Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities

TL;DR

This paper introduces a new simulation-based statistical test for the local colocation quotient, incorporating street network distances, to analyze spatial associations between crimes and facilities with improved local variability detection.

Contribution

It develops a novel simulation-based significance test for the local colocation quotient, considering street network distances, enhancing spatial association analysis of point data.

Findings

The method effectively identifies significant colocation patterns.

Application reveals meaningful associations between crimes and facilities.

Demonstrates the method's utility in urban spatial analysis.

Abstract

Most existing point-based colocation methods are global measures (e.g., join count statistic, cross K function, and global colocation quotient). Most recently, a local indicator such as the local colocation quotient is proposed to capture the variability of colocation across areas. Our research advances this line of work by developing a simulation-based statistic test for the local indicator of colocation quotient (LCLQ). The study applies the indicator to examine the association of land use facilities with crime patterns. Moreover, we use the street network distance in addition to the traditional Euclidean distance in defining neighbors since human activities (including facilities and crimes) usually occur along a street network. The method is applied to analyze the colocation of three types of crimes and three categories of facilities in a city in Jiangsu Province, China. The findings demonstrate the value of the proposed method in colocation analysis of crime and facilities, and in general colocation analysis of point data.