Spatial Analysis: Evolution, Methods, and Applications

  • Yuji Murayama
  • Rajesh B. Thapa
Part of the GeoJournal Library book series (GEJL, volume 100)


In a narrow sense, spatial analysis has been described as a method for analyzing spatial data, while in a broad sense it includes revealing and clarifying processes, structures, etc., of spatial phenomena that occur on the Earth’s surface. Ultimately, it is designed to support spatial decision-making, and to serve as a tool for assisting with regional planning and the formulation of government policies, among other things. The world of GIS includes such terms as spatial data manipulation, spatial data analysis, spatial statistical analysis, and spatial modeling. While there are admittedly slight differences in the definitions of these terms (O’Sullivan & Unwin, 2003), they are subsumed in this chapter, which will examine spatial analysis in a broad sense.


Analytical Hierarchy Process Spatial Autocorrelation Cellular Automaton Spatial Analysis Voronoi Diagram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Ahmadi Nejad Masouleh, F. (2006). A geographical study of school attendance areas using multiplicatively weighted Voronoi diagrams: A case of Rasht City, Iran. Geographical Review of Japan, 79, 700–714.Google Scholar
  2. Alberti, M., & Waddell, P. (2000). An integrated urban development and ecological simulation model. Integrated Assessment, 1, 215–227.CrossRefGoogle Scholar
  3. An, L., Linderman, M., Qi, J., Shortridge, A., & Liu, J. (2005). Exploring complexity in a human–environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers, 95, 54–79.CrossRefGoogle Scholar
  4. Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer.Google Scholar
  5. Anselin, L., Syabri, I., & Kho, Y. (2010). GeoDa: An introduction to spatial data analysis. In M. M. Fischer & A. Getis (Eds.), Handbook of spatial data analysis (pp. 73–89). Berlin: Springer.CrossRefGoogle Scholar
  6. Bell, E. J. (1974). Markov analysis of land use change: An application of stochastic processes to remotely sensed data. Socio-Economic Planning Sciences, 8, 311–316.CrossRefGoogle Scholar
  7. Benenson, I., & Torrens, P. M. (2004). Geosimulation. Chichester: Wiley.CrossRefGoogle Scholar
  8. Berger, T. (2001). Agent-based spatial models applied to agriculture: A simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 25, 245–260.CrossRefGoogle Scholar
  9. Berry, B. J. L., & Marble, D. F. (1968). Spatial analysis: A reader in statistical geography. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  10. Bonham-Carter, G. (1994). Geographic information systems for geoscientists: Modeling with GIS. New York: Pergamon.Google Scholar
  11. Boyle, P. J., & Dunn, C. E. (1991). Redefinition of enumeration district centroids: A test of their accuracy using Thiessen polygons. Environmental Planning A, 23, 1111–1119.CrossRefGoogle Scholar
  12. Clarke, K. C., Hoppen, S., & Gaydos, L. J. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24, 247–261.CrossRefGoogle Scholar
  13. Clement, F., Orange, D., Williams, M., Mulley, C., & Epprecht, M. (2009). Drivers of afforestation in Northern Vietnam: Assessing local variations using geographically weighted regression. Applied Geography, 29, 561–576.CrossRefGoogle Scholar
  14. Cliff, A. D., & Ord, J. K. (1973). Spatial autocorrelation. London: Pion.Google Scholar
  15. Davidson, D., Theocharopoulos, S., & Bloksma, R. (1994). A land evaluation project in Greece using GIS and based on Boolean and fuzzy set methodologies. International Journal of Geographical Information Systems, 8, 369–384.CrossRefGoogle Scholar
  16. Deadman, P., Robinson, D., Moran, E., & Brondizio, E. (2004). Colonist household decision making and land-use change in the Amazon rainforest: An agent based simulation. Environment and Planning B, 31, 693–709.CrossRefGoogle Scholar
