Abstract
The spatial pattern of a distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The capability of evaluating spatial patterns is a prerequisite to understanding the complicated spatial processes underlying the distribution of a phenomenon. Spatial autocorrelation indicates the extent to which the occurrence of one feature is influenced by similar features in the adjacent area. As such, statistics of spatial autocorrelation provide a useful indicator of spatial patterns. This study shows that quadrat analysis of spatial autocorrelation is not suitable for evaluating point patterns. The evaluation of spatial patterns for area features, using Moran's I coefficient, must take into consideration the log-linear relationship between map resolution and spatial autocorrelation. The topological structure of a complex spatial pattern can be revealed by the correlograms constructed based on higher-order spatial relationships. The spatial pattern can be characterized by the behavior of the correlogram's wavelength and amplitude within a specific range of spatial orders.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
P.A. Burrough, Sampling designs for quantifying map unit composition, in Mausbach and Wilding (eds), Spatial Variability of Soils and Landforms, Soil Science Soc. America Special Pub. #28, 89–125 (1991).
Y.H. Chou: Analyzing the spatial autocorrelation of polygonal data in GIS, Proceedings of Urban and Regional Information Systems Association, IV:138–148 (1989).
Y.H. Chou, R.A. Minnich, L.A. Salazar, J.D. Power, and R.J. Dezzani: Spatial auto correlation of wildfire distribution in the Idyllwild Quadrangle, San Jacinto Mountain, California, Photogrammetric Engineering and Remote Sensing, 56:1507–1513 (1990).
Y.H. Chou: Map resolution and spatial autocorrelation. Geographical Analysis, 23, 228–246 (1991).
Y.H. Chou: Management of wildfires with a geographical information system. International Journal of Geographic Information Systems, 6, 123–140 (1992).
Y.H. Chou: Spatial autocorrelation and weighting functions in the distribution of wildland fires. International Journal of Wildland Fire, 2, 169–176 (1992).
Y.H. Chou: Critical issues in the evaluation of spatial autocorrelation, Lecture Notes in Computer Science, 716: 421–433, Springer-Verlag (1993).
Y.H. Chou, R.A. Minnich, and R.A. Chase: Mapping probability of fire occurrence in the San Jacinto Mountains, California. Environmental Management, 17, 129–140 (1993).
A.D. Cliff and J.K. Ord: 1973, Spatial Autocorrelation, Pion (1973).
A.D. Cliff and J.K. Ord: Spatial Processes: Models and Applications, London: Pion (1981).
M.F. Dacey: A review of measures of contiguity for two and K-color maps, in B.J.L. Barry and D.F. Marble (editors), Spatial Analyses: A Reader in Statistical Geography, Englewood Cliffs, New Jersey: Prentice-Hall (1965).
P.J. Diggle: Statistical Analysis of Spatial Point Patterns, Academic Press (1983).
R.C. Geary: The contiguity ratio and statistical mapping, The Incorporated Statistician, 5, 115–145 (1954).
A. Getis: Spatial interaction and spatial autocorrelation: a cross-product approach, Environment and Planning A, 23, 1269–1277 (1991).
A. Getis and J.K. Ord: The analysis of spatial association by use of distance statistics, Geographical Analysis, 24, 189–206 (1992).
D.A. Griffith: Spatial Autocorrelation: A Primer, Association of American Geographers, Washington, D.C (1987).
L.J. Hubert, R.G. Golledge, and C.M. Costanzo: Generalized procedures for evaluating spatial autocorrelation, Geographical Analysis, 13: 224–33 (1981).
P. de Jong, C. Sprenger, and F. van Veen: On extreme values of Moran's I and Geary's C, Geographical Analysis, 16, 17–24 (1984).
P.S. Liu and Y.H. Chou: Quadrat size and spatial autocorrelation in point pattern analysis, Proceedings of 1994 ESRI User Conference, Palm Springs, California, 1371–1378 (1994).
P.A.P. Moran: The interpretation of statistical maps, Journal of the Royal Statistical Society, Series B, 37, 243–51 (1948).
P.A.P. Moran: Notes on continuous stochastic phenomena, Biometrika, 37, 17–23 (1950)
N.L. Oden: Assessing the significance of a spatial correlogram, Geographical Analysis, 16: 1–16 (1984).
J. Odland: Spatial Autocorrelation, Sage (1988).
B.D. Ripley: Spatial Statistics, Wiley & Sons (1981).
R.R. Sokal and N.L. Oden: Spatial autocorrelation in biology 1: methodology, Biological Journal of the Linnean Society, 10: 199–228 (1978a).
R.R. Sokal and N.L. Oden: Spatial autocorrelation in biology 2: some biological applications of evolutionary and ecological interest, Biological Journal of the Linnean Society, 10: 229–49 (1978b).
W.R. Tobler: A computer movie simulating urban growth in the Detroit region, Economic Geography, 46, 234–240 (1970).
G.L. Upton and B. Fingleton: Spatial Data Analysis by Examples, Vol. 1: Point Pattern and Quantitative Data. Wiley (1985).
D. Watenberg: Multivariate spatial correlation: a method for exploratory geographical analysis. Geographical Analysis 17: 263–83 (1985).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chou, Y.H. (1995). Spatial pattern and spatial autocorrelation. In: Frank, A.U., Kuhn, W. (eds) Spatial Information Theory A Theoretical Basis for GIS. COSIT 1995. Lecture Notes in Computer Science, vol 988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60392-1_24
Download citation
DOI: https://doi.org/10.1007/3-540-60392-1_24
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60392-4
Online ISBN: 978-3-540-45519-6
eBook Packages: Springer Book Archive