Exploring metro vibrancy and its relationship with built environment: a cross-city comparison using multi-source urban data

References

• Aiello, L. M., R. Schifanella, D. Quercia, F. Aletta, B. Labs, and D. Quercia. 2016. “Chatty Maps: Constructing Sound Maps of Urban Areas from Social Media Data.” Royal Society Open Science 3 (3): 1–19. doi:10.1098/rsos.150690.
   Web of Science ®Google Scholar
• Best, K., and S. Butler. 2015. “Virtual Space: Creating a Place for Social Support in Second Life.” Space and Culture 18 (2): 183–197. doi:10.1177/1206331213512235.
   Web of Science ®Google Scholar
• Ceapa, I., C. Smith, and L. Capra. 2012. “Avoiding the Crowds: Understanding Tube Station Congestion Patterns from Trip Data.” Paper presented at the Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, 134–141. doi: 10.1145/2346496.2346518
   Google Scholar
• Chakour, V., and N. Eluru. 2016. “Examining the Influence of Stop Level Infrastructure and Built Environment on Bus Ridership in Montreal.” Journal of Transport Geography 51: 205–217. doi:10.1016/j.jtrangeo.2016.01.007.
   Web of Science ®Google Scholar
• Chen, T., E. C. M. Hui, J. Wu, W. Lang, and X. Li. 2019. “Identifying Urban Spatial Structure and Urban Vibrancy in Highly Dense Cities Using Georeferenced Social Media Data.” Habitat International 89: 102005. doi:10.1016/j.habitatint.2019.102005.
   Web of Science ®Google Scholar
• De Jong, P., C. Sprenger, and F. V. Veen. 1984. “On Extreme Values of Moran’s I and Geary’s C.” Geographical Analysis 16 (1): 17–24. doi:10.1111/j.1538-4632.1984.tb00797.x.
   Web of Science ®Google Scholar
• De Nadai, M., J. Staiano, R. Larcher, N. Sebe, D. Quercia, and B. Lepri. 2016. “The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective.” Paper presented at the International World Wide Web Conferences, Montréal Québec, Canada, 413–423. doi: 10.1145/2872427.2883084
   Google Scholar
• Diniz-Filho, J. A. F., L. M. Bini, and B. A. Hawkins. 2003. “Spatial Autocorrelation and Red Herrings in Geographical Ecology.” Global Ecology and Biogeography 12 (1): 53–64. doi:10.1046/j.1466-822X.2003.00322.x.
   Web of Science ®Google Scholar
• Elhorst, J. P. 2014. “Matlab Software for Spatial Panels.” International Regional Science Review 37 (3): 389–405. doi:10.1177/0160017612452429.
   Web of Science ®Google Scholar
• Fang, Z., S. L. Shaw, W. Tu, Q. Li, and Y. Li. 2012. “Spatiotemporal Analysis of Critical Transportation Links Based on Time Geographic Concepts: A Case Study of Critical Bridges in Wuhan, China.” Journal of Transport Geography 23: 44–59. doi:10.1016/j.jtrangeo.2012.03.018.
   Web of Science ®Google Scholar
• Getis, A., and J. Aldstadt. 2004. “Constructing the Spatial Weights Matrix Using a Local Statistic.” Geographical Analysis 36 (2): 90–104. doi:10.1111/j.1538-4632.2004.tb01127.x.
   Web of Science ®Google Scholar
• González, M. C., C. A. Hidalgo, and A.-L. Barabási. 2008. “Understanding Individual Human Mobility Patterns.” Nature 453 (7196): 779–782. doi:10.1038/nature06958.
   PubMed Web of Science ®Google Scholar
• Goulet-Langlois, G., H. N. Koutsopoulos, Z. Zhao, and J. Zhao. 2018. “Measuring Regularity of Individual Travel Patterns.” IEEE Transactions on Intelligent Transportation Systems 19 (5): 1583–1592. doi:10.1109/TITS.2017.2728704.
   Web of Science ®Google Scholar
• Gutiérrez, J., O. D. Cardozo, and J. C. García-Palomares. 2011. “Transit Ridership Forecasting at Station Level: An Approach Based on Distance-decay Weighted Regression.” Journal of Transport Geography 19 (6): 1081–1092. doi:10.1016/j.jtrangeo.2011.05.004.
