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International Symposium on Web and Wireless Geographical Information Systems

W2GIS 2018: Web and Wireless Geographical Information Systems pp 61–76Cite as

Extraction of Usage Patterns for Land-Use Types by Pedestrian Trajectory Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10819))

Abstract

Research on moving objects and analysis of movement patterns in urban networks can help us evaluate urban land-use types. With the help of technologies such as global positioning systems, spatial information systems and spatial data the study of movement patterns is possible. By understanding and quantifying the patterns of pedestrian trajectories, we can find effects of and relations between urban land-use types and movements of pedestrians. Understanding urban land-use and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we use the data of various urban land-use types and the trajectory of pedestrians in an urban environment. This paper presents a new approach for identifying busy urban land-use by semantic spatial trajectory in which urban land-uses are assessed according to the pedestrian trajectories. Undoubtedly, the extraction of popular urban land-uses and analysis of the association between popular places and the spatial and semantic movement allow us to improve the urban structure and city marketing system. In this regard, for semantic analysis of urban land-use, all stop points are extracted by a time threshold and they are enriched according to semantic information such as age, occupation, and gender. We examine if and how habits of using land-use types depend on qualities such as age, gender and occupation. For analysis of effects of various urban land-use types, all stop points near each urban land-use are detected. Determining what type of urban land-use cause pedestrian traffic and high absorption coefficient and what relation such high traffic has with semantic information such as age, occupation and gender. By clustering the stop points, the results indicate that stop at urban networks for each gender have a spatial correlation. Also, the results show that some urban land-use types have high traffic and we have a correlation with some semantic information such as age, gender and occupation.

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Notes

  1. 1.

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References

  1. Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering: Annals of GIS, vol. 20(2). http://www.tandfonline.com/doi/full/10.1080/19475683.2014.904560. Accessed 28 Apr 2017

  2. Millonig, A., Gartner, G.: Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools. J. Locat. Based Serv. 5(1), 3–21 (2011)

    Article  Google Scholar 

  3. Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Build. Environ. 44(3), 437–445 (2009)

    Article  Google Scholar 

  4. Castro, P.S., Zhang, D., Li, S.: Urban traffic modelling and prediction using large scale taxi GPS traces. In: Pervasive Computing, pp. 57–72 (2012)

    Google Scholar 

  5. van der Spek, S.: Tracking tourists in historic city centres. In: Gretzel, U., Law, R., Fuchs, M. (eds.) Information and Communication Technologies in Tourism, pp. 185–196. Springer, Vienna (2010). https://doi.org/10.1007/978-3-211-99407-8_16

    Chapter  Google Scholar 

  6. Eubank, S., et al.: Modelling disease outbreaks in realistic urban social networks. Nature 429(6988), 180–184 (2004)

    Article  Google Scholar 

  7. Kwan, M.-P., Neutens, T.: Space-time research in GIScience. Int. J. Geogr. Inf. Sci. 28(5), 851–854 (2014)

    Article  Google Scholar 

  8. Van Schaick, J.: Timespace matters - Exploring the gap between knowing about activity patterns of people and knowing how to design and plan urban areas and regions (2011)

    Google Scholar 

  9. Purves, R.S., Laube, P., Buchin, M., Speckmann, B.: Moving beyond the point: An agenda for research in movement analysis with real data, vol. 47 (2014)

    Google Scholar 

  10. Ewing, R., Cervero, R.: Travel and the built environment. J. Am. Plann. Assoc. 76(3), 265–294 (2010)

    Article  Google Scholar 

  11. Van Vugt, M., Van Lange, P.A.M., Meertens, R.M.: Commuting by car or public transportation? A social dilemma analysis of travel mode judgements. Eur. J. Soc. Psychol. 26(3), 373–395 (1996)

    Article  Google Scholar 

  12. Wener, R.E., Evans, G.W.: A morning stroll: levels of physical activity in car and mass transit commuting. Environ. Behav. 39(1), 62–74 (2007)

    Article  Google Scholar 

  13. Parent, C.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45, 1–32 (2013)

    Article  Google Scholar 

  14. Sester, M., Feuerhake, U., Kuntzsch, C., Zhang, L.: Revealing underlying structure and behaviour from movement data. Künstl. Intell. 26(3), 223–231 (2012)

    Article  Google Scholar 

  15. Xiao, X., Zheng, Y., Luo, Q., Xie, X.: Inferring social ties between users with human location history. J. Ambient Intell. Humaniz. Comput. (2012)

    Google Scholar 

  16. Palmer, J.R.B., Espenshade, T.J., Bartumeus, F., Chung, C.Y., Ozgencil, N.E., Li, K.: New approaches to human mobility: using mobile phones for demographic research. Demography 50(3), 1105–1128 (2013)

    Article  Google Scholar 

  17. Kwan, M.-P.: Beyond space (As We Knew It): toward temporally integrated geographies of segregation, health, and accessibility. Ann. Assoc. Am. Geogr. 103(5), 1078–1086 (2013)

    Article  Google Scholar 

  18. Tranos, E., Nijkamp, P.: Mobile phone usage in complex urban systems: a space–time, aggregated human activity study. J. Geogr. Syst. 17(2), 157–185 (2015)

    Article  Google Scholar 

  19. Torrens, P., Li, X., Griffin, W.A.: Building agent-based walking models by machine-learning on diverse databases of space-time trajectory samples. Trans. GIS 15, 67–94 (2011)

    Article  Google Scholar 

  20. Hu, W., Xie, D., Tan, T.: A hierarchical self-organizing approach for learning the patterns of motion trajectories. IEEE Trans. Neural Netw. 15(1), 135–144 (2004)

    Article  Google Scholar 

  21. Liao, L., Fox, D., Kautz, H.: Extracting places and activities from GPS traces using hierarchical conditional random fields. Int. J. Robot. Res. 26(1), 119–134 (2007)

    Article  Google Scholar 

  22. Rodrigues, A., Damásio, C., Cunha, J.E.: Using GPS logs to identify agronomical activities. In: Huerta, J., Schade, S., Granell, C. (eds.) Connecting a Digital Europe Through Location and Place. LNGC, pp. 105–121. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-03611-3_7

    Chapter  Google Scholar 

  23. Van der Hoeven, F.D., Van der Spek, S.C., Smit, M.G.J.: Street-Level Desires, Discovering the City on Foot: Pedestrian Mobility and the Regeneration of the European City Centre (2008)

    Google Scholar 

  24. Van der Spek, S.C., Van Langelaar, C.M., Kickert, C.C.: Evidence-based design: satellite positioning studies of city centre user groups. Proc. Inst. Civ. Eng. Urban Des. Plan. 166(4), 206–216 (2013)

    Google Scholar 

  25. Gong, L., Liu, X., Wu, L., Liu, Y.: Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartogr. Geogr. Inf. Sci. 43(2), 103–114 (2016)

    Article  Google Scholar 

  26. Shin, D., et al.: Urban sensing: using smartphones for transportation mode classification. Comput. Environ. Urban Syst. 53(Supplement C), 76–86 (2015)

    Article  Google Scholar 

  27. Widhalm, P., Yang, Y., Ulm, M., Athavale, S., González, M.C.: Discovering urban activity patterns in cell phone data. Transportation 42(4), 597–623 (2015)

    Article  Google Scholar 

  28. Umair, M., Kim, W.S., Choi, B.C., Jung, S.Y.: Discovering personal places from location traces. In: 16th International Conference on Advanced Communication Technology, pp. 709–713 (2014)

    Google Scholar 

  29. Lenormand, M., et al.: Comparing and modelling land use organization in cities. R. Soc. Open Sci. 2(12), 150449 (2015)

    Article  MathSciNet  Google Scholar 

  30. Ahas, R., et al.: Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. Int. J. Geogr. Inf. Sci. 29(11), 2017–2039 (2015)

    Article  Google Scholar 

  31. Guc, B., May, M., Saygin, Y., et al.: Semantic annotation of GPS trajectories. In: Proceedings of the AGILE 2008, CD-ROM, p. 9 (2008)

    Google Scholar 

  32. Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)

    Article  Google Scholar 

  33. van Hage, W.R., Malaisé, V., de Vries, G., Schreiber, G., van Someren, M.: Combining ship trajectories and semantics with the simple event model (SEM). In: Proceedings of the 1st ACM International Workshop on Events in Multimedia, New York, pp. 73–80 (2009)

    Google Scholar 

  34. An Outlook for OpenStreetMap. https://www.researchgate.net/publication/290522341_An_Outlook_for_OpenStreetMap. Accessed 21 Apr 2017

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Author information

Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Mehdi Jalili & Farshad Hakimpour

  2. Department of Urbanism, Delft University of Technology, Delft, The Netherlands

    Stefan Christiaan Van der Spek

Authors
  1. Mehdi Jalili
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  2. Farshad Hakimpour
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  3. Stefan Christiaan Van der Spek
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Corresponding author

Correspondence to Farshad Hakimpour .

Editor information

Editors and Affiliations

  1. Universidade da Coruña, A Coruña, Spain

    Miguel R. Luaces

  2. University of Tehran, Tehran, Iran

    Farid Karimipour

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Jalili, M., Hakimpour, F., Van der Spek, S.C. (2018). Extraction of Usage Patterns for Land-Use Types by Pedestrian Trajectory Analysis. In: R. Luaces, M., Karimipour, F. (eds) Web and Wireless Geographical Information Systems. W2GIS 2018. Lecture Notes in Computer Science(), vol 10819. Springer, Cham. https://doi.org/10.1007/978-3-319-90053-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-90053-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90052-0

  • Online ISBN: 978-3-319-90053-7

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