  17. Fisher, M., Scholten, H. J., & Unwin, D. (1996). Spatial analytical perspectives on GIS, new potential and new models. London: Taylor & Francis.Google Scholar
  18. Fortin, M.-J., & Dale, M. R. T. (2009). Spatial autocorrelation. In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis (pp. 89–103). London: Sage.Google Scholar
  19. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. New York: Wiley.Google Scholar
  20. Fotheringham, A. S., & Wegener, M. (2000). Spatial models and GIS. London: Taylor & Francis.Google Scholar
  21. Fotheringham, S., & Rogerson, P. (1994). Spatial analysis and GIS. London: Taylor & Francis.Google Scholar
  22. Geoghegan, J., Wainger, L. A., & Bockstael, N. E. (1997). Spatial landscape indices in a hedonic framework: An ecological economics analysis using GIS. Ecological Economics, 23, 251–264.CrossRefGoogle Scholar
  23. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206.CrossRefGoogle Scholar
  24. Hagen, A. (2003). Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17, 235–249.CrossRefGoogle Scholar
  25. Hoffman, M., Kelley, H., & Evans, T. (2002). Simulating land cover change in South-central Indiana: An agent-based model of deforestation and afforestation. In M. E. Janssen (Ed.), Complexity and ecosystem management: The theory and practice of multi-agent systems (pp. 218–247). Cheltenham: Edward Elgar.Google Scholar
  26. Huigen, M. G. A. (2004). First principles of the MameLuke multi-actor modeling framework for land-use change, illustrated with a Philippine case study. Journal of Environmental Management, 72, 5–12.CrossRefGoogle Scholar
  27. Irwin, E. G., & Bockstael, N. E. (2002). Interacting agents, spatial externalities and the evolution of residential land use patterns. Journal of Economic Geography, 2, 31–54.CrossRefGoogle Scholar
  28. Isard, W. (1956). Location and space-economy. New York: Wiley.Google Scholar
  29. Jaimes, N. B. P., Sendra, J. B., Delgado, M. G., & Plata, R. F. (2010). Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Applied Geography, 30, 576–591.Google Scholar
  30. Laine, T., & Busemeyer, J. (2004). Comparing agent-based learning models of land-use decision making. In C. L. M. Lovett, C. Schunn, & P. Munro (Eds.), Proceedings of the 6th international conference on cognitive modeling (pp. 142–147). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  31. Li, X., & Yeh, A. G. (2001). Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environment and Planning A, 33, 1445–1462.CrossRefGoogle Scholar
  32. Ligtenberg, A., Bregt, A. K., & van Lammeren, R. (2001). Multi-actor-based land use modelling: Spatial planning using agents. Landscape and Urban Planning, 56, 21–33.CrossRefGoogle Scholar
  33. Liu, Y. (2009). Modelling urban development with geographical information system and cellular automata. Boca Raton, FL: Taylor and Francis.Google Scholar
  34. Longley, P., & Batty, M. (1996). Spatial analysis: Modelling in a GIS environment. Cambridge: GeoInformation International.Google Scholar
  35. Lopez, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land cover and land use change in the urban fringe: A case in Morelia city Mexico. Landscape and Urban Planning, 55, 271–285.CrossRefGoogle Scholar
  36. Manson, S. M. (2000). Agent-based dynamic spatial simulation of land use/cover change in the Yucatan peninsula, Mexico. Proceedings of the 4th international conference on integrating GIS and environmental modeling (GIS/EM4): Problems, prospects and research needs, Banff, AB.Google Scholar
  37. Mendelbrot, B. B. (1983). The fractal geometry of nature. New York: W. H. Freeman.Google Scholar
  38. Mu, L. (2004). Polygon characterization with the multiplicatively weighted Voronoi diagram. Professional Geographer, 56, 223–239.Google Scholar
  39. Murayama, Y. (2000). Land use change in Tokyo. In Y. Murayama (Ed.), Japanese urban system (pp. 227–236). Dordrecht: Kluwer.Google Scholar