   Web of Science ®Google Scholar
• Hasan, S., X. Zhan, and S. V. Ukkusuri. 2013. “Understanding Urban Human Activity and Mobility Patterns Using Large-scale Location-based Data from Online Social Media.” Paper presented at the Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, Chicago, USA. ACM, 6. doi: 10.1145/2505821.2505823
   Google Scholar
• Huang, B., and J. H. Wang. 2020. “Big Spatial Data for Urban and Environmental Sustainability.” Geo-spatial Information Science 23 (2): 125–140. doi:10.1080/10095020.2020.1754138.
   Web of Science ®Google Scholar
• Huang, B., Y. L. Zhou, Z. Li, Y. Song, J. Cai, and W. Tu. 2020. “Evaluating and Characterizing Urban Vibrancy Using Spatial Big Data: Shanghai as a Case Study.” Environment and Urban Planning B: Urban Analytics and City Science 47 (9): 1543–1559. doi:10.1177/2399808319828730.
   Google Scholar
• Huang, S.-L., J.–H. Wong, and T.–C. Chen. 1998. “A Framework of Indicator System for Measuring Taipei’s Urban Sustainability.” Landscape and Urban Planning 42: 15–27. doi:10.1016/S0169-2046(98)00054-1.
   Web of Science ®Google Scholar
• Jacob, J. 1961. The Life and Death of Great American Cities. New York: Random House.
   Google Scholar
• Jacobs-Crisioni, C., P. Rietveld, E. Koomen, and E. Tranos. 2014. “Evaluating the Impact of Land-use Density and Mix on Spatiotemporal Urban Activity Patterns: An Exploratory Study Using Mobile Phone Data.” Environment & Planning A 46 (11): 2769–2785. doi:10.1068/a130309p.
   Web of Science ®Google Scholar
• Kusakabe, T., and Y. Asakura. 2014. “Behavioral Data Mining of Transit Smart Card Data: A Data Fusion Approach.” Transportation Research Part C: Emerging Technologies 46: 179–191. doi:10.1016/j.trc.2014.05.012.
   Web of Science ®Google Scholar
• Lansley, G., and P. A. Longley. 2016. “The Geography of Twitter Topics in London.” Computers, Environment and Urban Systems 58: 85–96. doi:10.1016/j.compenvurbsys.2016.04.002.
   Web of Science ®Google Scholar
• Li, C., M. Wang, J. Wang, and W. Wu. 2016. “The Geography of City Liveliness and Land Use Configurations: Evidence from Location-based Big Data in Beijing.” Paper presented at the Spatial Economics Research Centre, London, UK. LSE. https://EconPapers.repec.org/RePEc:cep:sercdp:0201
   Google Scholar
• Li, D., W. Guo, X. Chang, and X. Li. 2020. “From Earth Observation to Human Observation: Geocomputation for Social Science.” Journal of Geographical Sciences 30 (2): 233–250. doi:10.1007/s11442-020-1725-8.
   Web of Science ®Google Scholar
• Liu, L., B. Zhou, J. Zhao, and B. D. Ryan. 2016. “C-IMAGE: City Cognitive Mapping through Geo-tagged Photos.” GeoJournal 81 (6): 817–861. doi:10.1007/s10708-016-9739-6.
   Web of Science ®Google Scholar
• Lock, O., and C. Pettit. 2020. “Social Media as Passive Geo-participation in Transportation Planning – How Effective are Topic Modeling & Sentiment Analysis in Comparison with Citizen Surveys?” Geo-spatial Information Science 23 (4): 275–292. doi:10.1080/10095020.2020.1815596.
   Web of Science ®Google Scholar
• Lynch, K. 1984. Good City Form. Boston, USA: MIT press.
   Google Scholar
• Martí, P., L. Serrano-Estrada, and A. Nolasco-Cirugeda. 2017. “Using Locative Social Media and Urban Cartographies to Identify and Locate Successful Urban Plazas.” Cities 64: 66–78. doi:10.1016/j.cities.2017.02.007.
   Web of Science ®Google Scholar
• Monika, S. 2020. “Analysis of Mobility Data – A Focus on Mobile Mapping Systems.” Geo-spatial Information Science 23 (1): 68–74. doi:10.1080/10095020.2020.1730713.
