Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data
Abstract
:1. Introduction
2. Review of Related Works
2.1. Theoretical Approaches and Crime Shifting during the COVID-19 Pandemic
2.2. Geo-Tagged Big Data for Crime Analytics
3. Data
3.1. Study Area and Unit of Analysis
3.2. London-Police-Recorded Data
3.3. Socioeconomic Data
3.4. Place Data
3.5. Mobile Phone GPS Trajectory Data
4. Methods
4.1. Generation of Human Activity Variables
4.1.1. Footfall Generation from GPS Trajectory
4.1.2. Characterising Dynamic Human Activity
4.2. Models
4.2.1. LASSO Regression Model
4.2.2. LASSO Model Training Setup
4.2.3. Geographically Weighted Regression Model
5. Results
5.1. Description of Explanatory Variables and Property Crime Rates
5.2. Optimised LASSO Models for Property Crimes
5.3. The Global Relationships between All Human Activity Variables and Theft in Four Pandemic Periods
5.4. The Spatial Relationships between Selected Human Activity Variables and Theft in Four Pandemic Periods
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
RAT | Routine activity theory |
CPT | Crime pattern theory |
SDT | Social disorganisation theory |
CDR | Call detail record |
GPS | Global positioning system |
UK | United Kingdom |
ONS | Office for National Statistics |
LSOAs | Lower super output areas |
POI | Point of interest |
WD | Weekdays |
WE | Weekends |
MDAF | Monthly daily average footfall |
LASSO | Least Absolute Shrinkage and Selection Operator |
RMSE | Root-mean-squared error |
Appendix A
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Category | Fields Used | Description | Source |
---|---|---|---|
Geographical boundary data | LSOA ID; names; geographical information | Local super output area (LSOA) geographical polygon information for the U.K. | Office for National Statistics |
Police-recorded data | Bicycle theft; burglary; criminal damage and arson; robbery; shoplifting; theft from a person; vehicle crime; | Police-recorded data of different crime types | data.police.uk |
Socioeconomic data | Residents; unemployment; rent social house; education above level 4 (higher education); young residents; own cars above 3 | U.K. 2011 census with demographic and socioeconomic conditions (LSOA-level) | Office for National Statistics |
Place data | Eating and drinking; public transport; stations and infrastructure; tourism; gambling; venues, stage, and screen; food, drink, and multi-item retail | Point of interest with latitude, longitude, and classifications | Ordnance survey |
Mobile phone GPS data | Latitude, longitude, date/time | Mobile phone GPS trajectories | Location Sciences |
Variables | Mean | Std | Min | Max |
---|---|---|---|---|
Static variables in social disorganisation (proportion) | ||||
Unemployment | 0.05 | 0.02 | 0.01 | 0.18 |
Rent social house | 0.23 | 0.20 | 0.00 | 0.91 |
Education above level 4 | 0.37 | 0.15 | 0.08 | 0.84 |
Young residents 16–34 | 0.32 | 0.09 | 0.13 | 0.77 |
Own cars above 3 | 0.04 | 0.04 | 0.00 | 0.31 |
Static crime generators (proportion) | ||||
Eating and drinking | 6.28 | 18.37 | 0.00 | 792 |
Public transport, stations, and infrastructure | 4.41 | 5.01 | 0.00 | 140 |
Tourism | 0.69 | 3.78 | 0.00 | 164 |
Gambling | 0.36 | 0.99 | 0.00 | 24 |
Venues, stage, and screen | 0.28 | 1.39 | 0.00 | 50 |
