Spatial Economic Analysis in Data-Rich Environments

Bayesian Prediction in Spatial Data Analysis

Computing environments for spatial data analysis

This paper describes the functionality and architecture of SpaceStat, the SpaceStat Extension for ArcView and the DynESDA Extension for ArcView. It compares the features of these packages to five other software implementations for spatial data analysis. Some ideas are formulated on generic requirements and future directions pertaining to computing environments for spatial data analysis.

Data Rich Digital Architectural Environments

The development of data rich digital environments for the construction industry has been problematic despite the initial optimism when their application to design and construction was first considered. This paper reviews the current state of the art research into the application of information technology in design and construction and identifies the more critical issues in its adoption. In conc...

Discovering Geographic Knowledge in Data Rich Environments

1 EXECUTIVE SUMMARY Similar to many scientific and applied research fields, geography has moved from a data-poor and computation-poor to a data-rich and computation-rich environment. The scope, coverage and volume of digital geographic datasets are growing rapidly due to new high-resolution satellite systems, initiatives such as the U.S. National Spatial Data Infrastructure and the automated co...

Crime Data Mining: Combining Socio-economic and Spatial Analysis

Public security is an important issue for a society. With the massive increase in electronic data availability over the last few years, characterising and predicting crime has become a task that can be approached using different data mining techniques. However, previous studies have concentrated on the spatial patterning of crimes without investigating potential predictors or causes. In this pa...