   Web of Science ®Google Scholar
• Montgomery J (1998) Making a city: Urbanity, vitality and urban design, Journal of Urban Design, 3:(1), 93–116, DOI: 10.1080/13574809808724418
   Google Scholar
• Morency, C., M. Trepanier, and B. Agard. 2008. “Analysing the Variability of Transit Users Behaviour with Smart Card Data.” Paper presented at the IEEE Intelligent Transportation Systems Conference, Beijing, China, 44–49. doi: 10.1109/ITSC.2006.1706716
   Google Scholar
• Nicodemus, A. G. 2013. “Fuzzy Vibrancy: Creative Placemaking as Ascendant US Cultural Policy.” Cultural Trends 22 (3–4): 213–222. doi:10.1080/09548963.2013.817653.
   Google Scholar
• Pelletier, M. P., M. Trépanier, and C. Morency. 2011. “Smart Card Data Use in Public Transit: A Literature Review.” Transportation Research Part C: Emerging Technologies 19 (4): 557–568. doi:10.1016/j.trc.2010.12.003.
   Web of Science ®Google Scholar
• Sawada, M. 2001. Global Spatial Autocorrelation indices-Moran’s I, Geary’s C and the General Cross-product Statistics. Mimeo: Laboratory of Paleoclimatology and Climatology, Dept. Geography, University of Ottawa.
   Google Scholar
• Shaw, S. L., and D. Sui. 2018. Human Dynamic Research in Smart and Connected Communities. Springer, Cham.
   Google Scholar
• Shen, Y., and K. Karimi. 2016. “Urban Function Connectivity: Characterisation of Functional Urban Streets with Social Media Check-in Data.” Cities 55: 9–21. doi:10.1016/j.cities.2016.03.013.
   Web of Science ®Google Scholar
• Song, Y., L. Merlin, and D. Rodriguez. 2013. “Comparing Measures of Urban Land Use Mix.” Computers, Environment and Urban Systems 42: 1–13. doi:10.1016/j.compenvurbsys.2013.08.001.
   Web of Science ®Google Scholar
• Sulis, P., E. Manley, C. Zhong, and M. Batty. 2018. “Using Mobility Data as Proxy for Measuring Urban Vitality.” Journal of Spatial Information Science 16: 137–162. doi:10.5311/JOSIS.2018.16.384.
   Google Scholar
• Sung, H.–G., D.-H. Go, and C. G. Choi. 2013. “Evidence of Jacobs’s Street Life in the Great Seoul City: Identifying the Association of Physical Environment with Walking Activity on Streets.” Cities 35: 164–173. doi:10.1016/j.cities.2013.07.010.
   Web of Science ®Google Scholar
• Tu, W., J. Cao, Y. Yue, S. L Shaw, M. Zhou, Z. Wang, Y. Xu, and Q. Li. 2017. “Coupling Mobile Phone and Social Media Data: A New Approach to Understanding Urban Functions and Diurnal Patterns.” International Journal of Geographical Information Science 31 (12): 2331–2358. doi:10.1080/13658816.2017.1356464.
   Web of Science ®Google Scholar
• Tu, W., R. Cao, Y. Yue, B. Zhou, Q. Li, and Q. Li. 2018. “Spatial Variations in Urban Public Ridership Derived from GPS Trajectories and Smart Card Data.” Journal of Transport Geography 69 (3688): 45–57. doi:10.1016/j.jtrangeo.2018.04.013.
   Google Scholar
• Tu, W., T. Zhu, J. Xia, Y. Zhou, Y. Lai, J. Jiang, and Q. Li. 2020. “Portraying the Spatial Dynamics of Urban Vibrancy Using Multisource Urban Big Data.” Computer Environment and Urban Systems 80 (101428): 101428. doi:10.1016/j.compenvurbsys.2019.101428.
   Web of Science ®Google Scholar
• Wu, C., X. Ye, F. Ren, and Q. Du. 2018a. “Check-in Behaviour and Spatio-temporal Vibrancy: An Exploratory Analysis in Shenzhen, China.” Cities 77: 104–116. doi:10.1016/j.cities.2018.01.017.
