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Cities as Spatial and Social Networks: Towards a Spatio-Socio-Semantic Analysis Framework

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Cities as Spatial and Social Networks

Part of the book series: Human Dynamics in Smart Cities ((HDSC))

Abstract

Cities have been studied as geo-social systems embedded with intricate and complicated spatial and social networks (e.g., transportation, telecommunication, and internet). In addition to the duo of spatial and social aspects, semantics, which study the logic aspects of meanings behind behaviours and phenomena, come underneath as the latent information (e.g., activity types of people) to enrich the geo-social models for spatial phenomena. For example, individual-level similarity of semantic trajectories for location-based social networks can be used to recommend potential friends or develop collaborative travels. Semantics infer the activity behind people’s spatial choices and the functions of places, transform coordinates of trajectories/spatial flows into certain types of activities, and remark locations in space with meaningful labels of functions of cities. Although the interconnections of spatial, social, and semantic domains are widely observed, the deeper theoretical integration of geography, social network, and semantic spaces, as well as the corresponding research challenges, is not yet sufficient for a comprehensive understanding of urban systems. In order to address this gap, this work proposes a novel theoretical framework for the integration of semantic perspective into geographic and social network perspectives applied to understanding urban systems. We discuss the advantages and disadvantages in terms of available data sets in urban studies within the theoretical framework. We also discuss research challenges in terms of integrating heterogeneous data sources and creating innovative analytical approach based on the theoretical framework. We believe that the proposed theoretical framework can shed light on a wide range of urban related research fields and decision-making, such as transportation, public health, urban planning, and emergency management.

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References

  • Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., & Liu, B. (2011) Predicting flu trends using twitter data. In 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 702–707.

    Google Scholar 

  • Adams, B., & McKenzie, G. (2013) Inferring thematic places from spatially referenced natural language descriptions. In Crowdsourcing geographic knowledge (pp. 201–221). Dordrecht: Springer.

    Google Scholar 

  • Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo J. A. F., Moelans, B., & Vaisman, A. (2007) A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, p. 22. ACM.

    Google Scholar 

  • Andris, C. (2016). Integrating social network data into GISystems. International Journal of Geographical Information Science, 30(10), 1–23.

    Article  Google Scholar 

  • Andris, C., Xi L., & Joseph F. Jr. (2018). Challenges for social flows. Computers, Environment and Urban Systems, 70, 197–207.

    Article  Google Scholar 

  • Aramaki, E., Maskawa, S., & Morita, M. (2011) Twitter catches the flu: Detecting influenza epidemics using Twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp 1568–1576). Association for Computational Linguistics.

    Google Scholar 

  • Axhausen, K. W., & Gärling, T. (1992). Activity-based approaches to travel analysis: Conceptual frameworks, models, and research problems. Transport Reviews, 12(4), 323–341.

    Article  Google Scholar 

  • Bailey, T. C., & Gatrell, A. C. (1995) Interactive spatial data analysis. Longman Scientific & Technical Essex.

    Google Scholar 

  • Bapierre, H., Jesdabodi, C., & Groh, G. (2015) Mobile homophily and social location prediction. arXiv preprint arXiv:150607763.

  • Batty, M. (2003). Network geography: Relations, interactions, scaling and spatial processes in GIS. In D. Unwin (Ed.), Re-presenting GIS (pp. 149–170). UK: John Wiley.

    Google Scholar 

  • Batty, M. (2013). The new science of cities. Cambridge, MA: MIT Press.

    Google Scholar 

  • Bhat, C. R., & Koppelman, F. S. (1999) Activity-based modeling of travel demand. In Handbook of transportation Science (pp. 35–61). Boston, MA: Springer.

    Google Scholar 

  • Bian, L. (2004). A conceptual framework for an individual-based spatially explicit epidemiological model. Environment and Planning B, 31(3), 381–396. https://doi.org/10.1068/b2833.

    Article  Google Scholar 

  • Butler, D. (2013). When Google got flu wrong. Nature, 494(7436), 155.

    Article  Google Scholar 

  • Cairncross, F. (2001). The death of distance: How the communications revolution is changing our lives. Cambridge, MA: Harvard Business Press.

    Google Scholar 

  • Cattuto, C., Van den Broeck, W., Barrat, A., Colizza, V., Pinton, J.-F., & Vespignani, A. (2010). Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS ONE, 5(7), e11596. https://doi.org/10.1371/journal.pone.0011596.