  40. Murayama, Y. (2006). Kukan-bunseki to GIS (Spatial analysis with GIS). In A. Okabe, & Y. Murayama (Eds.), GIS de Kukan-bunseki (Spatial analysis using GIS) (pp. 1–20). Tokyo: Kokon-shoin in Japanese.Google Scholar
  41. Nakaya, T. (2008). Geographically weighted regression (GWR). In K. K. Kemp (Ed.), Encyclopedia of geographic information science (pp. 179–184). London: Sage.Google Scholar
  42. Nordbeck, S., & Rystedt, B. (1972). Computer cartography: The mapping system NORMAP: Location models. Lund: Studentlitteratur.Google Scholar
  43. Ogneva-Himmelberger, Y., Pearsall, H., & Rakshit, R. (2009). Concrete evidence & geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts. Applied Geography, 29, 478–487.CrossRefGoogle Scholar
  44. Okabe, A., Boots, B., Sugihara, K., & Chiu, S. N. (2000). Spatial tessellations: Concepts and applications of Voronoi diagrams. Chichester: Wiley.Google Scholar
  45. Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and application. Geographical Analysis, 27, 286–306.CrossRefGoogle Scholar
  46. O’Sullivan, D., & Unwin, D. J. (2003). Geographic information analysis. Hoboken, NJ: Wiley.Google Scholar
  47. Páez, A., & Wheeler, D. C. (2010). Geographically weighted regression. In M. M. Fischer, & A. Getis (Eds.), Handbook of spatial data analysis (pp. 461–486). Berlin: Springer.Google Scholar
  48. Parker, D. C., Evans, T. P., & Meretsky, V. (2001). Measuring emergent properties of agent-based landuse/landcover models using spatial metrics. Proceedings of 7th annual conference of the international society for computational economics, Yale University.Google Scholar
  49. Parker, D., Manson, S., Janssen, M., Hoffman, M., & Deadman, P. (2003). Multiagent system models for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93, 316–340.CrossRefGoogle Scholar
  50. Ripley, B. (1981). Spatial statistics. Chichester: Wiley.CrossRefGoogle Scholar
  51. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  52. Soares-Filho, B. S., Alencar, A., Nespad, D., Cerqueira, G. C., Dial, M., Del, C., et al. (2004). Simulating the response of land-cover changes to road paving and governance along a major Amazon Highway: The Santarem-Cuiaba corridor. Global Change Biology, 10, 745–764.CrossRefGoogle Scholar
  53. Stefanakis, E., Vazirgiannis, M., & Sellis, T. (1999). Incorporation fuzzy set methodologies in DBMS repository for the application domain of GIS. International Journal of Geographical Information Science, 13, 657–675.CrossRefGoogle Scholar
  54. Tang, J., Wang, L., & Yao, Z. (2007). Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm. International Journal of Remote Sensing, 28, 3255–3271.CrossRefGoogle Scholar
  55. Thapa, R. B., & Murayama, Y. (2009). Land use change factors in Kathmandu valley: A GWR approach. In B. G. Lees, & S. W. Laffan (Eds.), Proceedings of the 10th international conference on geocomputation (pp. 255–260). Sydney, NSW: The University of New South Wales.Google Scholar
  56. Thapa, R. B., & Murayama, Y. (2010a). Drivers of urban growth in the Kathmandu valley, Nepal: Examining the efficacy of the analytic hierarchy process. Applied Geography, 30, 70–83.CrossRefGoogle Scholar
  57. Thapa, R. B., & Murayama, Y. (2010b). Urban growth modelling of Kathmandu metropolitan region, Nepal. Computers, Environment and Urban Systems. DOI: 10.1016/j.compenvurbsys.2010.07.005.Google Scholar
  58. Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.CrossRefGoogle Scholar
  59. Wolfram, S. (1984). Cellular automata as models of complexity. Nature, 311, 419–424.CrossRefGoogle Scholar
  60. Yuan, H., Van Der Wiele, C. F., & Khorram, S. (2009). An automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sensing, 1, 243–265.CrossRefGoogle Scholar
  61. Zadeh, L. A. (1965). Fuzzy sets. Information Control, 8, 338–353.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Division of Spatial Information Science, Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan

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