   Web of Science ®Google Scholar
• Wu, J., N. Ta, Y. Song, J. Lin, and Y. Chai. 2018b. “Urban Form Breeds Neighborhood Vibrancy: A Case Study Using A GPS-based Activity Survey in Suburban Beijing.” Cities 74: 100–108. doi:10.1016/j.cities.2017.11.008.
   Web of Science ®Google Scholar
• Xu, Z., Y. Liu, N. Yen, L. Mei, X. Luo, X. Wei, and C. Hu. 2016. “Crowdsourcing Based Description of Urban Emergency Events Using Social Media Big Data.” IEEE Transactions on Cloud Computing 7161 (c): 1. doi:10.1109/TCC.2016.2517638.
   Google Scholar
• Yu, H. B., and S. L. Shaw. 2008. “Exploring Potential Human Activities in Physical and Virtual Spaces: A Spatio‐temporal Gis Approach.” International Journal of Geographical Information Systems 22 (4): 22. doi:10.1080/13658810701427569.
   Web of Science ®Google Scholar
• Yue, Y., Y. Zhuang, A. G. J. Xie, C. Ma, and Q. Lin. 2017. “Measurements of POI-based Mixed Use and Their Relationships with Neighbourhood Vibrancy.” International Journal of Geographical Information Science 31: 658–675. doi:10.1080/13658816.2016.1220561.
   Web of Science ®Google Scholar
• Zarin, S. Z., M. Niroomand, and A. A. Heidari. 2015. “Physical and Social Aspects of Vitality Case Study: Traditional Street and Modern Street in Tehran.” Procedia - Social and Behavioral Sciences 170: 659–668. doi:10.1016/j.sbspro.2015.01.068.
   Google Scholar
• Zhang, X., Y. Xu, W. Tu, and C. Ratti. 2018. “Do Different Datasets Tell the Same Story about Urban Mobility — A Comparative Study of Public Transit and Taxi Usage.” Journal of Transport Geography 70 (May): 1–23. doi:10.1016/j.jtrangeo.2018.05.002.
   Google Scholar
• Zhang, Y., Q. Li, W. Tu, K. Mai, Y. Yao, and Y. Chen. 2019. “Functional Urban Land Use Recognition Integrating Multi-source Geospatial Data and Cross-correlations.” Computers, Environment and Urban Systems 78: 101374. doi:10.1016/j.compenvurbsys.2019.101374.
   Web of Science ®Google Scholar
• Zhao, J., W. Deng, Y. Song, and Y. Zhu. 2013. “What Influences Metro Station Ridership in China? Insights from Nanjing.” Cities 35: 114–124. doi:10.1016/j.cities.2013.07.002.
   Web of Science ®Google Scholar
• Zhao, Z., H. N. Koutsopoulos, and J. Zhao. 2018. “Detecting Pattern Changes in Individual Travel Behavior: A Bayesian Approach.” Transportation Research Part B: Methodological 112: 73–88. doi:10.1016/j.trb.2018.03.017.
   Web of Science ®Google Scholar
• Zheng, S., X. Hu, J. Wang, and R. Wang. 2016. “Subways near the Subway: Rail Transit and Neighborhood Catering Businesses in Beijing.” Transport Policy 51: 81–92. doi:10.1016/j.tranpol.2016.03.008.
   Web of Science ®Google Scholar
• Zhong, C., M. Batty, E. Manley, J. Wang, Z. Wang, F. Chen, and G. Schmitt. 2016. “Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore, and Beijing Using Smart-card Data.” PLoS ONE 11 (2): 1–17. doi:10.1371/journal.pone.0149222.
   Web of Science ®Google Scholar
• Zhu, Y., and J. Ferreira. 2015. “Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations.” Planning Support Systems and Smart Cities. Springer, Cham. 121–141. doi:10.1007/978-3-319-18368-8_7.
   Google Scholar
• Zielstra, D., and H. H. Hochmair. 2011. “Comparative Study of Pedestrian Accessibility to Transit Stations Using Free and Proprietary Network Data.” Transportation Research Record: Journal of the Transportation Research Board 2217 (1): 145–152. doi:10.3141/2217-18.
   Web of Science ®Google Scholar