The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Study Region
3.2. Data
3.2.1. Crime Data
3.2.2. Land-Use Feature Data
3.3. Methods
3.3.1. Colocation Quotient
3.3.2. Local Colocation Quotient
4. Results and Discussion
4.1. Exploratory Spatial Data Analysis
4.2. Global Colocation Patterns
4.3. Local Colocation Patterns
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Tobler, W.R. A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 1970, 46, 234–240. [Google Scholar] [CrossRef]
- Leslie, T.F.; Frankenfeld, C.L.; Makara, M.A. The spatial food environment of the DC metropolitan area: Clustering, co-location, and categorical differentiation. Appl. Geogr. 2012, 35, 300–307. [Google Scholar] [CrossRef]
- Huang, Y.; Pei, J.; Xiong, H. Mining co-location patterns with rare events from spatial data sets. GeoInformatica 2006, 10, 239–260. [Google Scholar] [CrossRef]
- Arbia, G.; Espa, G.; Quah, D. A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empir. Econ. 2008, 34, 81–103. [Google Scholar] [CrossRef]
- Leslie, T.F.; Kronenfeld, B.J. The colocation quotient: A new measure of spatial association between categorical subsets of points. Geogr. Anal. 2011, 43, 306–326. [Google Scholar] [CrossRef]
- Cromley, R.G.; Hanink, D.M.; Bentley, G.C. Geographically weighted colocation quotients: Specification and application. Prof. Geogr. 2014, 66, 138–148. [Google Scholar] [CrossRef]
- Sypion-Dutkowska, N.; Leitner, M. Land use influencing the spatial distribution of urban crime: A case study of Szczecin, Poland. ISPRS Int. J. Geo-Inf. 2017, 6, 74. [Google Scholar] [CrossRef]
- Wang, F.; Hu, Y.; Wang, S.; Li, X. Local indicator of colocation quotient with a statistical significance test: Examining spatial association of crime and facilities. Prof. Geogr. 2017, 69, 22–31. [Google Scholar] [CrossRef]
- Brantingham, P.J.; Brantingham, P.L. Environment, routine and situation: Toward a pattern theory of crime. Adv. Criminol. Theory 1993, 5, 259–294. [Google Scholar]
- Gabor, T. Situational crime prevention: Successful case studies. Can. J. Criminol. 1994, 36, 475–480. [Google Scholar]
- Brantingham, P.; Brantingham, P. Criminality of place. Eur. J. Crim. Policy Res. 1995, 3, 5–26. [Google Scholar] [CrossRef]
- Kinney, J.B.; Brantingham, P.L.; Wuschke, K.; Kirk, M.G.; Brantingham, P.J. Crime attractors, generators and detractors: Land use and urban crime opportunities. Built Environ. 2008, 34, 62–74. [Google Scholar] [CrossRef]
- Mccord, E.S. Intensity value analysis and the criminogenic effects of land use features on local crime patterns. Crime Patterns Anal. 2009, 2, 17–30. [Google Scholar]
- Frisbie, D.W.; Fishbine, G.; Hintz, R.; Joelson, M.; Nutter, J.M. Crime in Minneapolis: Proposals for Prevention; Governor’s commission on crime prevention and control: St. Paul, MN, USA, 1977.
- Roncek, D.W.; Bell, R. Bars, blocks, and crimes. J. Environ. Syst. 1981, 11, 35–47. [Google Scholar] [CrossRef]
- Roncek, D.W.; Maier, P.A. Bars, blocks, and crimes revisited: Linking the theory of routine activities to the empiricism of “hot spots”. Criminology 1991, 29, 725–753. [Google Scholar] [CrossRef]
- Gruenewald, P.J.; Freisthler, B.; Remer, L.; LaScala, E.A.; Treno, A. Ecological models of alcohol outlets and violent assaults: Crime potentials and geospatial analysis. Addiction 2006, 101, 666–677. [Google Scholar] [CrossRef] [PubMed]
- Grubesic, T.H.; Pridemore, W.A. Alcohol outlets and clusters of violence. Int. J. Health Geogr. 2011, 10, 30. [Google Scholar] [CrossRef] [PubMed]
- Livingston, M. A longitudinal analysis of alcohol outlet density and domestic violence. Addiction 2011, 106, 919–925. [Google Scholar] [CrossRef] [PubMed]
- Day, P.; Breetzke, G.; Kingham, S.; Campbell, M. Close proximity to alcohol outlets is associated with increased serious violent crime in New Zealand. Aust. N. Z. J. Public Health 2012, 36, 48–54. [Google Scholar] [CrossRef] [PubMed]
- Groff, E.; Mccord, E.S. The role of neighborhood parks as crime generators. Secur. J. 2012, 25, 1–24. [Google Scholar] [CrossRef]
- Demotto, N.; Davies, C.P. A GIS analysis of the relationship between criminal offenses and parks in Kansas City, Kansas. Cartogr. Geogr. Inf. Sci. 2006, 33, 141–157. [Google Scholar] [CrossRef]
