Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age

Spatial Cognition and Computation

Volumn 15, Issue 2, 2015, Pages 86-114

  Gao, Song ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

human mobility; mobile phone data; space time GIS; space time kernel density estimation; spatio temporal analytics; spatio temporal autocorrelation analysis; spatio temporal visualization

Indexed keywords

AUTOCORRELATION; CELLULAR TELEPHONES; DATA VISUALIZATION; STATISTICS; VISUALIZATION;

AUTOCORRELATION ANALYSIS; HUMAN MOBILITY; KERNEL DENSITY ESTIMATION; MOBILE PHONE DATUM; SPACE TIME; SPATIO TEMPORAL; SPATIO-TEMPORAL VISUALIZATIONS;

TIME SERIES ANALYSIS;

EID: 84927644229     PISSN: 13875868     EISSN: 15427633     Source Type: Journal    
DOI: 10.1080/13875868.2014.984300     Document Type: Article

References (70)

1. AndrienkoG., & AndrienkoN. (2008). Spatio-temporal aggregation for visual analysis of movements. In D.Ebert & T.Ertl (Eds.), Visual Analytics Science and Technology, 2008. VAST'08. IEEE Symposium (pp. 51–58). Piscataway, NJ: IEEE.
2. AndrienkoG., AndrienkoN., DemsarU., DranschD., DykesJ., FabrikantS. I., JernM., KraakM. J., SchumannH., & TominskiC. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577–1600.
3. AndrienkoG., AndrienkoN., DykesJ., FabrikantS., & WachowiczM. (2008). Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Information Visualization, 7(3/4), 173–180.
4. AdrienkoN., & AdrienkoG. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.
5. AnselinL. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
6. BattyM. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–279.
7. BoxG. E., JenkinsG. M., & ReinselG. C. (2008). Time series analysis: Forecasting and control. New York, NY: John Wiley & Sons.
8. BrunsdonC., CorcoranJ., & HiggsG. (2007). Visualizing space and time in crime patterns: A comparison of methods. Computers, Environment and Urban Systems, 31(1), 52–75.
9. CalabreseF., PereiraF. C., Di LorenzoG., LiuL., & RattiC. (2010). The geography of taste: analyzing cell-phone mobility and social events. In P.Floréen, A.Krüger, & M.Spasojevic (Eds.), Pervasive computing (pp. 22–37). Berlin/Heidelberg: Springer.
10. ChaiY. (2013). Space–time behavior research in China: Recent development and future prospect. Annals of the Association of American Geographers, 103(5), 1093–1099.
11. ChenJ., ShawS. L., YuH., LuF., ChaiY., & JiaQ. (2011). Exploratory data analysis of activity diary data: a space–time GIS approach. Journal of Transport Geography, 19(3), 394–404.
12. ChengT., HaworthJ., & WangJ. (2012). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14(4), 389–413.
13. CliffA. D., & OrdJ. K. (1973). Spatial autocorrelation (Vol. 5). London: Pion.
14. de MontjoyeY. A., HidalgoC. A., VerleysenM., & BlondelV. D. (2013). Unique in the crowd: The privacy bounds of human mobility. Scientific Reports, 3, 1376.
15. DelmelleE., DonyC., CasasI., JiaM., & TangW. (2014). Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information Science, 28(5), 1107–1127.
16. DemšarU., & VirrantausK. (2010). Space–time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24(10), 1527–1542.
17. DijstM. (2013). Space–time integration in a dynamic urbanizing world: Current status and future prospects in geography and GIScience: Space–time integration in geography and GIScience. Annals of the Association of American Geographers, 103(5), 1058–1061.
18. EpanechnikovV. A. (1969). Non-parametric estimation of a multivariate probability density. Theory of Probability & its Applications, 14(1), 153–158.
19. FarinG. E. (1996). Curves and surfaces for computer-aided geometric design: A practical code. San Diego, CA: Academic Press, Inc.
20. GaoS., HuY., JanowiczK., & McKenzieG. (2013a). A spatiotemporal scientometrics framework for exploring the citation impact of publications and scientists. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 204–213). New York, NY: ACM.
21. GaoS., LiuY., WangY., & MaX. (2013b). Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17(3), 463–481.
22. GaoS., WangY., GaoY., & LiuY. (2013c). Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environment and Planning B: Planning and Design, 40(1), 135–153.
23. GearyR. C. (1954). The contiguity ratio and statistical mapping. The incorporated statistician, 5(3), 115–146.
24. GetisA. (1991). Spatial interaction and spatial autocorrelation: a cross-product approach. Environment and Planning A, 23(9), 1269–1277.
25. GetisA., & AldstadtJ. (2010). Constructing the spatial weights matrix using a local statistic. In Perspectives on spatial data analysis (pp. 147–163). Berlin/Heidelberg: Springer.
26. GetisA., & OrdJ. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.
27. GiannottiF., NanniM., PedreschiD., PinelliF., RensoC., RinzivilloS., & TrasartiR. (2011). Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal—The International Journal on Very Large Data Bases, 20(5), 695–719.
28. GonzálezM. C., HidalgoC. A., & BarabasiA. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.
29. GoodchildM. F. (2013). Prospects for a space–time GIS: Space–time integration in geography and GIScience. Annals of the Association of American Geographers, 103(5), 1072–1077.
30. GriffithD. A. (2010). Modeling spatio-temporal relationships: retrospect and prospect. Journal of Geographical Systems, 12(2), 111–123.
31. GuoD., ChenJ., MacEachrenA. M., & LiaoK. (2006). A visualization system for space-time and multivariate patterns (vis-stamp). Visualization and Computer Graphics, IEEE Transactions, 12(6), 1461–1474.
32. GuoD., GaheganM., MacEachrenA. M., & ZhouB. (2005). Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Cartography and Geographic Information Science, 32(2), 113–132.
