Spatial Statistics

The term spatial statistics refers to the application of statistical concepts and methods to data that has a spatial location associated with it, and in which the locational element is used as an important and necessary part of the analysis. Such analysis is typically descriptive and exploratory, but it may also employ the full power of statistical theory to test and/or calibrate models of the spatial processes assumed to be at work. This approach is sometimes referred to as spatial analysis, along with deterministic, mathematical procedures applied to location data, such as optimal facility location, area coverage problems, the travelling salesman problem, and so on. This article only discusses spatial analytical methods that are based on statistical concepts and logic.
Spatial Statistics publishes articles on spatial and spatiotemporal statistics theory and application. It prefers manuscripts that present theory generated by new applications or that apply new theory to a significant practical case. A purely theoretical study is unlikely to be accepted. Case studies that lack methodological development are not suitable for publication.

KEY FEATURE

Current trends in statistical methods reflect both the specifics of the problems being investigated and the accessibility of computational resources.

Spatial statisticians are becoming increasingly important to the GIS community as they help to make sense of spatial data.

Those who came before modern practitioners of spatial statistics included those interested in characterising areal distributions, spatial interactions, and the complexities of spatial correlation.

Spatial association, pattern analysis, scale and zoning, geostatistics, classification, spatial sampling, and spatial econometrics are some of the spatial methods currently in use.

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Key Topics

    Spatial Statistics

    Spatial Statistics is concerned with the quantitative analysis of spatial and spatiotemporal data, as well as their statistical dependencies, accuracy, and uncertainties. Spatial statistics methodology is commonly found in probability theory, stochastic modelling, mathematical statistics, and information science. Spatial statistics is used in mapping, assessing the quality of spatial data, optimising sampling designs, modelling dependence structures, and drawing valid inferences from a limited set of spatiotemporal data. Application fields include
    Agriculture, geology, soil science, hydrology, ecology, mining, oceanography, forestry, air quality, and remote sensing are examples of physical domains. Spatial econometrics, epidemiology, and disease mapping are examples of social/economic domains.

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    Spatial analysis, also known as spatial statistics, refers to any of the formal techniques used to study entities based on their topological, geometric, or geographic properties. Spatial analysis encompasses a wide range of techniques, many of which are still in their early stages, that use various analytic approaches and are used in fields ranging from astronomy, which studies the placement of galaxies in the universe, to chip fabrication engineering, which uses “place and route” algorithms to build complex wiring structures. In a more limited sense, spatial analysis is the technique used to analyse human-scale structures, most notably in the analysis of geographic data or transcriptomics data.
    In spatial analysis, complex issues arise, many of which are neither clearly defined nor completely resolved but serve as the foundation for current research. The most fundamental of these is determining the spatial location of the entities under investigation.
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