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Original Article

Using remote sensing data to derive built-form indexes to analyze the geography of residential burglary and street thefts

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Received 26 Jun 2023, Accepted 13 Dec 2023, Published online: 23 Jan 2024

References

  • Algahtany, M., & Kumar, L. (2016). A method for exploring the link between urban area expansion over time and the opportunity for crime in Saudi Arabia. Remote Sensing, 8(10) Article 863. https://doi.org/10.3390/rs8100863
  • Arbab, P. (2019). Global and globalizing cities from the global south: Multiple realities and pathways to form a new order. Perspectives on Global Development and Technology, 18(3), 327–337. https://doi.org/10.1163/15691497-12341518
  • Bernasco, W., & Elffers, H. (2010). Statistical analysis of spatial crime data. https://doi.org/10.1007/978-0-387-77650-7_33
  • Bhangale, U., Patil, S., Vishwanath, V., Thakker, P., Bansode, A., & Navandhar, D. (2020). Near real-time crowd counting using deep learning approach. Procedia Computer Science, 171, 770–779. https://doi.org/10.1016/j.procs.2020.04.084
  • Bottoms, A. (2012). Developing socio-spatial criminology. The Oxford Handbook of Criminology, 5, 450–489. https://doi.org/10.1093/he/9780199590278.003.0016
  • Bottoms, A., & Wiles, P. (1988). Crime and Housing Policy: A Framework for Crime Prevention Analysis (From Communities and Crime Reduction. In T. Hope & M. Shaw (Eds.), Communities and Crime Reduction (pp. 84–98). His Majesty’s Stationery Office (HMSO).
  • Bowers, K. (2014). Risky facilities: Crime radiators or crime absorbers? A comparison of internal and external levels of theft. Journal of Quantitative Criminology, 30(3), 389–414. https://doi.org/10.1007/s10940-013-9208-z
  • Brantingham, P. J. B. P. (1984). Patterns in crime. Macmillan.
  • Brantingham, P., & Brantingham, P. (1995). Criminality of place. European Journal on Criminal Policy and Research, 3(3), 5–26. https://doi.org/10.1007/BF02242925
  • Ceccato, Haining, R., & Signoretta, P. (2001). Exploring offence statistics in Stockholm city using spatial analysis tools. https://EconPapers.repec.org/RePEc:wiw:wiwrsa:ersa01p97
  • Ceccato, V., & Ioannidis, I. (2024). Using remote sensing data in urban crime analysis: A systematic review of English-language literature from 2003 to 2023. International Criminal Justice Review (under review).
  • Ceccato, V., & Wilhelmsson, M. (2011). The impact of crime on apartment prices: Evidence from Stockholm, sweden. Geografiska Annaler: Series B, Human Geography, 93(1), 81–103. https://doi.org/10.1111/j.1468-0467.2011.00362.x
  • Chen, Y., Li, Y., & Li, J. 2016. Investigating the influence of tree coverage on property crime: A case study in the city of vancouver, British Columbia, Canada. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B2, 695–702.
  • Clarke, R. V. (1983). Situational crime prevention: Its theoretical basis and practical scope. Crime and Justice, 4, 225–256. https://doi.org/10.1086/449090
  • Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44(4), 588–608. https://doi.org/10.2307/2094589
  • Corbane, C., Syrris, V., Sabo, F., Politis, P., Melchiorri, M., Pesaresi, M., Soille, P., & Kemper, T. (2021). Convolutional neural networks for global human settlements mapping from sentinel-2 satellite imagery. Neural Computing and Applications, 33(12), 6697–6720. https://doi.org/10.1007/s00521-020-05449-7
  • Demotto, N., & Davies, C. P. (2006). A GIS analysis of the relationship between criminal offenses and parks in Kansas City, Kansas. Cartography and Geographic Information Science, 33(2), 141–157. https://doi.org/10.1559/152304006777681715
  • Dempsey, N., Bramley, G., Power, S., & Brown, C. (2011). The social dimension of sustainable development: Defining urban social sustainability. Sustainable Development, 19(5), 289–300. https://doi.org/10.1002/sd.417
  • Duque, J. C., Patino, J. E., Ruiz, L. A., & Pardo-Pascual, J. E. (2015). Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning, 135, 11–21. https://doi.org/10.1016/j.landurbplan.2014.11.009
  • Eck, J., & Weisburd, D. (1995). Crime places in crime theory. Crime and Place, Crime Prevention Studies, 4. https://ssrn.com/abstract=2629856
