Journal of Geographical Systems

, Volume 9, Issue 3, pp 207–227 | Cite as

STAMP: spatial–temporal analysis of moving polygons

  • Colin RobertsonEmail author
  • Trisalyn A. Nelson
  • Barry Boots
  • Michael A. Wulder
Original Article


Research questions regarding temporal change in spatial patterns are increasingly common in geographical analysis. In this research, we explore and extend an approach to the spatial–temporal analysis of polygons that are spatially distinct and experience discrete changes though time. We present five new movement events for describing spatial processes: displacement, convergence, divergence, fragmentation and concentration. Spatial–temporal measures of events for size and direction are presented for two time periods, and multiple time periods. Size change metrics are based on area overlaps and a modified cone-based model is used for calculating polygon directional relationships. Quantitative directional measures are used to develop application specific metrics, such as an estimation of the concentration parameter for a von Mises distribution, and the directional rate of spread. The utility of the STAMP methods are demonstrated by a case study on the spread of a wildfire in northwestern Montana.


Spatial pattern analysis Spatial–temporal analysis Polygons Events Geocomputation Spread 

JEL classification

C0 C69 C69 C0 Q0 



This project was funded by the Government of Canada through the Mountain Pine Beetle Initiative, a 6 year, $40 million Program administered by Natural Resources Canada, Canadian Forest Service. Publication does not necessarily signify that the contents of this report reflect the views or policies of Natural Resources Canada–Canadian Forest Service. We would also like to thank three anonymous reviewers for thoughtful comments and suggestions.


