The impact of scale on extracting urban mobility patterns using texture analysis
Tóm tắt
The development of high-precision location tracking devices and advancements in data collection, storage, transmission technologies, and data mining algorithms have led to the availability of large datasets with high spatiotemporal resolution. These geospatial big data can be used to identify human movement patterns in urban areas. However, identifying human movement patterns may yield different results depending on the scale size used. In this paper, we employed first and second order texture analysis algorithms to identify spatial patterns of human movement for various scale sizes based on taxi trajectory data from Nanjing, China. The results demonstrated that texture analysis can quantify changes in human movement patterns for different scale sizes in an urban area. Furthermore, the results may differ based on the location of the study area. This study contributed both methodologically and empirically. Methodologically, we used texture analysis to examine the impact of different scale sizes on the extraction of aggregated human travel patterns. Empirically, we quantified the effects of different scale sizes on extracting aggregated travel patterns of an urban area. Overall, the findings of this study can have significant implications for urban planning and policy-making, as understanding human movement patterns at different scales can provide valuable insights for optimizing transportation systems and enhancing overall urban mobility.
Tài liệu tham khảo
Abbasi, A., Rashidi, T.H., Maghrebi, M., & Waller, S.T. (2015). Utilising Location Based Social Media in Travel Survey Methods: bringing Twitter data into the play. Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (pp.1-9). Seattle. https://doi.org/10.1145/2830657.2830660.
Anda, C., Erath, A., & Fourie, P. J. (2017). Transport modelling in the age of big data. International Journal of Urban Sciences, 21(sup1), 19–42.
Armi, L., & Fekri-Ershad, S. (2019). Texture image analysis and texture classification methods-A review. arXiv preprint arXiv:1904.06554.
Atack, J. (2013). On the use of geographic information systems in economic history: The american transportation revolution revisited. The Journal of Economic History, 73(2), 313–338.
Atkinson, P. M., & Curran, P. J. (1997). Choosing an appropriate spatial resolution for remote sensing investigations. Photogrammetric Engineering and Remote Sensing, 63(12), 1345–1351.
Batty, M. (2013). Urban informatics and big data. A Report to the ESRC Cities Expert Group.
Batty, M. (2018). Visualizing aggregate movement in cities. Philosophical Transactions of the Royal Society b: Biological Sciences, 373(1753), 20170236.
Beaumont, R. G. (2018). The Evolution of Nanjing’s Xinjiekou. THE NANJINGER. Retrieved June 10, 2023, from https://www.thenanjinger.com/magazine/the-supplement/evolution-nanjings-xinjiekou/.
Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature, 439(7075), 462–465.
Castro, P. S., Zhang, D., Chen, C., Li, S., & Pan, G. (2013). From taxi GPS traces to social and community dynamics: A survey. ACM Computing Surveys (CSUR), 46(2), 1–34.
Chen, C., Ma, J., Susilo, Y., Liu, Y., & Wang, M. (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part c: Emerging Technologies, 68, 285–299.
Chen, L., Gao, Y., Zhu, D., Yuan, Y., & Liu, Y. (2019). Quantifying the scale effect in geospatial big data using semi-variograms. PLoS ONE, 14(11), e0225139. https://doi.org/10.1371/journal.pone.0225139.
Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining,1082-1090. San Diego: ACM.
Dabiri, Z., & Blaschke, T. (2019). Scale matters: A survey of the concepts of scale used in spatial disciplines. European Journal of Remote Sensing, 52(1), 419–434.
Erlander, S., & Stewart, N. F. (1990). The gravity model in transportation analysis: theory and extensions. Bacon Raton: CRC Press.
Eubank, S., Guclu, H., Anil Kumar, V., Marathe, M. V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429(6988), 180–184.
Gao, S. (2015). Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age. Spatial Cognition & Computation, 15(2), 86–114.
Hall-Beyer, M. (2017a). GLCM texture: a tutorial (V. 3.0). Retrieved June 1, 2023, from https://prism.ucalgary.ca/server/api/core/bitstreams/8f9de234-cc94-401d-b701-f08ceee6cfdf/content.
