Human Mobility and Smart City: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Lily Guo and Version 1 by Anshu Zhang.

Human mobility, the movement of human beings in space and time, reflects the spatial-temporal characteristics of human behavior. With big data analytics, human mobility research can be used to facilitate smart city development, in multiple disciplines such as smart traffic, smart urban planning, smart health, smart safety, smart commerce, etc. A framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literatures regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

  • Human Mobility Research
  • smart city
  • big data
Please wait, diff process is still running!

References

  1. Batty, M.; Axhausen, K.W.; Giannotti, F.; Pozdnoukhov, A.; Bazzani, A.; Wachowicz, M.; Ouzounis, G.; Portugali, Y. Smart cities of the future. Eur. Phys. J. Spéc. Top. 2012, 214, 481–518, doi:10.1140/epjst/e2012-01703-3.
  2. Yuan, Y.; Raubal, M. Analyzing the distribution of human activity space from mobile phone usage: An individual and ur-ban-oriented study. Int. J. Geogr. Inf. Sci. 2016, 30, 1594–1621, doi:10.1080/13658816.2016.1143555.
  3. Hilbert, M. Big Data for Development: A Review of Promises and Challenges. Dev. Policy Rev. 2016, 34, 135–174, doi:10.1111/dpr.12142.
  4. Neirotti, P.; De Marco, A.; Cagliano, A.C.; Mangano, G.; Scorrano, F. Current trends in Smart City initiatives: Some stylised facts. Cities 2014, 38, 25–36, doi:10.1016/j.cities.2013.12.010.
  5. Liu, J.; Li, J.; Li, W.; Liu, J. Rethinking big data: A review on the data quality and usage issues. ISPRS J. Photogramm. Remote. Sens. 2016, 115, 134–142, doi:10.1016/j.isprsjprs.2015.11.006.
  6. Pan, G.; Qi, G.; Zhang, W.; Li, S.; Wu, Z.; Yang, L.T. Trace analysis and mining for smart cities: Issues, methods, and applica-tions. IEEE Commun. Mag. 2013, 51, 120–126, doi:10.1109/mcom.2013.6525604.
  7. Yin, C.; Xiong, Z.; Chen, H.; Wang, J.; Cooper, D.; David, B. A literature survey on smart cities. Sci. China Inf. Sci. 2015, 58, 1–18, doi:10.1007/s11432-015-5397-4.
  8. Miller, H.J. Beyond sharing: Cultivating cooperative transportation systems through geographic information science. J. Transp. Geogr. 2013, 31, 296–308, doi:10.1016/j.jtrangeo.2013.04.007.
  9. Lee, J.-G.; Kang, M. Geospatial Big Data: Challenges and Opportunities. Big Data Res. 2015, 2, 74–81, doi:10.1016/j.bdr.2015.01.003.
  10. Yao, H.; Xiong, M.; Zeng, D.; Gong, J. Mining multiple spatial–temporal paths from social media data. Futur. Gener. Comput. Syst. 2018, 87, 782–791, doi:10.1016/j.future.2017.08.003.
  11. Sagl, G.; Loidl, M.; Beinat, E. A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic. ISPRS Int. J. Geo-Inf. 2012, 1, 256–271, doi:10.3390/ijgi1030256.
  12. Sainio, J.; Westerholm, J.; Oksanen, J. Generating Heat Maps of Popular Routes Online from Massive Mobile Sports Track-ing Application Data in Milliseconds While Respecting Privacy. ISPRS Int. J. Geo-Inf. 2015, 4, 1813–1826, doi:10.3390/ijgi4041813.
  13. Jiang, X.; Zheng, C.; Tian, Y.; Liang, R.-H. Large-scale taxi O/D visual analytics for understanding metropolitan human movement patterns. J. Vis. 2015, 18, 185–200, doi:10.1007/s12650-015-0278-x.
  14. Fang, Z.; Yang, X.; Xu, Y.; Shaw, S.-L.; Yin, L. Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns. Int. J. Geogr. Inf. Sci. 2017, 31, 2119–2141, doi:10.1080/13658816.2017.1346256.
  15. Li, R.; Zhao, Z.; Yang, C.; Wu, C.; Zhang, H. Wireless big data in cellular networks: The cornerstone of smart cities. IET Commun. 2018, 12, 1517–1523, doi:10.1049/iet-com.2017.1278.
  16. Traunmueller, M.W.; Johnson, N.E.; Malik, A.; Kontokosta, C.E. Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities. Comput. Environ. Urban Syst. 2018, 72, 4–12, doi:10.1016/j.compenvurbsys.2018.07.006.
