Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

Ord, J. Keith; Getis, Arthur

doi:10.1111/j.1538-4632.1995.tb00912.x

Cited by 2,793 publications

(1,590 citation statements)

References 13 publications

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“…The "hotspots" refer to an area with relatively higher concentrations of cat colonies as compared to surrounding areas, whereas "coldspots" indicate the opposite. The major diagnostic value for determining the hotspots are the zscores and p-values of the analysis (Getis & Ord, 1992;Ord & Getis, 1995, 2001). The Gi* statistic returned for the feature in the dataset is a z-score.…”

Section: Methodsmentioning

confidence: 99%

Distribution characteristics of unmanaged cat colonies over a 20 year period in Auckland, New Zealand

Aguilar

Farnworth

2013

Applied Geography

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a b s t r a c tCat colony records for the period 1991 to 2011 were used to determine spatial characteristics and relationships between cat colony density, human population, social conditions and the physical environment. Results show a positive correlation between cat colonies, population density, social indicators and the types of land where they are located. A temporal increase in the density of cat colonies is noticeable in areas where they are frequently reported, supporting the hypothesis that cat colonies are a persistent feature of Auckland's urban landscape. Complementing an earlier paper on stray cats, this paper seeks to provide information to support the implementation of management measures to mitigate both social and animal welfare concerns.

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Section: Methodsmentioning

confidence: 99%

Distribution characteristics of unmanaged cat colonies over a 20 year period in Auckland, New Zealand

Aguilar

Farnworth

2013

Applied Geography

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“…We then applied the Getis-Ord G ‫ء‬ statistic for geographical clustering analysis (also known as "hotspot analysis") to locate geographical concentration or concentrations of high and low values of profile correlation values. The G ‫ء‬ statistic identifies areas that have high (or low) values and that also have neighboring areas that have high (or low) values in the profile correlations (Ord & Getis, 1995). The statistic is interpreted as a z-score, with values above 1.96 or below Ϫ1.96 indicating statistically significant clustering.…”

Section: Focal Analyses: Identification Of Psychological Regionsmentioning

confidence: 99%

Divided we stand: Three psychological regions of the United States and their political, economic, social, and health correlates.

Rentfrow¹,

Gosling²,

Jokela³

et al. 2013

Journal of Personality and Social Psychology

261

255

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There is overwhelming evidence for regional variation across the United States on a range of key political, economic, social, and health indicators. However, a substantial body of research suggests that activities in each of these domains are typically influenced by psychological variables, raising the possibility that psychological forces might be the mediating or causal factors responsible for regional variation in the key indicators. Thus, the present article examined whether configurations of psychological variables, in this case personality traits, can usefully be used to segment the country. Do regions emerge that can be defined in terms of their characteristic personality profiles? How are those regions distributed geographically? And are they associated with particular patterns of key political, economic, social, and health indicators? Results from cluster analyses of 5 independent samples totaling over 1.5 million individuals identified 3 robust psychological profiles: Friendly & Conventional, Relaxed & Creative, and Temperamental & Uninhibited. The psychological profiles were found to cluster geographically and displayed unique patterns of associations with key geographical indicators. The findings demonstrate the value of a geographical perspective in unpacking the connections between microlevel processes and consequential macrolevel outcomes.

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“…For the period 1999-2010 we calculate yearly sets of coverages and the mean coverage over the period; for the period 1968-1976 only one set of mussel bed coverage can be calculated. Ord and Getis (1995) have developed two local statistics, G i and G i * , to evaluate the degree of spatial clustering around a focal location, i. Statistic G i excludes location i from the cluster whereas G i * includes it. The G and G* statistics express the sum of the values in the vicinity of a particular location as the proportion of the sum of the values for the entire area.…”

Section: Mussel Bed Area Per Tidal Basin and Spatial Clusteringmentioning

confidence: 99%

Large-Scale Spatial Dynamics of Intertidal Mussel (Mytilus edulis L.) Bed Coverage in the German and Dutch Wadden Sea

et al. 2014

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Intertidal blue mussel beds are important for the functioning and community composition of coastal ecosystems. Modeling spatial dynamics of intertidal mussel beds is complicated because suitable habitat is spatially heterogeneously distributed and recruitment and loss are hard to predict. To get insight into the main determinants of dispersion, growth and loss of intertidal mussel beds, we analyzed spatial distributions and growth patterns in the German and Dutch Wadden Sea. We considered yearly distributions of adult intertidal mussel beds from 36 connected tidal basins between 1999 and 2010 and for the period 1968-1976. We found that in both periods the highest coverage of tidal flats by mussel beds occurs in the sheltered basins in the southern Wadden Sea. We used a stochastic growth model to investigate the effects of density dependence, winter temperature and storminess on changes in mussel bed coverage between 1999 and 2010. In contrast to expectation, we found no evidence that cold winters consistently induced events of synchronous population growth, nor did we find strong evidence for increased removal of adult mussel beds after stormy winter seasons. However, we did find synchronic growth within groups of proximate tidal basins and that synchrony between distant groups is mainly low or negative. Because the boundaries between synchronic groups are located near river mouths and in areas lacking suitable mussel bed habitat, we suggest that the metapopulation is under the control of larval dispersal conditions. Our study demonstrates the importance of moving from simple habitat suitability models to models that incorporate metapopulation processes to

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Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

Abstract: The statistics Gi ( d )

Cited by 2,793 publications

References 13 publications

Distribution characteristics of unmanaged cat colonies over a 20 year period in Auckland, New Zealand

Distribution characteristics of unmanaged cat colonies over a 20 year period in Auckland, New Zealand

Divided we stand: Three psychological regions of the United States and their political, economic, social, and health correlates.

Large-Scale Spatial Dynamics of Intertidal Mussel (Mytilus edulis L.) Bed Coverage in the German and Dutch Wadden Sea

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