  1. Boots B (2000) Using GIS to promote spatial analysis. J Geograph Syst 2:17–21CrossRefGoogle Scholar
  2. Burrough P, McDonnell R (1998) Principles of geographical information systems. Oxford University Press, New YorkGoogle Scholar
  3. Chen J, Li C, Li Z, Gold C (2001) A Voronoi-based 9-intersection model for spatial relations. Int J Geograph Informat Sci 15:201–220CrossRefGoogle Scholar
  4. Christakos G., Bogaert P., Serre M.L. (2002) Temporal GIS: advanced functions for field-based applications. Springer, New YorkGoogle Scholar
  5. Claramunt C, Thériault M (1996) Towards semantics for modelling spatio-temporal processes within GIS. In: Kraak M, Molenaar (eds) 7th international symposium on spatial data handling. Taylor and Francis, London, pp 47–63Google Scholar
  6. Cova TJ, Dennison PE, Kim TH, Moritz MA (2005) Setting wildfire evacuation trigger points using fire spread modeling and GIS. Transactions in GIS 9:603–617CrossRefGoogle Scholar
  7. Dragicevic S, Marceau D (2000) A fuzzy set approach for modelling time in GIS. Int J Geograph Informat Sci 14:225–245CrossRefGoogle Scholar
  8. Egenhofer MJ, Franzosa RD (1991) Point-set topological spatial relations. Int J Geograph Informat Syst 5:161–176CrossRefGoogle Scholar
  9. Egenhofer M and Golledge R (eds) (1998) Spatial and temporal reasoning in geographic information systems. Oxford University Press, New YorkGoogle Scholar
  10. Egenhofer MJ, Sharma J, Mark DM (1993) A critical comparison of the 4-intersection and 9-intersection models for spatial relations: a formal analysis. Auto-Carto 11:1–11Google Scholar
  11. Finney MA (1998) FARSITE: fire area simulator—model development and evaluation. Res. Pap. RMRS-RP-4, USDA, Forest Service, Rocky Mountain Research Station, p 47Google Scholar
  12. Fortin M-J, Dale M (2005) Spatial analysis: a guide for ecologists. Cambridge University Press, CambridgeGoogle Scholar
  13. Fortin MJ, Keitt BA, Maurer ML, Kaufman D, Blackburn TM (2005) Species’ geographic ranges and distributional limits: pattern analysis and statistical issues. Oikos 108:7–17CrossRefGoogle Scholar
  14. Frank AU (1992) Qualitative reasoning about distances and directions in geographic space. J Vis Language Comput 3:343–371CrossRefGoogle Scholar
  15. Galton A (1998) Modes of overlap. J Vis Language Comput 9:61–79CrossRefGoogle Scholar
  16. Gillis M, Leckie D (1996) Forest inventory update in Canada. Fores Chronicle 72(2):138–156Google Scholar
  17. Gong P, Xu B (2003) Remote sensing of forests over time: change types, methods, and opportunities. In: Wulder MA, Franklin SE (eds) Remote sensing of forest environments. Kluwer Academic Publishers, Norwell, pp 301–334Google Scholar
  18. Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geograph Informat Sci 17:235–249CrossRefGoogle Scholar
  19. Knox E (1964) The detection of space–time interactions. Appl Stat 13:25–29CrossRefGoogle Scholar
  20. Kulldorff M, Heffernan R, Hartman J, Assuncao RM, Mostashari F (2005) A space–time permutation scan statistic for the early detection of disease outbreaks. PLoS Med 2:216–224CrossRefGoogle Scholar
  21. Langran G (1992) Time in geographic information systems. Taylor & Francis, New YorkGoogle Scholar
  22. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Canc Res 27:209–220Google Scholar
  23. Maruca S, Jacquez G (2002) Area-based tests for association between spatial patterns. J Geograph Syst 4:69–83CrossRefGoogle Scholar
  24. McIntosh J, Yuan M (2005) A framework to enhance semantic flexibility for analysis of distributed phenomena. Int J Geograph Informat Sci 19:999–1018CrossRefGoogle Scholar
  25. Miller HJ, Wentz EA (2003) Representation and spatial analysis in geographic information systems. Ann Associat Am Geograph 93:574–594CrossRefGoogle Scholar
  26. O’Sullivan D, Unwin J (2003) Geographic information analysis. Wiley, Hoboken, NJGoogle Scholar
  27. Okabe A, Miller HJ (1996) Exact computational methods for calculating distances between objects in a cartographic database. Cartograph Geograph Informat Syst 23:180–195Google Scholar
  28. Openshaw S, Charlton M, Wymer C, Craft A (1987) A mark 1 geographical analysis machine for the automated analysis of point data sets. Int J Geograph Informat Syst 1(4):335–358CrossRefGoogle Scholar
  29. Peuquet D, Zhang CX (1987) An algorithm to determine the directional relationship between arbitrarily-shaped polygons in the plane. Pattern Recognition 20:65–74CrossRefGoogle Scholar
  30. Peuquet D (1994) It’s about time: a conceptual framework for the representation of temporal dynamics in geographic information systems. Ann Associat Am Geograph 84:441–461CrossRefGoogle Scholar
  31. Peuquet D (2001) Making space for time: Issues in space–time data representation. GeoInformatica 5:11–32CrossRefGoogle Scholar
  32. Rey SJ, Janikas MV (2006) STARS: space–time analysis of regional systems. Geograph Anal 38:67–86CrossRefGoogle Scholar
  33. Rao JS, SenGupta S (2001) Topics in circular statistics. World Scientific, SingaporeGoogle Scholar
  34. Sadahiro Y (2001) Exploratory method for analyzing changes in polygon distributions. Environ Plann B: Plann Des 28:595–609CrossRefGoogle Scholar
  35. Sadahiro Y, Umemura M (2001) A computational approach for the analysis of changes in polygon distributions. J Geograph Syst 3:137–154CrossRefGoogle Scholar
  36. Skiadopoulos S, Koubarakis M (2004) Composing cardinal directions relations. Artific Intell 152:143–171CrossRefGoogle Scholar
  37. Skiadopoulos S, Giannoukos C, Sarkas N, Vassiliadis P, Sellis T, Koubarakis M (2005) Computing and managing directional relations. IEEE Trans Knowledge Data Eng 17:1610–1623CrossRefGoogle Scholar
  38. Upton G, Fingleton B (1989) Spatial data analysis by example: categorical and directional data. Wiley, ChichesterGoogle Scholar
  39. Visser H, de Nijis T (2006) The map comparison kit. Environ Modell Software 21:346–358CrossRefGoogle Scholar
  40. Worboys M (1994) A unified model of spatial and temporal information. Comput J 37(1):26–34CrossRefGoogle Scholar
  41. Yan H, Chu Y, Li Z, Guo R (2006) A quantitative description model for the direction relations based on direction groups. Geoinformatica 10:177–196CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Colin Robertson
    • 1
    Email author
  • Trisalyn A. Nelson
    • 1
  • Barry Boots
    • 2
  • Michael A. Wulder
    • 3
  1. 1.Spatial Pattern Analysis and Research (SPAR) Laboratory, Department of Geography University of VictoriaVictoriaCanada
  2. 2.Department of Geography and Environmental StudiesWilfrid Laurier UniversityWaterlooCanada
  3. 3.Canadian Forest Service (Pacific Forestry Centre), Natural Resources CanadaVictoriaCanada

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