Enterprise AI Analysis
Disentangling the impacts of collective mobility of residents and non-residents on burglary levels
This study investigates how the collective mobility (including movement and visiting) of residents and non-residents affects neighbourhood burglary levels. While past research has linked mobility to urban crime, this study explores how these relationships vary across population groups and social contexts at the neighbourhood level. Using mobile phone GPS data, we distinguished between residents and non-residents based on daily movement patterns. We then measured their mobility within defined spatial and temporal units. An explainable machine learning method (XGBoost and SHAP) was used to assess how mobility patterns influence burglary in London's LSOAs from 2020 to 2021. Results show that increased collective mobility is generally associated with higher burglary levels. Specifically, non-resident footfall and residents' stay-at-home time have a stronger influence than other variables like residents' travelled distance. The impact also varies across neighbourhoods and shifts during periods of COVID-19 restrictions and relaxations. These findings confirm the dynamic link between mobility and crime, highlighting the value of understanding population-specific patterns to inform more targeted policing strategies.
Quantifiable Impact: Urban Science
Our analysis reveals critical metrics on how mobility and deprivation factors influence burglary rates, providing a clear understanding of risk elements.
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Increased Burglary Risk with High NRF
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Reduced Burglary with Extended RSHDT
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Increased Burglary with High IMD
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Increased Burglary with High RF
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Flow for Mobility Measurement
Mobile Phone GPS points
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Stay detection
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Stay trajectory
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Home location delineation
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Residents' stays / Non-residents' stays
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Movement & Visiting behaviours
Our robust methodology distinguishes residents from non-residents using daily GPS patterns and measures collective mobility (movement & visiting) at LSOA- and month-levels. This comprehensive approach ensures high-fidelity data for granular analysis.
Non-Resident Footfall vs. Burglary Levels
35% Samples showing positive NRF impact on burglaryAnalysis of SHAP values reveals a strong positive correlation between higher non-resident footfall (NRF) and increased burglary rates, particularly in urban centers during normal periods. This highlights the opportunity crime theory, where increased activity provides more targets.
Resident Stay-at-Home Time vs. Burglary Levels
60% Samples showing negative RSHDT impact on burglaryIncreased resident stay-at-home duration (RSHDT) significantly correlates with decreased burglary levels. This underscores the importance of 'natural guardianship' in crime prevention, with homes being better protected when residents are present.
Deprivation (IMD) vs. Burglary Levels
57% Samples showing positive IMD impact on burglaryThe Index of Multiple Deprivation (IMD) shows a consistent positive impact on burglary rates, aligning with social disorganization theories. Higher deprivation levels are associated with increased crime, though this relationship can be modulated by mobility patterns.
Impact of COVID-19 Restrictions on Mobility & Crime
The COVID-19 pandemic significantly altered mobility patterns. Lockdowns led to decreased non-resident footfall and increased resident stay-at-home time, which in turn correlated with shifts in burglary rates. These changes were more pronounced in central urban areas.
| High Deprivation Areas | Low Deprivation Areas |
|---|---|
Low NRF, High Burglary Risk
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High NRF, Reduced Burglary
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Targeted Policing Strategies
Challenge: Traditional policing often overlooks dynamic mobility patterns, leading to sub-optimal resource deployment.
Solution: Integrate real-time mobility data (resident vs. non-resident, movement vs. visiting) with crime prediction models.
Outcome: Improved spatio-temporal accuracy in predicting burglary hotspots, allowing for pre-emptive and targeted interventions.
Understanding population-specific mobility patterns allows for more effective resource allocation. For instance, increased police patrols in high-footfall areas during peak times, or community engagement in areas with lower resident stay-at-home rates, can deter crime.
Advanced ROI Calculator
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