Recent Advances in Spatial Data Analysis

This article views spatial analysis as a research paradigm that provides a unique set of
specialised techniques and models for a wide range of research questions in which the prime
variables of interest vary significantly over space. The heart of spatial analysis is concerned
with the analysis and modeling of spatial data. Spatial point patterns and area referenced data
represent the most appropriate perspectives for applications in the social sciences. The
researcher analysing and modeling spatial data tends to be confronted with a series of
problems such as the data quality problem, the ecological fallacy problem, the modifiable
areal unit problem, boundary and frame effects, and the spatial dependence problem. The
problem of spatial dependence is at the core of modern spatial analysis and requires the use of
specialised techniques and models in the data analysis. The discussion focuses on exploratory
techniques and model-driven [confirmatory] modes of analysing spatial point patterns and
area data. In closing, prospects are given towards a new style of data-driven spatial analysis
characterized by computational intelligence techniques such as evolutionary computation and
neural network modeling to meet the challenges of huge quantities of spatial data
characteristic in remote sensing, geodemographics and marketing. (author's abstract)