    Article  Google Scholar 

  • Chang, L. W., Grabowski, M. K., Ssekubugu, R., Nalugoda, F., Kigozi, G., Nantume, B., et al. (2016). Heterogeneity of the HIV epidemic in agrarian, trading, and fishing communities in Rakai, Uganda: An observational epidemiological study. The Lancet HIV, 3(8), e388–e396.

    Article  Google Scholar 

  • 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 (pp. 1082–1090). New York, NY, USA: ACM. https://doi.org/10.1145/2020408.2020579.

  • Coffey, C., & Pozdnoukhov, A. (2013) Temporal decomposition and semantic enrichment of mobility flows. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (pp. 34–43). ACM.

    Google Scholar 

  • Crandall, D. J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., & Kleinberg, J. (2010). Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences, 107(52), 22436–22441. https://doi.org/10.1073/pnas.1006155107.

    Article  Google Scholar 

  • Cranshaw, J., Schwartz, R, Hong, J. I., & Sadeh, N. (2012) The livehoods project: Utilizing social media to understand the dynamics of a city. In International AAAI Conference on Weblogs and Social Media p. 58.

    Google Scholar 

  • Cranshaw, J., Toch, E., Hong, J., Kittur, A., Sadeh, N. (2010) Bridging the gap between physical location and online social networks. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing (pp. 119–128). ACM.

    Google Scholar 

  • Dashdorj, Z., Serafini, L., Antonelli, F., & Larcher, R. (2013) Semantic enrichment of mobile phone data records. In Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia (p. 35). ACM.

    Google Scholar 

  • Eagle, N., Pentland, A. S., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274–15278.

    Article  Google Scholar 

  • Gao, S., Janowicz, K., MckKnzie, G., & Li, L. (2013a) Towards platial joins and buffers in place-based GIS. In COMP@ SIGSPATIAL (pp. 42–49).

    Google Scholar 

  • Gao, S., Liu, Y., Wang, Y., & Ma, X. (2013b). Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17(3), 463–481.

    Article  Google Scholar 

  • Gao, S., Yan, B., Gong, L., Regalia, B., Ju, Y., & Hu, Y. (2015) Uncovering digital divide and physical divide in senegal using mobile phone data. In D. A. Griffith, Y. Chun, D. J. Dean (Eds.), Geocomputation 2015–The 13th International Conference.

    Google Scholar 

  • Goddard, J. B. (1970) Functional regions within the city centre: A study by factor analysis of taxi flows in central London. Transactions of the Institute of British Geographers (pp. 161–182).

    Google Scholar 

  • Gong, L., Liu, X., Wu, L., & Liu, Y. (2016). Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartography and Geographic Information Science, 43(2), 103–114.

    Article  Google Scholar 

  • Grenfell, B., Bjørnstad, O., & Kappey, J. (2001). Travelling waves and spatial hierarchies in measles epidemics. Nature, 414(6865), 716–723.

    Article  Google Scholar 

  • Guo, D. (2007). Visual analytics of spatial interaction patterns for pandemic decision support. International Journal of Geographical Information Science, 21(8), 859–877. https://doi.org/10.1080/13658810701349037.

    Article  Google Scholar 

  • Han, S. Y., Tsou, M.-H., & Clarke, K. C. (2015). Do global cities enable global views? Using Twitter to quantify the level of geographical awareness of US cities. PLoS ONE, 10(7), e0132464.

    Article  Google Scholar 

  • Hu, D., Huang, B., Tu, L., & Chen, S. (2015). Understanding social characteristic from spatial proximity in mobile social network. International Journal of Computers Communications & Control, 10(4), 539–550.

    Article  Google Scholar 

  • Imran, M., Mitra, P., & Castillo, C. (2016a) Twitter as a lifeline: Human-annotated Twitter corpora for NLP of crisis-related messages. arXiv preprint arXiv:160505894.

  • Imran, M., Mitra, P., & Srivastava, J. (2016b) Cross-language domain adaptation for classifying crisis-related short messages. arXiv preprint arXiv:160205388.

  • Karimzadeh, M., Huang, W., Banerjee, S., Wallgrün, J. O., Hardisty, F., Pezanowski, S., et al. (2013) GeoTxt: A web API to leverage place references in text. In Proceedings of the 7th Workshop on Geographic Information Retrieval (pp. 72–73). ACM.

    Google Scholar 

  • Kling, F., Pozdnoukhov, A. (2012) When a city tells a story: Urban topic analysis. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (pp. 482–485). ACM.