Hall-Beyer, M. (2017b). Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. International Journal of Remote Sensing, 38(5), 1312–1338.
Hao, Q., Cai, R., Wang, C., Xiao, R., Yang, J. M., Pang, Y., & Zhang, L. (2010). Equip tourists with knowledge mined from travelogues. In Proceedings of the 19th international conference on World wide web, 401-410. Raleigh: ACM.
Huang, J., Levinson, D., Wang, J., Zhou, J., & Wang, Z.-J. (2018). Tracking job and housing dynamics with smartcard data. Proceedings of the National Academy of Sciences, 115(50), 12710–12715.
Jeong, H., Park, B., Park, M., Kim, K.-B., & Choi, K. (2019). Big data and rule-based recommendation system in Internet of Things. Cluster Computing, 22(1), 1837–1846. https://doi.org/10.1007/s10586-017-1078-y.
Kwan, M.-P. (2016). Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Annals of the American Association of Geographers, 106(2), 274–282.
Lam, N.S.-N., Catts, D., McMaster, R., Quattrochi, D., and Brown, D. (2004). Scale. In: A Research Agenda for Geographic Information Science, R. McMaster & L. Usery, (eds.), 93-128. Bacon Raton: CRC Press.
Lam, N.S.-N., & Quattrochi, D. A. (1992). On the issues of scale, resolution, and fractal analysis in the mapping sciences. The Professional Geographer, 44(1), 88–98.
Levin, S. A. (1992). The problem of pattern and scale in ecology: The Robert H. MacArthur Award Lecture. Ecology, 73(6), 1943–1967.
Liang, X., Zhao, J., Dong, L., & Xu, K. (2013). Unraveling the origin of exponential law in intra-urban human mobility. Scientific Reports, 3(1), 2983.
Liu, J., Li, J., Li, W., & Wu, J. (2016). Rethinking big data: A review on the data quality and usage issues. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 134–142.
Liu, L., Hou, A., Biderman, A., Ratti, C., & Chen, J. (2009). Understanding individual and collective mobility patterns from smart card records: A case study in Shenzhen. International IEEE Conference on Intelligent Transportation Systems, 12, 1–6.
Liu, X., Gong, L., Gong, Y., & Liu, Y. (2015a). Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43, 78–90.
Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., Chi, G., & Shi, L. (2015b). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512–530.
Liu, Z., Yang, J., Chang, Y., Weisberg, P. J., & He, H. S. (2012). Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Global Change Biology, 18(6), 2041–2056.
Luca, M., Barlacchi, G., Lepri, B., & Pappalardo, L. (2021). A survey on deep learning for human mobility. ACM Computing Surveys (CSUR), 55(1), 1–44.
Manjunath, B. S., & Ma, W.-Y. (1996). Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 837–842.
Manley, D. J. (2006). The modifiable areal unit phenomenon: An investigation into the scale effect using UK census data. Doctoral dissertation, University of St Andrews-St Andrews, UK. St Andrews Research Repository. https://research-repository.st-andrews.ac.uk/handle/10023/465.
Mao, F., Ji, M., & Liu, T. (2016). Mining spatiotemporal patterns of urban dwellers from taxi trajectory data. Frontiers of Earth Science, 10, 205–221.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: architecture, opportunities, and open research challenges. Ieee access, 5, 5247–5261.
Mennis, J. (2019). Problems of Scale and Zoning. The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2019 Edition), John P. Wilson (Ed.). https://doi.org/10.22224/gistbok/2019.1.2.
Miller, H. J., & Shaw, S. L. (2015). Geographic information systems for transportation in the 21st century. Geography Compass, 9(4), 180–189.
Nanjing Municipal People’s Government. (2012). The History of Nanjing. Retrieved June 20, 2023, from https://web.archive.org/web/20130609184340/http://www.nanjing.gov.cn/njgk/csgk/csgk3/.
Openshaw, S. (1984). The modifiable areal unit problem. Concepts and techniques in modern geography.
Pathak, B., & Barooah, D. (2013). Texture analysis based on the gray-level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(9), 4206–4212.
