Geographically Weighted Colocation Quotients: Specification and Application

Cromley, Robert G.; Hanink, Dean M.; Bentley, George C.

doi:10.1080/00330124.2013.768130

Cited by 63 publications

(59 citation statements)

References 26 publications

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“…Leslie and Kronenfeld [5] found that business establishments and trees exhibited individual spatial autocorrelation, as well as spatial association between categories. Subsequently, Leslie et al [2] confirmed the spatial co-occurrence of sub-categories of food sources, and housing types have been verified as having spatial colocation patterns [6]. Regarding the spatial associations between crimes and land-use features, studies have found that land-use features influence the spatial distributions of various crimes in different ways and to different extents [7,8].…”

Section: Introductionmentioning

confidence: 91%

“…The LCLQ is expected to be 1 [6]. Therefore, a LCLQ Ai→B larger than 1 demonstrates a local colocation pattern between A i and type B points.…”

Section: Local Colocation Quotientmentioning

confidence: 99%

“…This is not correct because, in most cases, spatial processes are not evenly distributed over space [33,34]. To determine the spatial heterogeneity of colocation patterns, Cromley et al [6] proposed a local version of the CLQ, as follows.…”

Section: Colocation Quotientmentioning

confidence: 99%

“…Specifically, the numerator is the observed proportion of type B points that are neighbors of A i . The denominator is the expected proportion under a specific null model, in this case, restricted random labeling [6,8]. As in the case of the global CLQ, N − 1 rather than N as the denominator.…”

Section: Local Colocation Quotientmentioning

confidence: 99%

“…The Local Colocation Quotient (LCLQ) proposed by Cromley et al [6] measures the extent to which category A points (crimes in the within study) are attracted to category B points (land-use features in the within study) in local given areas. For a type A point A i , the LCLQ is calculated as per [8], as follows:…”

Section: Local Colocation Quotientmentioning

confidence: 99%

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The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

Han

Zhu

et al. 2017

IJGI

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Most studies of spatial colocation patterns of crime and land-use features in geographical information science and environmental criminology employ global measures, potentially obscuring spatial inhomogeneity. This study investigated the relationships of three types of crime with 22 types of land-use in Wuhan, China. First, global colocation patterns were examined. Then, local colocation patterns were examined based on the recently-proposed local colocation quotient, followed by a detailed comparison of the results. Different types of crimes were encouraged or discouraged by different types of land-use features with varying intensity, and the local colocation patterns demonstrated spatial inhomogeneity.

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Section: Introductionmentioning

confidence: 91%

“…The LCLQ is expected to be 1 [6]. Therefore, a LCLQ Ai→B larger than 1 demonstrates a local colocation pattern between A i and type B points.…”

Section: Local Colocation Quotientmentioning

confidence: 99%

Section: Colocation Quotientmentioning

confidence: 99%

Section: Local Colocation Quotientmentioning

confidence: 99%

Section: Local Colocation Quotientmentioning

confidence: 99%

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The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

Han

Zhu

et al. 2017

IJGI

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Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure

Kronenfeld

Leslie

2015

J Geogr Syst

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, "Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure" (2015). AbstractStatistical measures of spatial interaction between multiple types of entities are commonly assessed against a null model of either toroidal shift (TS), which controls for spatial structure of individual subpopulations, or random labeling (RL) which controls for spatial structure of the joint population. Neither null model controls for both types of spatial structure simultaneously, although this may sometimes be desirable when more than two subpopulations are present. To address this, we propose a flexible framework for specifying null models that we refer to as restricted random labeling (rRL). Under rRL, a specified subset of individuals is restricted, and other individuals are randomly relabeled. Within this framework two specific null models are proposed for pairwise analysis within populations consisting of three or more subpopulations, to simultaneously control for spatial structure in the joint population and one or the other of the two subpopulations being analyzed. Formulas are presented for calculating expected nearest neighbor counts and co-location quotients within the proposed framework. Differences between TS, RL and rRL are illustrated by application to six types of generating processes in a simulation study, and to empirical datasets of tree species in a forest and crime locations in an urban setting. These examples show that rRL null models are typically stricter than either TS or RL, which often detect "interactions" that are an expected consequence either of the joint population pattern or of individual subpopulation patterns.

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Operational local join count statistics for cluster detection

Anselin

2019

J Geogr Syst

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This paper operationalizes the idea of a local indicator of spatial association (LISA) for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

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Geographically Weighted Colocation Quotients: Specification and Application

Cited by 63 publications

References 26 publications

The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure

Operational local join count statistics for cluster detection

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