    Google Scholar 

  • Lampos, V., & Cristianini, N. (2010) Tracking the flu pandemic by monitoring the social web. In 2010 2nd International Workshop on Cognitive Information Processing (pp. 411–416). IEEE.

    Google Scholar 

  • Li, Q., Zheng, Y., Xie, X., Chen, Y., & Liu, W., Ma, W.-Y. (2008) Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (p. 34). ACM.

    Google Scholar 

  • Liao, L., Fox, D., Kautz, H. (2005) Location-based activity recognition using relational Markov networks. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (pp 773–778). Edinburgh, Scotland.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Liu, X., Kang, C., Gong, L., & Liu, Y. (2016). Incorporating spatial interaction patterns in classifying and understanding urban land use. International Journal of Geographical Information Science, 30(2), 334–350.

    Article  Google Scholar 

  • 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, pp. 1–19. (ahead-of-print).

    Article  Google Scholar 

  • Luo, W. (2016). Visual analytics of geo-social interaction patterns for epidemic control. International Journal of Health Geographics, 15(1), 1–16. https://doi.org/10.1186/s12942-016-0059-3.

    Article  Google Scholar 

  • Luo, W., & MacEachren, A. M. (2014). Geo-social visual analytics. Journal of spatial information science, 8, 27–66. https://doi.org/10.5311/JOSIS.2014.8.139.

    Article  Google Scholar 

  • Luo, W., MacEachren, A. M., Yin, P., & Hardisty, F. (2011) Spatial-social network visualization for exploratory data analysis. In SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN 2011) in conjunction with the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS) 2011. Chicago, Illinois: ACM. https://doi.org/10.1145/2063212.2063216.

  • Luo, W., Yin, P., Di, Q., Hardisty, F., & MacEachren, A. M. (2014). A geovisual analytic approach to understanding Geo-Social relationships in the international trade network. PLoS ONE, 9(2), e88666.

    Article  Google Scholar 

  • Luo, W., Gao, P., & Cassels, S. (2018). A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area. Computers, Environment and Urban Systems Special Issue: Human Dynamics in the Mobile and Big Data Era: Towards Smart and Connected Communities. https://doi.org/10.1016/j.compenvurbsys.2018.06.008.

  • Mao, L., & Bian, L. (2010a). A dynamic network with individual mobility for designing vaccination strategies. Transactions in GIS, 14(4), 533–545. https://doi.org/10.1111/j.1467-9671.2010.01201.x.

    Article  Google Scholar 

  • Mao, L., & Bian, L. (2010b). Spatial-temporal transmission of influenza and its health risks in an urbanized area. Computers, Environment and Urban Systems, 34(3), 204–215. https://doi.org/10.1016/j.compenvurbsys.2010.03.004.

    Article  Google Scholar 

  • McKenzie, G., & Janowicz, K. (2015). Where is also about time: A location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures. Computers, Environment and Urban Systems, 54, 1–13.

    Article  Google Scholar 

  • Meade, M., & Earickson, R. (2005) Medical geography. New York: The Guilford Press.

    Google Scholar 

  • Miklas, A. G., Gollu, K. K., Chan, K. K., Saroiu, S., Gummadi, K. P., De Lara, E. (2007) Exploiting social interactions in mobile systems. In International Conference on Ubiquitous Computing (pp. 409–428). Berlin: Springer.

    Chapter  Google Scholar 

  • Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., et al. (2013). Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR), 45(4), 42.

    Article  Google Scholar 

  • Pham, H., Shahabi, C., & Liu, Y. (2013) EBM: An entropy-based model to infer social strength from spatiotemporal data. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (pp. 265–276). ACM.

    Google Scholar 

  • Radil, S. M., Flint, C., & Chi, S.-H. (2013). A relational geography of war: Actor–context interaction and the spread of World War I. Annals of the Association of American Geographers, 103(6), 1468–1484.

    Article  Google Scholar 

  • Radil, S., Flint, C., & Tita, G. (2010). Spatializing social networks: Using social network analysis to investigate geographies of gang rivalry, territoriality, and violence in Los Angeles. Annals of the Association of American Geographers, 100(2), 307–326. https://doi.org/10.1080/00045600903550428.

    Article  Google Scholar 

  • Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, M., et al. (2010). Redrawing the map of Great Britain from a network of human interactions. PLoS ONE, 5(12), e14248.