Pei, T., Sobolevsky, S., Ratti, C., Shaw, S.-L., Li, T., & Zhou, C. (2014). A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28(9), 1988–2007.
Rashidi, T. H., Abbasi, A., Maghrebi, M., Hasan, S., & Waller, T. S. (2017). Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges. Transportation Research Part c: Emerging Technologies, 75, 197–211.
Ruddell, D., & Wentz, E. A. (2009). Multi-tasking: Scale in geography. Geography Compass, 3(2), 681–697.
Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96–100.
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.
Stone, K. H. (1972). A geographer’s strength: The multiple-scale approach. Journal of Geography, 71(6), 354–362.
Tobler, W. (1988). The quadratic transportation problem as a model of spatial interaction patterns. Geographical Systems and Systems of Geography: Essays in Honor of William Warntz (pp. 75–88). London: Methuen.
Toch, E., Lerner, B., Ben-Zion, E., & Ben-Gal, I. (2019). Analyzing large-scale human mobility data: A survey of machine learning methods and applications. Knowledge and Information Systems, 58, 501–523.
Toole, J. L., de Montjoye, Y.-A., González, M. C., & Pentland, A. (2015). Modeling and understanding intrinsic characteristics of human mobility. Social phenomena: From data analysis to models (pp. 15–35).
Tortini, R., Mayer, A. L., & Maianti, P. (2015). Using an OBCD approach and Landsat TM data to detect harvesting on nonindustrial private property in Upper Michigan. Remote Sensing, 7(6), 7809–7825.
Tranos, E., & Mack, E. (2019). Big data: A new opportunity for transport geography? Journal of Transport Geography, 76, 232–234.
Wang, J., Kong, X., Xia, F., & Sun, L. (2019). Urban human mobility: Data-driven modeling and prediction. ACM SIGKDD Explorations Newsletter, 21(1), 1–19.
Woodcock, C. E., Strahler, A. H., & Jupp, D. L. (1988). The use of variograms in remote sensing: I. Scene models and simulated images. Remote sensing of Environment., 25(3), 323–348.
Wu, J., & Li, H. (2006). Concepts of scale and scaling. Scaling and uncertainty analysis in ecology, 3–15.
Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Lu, F., Chen, J., Fang, Z., & Li, Q. (2016). Another tale of two cities: Understanding human activity space using actively tracked cellphone location data. Annals of the American Association of Geographers, 106(2), 489–502.
Yang, M., Yuan, Y., & Zhan, F. B. (2023). Explore urban interactions based on floating car data – a case study of Chengdu. China. Annals of GIS, 29(1), 37–53. https://doi.org/10.1080/19475683.2023.2166109.
Yuan, Y., & Raubal, M. (2016). Analyzing the distribution of human activity space from mobile phone usage: An individual and urban-oriented study. International Journal of Geographical Information Science, 30(8), 1594–1621. https://doi.org/10.1080/13658816.2016.1143555.
Yuan, Y., Kang, C., Cao, P., & Liu, Y. (2017). Big geospatial data and its applications in urban studies. In: Frontier of GeoInformatics. H. Lin & X. Shi (eds.), 167–189. Beijing: Higher Education Press.
Zhang, H., Cheng, J., Wu, Z., Li, C., Qin, J., & Liu, T. (2018). Effects of impervious surface on the spatial distribution of urban waterlogging risk spots at multiple scales in Guangzhou. South China. Sustainability, 10(5), 1589.
Zhang, J., Atkinson, P., & Goodchild, M. F. (2014). Scale in spatial information and analysis. CRC Press.
Zhang, J., Wang, F. Y., Wang, K., Lin, W. H., Xu, X., & Chen, C. (2011). Data-Driven Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624–1639. https://doi.org/10.1109/TITS.2011.2158001
Zhang, S., Tang, J., Wang, H., Wang, Y., & An, S. (2017). Revealing intra-urban travel patterns and service ranges from taxi trajectories. Journal of Transport Geography, 61, 72–86.
Zipf, G. K. (1946). The P 1 P 2/D hypothesis: On the intercity movement of persons. American Sociological Review, 11(6), 677–686.