Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data
Abstract
:1. Introduction
2. Background
2.1. Ambient Population in Crime Analysis
2.2. Mobile Phone Data for Crime Analysis
2.3. Routine Activity Theory
2.4. Crime Pattern Theory
3. Study Area, Data, and Methods
3.1. Study Area
3.2. Data
3.2.1. Crime Data
3.2.2. Spatially Referenced Mobile Phone Big Data
3.2.3. Point of Interest (POI) Data
3.2.4. Luojia 1-01 Nighttime Light Imaging
3.3. Methods
3.3.1. Exploratory Spatial Data Analysis
3.3.2. Negative Binomial Regression
4. Analysis and Results
4.1. Exploratory Spatial Data Analysis
4.2. Regression Analysis
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Code | Variables | Mean | Std. | Min | Max |
---|---|---|---|---|---|
Dependent variable | |||||
Y | # of larceny-theft | 279.71 | 353.53 | 0.00 | 2491.00 |
Ambient population variables | |||||
X1 | % of non-local population (ln) | −0.61 | 0.19 | −1.06 | −0.27 |
X2 | % of population with regular social activity (SR) (ln) | −0.02 | 0.01 | −0.03 | 0.00 |
X3 | Diversity index of population’s native place (DINP) | 2.79 | 0.47 | 0.79 | 3.67 |
Crime attractors | |||||
X4 | # of internet bars, billiards rooms (ln) | −0.68 | 4.92 | −9.21 | 4.57 |
X5 | # of bars, card rooms, bath centers, KTV rooms (ln) | −0.71 | 5.07 | −9.21 | 4.49 |
Crime generators | |||||
X6 | # of bus stops, metro stations | 40.71 | 38.74 | 0.00 | 239.00 |
X7 | # of convenience stores, supermarkets, shopping malls (ln) | 1.61 | 2.76 | −9.21 | 4.33 |
X8 | # of restaurants (ln) | 4.65 | 1.91 | −9.21 | 7.56 |
Crime detractors | |||||
X9 | # of industrial plants, public security organs (ln) | 3.49 | 1.54 | −9.21 | 6.00 |
Socio-economic status | |||||
X10 | Radiance mean value of NTL (ln) | −24.24 | 3.68 | −39.14 | −18.33 |
Y | 1 | ||||||||||
X1 | 0.427 | 1 | |||||||||
X2 | −0.406 | −0.393 | 1 | ||||||||
X3 | 0.181 | −0.281 | −0.325 | 1 | |||||||
X4 | 0.488 | 0.398 | −0.397 | 0.172 | 1 | ||||||
X5 | 0.513 | 0.374 | −0.511 | 0.292 | 0.615 | 1 | |||||
X6 | 0.487 | 0.162 | −0.323 | 0.158 | 0.411 | 0.405 | 1 | ||||
X7 | 0.377 | 0.162 | −0.290 | 0.019 | 0.495 | 0.471 | 0.398 | 1 | |||
X8 | 0.600 | 0.411 | −0.509 | 0.197 | 0.734 | 0.684 | 0.493 | 0.596 | 1 | ||
X9 | 0.493 | 0.385 | −0.386 | 0.137 | 0.592 | 0.646 | 0.510 | 0.551 | 0.682 | 1 | |
X10 | 0.493 | 0.608 | −0.579 | 0.214 | 0.581 | 0.599 | 0.225 | 0.263 | 0.632 | 0.443 | 1 |
Y | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 |
Variables | Coefficient | p-Value |
---|---|---|
Constant | 6.390 | 0.000 |
Ambient population variables | ||
% of non-local population (ln 1) | 0.929 | 0.047 |
% of population with regular social activity (SR) (ln) | −30.238 | 0.027 |
Diversity index of population’s native place (DINP) | - | - |
Crime attractors | ||
# of internet bars, billiards rooms (ln) | 0.075 | 0.001 |
# of bars, card rooms, bath centers, KTV rooms (ln) | 0.086 | 0.000 |
Crime generators | ||
# of bus stops, metro stations | 0.005 | 0.016 |
# of convenience stores, supermarkets, shopping malls (ln) | 0.132 | 0.000 |
# of restaurants (ln) | - | - |
Crime detractors | ||
# of industrial plants, public security organs (ln) | −0.126 | 0.035 |
Socio-economic status | ||
Radiance mean value of NTL (ln) | 0.046 | 0.047 |
lnalpha | −0.224 | |
alpha | 0.799 | |
Pseudo R2 | 0.071 | 0.000 |
AIC | 2278.504 | |
BIC | 2310.815 | |
Moran’s I in residuals 2 | 0.037 | 0.190 |
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He, L.; Páez, A.; Jiao, J.; An, P.; Lu, C.; Mao, W.; Long, D. Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data. ISPRS Int. J. Geo-Inf. 2020, 9, 342. https://doi.org/10.3390/ijgi9060342
He L, Páez A, Jiao J, An P, Lu C, Mao W, Long D. Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data. ISPRS International Journal of Geo-Information. 2020; 9(6):342. https://doi.org/10.3390/ijgi9060342
Chicago/Turabian StyleHe, Li, Antonio Páez, Jianmin Jiao, Ping An, Chuntian Lu, Wen Mao, and Dongping Long. 2020. "Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data" ISPRS International Journal of Geo-Information 9, no. 6: 342. https://doi.org/10.3390/ijgi9060342