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Frontiers of Computer Science

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ISSN 2095-2236(Online)

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Front. Comput. Sci.    2022, Vol. 16 Issue (4) : 164316    https://doi.org/10.1007/s11704-020-0007-z
RESEARCH ARTICLE
Exploring associations between streetscape factors and crime behaviors using Google Street View images
Mingyu DENG1, Wei YANG2(), Chao CHEN1,3(), Chenxi LIU1
1. School of Computer Science, Chongqing University, Chongqing 400044, China
2. School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
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Abstract

Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management. Recently, the development of deep learning technology and big data of street view images, makes it possible to quantitatively explore the relationship between streetscape and crime. This study computed eight streetscape indexes of the street built environment using Google Street View images firstly. Then, the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model. An experiment was conducted in downtown and uptown Manhattan, New York. Global regression results show that the influences of Motorization Index on crimes are significant and positive, while the effects of the Light View Index and Green View Index on crimes depend heavily on the socio-economic factors. From a local perspective, the Pedestrian Space Index, Green View Index, Light View IndexandMotorization Index have a significant spatial influence on crimes, while the same visual streetscape factors have different effects on different streets due to the combination differences of socio-economic, cultural and streetscape elements. The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association. The results provide both theoretical and practical implications for crime theories and crime prevention efforts.

Keywords crime      Google Street View      streetscape      spatial analysis      geographically weighted poisson regression     
Corresponding Author(s): Wei YANG,Chao CHEN   
Just Accepted Date: 29 May 2020   Issue Date: 18 November 2021
 Cite this article:   
Mingyu DENG,Wei YANG,Chao CHEN, et al. Exploring associations between streetscape factors and crime behaviors using Google Street View images[J]. Front. Comput. Sci., 2022, 16(4): 164316.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-0007-z
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I4/164316
Fig.1  Study area in Manhattan
Datasets Description Data source
Crime data Including crime location, date, time, etc. NYC Open Data
Socio-economic data Demographic, economy, education, housing, family type, etc. American Factfinder
Map vector data Road networks, community boundaries, etc. Open Street Map
GSV image data Google Street View images. Google Street View
Tab.1  Datasets of the study area
Crime variables Uptown Downtown
Min Max Mean S.D. Min Max Mean S.D.
Total 0 51 4.58 5.988 0 68 3.88 6.256
Property 0 28 2.30 3.010 0 56 2.36 4.245
Violence 0 43 2.27 4.285 0 31 1.52 3.070
Daytime 0 31 2.44 3.366 0 43 1.62 3.264
Nighttime 0 34 2.13 3.335 0 58 2.27 3.783
Tab.2  Descriptive statistics for crime events (dependent variables)
Fig.2  GSV panoramas captured in (a) three vertical views and (b) six horizontal views at a sample site
Fig.3  Semantic segmentation results for the GSV images with the DeepLabv3+ model
Covariates Uptown Downtown
Min Max Mean S.D. Min Max Mean S.D.
Population density (people per acre) 34.39 461.66 175.51 71.19 12.05 463.93 115.58 58.65
Vulnerable population/% 10.92 55.76 34.53 9.39 3.26 45.84 22.67 9.60
Media income/k$ 1.25 25.00 10.43 5.79 1.11 25.00 10.33 5.84
Poverty/% 0.00 57.62 12.88 11.25 0.00 78.91 13.93 12.38
Low education/% 0.00 42.53 5.30 7.42 0.00 65.60 8.93 13.75
Unemployment/% 0.00 37.10 7.99 5.75 0.00 39.30 6.76 5.01
Female alone/% 11.07 44.95 27.98 6.38 7.17 38.98 23.14 6.69
Single-parent family/% 0.29 26.16 5.15 5.56 0.13 20.28 2.54 2.37
Housing vacancy/% 0.00 47.71 12.46 8.60 0.30 64.85 9.77 8.49
Tab.3  Descriptive statistics for nine covariates
Fig.4  The results of the Getis-Ord-Gi* for different crime types. (a) Crime_all; (b) Crime_P; (c) Crime_V; (d) Crime_D; (e) Crime_N
Visual factors/% Uptown Downtown
Min Max Mean S.D. Min Max Mean S.D.
