Autocorrelation occurs whenever a variable exhibits a regular pattern over space, when its values at a set of locations depend on values of the same variables at other locations.Odland introduces spatial autocorrelation to the reader in a concise and readable fashion, and describes the statistical p.

Scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. The text introduces a nonverbal model to subdisciplines that until now has mostly employed mathematical or verbal-conceptual models. The focus is on how scientific visualization can help revolutionize the manner in which the tendencies for (dis)similar numerical values to cluster together in location on a map are explored and analyzed. In doing so, the concept known as spatial autocorrelation - which characterizes these tendencies - is further demystified.

Monograph on statistical analysis methodology for measurement of geographic distribution - develops the theory of spatial autocorrelation together with sample procedures, and considers their use in the presentation and evaluation of maps showing statistical tables in the fields of economic geography and human geography. Bibliography pp. 173 to 176 and graphs.

An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results.

This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data. Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency. The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach. This book is a popularized reference for students looking to work with spatialized data, but who do not have the advanced statistical theoretical basics.

Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat