• Acta Geographica Sinica
  • Vol. 75, Issue 7, 1523 (2020)
Yu LIU1、*, Xin YAO1, Yongxi GONG2, Chaogui KANG3、4, Xun SHI5, Fahui WANG6, Jiao'e WANG7, Yi ZHANG1, Pengfei ZHAO1, Di ZHU1, and Xinyan ZHU8
Author Affiliations
  • 1Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
  • 2Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 4Center for Urban Science and Progress, New York University, Brooklyn, NY 11201, USA
  • 5Department of Geography, Dartmouth College, Hanover, NH 03755, USA
  • 6Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
  • 7Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 8State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • show less
    DOI: 10.11821/dlxb202007014 Cite this Article
    Yu LIU, Xin YAO, Yongxi GONG, Chaogui KANG, Xun SHI, Fahui WANG, Jiao'e WANG, Yi ZHANG, Pengfei ZHAO, Di ZHU, Xinyan ZHU. Analytical methods and applications of spatial interactions in the era of big data[J]. Acta Geographica Sinica, 2020, 75(7): 1523 Copy Citation Text show less
    References

    [1] Geography as spatial interaction[J]. Annals of Association of the American Geographers, 44, 283-284(1954).

    [2] Spatial interaction patterns[J]. Journal of Environmental Systems, 6, 271-301(1976).

    [3] Spatial Interaction Models: Formulations and Applications[J]. Boston, MA: Kluwer(1989).

    [4] Spatial interaction modelling//Florax R J G M, Plane D A. Fifty Years of Regional Science[J]. Heidelberg: Springer, 339-361(2004).

    [5] Locational Analysis in Human Geography[J]. London: Edward Arnold(1977).

    [6] The laws of migration[J]. Journal of the Royal Statistical Society, 48, 167-227(1885).

    [7] . The Law of Retail Gravitation(1931).

    [8] Intervening opportunities: A theory relating to mobility and distance[J]. American Sociological Review, 5, 845-867(1940).

    [9] Spatially weighted interaction models (SWIM)[J]. Annals of the American Association of Geographers, 106, 990-1012(2016).

    [10] Mapping large spatial flow data with hierarchical clustering[J]. Transactions in GIS, 18, 421-435(2014).

    [11] et alUnderstanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 14, 463-483(2012).

    [12] et alExploring movement object patterns[J]. The Annals of Regional Science, 49, 471-484(2012).

    [13] et alRevealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data[J]. IEEE Transactions on Visualization and Computer Graphics, 23, 2120-2136(2017).

    [14] Towards a general theory of geographic representation in GIS[J]. International Journal of Geographical Information Science, 21, 239-260(2007).

    [15] et alSocial sensing: A new approach to understanding our socioeconomic environments[J]. Annals of the Association of American Geographers, 105, 512-530(2015).

    [16] Die Geographie: Ihre Geschichte, ihr Wesen und ihre Methoden[J]. Breslau: Ferdinand Hirt(1927).

    [17] Spatial interaction and spatial autocorrelation: A cross-product approach[J]. Environment and Planning A, 23, 1269-1277(1991).

    [18] Principles of geostatistics[J]. Economic Geology, 58, 1246-1266(1963).

    [19] Geography and spatial statistics: Current positions, future developments//Macmillan B. Remodelling Geography[J]. Oxford: Basil Blackwell, 191-203(1989).

    [20] Tobler's first law and spatial analysis[J]. Annals of the Association of American Geographers, 94, 284-289(2004).

    [21] A computer movie simulating urban growth in the Detroit region[J]. Economic Geography, 46, 234-240(1970).

    [22] The Geography of Transport Systems[J]. New York: Routledge(2013).

    [23] et alRedrawing the map of Great Britain from a network of human interactions[J]. PLoS ONE, 5, e14248(2010).

    [24] et alUncovering patterns of inter-urban trip and spatial interaction from social media check-in data[J]. PLoS ONE, 9, e86026(2014). https://www.ncbi.nlm.nih.gov/pubmed/24465849

    [25] Effects of scale in spatial interaction models[J]. Journal of Geographical Systems, 15, 249-264(2013).

    [26] The origins of scaling in cities[J]. Science, 340, 1438-1441(2013).

    [27] [J], 67, 1339-1352(2012).

    [28] et alExploratory calibration of a spatial interaction model using taxi GPS trajectories[J]. Computers, Environment and Urban Systems, 36, 140-153(2012).

