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• W2609227564 abstract "This article examines techniques for analyzing the spatial patterns of point and area features. Section “ Descriptive Measures of Point Features ” introduces centrographic measures to describe the geographic distribution of point features in terms of central tendency and spatial dispersion. Central tendency measures include mean center, median center, and central feature, and spatial dispersion measures include standard distance and deviational ellipse. Section “ Inferential Measures of One Type of Points ” discusses inferential measures of one type of points to assess whether an observed pattern is random. The density-based approach, such as quadrat analysis, concerns whether the density of observed points exhibits a random, dispersed, or clustered pattern. The distance-based approach, for example ordered neighbor statistics, considers if distances between points are larger (dispersed) or smaller (clustered) than those of a random pattern. Ripley’s K-function also considers point density, but depicts changes in point density at different geographical scales (spatial lags). Section “ Collocation Analysis of Two Types of Points ” examines colocation analysis of two types of points in order to measure the extent of their vicinity from each other. The cross K (or bivariate K) function measures the ratio of observed overall density of type B points within a specified distance of a type A point over what we would expect by chance. The colocation quotient examines the overall association between observed and expected numbers of type B points in proximity to type A points, and assesses whether type A points are spatially colocated with, or isolated from, B points, or whether the pattern is random. The colocation quotient also has a local version to reveal the spatial variability of correlation between two point sets and a corresponding statistical significance test. Section “ Area-Based Analysis of Spatial Autocorrelation ” analyzes spatial autocorrelation of areal features. The methods include the join-count statistic (limited for data of binary values), and the more commonly known Moran’s I, Geary Ratio, and Getis-G statistic. Section “ Regionalization Methods ” introduces several GIS-automated regionalization methods, such as REDCAP and mixed-level regionalization, to aggregate smaller areal units into larger regions. Each section has some illustrative examples to explain the implementation process with sample data and programs to download." @default.
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• W2609227564 date "2018-01-01" @default.
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• W2609227564 title "Spatial Analysis Methods" @default.
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• W2609227564 doi "https://doi.org/10.1016/b978-0-12-409548-9.09598-1" @default.
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