PSI 0.00 33.32 5.54 5.01 0.00 59.02 8.21 7.58
GVI 0.00 78.43 13.47 10.95 0.00 61.54 9.16 10.45
BVI 0.00 57.37 19.23 11.45 0.00 68.68 22.25 15.07
SVI 0.00 36.99 10.95 7.97 0.00 42.93 11.63 8.76
LVI 0.00 1.70 0.23 0.19 0.00 3.14 0.33 0.28
SCE 0.00 81.12 33.79 16.92 0.00 78.19 33.04 18.03
MI 0.00 63.12 37.56 17.20 0.00 64.55 35.64 17.51
NAI 0.00 321.63 17.29 25.26 0.00 6066.70 22.09 165.28
Tab.4  Descriptive statistics for eight streetscape indexes (independent variables)
Fig.5  Visualization results of eight streetscape factors in uptown and downtown. (a) Streetscape factors quantification results in uptown; (b) streetscape factors quantification results in downtown
Variables Uptown Downtown
Model 1 (All crime) Model 2(Property) Model 3(Violence) Model 7(Daytime) Model 8(Nighttime) Model 4(All crime) Model 5(Property) Model 6(Violence) Model 9(Daytime) Model 10(Nighttime)
Visual fators
PSI ?0.042** ?0.019 ?0.086** ?0.054*** ?0.027 ?0.044*** ?0.047** ?0.041 ?0.124*** 0.020
GVI ?0.166 ?0.214 ?0.136 ?0.352** 0.065 0.132* 0.026 0.262** 0.235** 0.045
BVI 0.076 0.105 ?0.015 ?0.089 0.279 0.338*** 0.289** 0.303* 0.585*** 0.123
SVI 0.021 0.007 0.044 0.031 0.014 0.055** ?0.007 0.167*** ?0.100** 0.183***
LVI 0.085*** 0.089*** 0.080*** 0.128*** 0.028 ?0.224*** ?0.257*** ?0.165*** ?0.202*** ?0.238***
SCE 0.246 0.355 0.216 0.510 ?0.084 ?0.245** ?0.168 ?0.276 ?0.511*** ?0.015
MI 0.464*** 0.482*** 0.454*** 0.398*** 0.544*** 0.517*** 0.650*** 0.315*** 0.666*** 0.387***
NAI 0.119*** 0.118*** 0.108*** 0.127*** 0.107*** ?0.042** 0.008 ?0.160*** ?0.050 ?0.040*
Covariates
Population density 0.137*** 0.135*** 0.141*** 0.087*** 0.194*** 0.200*** 0.196*** 0.246*** 0.112*** 0.270***
Vulnerable ?0.162*** ?0.099*** ?0.244*** ?0.066** ?0.298*** ?0.161*** ?0.046** ?0.302*** ?0.043* ?0.267***
Media income ?0.031 0.068* ?0.286*** ?0.070* 0.001 ?0.155*** ?0.219*** ?0.112** ?0.227*** ?0.088**
Poverty 0.224*** 0.169*** 0.220*** 0.115*** 0.325*** 0.012 0.002 0.005 0.013 0.009
Below high 0.134*** 0.170*** 0.123*** 0.088** 0.184*** ?0.174*** ?0.382*** 0.079* ?0.209*** ?0.131***
Unemployment ?0.060*** ?0.010 ?0.087*** ?0.053** ?0.063** 0.028** 0.035* 0.039* 0.032 0.026
Housing vacant 0.173*** 0.146*** 0.188*** 0.142*** 0.199*** ?0.004 ?0.007 ?0.017 0.050** ?0.093***
Female alone 0.116*** 0.180*** ?0.020 0.124** 0.104*** 0.078*** 0.005 0.147*** ?0.001 0.151***
Female single 0.253*** ?0.085* 0.356*** 0.199*** 0.315*** 0.205*** ?0.059** 0.369*** 0.157*** 0.243***
Constant
Intercept 1.249*** 0.557*** 0.327*** 0.68*** 0.371*** 1.173*** 0.648*** 0.145*** 0.408*** 0.510***
AICc 3988.4 2355.4 2887.8 2882.6 2329.4 9672.7 6557.6 4960.9 5492.3 6142.7
Adjust R2 0.33 0.27 0.42 0.211 0.372 0.184 0.181 0.24 0.141 0.200
Tab.5  Poisson regression results for different types and times of crime
Variables Uptown (Model 1, 2, 3, 7, 8) Downtown (Model 4, 5, 6, 9, 10)
Min Max Mean S.D. AICc Adjusted R2 Min Max Mean S.D. AICc Adjusted R2
Model 1: (all crime) 2805.2 0.684 Model 4: (all crime) 6721.7 0.471
