Spatial autocorrelation is a concept that helps to define the field of spatial analysis. It is central to studies using spatial statistics and spatial econometrics. In this paper, we trace the early development of the concept and explain the academic links that brought the concept to the fore in the late 1960s. In geography, the importance of the work of Michael F. Dacey, Andrew D. Cliff, and J...

Ecological variables often fluctuate synchronously over wide geographical areas, a phenomenon known as spatial autocorrelation or spatial synchrony. Development of statistical approaches designed to test for spatial autocorrelation combined with the increasing accessibility of long-term, large-scale ecological datasets are now making it possible to document the patterns and understand the cause...

Spatial structure of genetic variation within populations, an important interacting influence on evolutionary and ecological processes, can be analyzed in detail by using spatial autocorrelation statistics. This paper characterizes the statistical properties of spatial autocorrelation statistics in this context and develops estimators of gene dispersal based on data on standing patterns of gene...

Raster spatial datasets are often analyzed at multiple spatial resolutions to understand natural phenomena such as global climate and land cover patterns. Given such datasets, a collection of user defined resolutions and a neighborhood definition, resolution sensitivity analysis (RSA) quantifies the sensitivity of spatial autocorrelation across different resolutions. RSA is important due to app...

objectives: to locate the road emergency stations in fars province based on using spatial multi-criteria decision making (delphi method).    methods: in this study, the criteria affecting the location of road emergency stations have been identified through delphi method and their importance was determined using analytical hierarchical process (ahp). with regard to the importance of the criteria...