Abstract
Change and movement across space and over time are observed in our everyday lives, with people commuting, traveling, communicating, moving, migrating, etc. Understanding how and why such change occurs is important for various reasons, including management of resources, planning for service improvements, detecting whether there are anomalies of some sort, etc. The analysis of spatial information associated with change and movement continues to be supported by a range of techniques, most notably cartography-based exploratory methods. Somewhat lacking, however, are confirmatory and predictive methods to support such analysis. This paper details a suite of approaches implemented in the Python programming language for exploratory analysis, as well as measures that enable statistical testing for pattern significance. Application results for housing movement in an urban region are used to demonstrate the efficacy and functionality of these methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andrienko G, Andrienko N, Demsar U, Dransch D, Dykes J, Fabrikant SI, Jern M, Kraak MJ, Schumann H, Tominski C (2010) Space, time and visual analytics. Int J Geogr Inf Sci 24: 1577–1600
Andrienko G, Andrienko N, Jankowski P, Keim D, Kraak M-J, MacEachren A, Wrobel S (2007) Geovisual analytics for spatial decision support: setting the research agenda. Int J Geogr Inf Sci 21: 839–857
Andrienko G, Andrienko N, Kopanakis I, Ligtenberg A, Wrobel S (2008) Visual analytics methods for movement data. In: Fiannotti F, Pedreschi D (eds) Mobility data mining and privacy. Springer, Berlin, pp 375–410
Andrienko N, Andrienko G (2005) Exploratory analysis of spatial and temporal data. Springer, Berlin
Anselin L, Bao S (1997) Exploratory spatial data analysis linking SpaceStat and Arc View. In: Fisher M, Getis A (eds) Recent developments in spatial analysis. Springer, Berlin, pp 35–59
Anselin L, Syabri I, Kho Y (2006) GeoDa: an introduction to spatial data analysis. Geogr Anal 38: 5–22
Brunsdon C, Corcoran J (2006) Using circular statistics to analyse time patterns in crime incidence. Comput Environ Urban Syst 30: 300–319
Chicago Area Transportation Study (1959) Final report, survey finding, vol 1. Chicago, IL
Chen J, MacEachren AM, Guo D (2008) Supporting the process of exploring and interpreting space-time multivariate patterns: the visual inquiry toolkit. Cartogr Geogr Inf Sci 35: 33–50
Demsar U, Virrantaus K (2010) Space-time density of trajectories: exploring spatiotemporal patterns in movement data. Int J Geogr Inf Sci 24: 1527–1542
Giannotti F, Nanni M, Pedreschi D, inelli F, Renso C, Rinzivillo S, Trastarti R (2010) Mobility data mining: discovering movement patterns from trajectory data. In: Proceedings of the WCTS
Guo D, Gahegan M, MacEachren AM, Zhou B (2005) Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Cartogr Geogr Inf Science 32: 113–132
Hagerstrand T (1970) What about people in regional science?. Pap Reg Sci Assoc 14: 7–21
Hanson B, Klink K, Matsuura K, Robeson SM, Willmott CJ (1992) Vector correlation: review, exposition, and geographic application. Ann Am Assoc Am Geogr 82: 103–116
Haynes KE, Fotheringham AS (1984) Gravity and spatial interaction models. Sage, Beverly Hills
Kern R, Ruston G (1969) A computer program for the production of flow maps, dot maps, and graduated symbol maps. Cartogr J 6: 131–136
Kwan MP (2000) Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transp Res C 8: 185–203
Miller HJ (1991) Modelling accessibility using space-time prism concepts within geographical information systems. Int J Geogr Syst 5: 287–301
Nanni M, Trasarti R, Renso C, Giannotti F, Pedreschi D (2010) Advanced knowledge discovery on movement data with the GeoPKDD System. In: Proceedings of the EDBT
Rae A (2009) From spatial interaction data to spatial interaction information? Geovisualisation and spatial structures of migration from the 2001 UK census. Comput Environ Urban Syst 33: 161–178
Ravenstein EG (1885) The laws of migration. J Stat Soc 48: 167–235
Rogerson PA (2006) Statistical methods for geography, 2nd edn. Sage, Thousand Oaks
Thomas J, Cook K (2005) Illuminating the path: the research and development agenda for visual analytics. National Visualization and Analytics Center, Richland
Tobler W (1987) Experiments in migration mapping by computer. Am Cartogr 14: 155–163
Tufte ER (1983) The visual display of quantitative information. Graphics Press, Cheshire
Tukey JW (1977) Exploratory data analysis. Addison-wesley, Reading
Wilson AG (1974) Urban and regional models in geography and planning. Wiley, London
Xiao N, Chun Y (2009) Visualizing migration flows using kriskograms. Cartogr Geogr Inf Sci 36: 183–191
Yu H, Shaw SL (2008) Exploring potential human activities in physical and virtual spaces: a spatio-temporal GIS approach. Int J Geogr Inf Sci 22: 409–430
Zhao J, Forer P, Harvey AS (2008) Activities, ringmaps and geovisualization of large human movement fields. Inf Vis 7: 198–209