    Article  Google Scholar 

  • Roth, C., Kang, S. M., Batty, M., & Barthélemy, M. (2011). Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS ONE, 6(1), e15923.

    Article  Google Scholar 

  • Sun, L., & Axhausen, K. W. (2016). Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transportation Research Part B: Methodological, 91, 511–524.

    Article  Google Scholar 

  • Thiemann, C., Theis, F., Grady, D., Brune, R., & Brockmann, D. (2010). The structure of borders in a small world. PLoS ONE, 5(11), e15422. https://doi.org/10.1371/journal.pone.0015422.

    Article  Google Scholar 

  • Toole, J. L., Herrera-Yaqüe, C., Schneider, C. M., & González, M. C. (2015). Coupling human mobility and social ties. Journal of the Royal Society, Interface, 12(105), 20141128.

    Article  Google Scholar 

  • Tuan, Y.-F. (2013) Topophilia: A study of environmental perceptions, attitudes, and values. New York: Columbia University Press.

    Google Scholar 

  • Wallgrün, J. O, Hardisty, F., MacEachren, A. M, Karimzadeh, M., Ju, Y., & Pezanowski, S. (2014) Construction and first analysis of a corpus for the evaluation and training of microblog/twitter geoparsers. In Proceedings of the 8th Workshop on Geographic Information Retrieval (p. 4). ACM.

    Google Scholar 

  • Walsh, F., & Pozdnoukhov, A. (2011) Spatial structure and dynamics of urban communities, pp. 1–8.

    Google Scholar 

  • Wang, Y., Kang, C., Bettencourt, L. M., Liu, Y., & Andris, C. (2015) Linked activity spaces: Embedding social networks in urban space. In Computational Approaches for Urban Environments (pp. 313–336). Berlin: Springer.

    Google Scholar 

  • Wang, Y., Kutadinata, R., & Winter, S. (2016) Activity-based ridesharing: Increasing flexibility by time geography. In Proceedings of the 24th ACM SIGSPATIAL. San Francisco Bay Area, CA: ACM.

    Google Scholar 

  • Xiao, G., Juan, Z., & Zhang, C. (2015). Travel mode detection based on GPS track data and Bayesian networks. Computers, Environment and Urban Systems, 54, 14–22.

    Article  Google Scholar 

  • Xie, K., Deng, K., & Zhou, X. (2009) From trajectories to activities: A spatio-temporal join approach. In Proceedings of the 2009 International Workshop on Location Based Social Networks (pp. 25–32). ACM.

    Google Scholar 

  • Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., & Aberer, K. (2011) SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In Proceedings of the 14th International Conference on Extending Database Technology (pp. 259–270). ACM.

    Google Scholar 

  • Yang, F., Jin, P. J., Wan, X., Li, R., & Ran, B. (2014) Dynamic origin-destination travel demand estimation using location based social networking data. In Transportation Research Board 93rd Annual Meeting.

    Google Scholar 

  • Ying, J., J.-C., Lee, W.-C., & Weng, T.-C. (2011) Tseng VS Semantic trajectory mining for location prediction. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 34–43). ACM.

    Google Scholar 

  • Yuan, J., Zheng, Y., & Xie, X. (2012a) Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 186–194). ACM.

    Google Scholar 

  • Yuan, Y., Raubal, M., & Liu, Y. (2012b). Correlating mobile phone usage and travel behavior–A case study of Harbin, China. Computers, Environment and Urban Systems, 36(2), 118–130.

    Article  Google Scholar 

  • Zheng, Y., Chen, Y., Li, Q., Xie, X., & Ma, W.-Y. (2010). Understanding transportation modes based on GPS data for web applications. ACM Transactions on the Web (TWEB), 4(1), 1.

    Article  Google Scholar 

  • Zheng, Y., Zhang, L., Ma, Z., Xie, X., & Ma, W.-Y. (2011). Recommending friends and locations based on individual location history. ACM Transactions on the Web (TWEB), 5(1), 5.

    Google Scholar 

  • Zhong, C., Arisona, S. M., Huang, X., Batty, M., & Schmitt, G. (2014). Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science, 28(11), 2178–2199.

    Article  Google Scholar 

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Luo, W., Wang, Y., Liu, X., Gao, S. (2019). Cities as Spatial and Social Networks: Towards a Spatio-Socio-Semantic Analysis Framework. In: Ye, X., Liu, X. (eds) Cities as Spatial and Social Networks. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-319-95351-9_3

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