Food, drink, and multi-item retail | 3.69 | 5.89 | 0.00 | 115 |
Dynamic human activity variables (MDAF) | ||||
Early Morning (WD *) | 20.27 | 21.15 | 0.22 | 1607.55 |
Morning (WD) | 30.38 | 63.12 | 0.15 | 9311.00 |
Midday (WD) | 39.35 | 74.92 | 0.90 | 8923.10 |
Afternoon (WD) | 25.18 | 55.32 | 0.36 | 7457.05 |
Evening (WD) | 7.13 | 14.81 | 0.09 | 1337.10 |
Early Morning (WE *) | 16.15 | 15.52 | 0.13 | 1302.11 |
Morning (WE) | 18.44 | 27.46 | 0.00 | 2297.00 |
Midday (WE) | 38.81 | 71.09 | 1.00 | 3222.44 |
Afternoon (WE) | 20.07 | 40.68 | 0.37 | 2291.50 |
Evening (WE) | 7.09 | 14.16 | 0.00 | 1014.56 |
Crime rate (per 1000 population) | ||||
All property crime | 3.30 | 5.76 | 0.00 | 462.98 |
Bicycle theft | 0.23 | 0.71 | 0.00 | 45.02 |
Burglary | 0.57 | 0.80 | 0.00 | 34.04 |
Criminal damage and arson | 0.52 | 0.75 | 0.00 | 25.86 |
Robbery | 0.24 | 0.69 | 0.00 | 44.26 |
Shoplifting | 0.34 | 1.54 | 0.00 | 96.17 |
Theft from the person | 0.35 | 2.66 | 0.00 | 321.70 |
Vehicle crime | 1.04 | 1.17 | 0.00 | 39.46 |
Optimised LASSO Models | S * | D * | S + D * | ||||||
---|---|---|---|---|---|---|---|---|---|
RMSE | RMSE | RMSE | |||||||
All property crime | 3.17 | 0.42 | 0.001 | 2.88 | 0.52 | 0.004 | 2.67 | 0.59 | 0.002 |
Bicycle theft | 0.49 | 0.26 | 0.024 | 0.51 | 0.19 | 0.001 | 0.48 | 0.27 | 0.022 |
Burglary | 0.66 | 0.10 | 0.004 | 0.67 | 0.07 | 0.002 | 0.66 | 0.11 | 0.007 |
Criminal damage and arson | 0.61 | 0.14 | 0.001 | 0.62 | 0.09 | 0.001 | 0.60 | 0.15 | 0.001 |
Robbery | 0.48 | 0.22 | 0.001 | 0.46 | 0.26 | 0.001 | 0.45 | 0.31 | 0.002 |
Shoplifting | 0.99 | 0.30 | 0.002 | 0.95 | 0.36 | 0.002 | 0.91 | 0.42 | 0.001 |
Theft from the person | 1.48 | 0.31 | 0.001 | 1.24 | 0.52 | 0.001 | 1.17 | 0.57 | 0.001 |
Vehicle crime | 1.01 | 0.05 | 0.001 | 1.00 | 0.06 | 0.02 | 1.00 | 0.07 | 0.007 |
Optimised LASSO Models | S * | D * | S + D * | |||
---|---|---|---|---|---|---|
RMSE | RMSE | RMSE | ||||
All property crime | 4.36 | 0.53 | 4.12 | 0.53 | 3.73 | 0.62 |
Bicycle theft | 0.47 | 0.31 | 0.51 | 0.17 | 0.46 | 0.32 |
Burglary | 0.62 | 0.14 | 0.64 | 0.08 | 0.62 | 0.14 |
Criminal damage and arson | 0.60 | 0.21 | 0.63 | 0.14 | 0.60 | 0.21 |
Robbery | 0.57 | 0.42 | 0.56 | 0.38 | 0.52 | 0.49 |
Shoplifting | 1.04 | 0.37 | 1.03 | 0.37 | 0.97 | 0.45 |
Theft from the person | 2.65 | 0.43 | 2.32 | 0.51 | 2.20 | 0.57 |
Vehicle crime | 1.08 | 0.09 | 1.08 | 0.08 | 1.07 | 0.10 |
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Chen, T.; Bowers, K.; Cheng, T. Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data. ISPRS Int. J. Geo-Inf. 2023, 12, 488. https://doi.org/10.3390/ijgi12120488
Chen T, Bowers K, Cheng T. Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data. ISPRS International Journal of Geo-Information. 2023; 12(12):488. https://doi.org/10.3390/ijgi12120488
Chicago/Turabian StyleChen, Tongxin, Kate Bowers, and Tao Cheng. 2023. "Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data" ISPRS International Journal of Geo-Information 12, no. 12: 488. https://doi.org/10.3390/ijgi12120488
APA StyleChen, T., Bowers, K., & Cheng, T. (2023). Applying Dynamic Human Activity to Disentangle Property Crime Patterns in London during the Pandemic: An Empirical Analysis Using Geo-Tagged Big Data. ISPRS International Journal of Geo-Information, 12(12), 488. https://doi.org/10.3390/ijgi12120488