33. HägerstrandT. (1970). What about people in regional science?Papers in Regional Science, 24(1), 7–24.
34. HardistyF., & KlippelA. (2010). Analysing spatio-temporal autocorrelation with LISTA-Viz. International Journal of Geographical Information Science, 24(10), 1515–1526.
35. HawelkaB., SitkoI., BeinatE., SobolevskyS., KazakopoulosP., & RattiC. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271.
36. HoS. S., & RuanS. (2011, November). Differential privacy for location pattern mining. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS (pp. 17–24). New York, NY: ACM.
37. JiangB., & ClaramuntC. (2004). Topological analysis of urban street networks. Environment and Planning B, 31(1), 151–162.
38. JonesM. C., MarronJ. S., & SheatherS. J. (1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 91(433), 401–407.
39. KangC., LiuY., MaX., & WuL. (2012a). Towards estimating urban population distributions from mobile call data. Journal of Urban Technology, 19(4), 3–21.
40. KangC., MaX., TongD., & LiuY. (2012b). Intra-urban human mobility patterns: An urban morphology perspective. Physica A: Statistical Mechanics and its Applications, 391(4), 1702–1717.
41. KaufmanA. (1990). Volume visualization. The Visual Computer, 6(1), 1.
42. KwanM. P. (2000). Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research Part C: Emerging Technologies, 8(1), 185–203.
43. KwanM. P. (2004). GIS methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler: Series B, Human Geography, 86(4), 267–280.
44. KwanM. P., XiaoN., & DingG. (2014). Assessing activity pattern similarity with multidimensional sequence alignment based on a multiobjective optimization evolutionary algorithm. Geographical Analysis, 46(3), 297–320.
45. LiuY., WangF., XiaoY., & GaoS. (2012). Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106(1), 73–87.
46. LiuY., SuiZ., KangC., & GaoY. (2014). Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PloS One, 9(1), e86026.
47. LongJ. A., & NelsonT. A. (2013). A review of quantitative methods for movement data. International Journal of Geographical Information Science, 27(2), 292–318.
48. MacEachrenA. M., & KraakM. J. (2001). Research challenges in geovisualization. Cartography and Geographic Information Science, 28(1), 3–12.
49. MavoaS., OliverM., WittenK., & BadlandH. M. (2011). Linking GPS and travel diary data using sequence alignment in a study of children's independent mobility. International Journal of Health Geographics, 10(64), 1–10.
50. MillerH. J. (2005). A measurement theory for time geography. Geographical Analysis, 37(1), 17–45.
51. MoranP. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2), 17–23.
52. NakayaT. (2013). Analytical data transformations in space–time region: Three stories of space–time cube. Annals of the Association of American Geographers, 103(5), 1093–1099.
53. NakayaT., & YanoK. (2010). Visualizing crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223–239.
54. PalmaA. T., BogornyV., KuijpersB., & AlvaresL. O. (2008, March). A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 2008 ACM symposium on Applied computing (pp. 863–868). New York, NY: ACM.
55. PhanD., XiaoL., YehR., & HanrahanP. (2005). Flow map layout. In J.Stasko & M.Ward (Eds.), IEEE Symposium In Information Visualization (INFOVIS) (pp. 219–224). MN, USA. October 23-25.
56. PindoliaD. K., GarciaA. J., HuangZ., FikT., SmithD. L., & TatemA. J. (2014). Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination. Malaria Journal, 13(169), 1–11.
57. RattiC., WilliamsS., FrenchmanD., & PulselliR. M. (2006). Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B Planning and Design, 33(5), 727.
58. RichardsonD. B. (2013). Real-time space–time integration in GIScience and geography: Space–time integration in geography and GIScience. Annals of the Association of American Geographers, 103(5), 1062–1071.
59. ShawS. L., YuH., & BombomL. S. (2008). A space-time GIS approach to exploring large individual-based spatiotemporal datasets. Transactions in GIS, 12(4), 425–441.
60. SheatherS. J., & JonesM. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B (Methodological), 53(3), 683–690.
61. ShenY., KwanM. P., & ChaiY. (2013). Investigating commuting flexibility with GPS data and 3D geovisualization: A case study of Beijing, China. Journal of Transport Geography, 32, 1–11.
62. ShovalN., & IsaacsonM. (2007). Sequence alignment as a method for human activity analysis in space and time. Annals of the Association of American Geographers, 97(2), 282–297.
63. SilvermanB. W. (1986). Density estimation for statistics and data analysis. Boca Raton, FL: CRC Press.
64. SongC., QuZ., BlummN., & BarabásiA. L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.
65. StewartK., FanJ., & WhiteE. (2013). Thinking about space-time connections: spatiotemporal scheduling of individual activities. Transactions in GIS, 17(6), 791–807.
66. ToblerW. R. (1987). Experiments in migration mapping by computer. The American Cartographer, 14(2), 155–163.
67. WilsonW. C. (1998). Activity pattern analysis by means of sequence-alignment methods. Environment and Planning A, 30(6), 1017–1038.
68. YuH., & ShawS. L. (2008). Exploring potential human activities in physical and virtual spaces: a spatio-temporal GIS approach. International Journal of Geographical Information Science, 22(4), 409–430.
69. YuanY., RaubalM., & LiuY. (2012). Correlating mobile phone usage and travel behavior—A case study of Harbin, China. Computers, Environment and Urban Systems, 36(2), 118–130.
70. YueY., LanT., YehA. G., & LiQ. Q. (2014). Zooming into individuals to understand the collective: A review of trajectory-based travel behavior studies. Travel Behavior and Society, 1(2), 69–78.