Lesson 2: Global and Local Measures of Spatial Autocorrelation

Overview

In this lesson, you will learn a collection of geospatial statistical methods specially designed for measuring global and local spatial association.

These spatial statistics are well suited for:

• detecting clusters or outliers;
• identifying hot spot or cold spot areas;
• assessing the assumptions of stationarity; and
• identifying distances beyond which no discernible association obtains.

Content

• What is Spatial Autocorrelation
  • Measures of Global Spatial Autocorrelation
  • Measures of Global High/Low Clustering
• Introducing Localised Geospatial Analysis
  • Local Indicators of Spatial Association (LISA)
• Cluster and Outlier Analysis
  • Local Moran and Local Geary
  • Moran scatterplot
  • LISA Cluster Map
• Hot Spot and Cold Spot Areas Analysis
  • Getis and Ord’s G-statistics
• Case Studies

Hands-on Exercise

• Global and Local Measures of Spatial Autocorrelation

In-class Exercise Notes

• Birds of a feather flock together: Do functionally similar water points positively co-occur in Nigeria?

Self-reading Before Meet-up

To read before class:

• Moran, P. A. P. (1950). “Notes on Continuous Stochastic Phenomena”. Biometrika. 37 (1): 17–23.

• Geary, R.C. (1954) “The Contiguity Ratio and Statistical Mapping”. The Incorporated Statistician, Vol. 5, No. 3, pp. 115-127.

• Getis, A., & Ord, K. (1992). “The Analysis of Spatial Association by Use of Distance Statistics”. Geographical Analysis, 24, 189–206.

• Anselin, L. (1995). “Local indicators of spatial association – LISA”. Geographical Analysis, 27(4): 93-115.

• Getis, A. and Ord, J.K. (1992) “The analysis of spatial association by use of distance statistics”. Geographical Analysis, 24(3): 189-206.

• Ord, J.K. and Getis, A. (2010) “Local spatial autocorrelation statistics: Distributional issues and an application”. Geographical Analysis, 27(4): 286-306.

These six papers are classics of Global and Local Spatial Autocorrelation. Be warned: All classic papers assume that the readers are academic researchers.

References

• D. A. Griffith (2009) “Spatial autocorrelation”.
• Getis, A., 2010 “B.3 Spatial Autocorrelation” in Fischer, M.M., and Getis, A. 2010 Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Springer.
• Anselin, L. (1996) “The Moran scatterplot as an ESDA tool to assess local instability in spatial association”
• Griffith, Daniel (2009) “Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows”. Journal of Geographical Systems, Vol.11(2), pp.117-140.
• Celebioglu, F., and Dall’erba, S. (2010) “Spatial disparities across the regions of Turkey: An exploratory spatial data analysis”. The Annals of Regional Science, 45:379–400.
• Mack, Z.W.V. and Kam T.S. (2018) “Is There Space for Violence?: A Data-driven Approach to the Exploration of Spatial-Temporal Dimensions of Conflict” Proceedings of 2nd ACM SIGSPATIAL Workshop on Geospatial Humanities (ACM SIGSPATIAL’18). Seattle, Washington, USA, 10 pages.
• TAN, Yong Ying and KAM, Tin Seong (2019). “Exploring and Visualizing Household Electricity Consumption Patterns in Singapore: A Geospatial Analytics Approach”, Information in Contemporary Society: 14th International Conference, iConference 2019, Washington, DC, USA, March 31–April 3, 2019, Proceedings. Pp 785-796.
• Singh A., Pathak P.K., Chauhan R.K., and Pan, W. (2011) “Infant and Child Mortality in India in the Last Two Decades: A Geospatial Analysis”. PLoS ONE 6(11), 1:19.