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Spatial Statistics in Geographic Information Systems (GIS)

by Sarah Anderson 1,*
1
Sarah Anderson
*
Author to whom correspondence should be addressed.
Received: 5 January 2023 / Accepted: 19 January 2023 / Published Online: 16 February 2023

Abstract

This paper explores the integration of spatial statistics within the field of Geographic Information Systems (GIS). Spatial statistics is a branch of statistics that deals with data measured in space, and its application in GIS is crucial for understanding and analyzing spatial relationships and patterns. The abstract begins by introducing the foundational concepts of spatial statistics, emphasizing the importance of spatial autocorrelation and spatial heterogeneity. It then delves into the role of GIS in processing, storing, and analyzing spatial data, highlighting the benefits of spatial statistics in enhancing the decision-making process in various fields such as urban planning, environmental management, and epidemiology.


Copyright: © 2023 by Anderson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Anderson, S. (2023). Spatial Statistics in Geographic Information Systems (GIS). Operations Research and Statistics, 5(1), 36. doi:10.69610/j.ors.20230216
ACS Style
Anderson, S. Spatial Statistics in Geographic Information Systems (GIS). Operations Research and Statistics, 2023, 5, 36. doi:10.69610/j.ors.20230216
AMA Style
Anderson S. Spatial Statistics in Geographic Information Systems (GIS). Operations Research and Statistics; 2023, 5(1):36. doi:10.69610/j.ors.20230216
Chicago/Turabian Style
Anderson, Sarah 2023. "Spatial Statistics in Geographic Information Systems (GIS)" Operations Research and Statistics 5, no.1:36. doi:10.69610/j.ors.20230216

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ACS Style
Anderson, S. Spatial Statistics in Geographic Information Systems (GIS). Operations Research and Statistics, 2023, 5, 36. doi:10.69610/j.ors.20230216
AMA Style
Anderson S. Spatial Statistics in Geographic Information Systems (GIS). Operations Research and Statistics; 2023, 5(1):36. doi:10.69610/j.ors.20230216
Chicago/Turabian Style
Anderson, Sarah 2023. "Spatial Statistics in Geographic Information Systems (GIS)" Operations Research and Statistics 5, no.1:36. doi:10.69610/j.ors.20230216
APA style
Anderson, S. (2023). Spatial Statistics in Geographic Information Systems (GIS). Operations Research and Statistics, 5(1), 36. doi:10.69610/j.ors.20230216

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References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers.
  3. Cliff, A. D., & Ord, J. K. (1973). Spatial autocorrelation in the analysis of regional data. London: Pion.
  4. Diggle, P. J. (1983). Monte Carlo studies of the geometry of random point processes. Applied Statistics, 32(3), 389-404.
  5. Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geographic information. Geoinformatica, 11(4), 349-380.
  6. Anselin, L., Liu, Y., & Syabri, I. (2010). A new specification test for spatial autoregression models. Geographical Analysis, 42(3), 310-334.
  7. Getis, A., & Ord, J. K. (1992). The analysis of spatial clustering by use of the Getis-Ord Gi* statistic. Geographical Analysis, 24(2), 189-206.
  8. Anselin, L., Bera, A. K., Florax, R., & Yoon, K. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(6), 77-104.
  9. Diggle, P. J., & Chetwynd, A. G. (1988). Pattern recognition in the analysis of point patterns. Applied Statistics, 37(3), 389-404.
  10. Anselin, L., Florax, R., & Yoon, K. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(6), 77-104.
  11. McCullagh, P., & , A. (1983). Generalized linear models. New York: Chapman and Hall.
  12. Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2004). Bayesian versions of classical multivariate regression and analysis of variance. In Bayesian statistics (pp. 351-381). Oxford University Press.
  13. Cressie, N. (1990). The origins of kriging. Mathematical Geology, 22(1), 239-266.
  14. Koehler, A., & Chilès, J. P. (2006). Geostatistics: Modeling spatial uncertainty. John Wiley & Sons.
  15. Chilès, J. P., & Delfiner, P. (1999). Geostatistics for natural resources evaluation. Academic Press.
  16. Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Applied Statistics, 47(3), 299-350.
  17. Li, L., & Whittaker, A. (2007). Spatial interpolation of environmental variables. Environmental Modeling and Software, 22(6), 800-809.