PSI ?0.1363 0.1288 ?0.0013 0.0412 ?0.0496 0.0253 ?0.0075 0.0178
GVI ?0.5189 0.3927 ?0.0579 0.1508 ?0.1674 0.411 0.0287 0.0993
BVI ?0.5231 0.3317 ?0.0361 0.1328 ?0.1636 0.4214 0.033 0.1024
SVI ?0.1927 0.2266 ?0.0045 0.0617 ?0.0762 0.0996 0.0012 0.0275
LVI ?2.3134 3.1007 0.1034 0.9398 ?1.8426 0.8817 ?0.6014 0.5034
SCE ?0.378 0.459 0.0525 0.1347 ?0.4359 0.1782 ?0.0232 0.1011
MI ?0.0553 0.3624 0.0468 0.0467 ?0.0049 0.086 0.0327 0.0183
NAI ?0.0459 0.0834 0.0072 0.0216 ?0.0711 0.0171 ?0.0078 0.0155
Model 2: (property) 1963.1 0.537 Model 5: (property) 4873.5 0.445
PSI ?0.0599 0.0596 0.002 0.0258 ?0.0514 0.0245 ?0.0095 0.0177
GVI ?0.3488 0.3185 ?0.0329 0.1472 ?0.1812 0.3566 0.0292 0.0999
BVI ?0.3232 0.2819 ?0.0131 0.1265 ?0.1526 0.3933 0.0397 0.1063
SVI ?0.108 0.1258 ?0.0095 0.0405 ?0.0827 0.0801 ?0.0035 0.0302
LVI ?1.3349 1.8515 0.2983 0.6152 ?1.905 0.161 ?0.6806 0.4561
SCE ?0.2959 0.351 0.0274 0.1383 ?0.3807 0.1917 ?0.0267 0.102
MI ?0.0176 0.1425 0.0478 0.0286 ?0.0015 0.0779 0.0402 0.0183
NAI ?0.0176 0.0463 0.0037 0.0139 ?0.0897 0.0246 ?0.0073 0.0186
Model 3: (violence) 2294 0.685 Model 6: (violence) 3846.2 0.47
PSI ?0.1409 0.1321 ?0.0035 0.0482 ?0.0541 0.0447 ?0.0035 0.0208
GVI ?0.5222 0.8727 ?0.0074 0.1943 ?0.1503 0.3608 0.0251 0.0921
BVI ?0.4231 0.8665 0.0235 0.1894 ?0.1828 0.3427 0.0212 0.0906
SVI ?0.2745 1.1034 0.0276 0.155 ?0.047 0.104 0.0179 0.0278
LVI ?3.4847 2.8345 0.2415 0.9886 ?1.9004 0.9853 ?0.5153 0.5348
SCE ?0.7001 0.8923 0.0333 0.1734 ?0.383 0.1649 ?0.0143 0.0924
MI ?0.0567 1.1143 0.0814 0.1635 ?0.0382 0.1091 0.0185 0.0247
NAI ?0.1283 0.1285 0.0059 0.0347 ?0.0803 0.0479 ?0.009 0.0171
Model 7: (daytime) 2286.5 0.563 Model 9: (daytime) 4173.8 0.418
PSI ?0.1347 0.0988 ?0.0063 0.0359 ?0.0608 0.0281 ?0.0155 0.0203
GVI ?0.4559 0.3755 ?0.0806 0.1553 ?0.2375 0.462 0.0384 0.1113
BVI ?0.4314 0.3325 ?0.0678 0.1389 ?0.2055 0.4727 0.0449 0.1163
SVI ?0.1739 0.2196 ?0.0058 0.0546 ?0.0955 0.1264 ?0.0057 0.0372
LVI ?2.4273 2.9505 0.4603 0.8929 ?2.1237 0.6904 ?0.6154 0.525
SCE ?0.3144 0.3969 0.075 0.1419 ?0.4946 0.1913 ?0.0365 0.1142
MI ?0.0443 0.2945 0.0446 0.043 ?0.0072 0.0906 0.0398 0.0215
NAI ?0.0405 0.084 0.0044 0.0209 ?0.0644 0.0284 ?0.0066 0.0165
Model 8: (nighttime) 1992 0.564 Model 10: (nighttime) 4509 0.464
PSI ?0.0712 0.0674 ?0.0001 0.0302 ?0.045 0.0345 ?0.0018 0.0186
GVI ?0.3796 0.2688 ?0.0226 0.1348 ?0.1933 0.3304 0.0288 0.0948
BVI ?0.282 0.3134 0.0107 0.1273 ?0.1725 0.3392 0.0287 0.0961
SVI ?0.1117 0.0543 ?0.0099 0.033 ?0.0445 0.0891 0.006 0.024
LVI ?2.3105 1.1658 ?0.0249 0.5636 ?2.0569 0.8576 ?0.5614 0.4995
SCE ?0.2805 0.3503 0.0145 0.1302 ?0.3447 0.1997 ?0.0191 0.094
MI ?0.0035 0.1505 0.0445 0.0269 ?0.0121 0.0791 0.029 0.0183
NAI ?0.0196 0.0555 0.007 0.0159 ?0.1036 0.0168 ?0.0116 0.0202
Tab.6  GWPR results for different types and times of crime
Fig.6  GWPR local coefficients for (a) GVI, (b) BVI, (c) SVI and (d) LVI
Fig.7  GWPR local coefficients for (a) SCE, (b) PSI, (c) MI, and (d) NAI
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