Perspectives on Spatial Data Analysis

  • Luc Anselin
  • Sergio J. Rey
Part of the Advances in Spatial Science book series (ADVSPATIAL)


This volume is inspired by the many contributions of Arthur Getis to the field of spatial analysis. In 2004, Arthur Getis formally retired as the Stephen and Mary Birch Foundation Chair of Geographical Studies in the Department of Geography at San Diego State University. That transition to emeritus status marked the end of a magnificent career spanning more than four decades. It started with undergraduate education in geography at Pennsylvania State University, followed by a PhD from the University of Washington in 1961. At Washington, he was part of the generation that initiated the “quantitative revolution” in geography under the tutelage of William Garrison. His graduate cohort included, among others, Brian Berry, Waldo Tobler, Duane Marble, John Nystuen, Richard Morrill and William Bunge. His academic appointments started with a position at Michigan State University, after which he moved to Rutgers University. He went on to become head of the Geography Department at the University of Illinois in 1977, and joined San Diego State University in 1989. In addition, he held many visiting scholar appointments at leading international institutions, including Cambridge University and the University of Bristol in the UK and the University of California, Santa Barbara and Harvard University in the USA. During his career, Arthur Getis was awarded several honors and distinctions, such as the 1995 Albert Johnson Research Lecture at San Diego State University (captured in Getis, 1995c), the Walter Isard Award from the North American Regional Science Council (1997), the Robinson Lecture at The Ohio State University (1999), and the 2002 Distinguished Scholarship Award from the Association of American Geographers (AAG). In 2005, he was elected Fellow of the Regional Science Association International. He served as president of the Western Regional Science Association (1999) and of the University Consortium of Geographic Information Science (2002).