    [29] et alModification of the gravity model and application to the metropolitan Seoul subway system[J]. Physical Review E, 86, 026102(2012).

    [30] Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing[J]. Computers, Environment and Urban Systems, 53, 19-35(2015).

    [31] et alIncorporating spatial interaction patterns in classifying and understanding urban land use[J]. International Journal of Geographical Information Science, 30, 334-350(2016).

    [32] Understanding operation behaviors of taxicabs in cities by matrix factorization[J]. Computers, Environment and Urban Systems, 60, 79-88(2016).

    [33] et alTracking job and housing dynamics with smartcard data[C]. Proceedings of the National Academy of Sciences of the United States of America, 115, 12710-12715(2018).

    [34] et alUncovering regional characteristics from mobile phone data: A network science approach[J]. Papers in Regional Science, 95, 613-631(2016).

    [35] et alAnother tale of two cities: Understanding human activity space using actively tracked cellphone location data[J]. Annals of the American Association of Geographers, 106, 489-502(2016).

    [36] et alDiscovering spatial interaction communities from mobile phone data[J]. Transactions in GIS, 17, 463-481(2013).

    [37] [J], 73, 1896-1909(2018).

    [38] et alA tale of many cities: Universal patterns in human urban mobility[J]. PLoS ONE, 7, e37027(2012). https://www.ncbi.nlm.nih.gov/pubmed/22666339

    [39] et alDelineation of an urban agglomeration boundary based on Sina Weibo microblog "check-in" data: A case study of the Yangtze River Delta[J]. Cities, 60, 180-191(2017).

    [40] et alIntra-urban human mobility and activity transition: Evidence from social media check-in data[J]. PLoS ONE, 9, e97010(2014).

    [41] et alThe missing parts from social media enabled smart cities: Who, where, when, and what?[J]. Annals of the American Association of Geographers, 110, 462-475(2020).

    [42] et alAnalyzing relatedness by toponym co-occurrences on web pages[J]. Transactions in GIS, 18, 89-107(2014).

    [43] Simulating urban growth in a metropolitan area based on weighted urban flows by using web search engine[J]. International Journal of Geographical Information Science, 29, 1721-1736(2015).

    [44] Extracting and analyzing semantic relatedness between cities using news articles[J]. International Journal of Geographical Information Science, 31, 2427-2451(2017).

    [45] et alAnalysis of co-occurrence toponyms in web pages based on complex networks[J]. Physica A: Statistical Mechanics and Its Applications, 466, 462-475(2017).

    [46] Mesoscale structures in world city networks[J]. Annals of the American Association of Geographers, 109, 887-908(2019).

    [47] A statistical theory of spatial distribution models[J]. Transportation Research, 1, 253-269(1967).

    [48] et alA universal model for mobility and migration patterns[J]. Nature, 484, 96-100(2012).

    [49] Modelling spatial interaction using a neural net//Fischer M M, Nijkamp P. Geographic Information Systems, Spatial Modelling and Policy Evaluation[J]. Heidelberg: Springer, 147-164(1993).

    [50] Spatial interaction modeling using artificial neural networks[J]. Journal of Transport Geography, 3, 159-166(1995).

    [51] Modeling freight distribution using artificial neural networks[J]. Journal of Transport Geography, 12, 141-148(2004).

    [52] et alGravity versus radiation models: On the importance of scale and heterogeneity in commuting flows[J]. Physical Review E, 88, 022812(2013).

    [53] et alUniversal predictability of mobility patterns in cities[J]. Journal of the Royal Society Interface, 11, 20140834(2014).

    [54] et alA generalized radiation model for human mobility: Spatial scale, searching direction and trip constraint[J]. PLoS ONE, 10, e0143500(2015). https://www.ncbi.nlm.nih.gov/pubmed/26600153

    [55] et alIntra-urban human mobility patterns: An urban morphology perspective[J]. Physica A: Statistical Mechanics and Its Applications, 391, 1702-1717(2012).

    [56] et alOn the levy-walk nature of human mobility[J]. IEEE/ACM Transactions on Networking, 19, 630-643(2011).

    [57] et alReturners and explorers dichotomy in human mobility[J]. Nature Communications, 6, 8166(2015).