- Ratcliffe, J.H.; Taniguchi, T.A. Is crime higher around drug-gang street corners? Two spatial approaches to the relationship between gang set spaces and local crime levels. Crime Patterns Anal. 2008, 1, 17–39. [Google Scholar]
- Kubrin, C.E.; Squires, G.D.; Graves, S.M.; Ousey, G.C. Does fringe banking exacerbate neighbourhood crime rates? Criminol. Public Policy 2011, 10, 437–466. [Google Scholar] [CrossRef]
- Chang, D. Social crime or spatial crime? Exploring the effects of social, economic, and spatial factors on burglary rates. Environ. Behav. 2011, 43, 26–52. [Google Scholar] [CrossRef]
- Isserman, A.M. The location quotient approach to estimating regional economic impacts. J. Am. Plan. Assoc. 1977, 43, 33–41. [Google Scholar] [CrossRef]
- Blair, J.P. Local Economic Development: Analysis and Practice; Sage: Newbury Park, CA, USA, 1995. [Google Scholar]
- Brantingham, P.L.; Brantingham, P.J. Location quotients and crime hot spots in the city. In Crime Analysis through Computer Mapping; Criminal Justice Information Authority: Chicago, IL, USA, 1993. [Google Scholar]
- Andresen, M.A. Location quotients, ambient populations, and the spatial analysis of crime in Vancouver, Canada. Environ. Plan. A 2007, 39, 2423–2444. [Google Scholar] [CrossRef]
- Zhang, H.; Peterson, M.P. A spatial analysis of neighbourhood crime in Omaha, Nebraska using alternative measures of crime rates. Internet J. Criminol. 2007, 31, 1–31. [Google Scholar]
- Pridemore, W.A.; Grubesic, T.H. A spatial analysis of the moderating effects of land use on the association between alcohol outlet density and violence in urban areas. Drug Alcohol Rev. 2012, 31, 385–393. [Google Scholar] [CrossRef] [PubMed]
- Kronenfeld, B.J.; Leslie, T.F. Restricted random labelling: Testing for between-group interaction after controlling for joint population and within-group spatial structure. J. Geogr. Syst. 2015, 17, 1–28. [Google Scholar] [CrossRef]
- Groff, E.; Weisburd, D.; Morris, N.A. Where the Action Is at Places: Examining Spatio-Temporal Patterns of Juvenile Crime at Places Using Trajectory Analysis and GIS; Springer: New York, NY, USA, 2009. [Google Scholar]
- Miller, H.J.; Han, J. Geographic Data Mining and Knowledge Discovery; CRC Press: Boca Raton, FL, USA, 2009. [Google Scholar]
- Silverman, B.W. Density Estimation for Statistics and Data Analysis; CRC press: Boca Raton, FL, USA, 1986. [Google Scholar]
- Bailey, T.C.; Gatrell, A.C. Interactive Spatial Data Analysis; Longman Scientific & Technical: Essex, UK, 1995. [Google Scholar]
- Anselin, L.; Cohen, J.; Cook, D.; Gorr, W.; Tita, G. Spatial analyses of crime. Crim. Justice 2000, 4, 213–262. [Google Scholar]
- Erdogan, S.; Yilmaz, I.; Baybura, T.; Gullu, M. Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar. Accid. Anal. Prev. 2008, 40, 174–181. [Google Scholar] [CrossRef] [PubMed]
- Lahr, H. An improved test for earnings management using kernel density estimation. Eur. Account. Rev. 2014, 23, 559–591. [Google Scholar] [CrossRef]
- Ye, X.; Xu, X.; Lee, J.; Zhu, X.; Wu, L. Space–time interaction of residential burglaries in Wuhan, China. Appl. Geogr. 2015, 60, 210–216. [Google Scholar] [CrossRef]
- Townsley, M.; Homel, R.; Chaseling, J. Infectious burglaries. A test of the near repeat hypothesis. Br. J. Criminol. 2003, 43, 615–633. [Google Scholar] [CrossRef]
- Bembenik, R.; Rybiński, H. FARICS: A method of mining spatial association rules and collocations using clustering and Delaunay diagrams. J. Intell. Inf. Syst. 2009, 33, 41–64. [Google Scholar] [CrossRef]
- Goodchild, M.F.; Anselin, L.; Appelbaum, R.P.; Harthorn, B.H. Toward spatially integrated social science. Int. Reg. Sci. Rev. 2000, 23, 139–159. [Google Scholar] [CrossRef]
- Guo, L.; Du, S.; Haining, R.; Zhang, L. Global and local indicators of spatial association between points and polygons: A study of land use change. Int. J. Appl. Earth Obs. Geoinf. 2013, 21, 384–396. [Google Scholar] [CrossRef]
- Goreaud, F.; Pelissier, R. On explicit formulas of edge effect correction for Ripley’s K-function. J. Veg. Sci. 1999, 10, 433–438. [Google Scholar] [CrossRef]
- Liu, H.; Zhu, X. Exploring the influence of neighborhood characteristics on burglary risks: A Bayesian random effects modeling approach. ISPRS Int. J. Geo-Inf. 2016, 5, 102. [Google Scholar] [CrossRef]
Type | Reclassified Land-Use Feature | n | Original Land-Use Feature Types in the PGIS Geo-Database |
---|---|---|---|
1 | Store * | 1030 | Clothing store, grocery store, flower shop, pharmacy, electronics store, bookstore, pastry shop, bird market, cosmetics store, building materials store, grain and oil store, farmer’s market, musical instrument shop, fruit and vegetable market, office supply store, liquor store, eyeglasses store, jewelry store, and others |
2 | Bank | 593 | Bank, ATM |
3 | Restaurant | 481 | Restaurant, Chinese tea house, fast food restaurant, coffeehouse, Western cuisine restaurant |
4 | Government | 420 | Governmental office, police station, surveillance room, and others |
5 | Industrial facility | 310 | Electronics equipment factory, machine factory, motor vehicle manufacturer, garment factory, textile mill, furniture factory, food factory, industrial park, chemical plant, metal factory, wood factory, plastics plant, rubber factory, paper mill, and others |
6 | Service facility | 278 | Barbershop, beauty shop, laundry, photo studio, and others |
7 | Hostel | 268 | Hostel, private hotel |
8 | School | 267 | Kindergarten, primary school, middle school |
9 | Commercial building | 241 | Trading company, communications company, logistics company, postal corporation, warehouse, power facility, fuel gas facility, water supply facility, and others |
10 | Hotel | 233 | Five-star hotel, four-star hotel, three-star hotel, unrated hotel |
11 | Hospital | 208 | General hospital, special hospital, clinic, community health station, epidemic prevention station |
12 | Market * | 202 | Supermarket, small market, mid-sized market, shopping center, bazaar, and others |
13 | Entertainment | 126 | Massage parlor, nightclub, Karaoke club, video game entertainment center, and others |
14 | University | 109 | University, university for the elderly, vocational school |
15 | Office building | 106 | Office building |
16 | Internet café | 81 | Internet café |
17 | Cultural building | 70 | Museum/art gallery, newspaper office, television station, cultural palace, library, and others |
18 | Parking lot | 63 | Parking lot |
19 | Station | 39 | Railway station, bus station, taxi stand, dock |
20 | Research Institute | 29 | Scientific research institution, science park, and others |
21 | Gas station | 25 | Gas station |
22 | Sport buildings | 24 | Gymnasium, fitness center, and others |
Type of Crime | Average Distance between Each Type of Crime and nth Nearest Neighborhood (m) | ||
---|---|---|---|
n = 1 | n = 10 | n = 20 | |
Theft of electric bicycle | 39 | 115 | 163 |
Burglary | 49 | 142 | 192 |
Robbery | 34 | 124 | 186 |
Land-Use Feature Type | E-Bike Theft | Burglary | Robbery |
---|---|---|---|
Store | 0.91 | 0.76 * | 0.89 |
Bank | 0.87 * | 0.67 * | 0.99 |
Restaurant | 0.71 * | 0.72 * | 0.59 * |
Government | 0.82 * | 0.8 * | 0.83 * |
Industrial facility | 0.72 * | 0.47 * | 0.66 * |
Service facility | 0.38 * | 0.28 * | 1.23 * |
Hostel | 0.9 * | 0.87 * | 0.86 * |
School | 0.85 * | 0.83 * | 1.08 |
Commercial building | 0.9 * | 0.47 * | 0.94 |
Hotel | 1.01 | 0.62 * | 1.27 * |
Hospital | 0.97 | 0.94 | 0.87 * |
Market | 0.82 * | 0.62 * | 0.88 * |
Entertainment | 0.88 * | 0.65 * | 1.15 * |
University | 0.88 * | 0.94 * | 0.69 * |
Office building | 0.88 * | 0.78 * | 0.78 * |
Internet café | 0.73 * | 0.63 * | 0.47 * |
Cultural building | 0.94 | 0.92 * | 0.96 |
Parking lot | 0.95 | 0.7 * | 1.12 * |
Station | 0.86 * | 0.79 * | 0.96 |
Research Institute | 0.81 * | 0.82 * | 0.87 * |
Gas station | 0.84 * | 0.78 * | 0.93 * |
Sport buildings | 0.97 | 0.94 * | 0.85 * |
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Yue, H.; Zhu, X.; Ye, X.; Guo, W. The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China. ISPRS Int. J. Geo-Inf. 2017, 6, 307. https://doi.org/10.3390/ijgi6100307
Yue H, Zhu X, Ye X, Guo W. The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China. ISPRS International Journal of Geo-Information. 2017; 6(10):307. https://doi.org/10.3390/ijgi6100307
Chicago/Turabian StyleYue, Han, Xinyan Zhu, Xinyue Ye, and Wei Guo. 2017. "The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China" ISPRS International Journal of Geo-Information 6, no. 10: 307. https://doi.org/10.3390/ijgi6100307