Geographic Information System Spatial Autocorrelation Point Pattern Spatial Weight Spatial Weight Matrix 
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. Aldstadt J, Getis A (2006) Using amoeba to create a spatial weights matrix and identify spatial clusters. Geogr Anal 38:327–343CrossRefGoogle Scholar
  2. Anselin L (1995) Local indicators of spatial association-LISA. Geogr Anal 27:93–115CrossRefGoogle Scholar
  3. Anselin L, Getis A (1992) Spatial statistical analysis and geographic information systems. Ann Reg Sci 26:19–33CrossRefGoogle Scholar
  4. Bailly A, Gibson LJ (2004) Applied geography: a world perspective. Kluwer, DordrechtGoogle Scholar
  5. Boots BN, Getis A (1987) Point pattern analysis. Sage, Newbury ParkGoogle Scholar
  6. Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transiet patterns in scientific literature. J Am Soc Inf Technol 57:359–377CrossRefGoogle Scholar
  7. Chen D, Getis A (1998) Point pattern analysis (PPA). Software manual. Department of Geography, San Diego State University, San Diego, CAGoogle Scholar
  8. Cressie N (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
  9. Fellmann JD, Getis A, Getis J (2008) Human geography, landscapes of human activities, 10th edn. McGraw-Hill, New YorkGoogle Scholar
  10. Fischer MM, Getis A (1997a) Advances in spatial analysis. In: Fischer MM, Getis A (eds) Recent developments in spatial analysis: spatial statistics, behavioral modeling, and computational intelligence. Springer, Berlin, Heidelberg and New York, pp 1–14Google Scholar
  11. Fotheringham AS (1997) Trends in quantitative methods I: stressing the local. Prog Hum Geogr 21:88–96CrossRefGoogle Scholar
  12. Fotheringham AS, Brunsdon C, Charlton ME (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New YorkGoogle Scholar
  13. Getis A (1957) A geographical analysis of rail freight shipments in Pennsylvania. Pa Bus Surv 51:4–5Google Scholar
  14. Getis A (1963) The determination of the location of retail activities with the use of a map transformation. Econ Geogr 39:14–22CrossRefGoogle Scholar
  15. Getis A (1964) Temporal land use pattern analysis with the use of nearest neighbor and quadrat method. Ann Assoc Am Geogr 54:391–399CrossRefGoogle Scholar
  16. Getis A (1983) Second-order analysis of point patterns: the case of Chicago as a multi-center urban region. Prof Geogr 35:73–80CrossRefGoogle Scholar
  17. Getis A (1984) Interaction modeling using second-order analysis. Environ Plan A 16:173–183CrossRefGoogle Scholar
  18. Getis A (1985b) A second-order approach to spatial autocorrelation. Ont Geogr 25:67–73Google Scholar
  19. Getis A (1989a) A spatial association model approach to the identification of spatial dependence. Geogr Anal 21:251–259CrossRefGoogle Scholar
  20. Getis A (1989b) A spatial causal model of economic interdependency among neighboring communities. Environ Plan A 21:115–120CrossRefGoogle Scholar
  21. Getis A (1990) Screening for spatial dependence in regression analysis. Pap Reg Sci Assoc 69: 69–81CrossRefGoogle Scholar
  22. Getis A (1991) Spatial interaction and spatial autocorrelation: a cross-product approach. Environ Plan A 23:1269–1277CrossRefGoogle Scholar
  23. Getis A (1993a) Introduction: mathematical models in geography. Pap Reg Sci 72:201–202CrossRefGoogle Scholar
  24. Getis A (1995a) Spatial filtering in a regression framework: examples using data on urban crime, regional inequality, and government expenditures. In: Anselin L, Florax R (eds) New directions in spatial econometrics. Springer, Berlin, pp 172–188Google Scholar
  25. Getis A (1995b) Spatial filtering in a regression framework: examples using data on urban crime, regional inequality, and government expenditures. In: Anselin L, Florax RJGM (eds) New directions in spatial econometrics. Springer, BerlinGoogle Scholar
  26. Getis A (1999) Some thoughts on the impact of large data sets on regional science. Ann Reg Sci 33:145–150CrossRefGoogle Scholar
  27. Getis A (2004a) A geographic approach to identifying disease clusters. In: Janelle DG, Warf B, Hansen K (eds) Worldminds: geographical perspectives on 100 problems. Kluwer, Dordrecht, pp 81–86Google Scholar
  28. Getis A (2004b) The role of geographic information science in applied geography. In: Bailly A, Gibson LJ (eds) Applied geography: a world perspective. Kluwer, Dordrecht, pp 95–112Google Scholar
  29. Getis A (2007) Reflections on spatial autocorrelation. Reg Sci Urban Econ 37:491–496CrossRefGoogle Scholar
  30. Getis A, Aldstadt J (2004) Constructing the spatial weights matrix using a local statistic. Geogr Anal 36:90–105CrossRefGoogle Scholar
  31. Getis A, Boots BN (1978) Models of spatial processes: an approach to the study of point, line, and area patterns. Cambridge University Press, CambridgeGoogle Scholar
  32. Getis A, Franklin J (1987) Second-order neighborhood analysis of mapped point patterns. Ecology 68:473–477CrossRefGoogle Scholar
  33. Getis A, Getis J (1968) Retail store spatial affinities. Urban Stud 5:317–332CrossRefGoogle Scholar
  34. Getis A, Griffith DA (2002) Comparative spatial filtering in regression analysis. Geogr Anal 34:130–140Google Scholar
  35. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206CrossRefGoogle Scholar
  36. Getis A, Ord JK (1996) Local spatial statistics: an overview. In: Longley P, Batty M (eds) Spatial analysis: modelling in a GIS environment. Geoinformation International, Cambridge, UK, pp 261–278Google Scholar
  37. Getis A, Ord JK (1998) Spatial modelling of disease dispersion using a local statistic: the case of aids. In: Griffith DA, Amrhein CG, Huriot JM (eds) Econometric advances in spatial modelling and methodology: essays in honour of Jean Paelinck. Kluwer, DordrechtGoogle Scholar
  38. Getis A, Drummy P, Gartin J, Gorr WL, Harries K, Rogerson P, Stoe D, Wright R (2000) Geographic information science and crime analysis. URISA J 12:7–14Google Scholar
  39. Getis A, Getis J, Quastler I (2001) The United States and Canada: the land and the people, 2nd edn. McGraw-Hill, New YorkGoogle Scholar
  40. Getis A, Morrison A, Gray K, Scott TW (2003) Characteristics of the spatial pattern of the Dengue vector Aedes Aegypti, in Iquitos, Peru. Am J Trop Med Hyg 69:494–505Google Scholar
  41. Getis A, Anselin L, Lea A, Ferguson M, Miller H (2004a) Spatial analysis and modeling in a GIS environment. In: McMaster RB, Usery EL (eds) A research agenda for geographic information science. CRC, Boca Raton, FL, pp 157–196Google Scholar
  42. Getis A, Getis J, Getis V, Fellmann JD (2008) Introduction to geography, 11th edn. Mc-Graw-Hill, New YorkGoogle Scholar
  43. Griffin P, Getis A, Griffin E (1996) Regional patterns of affirmative action compliance costs. Ann Reg Sci 30:321–340CrossRefGoogle Scholar
  44. Griffith DA (2003) Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization. Springer, BerlinGoogle Scholar
  45. Griffith DA, Peres-Neto PR (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analysis. Ecology 87:2603–2613CrossRefGoogle Scholar
  46. Morrison A, Astete H, Chapilliquen F, Ramirez-Prada C, Diaz G, Getis A, Gray K, Scott TW (2004a) Evaluation of a sampling methodology for rapid assessment of aedes aegypti infestation levels in Iquitos, Peru. J Med Entomol 41:502–510CrossRefGoogle Scholar
  47. Morrison AC, Getis A, Santiago M, Rigau-Perez JG, Reiter P (1998) Exploratory space–time analysis of reported dengue cases during an outbreak in Florida, Puerto Rico, 1991–1992. Am J Trop Med Hyg 58:287–298Google Scholar
  48. O’Brien D, Kaneene J, Getis A, Lloyd J, Rip M, Leader R (2000) Spatial and temporal distribution of selected canine cancers in michigan, USA, 1964–1994. Prev Vet Med 47:187–204CrossRefGoogle Scholar
  49. Ord JK, Getis A (2001) Testing for local spatial autocorrelation in the presence of global autocorrelation. J Reg Sci 41:411–432CrossRefGoogle Scholar
  50. Ripley BD (1976) The second-order analysis of stationary point processes. J Appl Probab 13: 255–266CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.GeoDa Center for Geospatial Analysis and Computation, School of Geographical SciencesArizona State UniversityTempeUSA

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