  17. Hasan, S.; Schneider, C.M.; Ukkusuri, S.V.; Gonzalez, M.C. Spatiotemporal Patterns of Urban Human Mobility. J. Stat. Phys. 2013, 151, 304–318, doi:10.1007/s10955-012-0645-0.
  18. Ríos, S.A.; Munoz, R. Land Use detection with cell phone data using topic models: Case Santiago, Chile. Comput. Environ. Urban Syst. 2017, 61, 39–48, doi:10.1016/j.compenvurbsys.2016.08.007.
  19. Zhong, C.; Arisona, S.M.; Huang, X.; Batty, M.; Schmitt, G. Detecting the dynamics of urban structure through spatial net-work analysis. Int. J. Geogr. Inf. Sci. 2014, 28, 2178–2199, doi:10.1080/13658816.2014.914521.
  20. Zhou, X.; Yeh, A.G.; Yue, Y. Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data. J. Transp. Geogr. 2018, 68, 102–108, doi:10.1016/j.jtrangeo.2017.12.006.
  21. Song, Y.; Huang, B.; Cai, J.; Chen, B. Dynamic assessments of population exposure to urban greenspace using multi-source big data. Sci. Total. Environ. 2018, 634, 1315–1325, doi:10.1016/j.scitotenv.2018.04.061.
  22. Paldino, S.; Bojic, I.; Sobolevsky, S.; Ratti, C.; González, M.C. Urban magnetism through the lens of geo-tagged photography. EPJ Data Sci. 2015, 4, 5, doi:10.1140/epjds/s13688-015-0043-3.
  23. Mizzi, C.; Fabbri, A.; Rambaldi, S.; Bertini, F.; Curti, N.; Sinigardi, S.; Luzi, R.; Venturi, G.; Davide, M.; Muratore, G.; et al. Unraveling pedestrian mobility on a road network using ICTs data during great tourist events. EPJ Data Sci. 2018, 7, 44, doi:10.1140/epjds/s13688-018-0168-2.
  24. Luo, F.; Cao, G.; Mulligan, K.; Li, X. Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago. Appl. Geogr. 2016, 70, 11–25, doi:10.1016/j.apgeog.2016.03.001.
  25. Rizwan, M.; Wanggen, W.; Cervantes, O.; Gwiazdzinski, L.; Wan, W. Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play. ISPRS Int. J. Geo-Inf. 2018, 7, 196, doi:10.3390/ijgi7050196.
  26. Semanjski, I.; Gautama, S.; Ahas, R.; Witlox, F. Spatial context mining approach for transport mode recognition from mobile sensed big data. Comput. Environ. Urban Syst. 2017, 66, 38–52, doi:10.1016/j.compenvurbsys.2017.07.004.
  27. Gong, L.; Liu, X.; Wu, L.; Liu, Y. Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartogr. Geogr. Inf. Sci. 2016, 43, 103–114, doi:10.1080/15230406.2015.1014424.
  28. Wang, Z.; Tu, L.; Guo, Z.; Yang, L.T.; Huang, B. Analysis of user behaviors by mining large network data sets. Futur. Gener. Comput. Syst. 2014, 37, 429–437, doi:10.1016/j.future.2014.02.015.
  29. Shen, J.; Cheng, T. A framework for identifying activity groups from individual space-time profiles. Int. J. Geogr. Inf. Sci. 2016, 30, 1785–1805, doi:10.1080/13658816.2016.1139119.
  30. Yang, J.; Li, X.; Wang, D.; Wang, J. A Group Mining Method for Big Data on Distributed Vehicle Trajectories in WAN. Int. J. Distrib. Sens. Networks 2015, 11, 756107–9, doi:10.1155/2015/756107.
  31. Wijesinghe, W.O.K.I.S.; Kumarasinghe, C. Relationship Among the Diameter of the Area of Influence & Refill Usage of Sri Lanka Using Anonymized Call Detail Records. ICST Trans. Scalable Inf. Syst. 2017, 4, 152104, doi:10.4108/eai.18-1-2017.152104.
  32. Rodríguez-Echeverría, J.; Semanjski, I.; Gautama, S.; Van De Weghe, N.; Ochoa, D. Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data. Sensors 2018, 18, 2972, doi:10.3390/s18092972.
  33. Toader, B.; Sprumont, F.; Faye, S.; Popescu, M.; Viti, F. Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals. ISPRS Int. J. Geo-Inf. 2017, 6, 62, doi:10.3390/ijgi6030062.