  • European Commission. (2023). GHSL – Global human settlement layer. https://ghsl.jrc.ec.europa.eu/index.php
  • Fermino, R. C., Reis, R. S., Hallal, P. C., & Júnior, J. C. D. F. (2013). Perceived environment and public open space use: A study with adults from Curitiba, Brazil. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 35. https://doi.org/10.1186/1479-5868-10-35
  • Gehl, J. (2010). Cities for people. http://site.ebrary.com/id/10437880
  • Groff, E., & McCord, E. S. (2012). The role of neighborhood parks as crime generators. Security Journal, 25(1), 1–24. https://doi.org/10.1057/sj.2011.1
  • Haining, R., & Li, G. (2020). Modelling spatial and spatial-temporal data: A bayesian approach. Chapman and Hall/CRC. https://doi.org/10.1201/9780429088933
  • He, Q., & Li, J. (2022). The roles of built environment and social disadvantage on the geography of property crime. Cities, 121, 103471. https://doi.org/10.1016/j.cities.2021.103471
  • Hipp, J. R., Lee, S., Ki, D., & Kim, J. H. (2022). Measuring the built environment with Google street view and machine learning: Consequences for crime on street segments. Journal of Quantitative Criminology, 38(3), 537–565. https://doi.org/10.1007/s10940-021-09506-9
  • Hoppe, L., & Gerell, M. (2019). Near-repeat burglary patterns in Malmö: Stability and change over time. European Journal of Criminology, 16(1), 3–17. https://doi.org/10.1177/1477370817751382
  • Horsefield, O. J., Lightowlers, C., & Green, M. A. (2023). The spatial effect of alcohol availability on violence: A geographically weighted regression analysis. Applied Geography, 150, 102824. https://doi.org/10.1016/j.apgeog.2022.102824
  • Jacobs, J. (1961). The death and life of great American cities. Random House.
  • Jeffery, C. R. (1972). Crime prevention through environmental design. Criminology, 10(2), 191. https://doi.org/10.1111/j.1745-9125.1972.tb00553.x
  • Khorshidi, S., Carter, J., Mohler, G., & Tita, G. (2021). Explaining crime diversity with Google street view. Journal of Quantitative Criminology, 37(2), 361–391. https://doi.org/10.1007/s10940-021-09500-1
  • Kounadi, O., Ristea, A., Araujo, A., & Leitner, M. (2020). A systematic review on spatial crime forecasting. Crime Science, 9(1), 7. https://doi.org/10.1186/s40163-020-00116-7
  • Kuo, M., & Sullivan, W. (2001). Environment and crime in the inner city: Does vegetation reduce crime? Environment and Behavior, 33(3), 343–367. https://doi.org/10.1177/00139160121973025
  • Leśniak, A., Polończyk, A., & Waśniowski, P. (2022). Variations in the spatial distribution of crime events in an urban environment during the COVID-19 lockdown. Cartography and Geographic Information Science, 49(2), 171–188. https://doi.org/10.1080/15230406.2021.2013945
  • Lisita, A., Sano, E. E., & Durieux, L. (2013). Identifying potential areas of cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data. International Journal of Remote Sensing, 34(15), 5409–5428. https://doi.org/10.1080/01431161.2013.790574
  • Liu, L., Zhou, H., Lan, M., & Wang, Z. (2020). Linking Luojia 1-01 nightlight imagery to urban crime [Article]. Applied Geography 125, 102267. https://doi.org/10.1016/j.apgeog.2020.102267
  • Li, Y., Xie, Y., & Shekhar, S. (2023). Spatial Data Science. In L. Rokach, O. Maimon, & E. Shmueli (Eds.), Machine learning for data science handbook: Data mining and knowledge discovery handbook (pp. 401–422). Springer International Publishing. https://doi.org/10.1007/978-3-031-24628-9_18
  • López-Caloca, A., Martínez-Viveros, E., & Chapela-Castañares, J. I. (2009). Application of a clustering-remote sensing method in analyzing security patterns. (ed.) (eds.). Proceedings of SPIE – The International Society for Optical Engineering.
  • MacDonald, J. (2015). Community design and crime: The impact of housing and the built environment. Crime and Justice, 44(1), 333–383. https://doi.org/10.1086/681558
  • Mansor, N. S., Shafri, H. Z. M., & Mansor, S. (2019). A GIS based method for assessing the association between urban development and crime pattern in Sungai Petani, Kedah Malaysia. The International Journal of Recent Technology & Engineering (IJRTE), 8(2), 4374–4380. https://doi.org/10.35940/ijrte.B3177.078219
  • Michael, S., & Hull, R. B. 1994. Effects of vegetation on crime in urban parks. U.S. Forest Service and the International Society of Arboriculture.