    [58] et alAn extended exploration and preferential return model for human mobility simulation at individual and collective levels[J]. Physica A: Statistical Mechanics and its Applications, 534, 121921(2019).

    [59] et alThe effect of recency to human mobility[J]. EPJ Data Science, 4, 21(2015).

    [60] Understanding individual human mobility patterns[J]. Nature, 453, 779-782(2008).

    [61] et alUniversal model of individual and population mobility on diverse spatial scales[J]. Nature Communications, 8, 1639(2017). https://www.ncbi.nlm.nih.gov/pubmed/29158475

    [62] Identifying local spatial association in flow data[J]. Journal of Geographical Systems, 1, 219-236(1999).

    [63] Assessing the cluster correspondence between paired point locations[J]. Geographical Analysis, 35, 290-309(2003).

    [64] Measuring spatial autocorrelation of vectors[J]. Geographical Analysis, 47, 300-319(2015).

    [65] Spatial cluster detection in spatial flow data[J]. Geographical Analysis, 48, 355-372(2016).

    [66] Tree-based and optimum cut-based origin-destination flow clustering[J]. ISPRS International Journal of Geo-Information, 8, 477(2019).

    [67] et alA stepwise spatio-temporal flow clustering method for discovering mobility trends[J]. IEEE Access, 6, 44666-44675(2018).

    [68] et alA multidimensional spatial scan statistics approach to movement pattern comparison[J]. International Journal of Geographical Information Science, 32, 1304-1325(2018).

    [69] et alDetecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization[J]. International Journal of Geographical Information Science, 33, 134-154(2019).

    [70] FlowAMOEBA: Identifying regions of anomalous spatial interactions[J]. Geographical Analysis, 51, 111-130(2019).

    [71] Constructing the spatial weights matrix using a local statistic[J]. Geographical Analysis, 36, 90-104(2004).

    [72] Experiments in migration mapping by computer[J]. The American Cartographer, 14, 155-163(1987).

    [73] A model of geographical movement[J]. Geographical Analysis, 13, 1-20(1981).

    [74] Spatial generalization and aggregation of massive movement data[J]. IEEE Transactions on Visualization and Computer Graphics, 17, 205-219(2011).

    [75] Flow mapping and multivariate visualization of large spatial interaction data[J]. IEEE Transactions on Visualization and Computer Graphics, 15, 1041-1048(2009).

    [76] et alGeometry-based edge clustering for graph visualization[J]. IEEE Transactions on Visualization and Computer Graphics, 14, 1277-1284(2008).

    [77] Force-directed edge bundling for graph visualization[J]. Computer Graphics Forum, 28, 983-990(2009).

    [78] Flow map layout via spiral trees[J]. IEEE Transactions on Visualization and Computer Graphics, 17, 2536-2544(2011).

    [79] et alForce-directed layout of origin-destination flow maps[J]. International Journal of Geographical Information Science, 31, 1521-1540(2017).

    [80] Design and evaluation of line symbolizations for origin-destination flow maps[J]. Information Visualization, 16, 309-331(2017).

    [81] et alDesign principles for origin-destination flow maps[J]. Cartography and Geographic Information Science, 45, 62-75(2018).

    [82] et alMobilitygraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering[J]. IEEE Transactions on Visualization and Computer Graphics, 22, 11-20(2016).

    [83] Quantifying global international migration flows[J]. Science, 343, 1520-1522(2014).

    [84] Spatial representation and spatial interaction[J]. Papers of the Regional Science Association, 38, 71-92(1977).

    [85] Visual analytics of spatial interaction patterns for pandemic decision support[J]. International Journal of Geographical Information Science, 21, 859-877(2007).

    [86] et alMany-to-many geographically-embedded flow visualisation: An evaluation[J]. IEEE Transactions on Visualization and Computer Graphics, 23, 411-420(2017).

    [87] et alInvestigating public facility characteristics from a spatial interaction perspective: A case study of Beijing hospitals using taxi data[J]. ISPRS International Journal of Geo-Information, 6, 38(2017). http://www.mdpi.com/2220-9964/6/2/38

    [88] Visualisation of origins, destinations and flows with OD maps[J]. The Cartographic Journal, 47, 117-129(2010).

    [89] et alVisualizing spatial interaction characteristics with direction-based pattern maps[J]. Journal of Visualization, 22, 555-569(2019).

    [90] Spatial structure and distance-decay parameters[J]. Annals of the Association of American Geographers, 71, 425-436(1981).

