Crime Risk Estimation with a Commuter-Harmonized Ambient Population

Abstract

Residential population data are frequently employed to link the crime incidence of an area with the number of residents to estimate the underlying risk. Human mobility patterns cause shifts in the baseline population, however, that can potentially influence the crime statistics. This study therefore employed an ambient population that combined residential population data with data depicting the commuting activity in small administrative areas. The effects of the commuter-harmonized ambient population on crime were then evaluated in a series of negative binomial regression models. The models also controlled for criminogenic factors and incorporated eigenvector spatial filtering to adjust for spatial effects. The results show significant effects of commuting patterns on crime outcomes. For certain crimes, such as violence, theft, and disorder, the inbound commuters are significantly associated with high risk. It was further discovered that an offset variable comprising the commuter-harmonized ambient population data models the crime outcomes more reliably than when residential population data are used. Spatial filtering was found to effectively eradicate residual spatial autocorrelation after accounting for effects of the predictor variables. We conclude that calculating crime rates using the residential population does not constitute an accurate risk measure and that the ambient population has crucial implications for realistic and reliable target representation and crime modeling.

居住人口数据经常用来连结一地的犯罪事件与常住人口数, 以评估潜在的风险。但人类的移动模式, 导致可能会影响犯罪统计的基线人口的转移。本研究因此运用结合小型行政区域居住人口数据和描绘通勤活动数据的周遭人口。本研究接着在负二项迴归模型的系列中, 评估调和勤者的周遭人口对犯罪的影响。这些模型同时控制犯罪因素, 并纳入特徵向量空间过滤, 以调整空间效应。研究结果显示通勤模式对于犯罪结果的显着影响。就暴力、偷窃和扰乱等若干犯罪而言, 向内的通勤者与高风险显着相关。本研究进一步发现, 包含调和通勤者周遭人口数据的偏移变量, 较运用居住人口数据所进行的模型化犯罪后果更佳可靠。本研究发现, 考量预测变项之后, 空间过滤能有效地根除空间自相关的残馀。我们于结论中指出, 运用常住人口来计算犯罪率, 无法组成准确的风险评估, 而周遭人口对于实际且可靠的目标再现与犯罪模式化而言具有重要的意涵。

Frecuentemente los datos de población residencial se utilizan para conectar la incidencia del crimen sobre un área con el número de residentes para calcular el riesgo subyacente. Sin embargo, los patrones de movilidad humana causan cambios en la población de referencia que potencialmente pueden influir las estadísticas de la criminalidad. Es por eso por lo que este estudio utilizó una población momentánea que combinó los datos de población residencial con los datos que representan la actividad de los viajes pendulares en áreas administrativas pequeñas. Los efectos de la población momentánea armonizada por el conmutante sobre el crimen fueron luego evaluados en una serie de modelos de regresión negativa. Los modelos controlaron también los factores criminogénicos e incorporaron el filtrado espacial eigenvector para hacer ajustes en razón de efectos espaciales. Los resultados muestran efectos significativos de los patrones del viaje pendular sobre la criminalidad resultante. En lo que se refiere a ciertos crímenes, como violencia, robo y alteraciones del orden, los conmutantes o viajeros pendulares orientados hacia adentro aparecen significativamente asociados con riesgo alto. Se descubrió además que una variable compensadora que comprenda los datos de población momentánea armonizada por el conmutante modela los crímenes resultantes de manera más fiable que cuando son utilizados los datos de la población residencial. Se halló que el filtrado espacial efectivamente erradicaba la autocorrelación espacial residual tras tomar en cuenta los efectos de las variables predictivas. Concluimos que calcular las tasas de crimen usando la población residencial no constituye una medida exacta de riesgo y que la población momentánea tiene implicaciones cruciales para una representación proyectada realista y confiable, y para la modelación criminalística.

Key Words:

• ambient population
• criminogenic factors
• eigenvector spatial filtering
• regression

关键词：:

• 周遭人口
• 犯罪因素
• 特徵向量空间过滤
• 迴归。

Palabras clave:

• población momentánea
• factores criminogénicos
• filtrado espacial eigenvector
• regresión

Acknowledgments

The authors thank Professor Mei-Po Kwan for efficiently handling the blind review process for this article. We are also grateful for the valuable comments and suggestions from the anonymous reviewers. The first author thanks Professor Alexander Zipf (Heidelberg University, Germany) for his support during the writing of this article.

Funding

The first author thanks the German Academic Exchange Service (DAAD) for funding this research.

Notes

1. The ambient population was adjusted to account for human mobility across observation areas when depicting the population at risk.

2. Spatial dependency refers to locational and attributional similarity. Positive spatial dependence, in which similar values are located close by across space, is the most prevalent type of dependence in empirical crime studies (Townsley 2009).

3. Lagrange multiplier tests (Hilbe 2014) were highly significant, indicating that the Poisson model was statistically insufficient for modeling the overdispersed crime data.

4. Condition indexes (Belsley, Kuh, and Welsch 2005) were used to test for multicollinearity in the predictors but no problems were found.

Additional information

Notes on contributors

Lucy W. Mburu

LUCY W. MBURU is a doctoral researcher with the GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany. E-mail: [email protected]. She is also a lecturer at the KCA University, Nairobi, Kenya. Her interests are in geospatial analysis and modeling.

Marco Helbich

MARCO HELBICH is an Assistant Professor in the Department of Human Geography and Planning at the Utrecht University, 3584 CS, The Netherlands. E-mail: [email protected]. His interests lie in urban geography and GIScience. In particular, he combines computational models and GIS-based approaches to investigate research challenges in real estate, crime, and health, among others.