This paper presents an overview of GeoDa, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for non-GIS specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environmen...

The integration of spatial and spectral responses in hyperspectral image data analysis has been identified as a desirable objective by the remote sensing community. However, most available attempts are based on the consideration of spectral information separately from spatial information, and thus the two types of information are not treated simultaneously. In this paper, we describe our backgr...

Issues of spatial dependence have arisen in several areas of research, such as the environmental sciences, economics and sociology, but may be more generally relevant in circumstances in which cross-sectional or panel data are collected. Book-length descriptions statistical methods of analyzing spatial data include Cliff and Ord (1981), Anselin (1988), Haining (1990), Cressie (1993), Guyon (199...

Are there geographic clusters of disease cases, or hotspots of crime? Can the geography of air quality be matched to where people hospitalized for respiratory complaints actually live? Spatial data is data about the...

the complexity of urban systems in our recent world, make it difficult to evaluation and explanation of their changes with traditional and customarily methods. in the past, remote sensing has shown an ability to detect and describe urban growth patterns at different spatial scales. with new remote sensing devices (e.g. ikonos), with innovative image processing techniques and with further develo...

Spatial data mining is a process to discover interesting and potentially useful spatial patterns embedded in spatial databases. EEcient tools for extracting information from spatial data sets can be of importance to organizations which own, generate and manage large geo-spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools aft...