    [91] Reverse-fitting the gravity model to inter-city airline passenger flows by an algebraic simplification[J]. Journal of Transport Geography, 12, 219-234(2004).

    [92] et alReconstructing gravitational attractions of major cities in China from air passenger flow data, 2001-2008: A particle swarm optimization approach[J]. The Professional Geographer, 65, 265-282(2013).

    [93] et alInferring properties and revealing geographical impacts of intercity mobile communication network of China using a subnet data set[J]. International Journal of Geographical Information Science, 27, 431-448(2013).

    [94] New estimates of gravitational attraction by linear programming[J]. Geographical Analysis, 27, 271-285(1995).

    [95] Nodal attractions in China's intercity air passenger transportation[J]. Papers of the Applied Geography Conferences, 29, 443-452(2006).

    [96] Modeling interregional interaction: Implications for defining functional regions[J]. Annals of the Association of American Geographers, 82, 86-102(1992).

    [97] The delimitation of functional regions, nodal regions, and hierarchies by functional distance approaches[J]. Journal of Regional Science, 11, 57-72(1971).

    [98] Hierarchical aggregation procedures for interaction data[J]. Environment and Planning A, 7, 509-523(1975).

    [99] Entropy and spatial geometry[J]. Area, 4, 230-236(1972).

    [100] Optimal zoning systems for spatial interaction models[J]. Environment and Planning A, 9, 169-184(1977).

    [101] et alSpatial optimization for regionalization problems with spatial interaction: A heuristic approach[J]. International Journal of Geographical Information Science, 30, 451-473(2016).

    [102] Community detection in graphs[J]. Physics Reports, 486, 75-174(2010).

    [103] et alThe structure of borders in a small world[J]. PLoS ONE, 5, e15422(2010). https://www.ncbi.nlm.nih.gov/pubmed/21124970

    [104] et alRevealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 43, 78-90(2015).

    [105] et alUncovering space-independent communities in spatial networks[C]. Proceedings of the National Academy of Sciences of the United States of America, 108, 7663-7668(2011).

    [106] Finding community structure in spatially constrained complex networks[J]. International Journal of Geographical Information Science, 29, 889-911(2015).

    [107] Network analysis of China's aviation system, statistical and spatial structure[J]. Journal of Transport Geography, 22, 109-117(2012).

    [108] [J], 69, 1833-1846(2014).

    [109] Interpolating spatial interaction data[J]. Transactions in GIS, 15, 541-555(2011).

    [110] et alInferring spatial interaction patterns from sequential snapshots of spatial distributions[J]. International Journal of Geographical Information Science, 32, 783-805(2018).

    [111] et alStructure of urban movements: Polycentric activity and entangled hierarchical flows[J]. PLoS ONE, 6, e15923(2011). https://www.ncbi.nlm.nih.gov/pubmed/21249210

    [112] et alRevealing centrality in the spatial structure of cities from human activity patterns[J]. Urban Studies, 54, 437-455(2016).

    [113] et alDetecting the dynamics of urban structure through spatial network analysis[J]. International Journal of Geographical Information Science, 28, 2178-2199(2014).

    [114] et alJob-worker spatial dynamics in Beijing: Insights from smart card data[J]. Cities, 86, 83-93(2019).

    [115] et al[J], 37, 397-406(2018).

    [116] [J], 31, 102-108(2016).

    [117] [J], 67, 1031-1043(2012).

    [118] et alGeo-located Twitter as proxy for global mobility patterns[J]. Cartography and Geographic Information Science, 41, 260-271(2014).

    [119] et al[J], 36, 1654-1660(2016).

    [120] et alExamining the effect of land-use function complementarity on intra-urban spatial interactions using metro smart card records[J]. Transportation(2019). https://www.ncbi.nlm.nih.gov/pubmed/20953273

    [121] et alInferring land use from mobile phone activity[C]. Beijing: Proceedings of the ACM SIGKDD International Workshop on Urban Computing(2012).

    [122] [J], 71, 484-499(2016).

    [123] et alExploring the network structure and nodal centrality of China's air transport network: A complex network approach[J]. Journal of Transport Geography, 19, 712-721(2011).

    [124] Evolution of air transport network of China 1930-2012[J]. Journal of Transport Geography, 40, 145-158(2014).

