Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran's index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran's...

We applied correlogram analysis to county-level AIDS data of four regionsthe Northeast (Delaware, the District of Columbia, Maryland, New Jersey, New York, and Pennsylvania), California, Florida, and Louisiana-for the period 1982-1990 to characterize the spatial-temporal spread of the AIDS epidemic. Correlograms computed from yearly incidence rates differ substantially among these four regions,...

This paper discusses spatial autocorrelation in mode choice models, including what kind of bias it introduces and how to remedy the problem. The research shows that a spatially autocorrelated mode choice model, not uncommon because of, in terms of transit characteristics homogeneous neighborhoods, systematically overestimates transit trips from suburban transit-unfriendly areas and underestimat...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more diicult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial...

Species composition patterns and vegetation-environment relationships were quantified for high-elevation rock outcrops of the Southern Appalachian Mountains, an infrequent and insular habitat in a forested landscape. Outcrops occur over a wide geographic range encompassing extensive variation in both geology and climate. Geographic-scale factors interact with site-scale factors to produce varia...

We demonstrate the connection between theoretical expressions for the correlation of ambient noise Rayleigh and Love waves and the exact surface-wave Green’s tensors for a point force. The surface-wave Green’s tensors are well known in the far-field limit. On the other hand, the imaginary part of the exact Green’s tensors, including near-field effects, arises in correlation techniques such as t...

The significance of land cover as an environmental variable has made land use change an important subject in global environmental change and sustainable development. Modeling land use change has attracted considerable attention. Currently, empirical estimation models using statistical techniques are one of mostly used spatial models to simulate land use pattern and its changes. Empirical estima...

In this paper we offer a relatively comprehensive introduction to estimation issues associated with time-series— cross-section analysis in criminology. We divide the estimation issues into two categories: (1) those that have received a fair amount of attention in the literature and (2) those that have not. Issues that have received attention are heterogeneity, autocorrelation, panel heteroskeda...

A case has been presented in this paper for the use of spatial statistical techniques in analyzing crash data to investigate spatial patterns and spatial autocorrelation. “Weather-related” crashes in Wisconsin, aggregated on a county level, have been analyzed using spatial statistical techniques. The Getis-Ord Gi * statistic was used to identify spatial patters of different types of weather-rel...

We present a new statistical model for linking spatial variation in ambient air pollution to mortality. The model incorporates risk factors measured at the individual level, such as smoking, and at the spatial level, such as air pollution. We demonstrate that the spatial autocorrelation in community mortality rates, an indication of not fully characterizing potentially confounding risk factors ...