spatial autocorrelation

spatial autocorrelation

[′spā·shəl ¦ȯd·ō‚kä·rə′lā·shən]
(geography)
In mathematical geography, the degree of interdependence among data arranged on a three-dimensional grid.
References in periodicals archive ?
The results support the work of Anselin and Sridharan (2002), which suggests that positive spatial autocorrelation (High-High and Low-Low) is associated with contagious mobility while negative spatial autocorrelation (High-Low and Low-High) is associated with spatial outliers or hierarchical mobility.
A statistic of spatial autocorrelation, such as Moran's I [15, 16], provides the tool to test this hypothesis.
This spatial weights matrix forms the basis from which the degree of spatial autocorrelation (for example, Moran's I, a statistic that measures clustering) can be calculated for the dependent variable.
To further evaluate the robustness of the estimates produced by Equations (1) and (2) we also conduct a spatial autocorrelation analysis.
(2005), by generalizing the notion of a map to include demographic and psychometric representations, spatial models can capture a variety of effects (spatial lags, spatial autocorrelation, and spatial drift) that affect firm or consumer decision behavior.
We develop and compare 40 different spatial weights matrices and choose the one that achieves a high coefficient of spatial autocorrelation in combination with a high level of statistical significance.
4:00 TESTING SPATIAL AUTOCORRELATION FOR USE IN DIFFERENTIATING EVAPORATIVE RESIDUES, Scott M.
Having established the spatial distribution of employment by sector, the paper analyses the spatial patterns of this distribution using a number of spatial statistical methods such as tests for spatial autocorrelation. This analysis uncovers the locational preferences of individual sectors, the degree to which specific sectors agglomerate and coagglomerate, and thus shows the degree of urbanisation effects and differences across urban and rural areas regarding economic activity.
Results were evaluated with Moran and Geary's spatial autocorrelation index, dispersion maps (Kriging technique) and variance analyses (ANOVA).
The main focus of this study was the estimation of spatial autocorrelation and the authors highlight the importance of testing for and estimating models that are sensitive to spatial effects.
Spatial autocorrelation statistics (Cliff and Ord, 1973; Goodchild, 1986) were used to test the null hypothesis [H.sub.0] of no spatial autocorrelation between WS% per square throughout the site.
Fourth, a spatial autocorrelation analysis (using the Moran's I index) provides statistical substantiation that the most influential FIRE firms (in terms of sales and employment) in the Toronto CMA continue to cluster in south-central Toronto.
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