Abstract
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 heartland 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)
techniques and models for a wide range of research questions in which the prime variables of interest
vary significantly over space. The heartland 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)
| Originalsprache | Englisch |
|---|---|
| Erscheinungsort | Vienna |
| Herausgeber | WU Vienna University of Economics and Business |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 1 März 1999 |
Publikationsreihe
| Reihe | Discussion Papers of the Institute for Economic Geography and GIScience |
|---|---|
| Nummer | 66/99 |
WU Working Paper Reihe
- Discussion Papers of the Institute for Economic Geography and GIScience