  34. Chorley, M.J.; Whitaker, R.M.; Allen, S.M. Personality and location-based social networks. Comput. Hum. Behav. 2015, 46, 45–56, doi:10.1016/j.chb.2014.12.038.
  35. Alessandretti, L.; Lehmann, S.; Baronchelli, A. Understanding the interplay between social and spatial behaviour. EPJ Data Sci. 2018, 7, 36, doi:10.1140/epjds/s13688-018-0164-6.
  36. Zhao, Z.-D.; Yang, Z.; Zhang, Z.; Zhou, T.; Huang, Z.-G.; Lai, Y.-C. Emergence of scaling in human-interest dynamics. Sci. Rep. 2013, 3, 3472, doi:10.1038/srep03472.
  37. Hu, T.; Bigelow, E.; Luo, J.; Kautz, H. Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Dis-tinctive Metropolitan Areas. IEEE Trans. Big Data 2017, 3, 55–66, doi:10.1109/tbdata.2016.2580542.
  38. Zhao, Z.-D.; Huang, Z.-G.; Huang, L.; Liu, H.; Lai, Y.-C. Scaling and correlation of human movements in cyberspace and physical space. Phys. Rev. E 2014, 90, 050802, doi:10.1103/physreve.90.050802.
  39. Zhao, Y.-M.; Zeng, A.; Xiao-Yong, Y.; Wang, W.-X.; Lai, Y.-C. Unified underpinning of human mobility in the real world and cyberspace. New J. Phys. 2016, 18, 53025, doi:10.1088/1367-2630/18/5/053025.
  40. Wang, H.; Kilmartin, L. Comparing Rural and Urban Social and Economic Behavior in Uganda: Insights from Mobile Voice Service Usage. J. Urban Technol. 2014, 21, 61–89, doi:10.1080/10630732.2014.888296.
  41. Xu, F.; Li, Y.; Chen, M.; Chen, S. Mobile cellular big data: Linking cyberspace and the physical world with social ecology. IEEE Netw. 2016, 30, 6–12, doi:10.1109/mnet.2016.7474338.
  42. Wu, W.; Wang, J.; Dai, T. The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts. Ann. Am. Assoc. Geogr. 2016, 106, 612–630, doi:10.1080/00045608.2015.1121804.
  43. Wang, J.; Kong, X.; Rahim, A.; Xia, F.; Tolba, A.; Al-Makhadmeh, Z. IS2Fun: Identification of Subway Station Functions Us-ing Massive Urban Data. IEEE Access 2017, 5, 27103–27113, doi:10.1109/access.2017.2766237.
  44. Niu, N.; Liu, X.; Jin, H.; Ye, X.; Liu, Y.; Li, X.; Chen, Y.; Li, S. Integrating multi-source big data to infer building functions. Int. J. Geogr. Inf. Sci. 2017, 31, 1–20, doi:10.1080/13658816.2017.1325489.
  45. Zhang, S.; Wang, Z. Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces. PLoS ONE 2016, 11, e0165597, doi:10.1371/journal.pone.0165597.
  46. Sobolevsky, S.; Sitko, I.; Combes, R.T.D.; Hawelka, B.; Arias, J.M.; Ratti, C. Cities through the Prism of People’s Spending Behavior. PLoS ONE 2016, 11, e0146291, doi:10.1371/journal.pone.0146291.
  47. Dong, X.; Suhara, Y.; Bozkaya, B.; Singh, V.K.; Lepri, B.; Pentland, A. ‘Sandy’ Social Bridges in Urban Purchase Behavior. ACM Trans. Intell. Syst. Technol. 2018, 9, 1–29, doi:10.1145/3149409.
  48. Lopez, C.; Leclercq, L.; Krishnakumari, P.; Chiabaut, N.; Van Lint, H. Revealing the day-to-day regularity of urban conges-tion patterns with 3D speed maps. Sci. Rep. 2017, 7, 1–11, doi:10.1038/s41598-017-14237-8.
  49. De Domenico, M.; Lima, A.; González, M.C.; Arenas, A. Personalized routing for multitudes in smart cities. EPJ Data Sci. 2015, 4, 1, doi:10.1140/epjds/s13688-015-0038-0.
  50. Pappalardo, L.; Simini, F. Data-driven generation of spatio-temporal routines in human mobility. Data Min. Knowl. Discov. 2018, 32, 787–829, doi:10.1007/s10618-017-0548-4.
