Extract the pickpocketing information implied in built environment by treating it as the anomalies

Title: Extracting the pickpocketing information implied in the built environment by treating it as the anomalies

Abstract

The practice of crime risk mapping, enabled by the utilization of geospatial big data such as street view images, has received significant research attention. However, in situations where available data is scarce, mapping models may suffer from underfitting and generate inaccurate spatial pattern estimations of crime risk. The covert nature of pickpocketing crimes results in limited observed areas relevant to such criminal events, leading to insufficient coverage of geospatial data. Moreover, the location of crime is also influenced by socio-economic characteristics that may introduce biases into crime risk estimates. These factors render it challenging for the model to capture a valid crime risk pattern, potentially yielding misleading conclusions. Therefore, effectively extracting crime risk with limited data remains a challenge, especially when relying on easily accessible, widespread, and unbiased geospatial data. To address this challenge, we propose a novel crime risk assessment framework based on deep anomaly detection techniques, assuming that urban landscape anomalies carry deep crime risk information. We take Shenzhen as the study area and map the distribution of pickpocketing risk using street view images, accurately revealing the spatial aggregation of pickpocketing crime risk. Our findings indicate that pickpocketing crime in China is caused by regional economic conditions, built environment factors, and human routine activities. This study provides valuable insights for policing and prevention strategies aimed at addressing pickpocketing crimes in large Chinese cities. By leveraging our proposed crime risk assessment framework, decision-makers can allocate resources more efficiently and develop targeted interventions to mitigate crime risks.

Keywords

Pickpocketing Crime;
Street View;
Deep Anomaly Detection;
Interpretable Analysis;
Social Disorder

Highlights

A deep learning-based model for mining pickpocketing crimes from street view images is proposed.
With the idea of anomaly detection, crime risk information can be extracted from a small number of street view images with crime labels.
The reliable parcel-level pickpocketing risk distribution was mapped in China's megacity.
Pickpocketing crime in China is multifactorial and spatially heterogeneous.
Pickpocketing crime risk is aggregated and affected by neighbourhood socioeconomic conditions and the built environment.