the minimal number of edit operation required to change one sequence of characters into the other.
- #9 For analyzing Spatio-temporal activities, first of all, we have to convert tourists’ activities into sequences of characters. Let me explain a procedure of converting. First, we have to divide a study area into some polygons, and assign characters to each polygons. Next, for example, if we get a tourist’ trajectory by GPS data like this, we can convert this trajectory into sequence of characters like this. Some trajectories of the subjects are converted into sequences. These sequences are analyzed by using SAM, we can get a tree like cluster analysis.
- #10 Let me introduce some previous studies.
- #12 We showed you some previous studies. However, techniques of SAM for typologies of spatio-temporal activities are not confirmed sufficiently. Some aspects still limit the potential of SAM for analysis of spatio-temporal activities. Thus, we have to find out some issues of SMA for typologies of spatio-temporal activities. So, as a first step, we decided to follow previous studies and conducted tourists activity surveys by using GPS and typologies by using SAM as preliminary analysis.
- #13 We selected the Ueno Zoo for our study because it is well suited for the application of SAM to GPS tracking data of tourists’ activity surveys. There are some advantages in conducting a GPS tracking survey in a Zoo. Most exhibitions are located outside. The number of its entrance and exit gates are limited. Zoo visitor’s activities are limited in time and space because zoo area is limited. Ueno Zoo is one of the most popular tourist facilities in Japan. Ueno Zoo has a 14.2 ha site area, two entrance gates and three exit gates. The site is partitioned clearly into two gardens; East Garden and West Garden. A bridge and a small monorail line connect the two gardens. Ueno Zoo has a footfall of 3.6 million or more visitors per year. In the holidays, tens of thousands visitors crowd into the zoo site. In spite of Managers of Ueno Zoo have difficulty about visitor‘s crowding, they didn’t have methodology for clarifying visitors’ activities. They just counted visitors at entrance gate. No information about exit gates visitors used and their routes.
- #14 So, we conducted the survey to obtain GPS data of visitors. At the Main Gate, we distributed small GPS loggers to visitors who agreed to participate in our survey. We set each GPS logger to record the location ONCE IN EVERY SECOND. When the visitors left the zoo, we collected the loggers from them, and asked them to answer questionnaires to obtain their attribute data. We obtained 113valid sets of GPS logs and visitors’ attribute data in a day.
- #15 We divide the zoo site into thirty zones based on their spatial connections and functions. Each of the zones was assigned a code with two alphabets.
- #16 Using the obtained GPS data, we made sequences of codes. We converted the subjects’ locations into codes representing zone once every minute. Actually, we have tested other time resolutions. for example: once every thirty seconds, once every 3 minutes, once every 5 minutes. As result, LOW-time resolution could not represent the subjects’ trajectories. It looks like teleportation. HI-time resolution got very complex trees that could not be interpreted as clusters.
- #17 Alignment software package ClustalTXY was applied.
- #18 As a preliminary analysis, we conducted typologies of all subjects. As a result, we obtained the tree and assigned nomenclature to the clusters like this. We found that the clusters have characteristics by two points: ・whether stayed for tens of minutes somewhere or not ・which zone they stayed for tens of minutes From this result, we could understand some characteristics of visitors’ activities as overall picture. This is a beneficial outcome in terms of that we are able to find the their rough tendency. However, we found a incomplete aspect of this typologies. Every cluster contained many type of routes. We tried to visualization of their typical routes, but we couldn't interpret their patterns.
- #19 As I mentioned earlier, the site of Ueno Zoo is partitioned clearly into two gardens. So, we divided the subjects into 3 groups based on their broad routes. Group (i) moved from East Garden to West Garden. Group (ii) got back to East Garden from West Garden. Group (iii) took other routes. Their spatio-temporal activities are clearly different from each other.
- #20 However, the subjects of each groups are interspersed into the tree. It is the most likely due to the influence of long halt time in one zone. Halting for tens of minutes in one zone is represented by continuous same codes. Such a continuous same code may cause unfairly high prioritization in calculating mismatch cost. Some previous studies pointed out this problem. There is no consensus method or standard calibration procedure for the setting of sequence alignment parameters.
- #21 Then, as a main analysis, we conducted typologies by SAM for earlier mentioned groups, but Group(iii) was excluded from this analysis.
- #22 As a result of this simple idea, we observed some typical routes in their spatio-temporal activities, and the detailed characteristics of the routes. First, we obtained this tree as the result of analysis for Group (i) and assigned nomenclature to the clusters like this. Group (i) has seven clusters as Cluster 1 to 7.
- #23 We tried to interpret the details of typical routes of each cluster by using time-space path maps and Kernel density estimation maps. Time-space path maps represent subjects’ movement by lines that increase in height by one meter for every elapsed minutes.
- #24 Kernel density estimation maps represent the hot spots from their GPS logs. Red places are high density.
- #25 This is typical routes of group (i). Cluster 1,2,3 and 4 have same characteristics of spatio-temporal activities. Cluster 1 is the most typical type of these 4 clusters. The subjects of Cluster 1 went around all over the East Garden, like this. They viewed the almost all exhibition in East Garden probably. Cluster 2,3,and 4 are considered as the derivatives of Cluster 1.
- #26 The subjects of Cluster 6 stayed for tens of minutes at Shinobazu Pond Terrace in West Garden.
- #27 The subjects of Cluster 7 went around on the north side of East Garden also but their direction is opposite to other clusters. The characteristics of Cluster 7 is beneficial data for management of zoo. Because some aisles in zoo are not wide, their behavior will probably affect the crowding.
- #28 Next, we obtained this tree as the result of analysis for Group (ii) and assigned nomenclature to the clusters like this. The subjects of Group (ii) got back to East Garden from West Garden. Group (ii) has five clusters as Cluster 8 to 12.
- #29 The subjects of Cluster 8,9 and 10 have same typical routes as Cluster 1 to 4 and have differences in terms of routes in West Garden.
- #30 The subjects of Cluster 11 stayed for tens of minutes at East Garden Cafeteria or near the free rest area. In contrast, the subjects of Group (i) tend to not stop at East Garden Cafeteria. Cluster 12 contains many routes
- #31 Let me summarize the result of our analysis. As preliminary analysis, we conducted tourists’ activity survey by using GPS and typologies of their spatio-temporal activities by using SAM to follow previous studies. These results are useful as overall pictures of visitors, but they are lacking some spatio-temporal aspects. Therefore, we divided the subjects into 3 groups based on their broad routes and conducted typologies for 2 groups. As a result, we observed some typical patterns in their activities and the detailed characteristics of the patterns.
- #32 Now, this is our conclusion. What we can say from our study is that the bias of sampling tracking data of tourists have very important influence for results of typologies by using SAM. Probably, Samples affect each other as noise However, we mitigated this problem with a simple work. We have to generalize over this results.
- #33 As future works, first, We consider a More precisely, we have to adopt validity measures in order to evaluate clusters. Second, we are concerned with a relationship Clusters and attribute data of subjects In addition, we have to conduct this analysis with larger number of subjects. Thank you for your attention.