  51. Xu, Y.; Gonzalez, M.C. Collective benefits in traffic during mega events via the use of information technologies. J. R. Soc. Interface 2017, 14, 20161041, doi:10.1098/rsif.2016.1041.
  52. Kim, D.Y.; Song, H.Y. Method of predicting human mobility patterns using deep learning. Neurocomputing 2018, 280, 56–64, doi:10.1016/j.neucom.2017.07.069.
  53. Bao, J.; Liu, P.; Yu, H.; Xu, C. Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas. Accid. Anal. Prev. 2017, 106, 358–369, doi:10.1016/j.aap.2017.06.012.
  54. Chen, C.; Zhang, D.; Li, N.; Zhou, Z.-H. B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces. IEEE Trans. Intell. Transp. Syst. 2014, 15, 1451–1465, doi:10.1109/tits.2014.2298892.
  55. Ranjit, S.; Witayangkurn, A.; Nagai, M.; Shibasaki, R. Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehi-cle Data. ISPRS Int. J. Geo-Inf. 2018, 7, 177, doi:10.3390/ijgi7050177.
  56. Chen, B.; Song, Y.; Jiang, T.; Chen, Z.; Huang, B.; Xu, B. Real-Time Estimation of Population Exposure to PM2.5 Using Mo-bile- and Station-Based Big Data. Int. J. Environ. Res. Public Heal. 2018, 15, 573, doi:10.3390/ijerph15040573.
  57. Gariazzo, C.; Pelliccioni, A.; Bolignano, A. A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic. Atmos. Environ. 2016, 131, 289–300, doi:10.1016/j.atmosenv.2016.02.011.
  58. Mao, L.; Yin, L.; Song, X.; Mei, S. Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data. Acta Trop. 2016, 162, 188–195, doi:10.1016/j.actatropica.2016.06.029.
  59. Kraemer, M.U.G.; Bisanzio, D.; Reiner, R.C.; Zakar, R.; Hawkins, J.B.; Freifeld, C.C.; Smith, D.L.; Hay, S.I.; Brownstein, J.S.; Perkins, T.A. Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: A case study using geolocated tweets from Lahore, Pakistan. EPJ Data Sci. 2018, 7, 1–17, doi:10.1140/epjds/s13688-018-0144-x.
  60. Mari, L.; Gatto, M.; Ciddio, M.; Dia, E.D.; Sokolow, S.H.; De Leo, G.A.; Casagrandi, R. Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis. Sci. Rep. 2017, 7, 1–11, doi:10.1038/s41598-017-00493-1.
  61. Wesolowski, A.; Buckee, C.O.; Engø-Monsen, K.; Metcalf, C.J.E. Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data. J. Infect. Dis. 2016, 214, S414–S420, doi:10.1093/infdis/jiw273.
  62. Google. COVID-19 Community Mobility Reports. 2020. Available online: https://www.google.com/covid19/mobility/ (ac-cessed on 7 December 2020).
  63. Baidu. Baidu Qianxi—Baidu Map Huiyan. 2020. Available online: https://qianxi.baidu.com/ (accessed on 7 December 2020).
  64. Badr, H.S.; Du, H.; Marshall, M.; Dong, E.; Squire, M.M.; Gardner, L. Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. Lancet Infect. Dis. 2020, 20, 1247–1254, doi:10.1016/s1473-3099(20)30553-3.
  65. Iacus, S.M.; Santamaria, C.; Sermi, F.; Spyratos, S.; Tarchi, D.; Vespe, M. Human mobility and COVID-19 initial dynamics. Nonlinear Dyn. 2020, 101, 1901–1919, doi:10.1007/s11071-020-05854-6.
  66. Kubota, Y.; Shiono, T.; Kusumoto, B.; Fujinuma, J. Multiple drivers of the COVID-19 spread: The roles of climate, interna-tional mobility, and region-specific conditions. PLoS ONE 2020, 15, e0239385, doi:10.1371/journal.pone.0239385.
  67. Santamaria, C.; Sermi, F.; Spyratos, S.; Iacus, S.M.; Annunziato, A.; Tarchi, D.; Vespe, M. Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A European regional analysis. Saf. Sci. 2020, 132, 104925, doi:10.1016/j.ssci.2020.104925.
  68. Venter, Z.S.; Barton, D.N.; Gundersen, V.; Figari, H.; Nowell, M. Urban nature in a time of crisis: Recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway. Environ. Res. Lett. 2020, 15, 104075, doi:10.1088/1748-9326/abb396.