  • Newman, O. (1972). Defensible space: Crime prevention through urban design. Macmillan.
  • Newman, O., & Franck, K. A. (1982). The effects of building size on personal crime and fear of crime. Population and Environment, 5(4), 203–220. https://doi.org/10.1007/BF01257071
  • Nordic_Co-Operation. (2022). Population of nordic countries. https://www.norden.org/en/information/population
  • Patino, J. E., Duque, J. C., Pardo-Pascual, J. E., & Ruiz, L. A. (2014). Using remote sensing to assess the relationship between crime and the urban layout. Applied Geography, 55, 48–60. https://doi.org/10.1016/j.apgeog.2014.08.016
  • See, L., Georgieva, I., Duerauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A. H., … Fritz, S. (2022). A crowdsourced global data set for validating built-up surface layers. Scientific Data, 9(1), 13. https://doi.org/10.1038/s41597-021-01105-4
  • Shaw, C., & McKay, H. (1976). Social disorganization theory. The British Journal of Criminology, 16(1), 1–19. https://doi.org/10.1093/oxfordjournals.bjc.a046684
  • Sohn, D.-W. (2016). Residential crimes and neighbourhood built environment: Assessing the effectiveness of crime prevention through environmental design (CPTED). Cities, 52, 86–93. https://doi.org/10.1016/j.cities.2015.11.023
  • Song, J., Andresen, M. A., Brantingham, P. L., & Spicer, V. (2017). Crime on the edges: Patterns of crime and land use change. Cartography and Geographic Information Science, 44(1), 51–61. https://doi.org/10.1080/15230406.2015.1089188
  • Stucky, T. D., & Ottensmann, J. R. (2009). Land use and violent crime*. Criminology, 47(4), 1223–1264. https://doi.org/10.1111/j.1745-9125.2009.00174.x
  • Twinam, T. (2014). Danger zone: The causal effects of high-density and mixed-use development on neighborhood crime. Correlates of Crime eJournal. https://doi.org/10.2139/ssrn.2508672
  • UN-Habitat. (2018). United Nations-Habitat, safe cities program. https://unhabitat.org/network/global-network-on-safer-cities
  • UN-SDG. (2015). The 2030 agenda for sustainable development. https://sdgs.un.org/goals
  • Waller, L. (2014). Putting spatial statistics (back) on the map. Spatial Statistics, 9, 4–19. https://doi.org/10.1016/j.spasta.2014.03.007
  • Weisburd, D. (2015). The law of crime concentration and the criminology of place. Criminology, 53(2), 133–157. https://doi.org/10.1111/1745-9125.12070
  • Weisburd, D., Groff, E. R., & Yang, S.-M. (2012). The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195369083.001.0001
  • White, G. F. (1990). Neighborhood permeability and burglary rates. Justice Quarterly, 7(1), 57–67. https://doi.org/10.1080/07418829000090471
  • Wikström, P.-O. H. (1991). Stockholm: Its crime and urban structure. In P.-O.-H. Wikström (Ed.), Urban crime, criminals, and victims: The Swedish experience in an anglo-american comparative perspective (pp. 111–129). Springer New York. https://doi.org/10.1007/978-1-4613-9077-0_6
  • Wilhelmsson, M., Ceccato, V., & Gerell, M. (2021). What effect does gun-related violence have on the attractiveness of a residential area? The case of Stockholm, Sweden. Journal of European Real Estate Research. https://doi.org/10.1108/JERER-03-2021-0015
  • Wolfe, M. K., & Mennis, J. (2012). Does vegetation encourage or suppress urban crime? Evidence from Philadelphia, PA. Landscape and Urban Planning, 108(2–4), 112–122. https://doi.org/10.1016/j.landurbplan.2012.08.006
  • Zahnow, R., Corcoran, J., Kimpton, A., & Wickes, R. (2021). Neighbourhood places, collective efficacy and crime: A longitudinal perspective. Urban Studies, 59(4), 789–809. https://doi.org/10.1177/00420980211008820
  • Zhou, H., Liu, L., Lan, M., Yang, B., & Wang, Z. (2019). Assessing the impact of nightlight gradients on street robbery and burglary in Cincinnati of Ohio State, USA. Remote Sensing, 11(17), 1958. https://doi.org/10.3390/rs11171958