    [125] et al[J], 71, 265-280(2016).

    [126] et alUniversal model of individual and population mobility on diverse spatial scales[J]. Nature Communications, 8, 1639(2017). https://www.ncbi.nlm.nih.gov/pubmed/29158475

    [127] et alA novel residual graph convolution deep learning model for short-term network-based traffic forecasting[J]. International Journal of Geographical Information Science, 34, 969-995(2020).

    [128] Data mining applied for accident prediction model in Indonesia toll road[C]. AIP Conference Proceedings, 1977, 060001(2018).

    [129] Weather effects on human mobility: A study using multi-channel sequence analysis[J]. Computers, Environment and Urban Systems, 71, 131-152(2018).

    [130] . Applications of Geospatial Information Technologies in Public Health(2016).

    [131] A simulation of the US influenza outbreak in 2009-2010 using a patch sir model based on airport transportation data[C]. Toronto: Proceedings of International Symposium on Mathematical and Computational Biology (BIOMAT 2013), 286-297(2013).

    [132] et alThe construction and analysis of epidemic trees with reference to the 2001 UK foot-and-mouth outbreak. Proceedings of the Royal Society of London[C]. Series B: Biological Sciences, 270, 121-127(2003).

    [133] et alSpatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic[C]. Proceedings of the National Academy of Sciences of USA, 114, 2337-2342(2017).

    [134] et alEpidemic forest: A spatiotemporal model for communicable diseases[J]. Annals of the American Association of Geographers, 109, 812-836(2019).

    [135] et alEstimating the risk on outbreak spreading of 2019-nCoV in China using transportation data[J]. MedRxiv(2020). https://www.ncbi.nlm.nih.gov/pubmed/32637978

    [136] Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study[J]. The Lancet(2020). https://www.ncbi.nlm.nih.gov/pubmed/32682457

    [137] et alLate-stage breast cancer diagnosis and health care access in Illinois[J]. Professional Geographer, 60, 54-69(2008).

    [138] Measurement, optimization, and impact of health care accessibility: A methodological review[J]. Annals of the Association of American Geographers, 102, 1104-1112(2012).

    [139] Delineating hierarchical hospital service areas in Florida[J]. Geographical Review, 107, 608-623(2017).

    [140] Using a Huff-based model to delineate hospital service areas[J]. Professional Geographer, 69, 522-530(2017).

    [141] A probabilistic analysis of shopping center trade areas[J]. Land Economics, 39, 81-90(1963).

    [142] Automated delineation of hospital service areas and hospital referral regions by modularity optimization[J]. Health Services Research, 53, 236-255(2018).

    [143] et alBig data in tourism research: A literature review[J]. Tourism Management, 68, 301-323(2018).

    [144] Extraction and analysis of city's tourism districts based on social media data[J]. Computers, Environment and Urban Systems, 65, 66-78(2017).

    [145] Spatial-temporal forecasting of tourism demand[J]. Annals of Tourism Research, 75, 106-119(2019).

    [146] Measuring tourism destinations using mobile tracking data[J]. Tourism Management, 57, 202-212(2016).

    [147] Stacked autoencoder with echo-state regression for tourism demand forecasting using search query data[J]. Applied Soft Computing, 73, 119-133(2018).

    [148] They arrive with new information. Tourism flows and production efficiency in the European regions[J]. Tourism Management, 32, 750-758(2011).

    [149] Public transport connectivity and intercity tourist flows[J]. Journal of Travel Research, 58, 25-41(2019).

    [150] et alThe scale effect on spatial interaction patterns: An empirical study using taxi OD data of Beijing and Shanghai[J]. IEEE Access, 6, 51994-52003(2018).

    [151] et alUnderstanding the interplay between bus, metro, and cab ridership dynamics in Shenzhen, China[J]. Transactions in GIS, 22, 855-871(2018).

    [152] et alUnderstanding place characteristics in geographic contexts through graph convolutional neural networks[J]. Annals of the American Association of Geographers, 110, 408-420(2020).

    Yu LIU, Xin YAO, Yongxi GONG, Chaogui KANG, Xun SHI, Fahui WANG, Jiao'e WANG, Yi ZHANG, Pengfei ZHAO, Di ZHU, Xinyan ZHU. Analytical methods and applications of spatial interactions in the era of big data[J]. Acta Geographica Sinica, 2020, 75(7): 1523
    Download Citation