  69. Jia, J.S.; Lu, X.; Yuan, Y.; Xu, G.; Jia, J.; Christakis, N.A. Population flow drives spatio-temporal distribution of COVID-19 in China. Nat. Cell Biol. 2020, 582, 389–394, doi:10.1038/s41586-020-2284-y.
  70. Wang, H.Y.; Yamamoto, N. Using a partial differential equation with Google Mobility data to predict COVID-19 in Arizona. Math. Biosci. Eng. 2020, 17, 4891–4904, doi:10.3934/mbe.2020266.
  71. Kraemer, M.; Yang, C.-H.; GutiérrezB.; Wu, C.-H.; Klein, B.; Pigott, D.M.; Du Plessis, L.; Faria, N.R.; Li, R.; Hanage, W.P.; et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 2020, 368, 493–497, doi:10.1126/science.abb4218.
  72. Gatto, M.; Bertuzzo, E.; Mari, L.; Miccoli, S.; Carraro, L.; Casagrandi, R.; Rinaldo, A. Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proc. Natl. Acad. Sci. USA 2020, 117, 10484–10491, doi:10.1073/pnas.2004978117.
  73. Bao, R.; Zhang, A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci. Total. Environ. 2020, 731, 139052, doi:10.1016/j.scitotenv.2020.139052.
  74. Cheval, S.; Adamescu, C.M.; Georgiadis, T.; Herrnegger, M.; Piticar, A.; LeGates, D.R. Observed and Potential Impacts of the COVID-19 Pandemic on the Environment. Int. J. Environ. Res. Public Heal. 2020, 17, 4140, doi:10.3390/ijerph17114140.
  75. Bonaccorsi, G.; Pierri, F.; Cinelli, M.; Flori, A.; Galeazzi, A.; Porcelli, F.; Schmidt, A.L.; Valensise, C.M.; Scala, A.; Quattro-ciocchi, W.; et al. Economic and social consequences of human mobility restrictions under COVID-19. Proc. Natl. Acad. Sci. USA 2020, 117, 15530–15535, doi:10.1073/pnas.2007658117.
  76. Boulos, M.N.K.; Geraghty, E.M. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute res-piratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int. J. Health Geogr. 2020, 19, 8.
  77. Zhou, C.; Su, F.; Pei, T.; Zhang, A.; Du, Y.; Luo, B.; Cao, Z.; Wang, J.; Yuan, W.; Zhu, Y.; et al. COVID-19: Challenges to GIS with Big Data. Geogr. Sustain. 2020, 1, 77–87, doi:10.1016/j.geosus.2020.03.005.
  78. Mazimpaka, J.D.; Timpf, S. How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern. ISPRS Int. J. Geo-Inf. 2017, 6, 15, doi:10.3390/ijgi6010015.
  79. Song, X.; Shibasaki, R.; Yuan, N.J.; Xie, X.; Li, T.; Adachi, R. DeepMob. ACM Trans. Inf. Syst. 2017, 35, 1–19, doi:10.1145/3057280.
  80. Bogomolov, A.; Lepri, B.; Staiano, J.; Letouzé, E.; Oliver, N.; Pianesi, F.; Pentland, A. Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics. Big Data 2015, 3, 148–158, doi:10.1089/big.2014.0054.
  81. Kadar, C.; Pletikosa, I. Mining large-scale human mobility data for long-term crime prediction. EPJ Data Sci. 2018, 7, 26, doi:10.1140/epjds/s13688-018-0150-z.
  82. Kong, X.; Liu, Y.; Wang, Y.; Tong, D.; Zhang, J. Investigating Public Facility Characteristics from a Spatial Interaction Per-spective: A Case Study of Beijing Hospitals Using Taxi Data. ISPRS Int. J. Geo-Inf. 2017, 6, 38, doi:10.3390/ijgi6020038.
  83. Giordani, I.; Archetti, F.; Candelieri, A. Data Science and Environmental Management in Smart Cities. Environ. Eng. Manag. J. 2015, 14, 2095–2102, doi:10.30638/eemj.2015.224.
  84. Ardanuy, J.F.; Castano-Solis, S.; Álvaro-Hermana, R.; Merino, J.; Castillo, Á. Using mobility information to perform a feasi-bility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet. Energy Convers. Manag. 2018, 157, 59–70, doi:10.1016/j.enconman.2017.11.070.
  85. Guo, Q.; Karimi, H.A. A novel methodology for prediction of spatial-temporal activities using latent features. Comput. En-viron. Urban Syst. 2017, 62, 74–85, doi:10.1016/j.compenvurbsys.2016.10.006.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback