Skip to main content

Advertisement

Springer Nature Link
Log in

Spatial−temporal forecast research of property crime under the driven of urban traffic factors

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

According to existing researches, robbery crime is remarkably impacted by urban transport development, road accessibility and complexity of land use. However, researches related to spatial−temporal data mining and forecasting on property crime driven by traffic factors are rare. The BP neural network model improved by genetic algorithm (GA−BP neural network), which utilize Machine Learning to mine the factor weights of the property crime, can describe these complicated relationships to forecast more effectively. This study collected spatialtemporal data of property crime that occurred at city A located in South China in the period from 2008 to 2012, filtered the factors of property crime by correlation analysis, and selected the neighbors by spatial autocorrelation. This study also standardized the data, with one year as the time window and 100m×100m as the spatial dimension. Property crime is forecasted based on the GA−BP neural network, and the root mean square error is 0.019. Current researches demonstrate that the correlation between property crime and factors, like urban transport, network density, floating population and economic indicator, is significant. This study confirms that the GA−BP neural network model based on GIS is a reasonable forecast model in the field of property crime forecast research. Given its features, the GA−BP neural network model can forecast other types of rational crime and can set the time window and spatial dimension to obtain the corresponding forecast result based on the accuracy of input data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Aitken C, Moore D, Higgs P, Kelsall J, Kerger M (2002) The impact of a police crackdown on a street drug scene: evidence from the street. Int J Drug Policy 13(3):193–202

    Article  Google Scholar 

  2. Almeida JS (2002) Predictive non-linear modeling of complex data by artificial neural networks. Curr Opin Biotechnol 13(1):72–76

    Article  Google Scholar 

  3. Anselin L, Cohen J, Cook D, Gorr W, Tita G (2000) Spatial analyses of crime. Crim Justice 4(2):213–262

    Google Scholar 

  4. Blonigen DM (2010) Explaining the relationship between age and crime: contributions from the developmental literature on personality. Clin Psychol Rev 30(1):89–100

    Article  Google Scholar 

  5. Brantingham PL, Brantingham PJ (1993) Nodes, paths and edges: considerations on the complexity of crime and the physical environment. J Environ Psychol 13(1):3–28

    Article  Google Scholar 

  6. Buonanno P, Leonida L (2009) Non-market effects of education on crime: evidence from italian regions. Econ Educ Rev 28(1):11–17

    Article  Google Scholar 

  7. Caplan JM, Kennedy LW, Miller J (2011a) Risk terrain modeling: brokering criminological theory and gis methods for crime forecasting. Justice Q 28(2):360–381

    Article  Google Scholar 

  8. Caplan JM, Kennedy LW, Petrossian G (2011b) Police-monitored cctv cameras in newark, nj: a quasi-experimental test of crime deterrence. J Exp Criminol 7(3):255–274

    Article  Google Scholar 

  9. Chainey S, Tompson L, Uhlig S (2008) The utility of hotspot mapping for predicting spatial patterns of crime. Secur J 21(1):4–28

    Article  Google Scholar 

  10. Chen G, Fu K, Liang Z, Sema T, Li C, Tontiwachwuthikul P, Idem R (2014) The genetic algorithm based back propagation neural network for mmp prediction in co 2-eor process. Fuel 126:202–212

    Article  Google Scholar 

  11. Cherkassky V, Lari-Najafi H (1992) Data representation for diagnostic neural networks. IEEE Expert 7(5):43–53

    Article  Google Scholar 

  12. Cohen J, Gorr WL, Olligschlaeger AM (2007) Leading indicators and spatial interactions: a crime-forecasting model for proactive police deployment. Geogr Anal 39 (1):105–127

    Article  Google Scholar 

  13. Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev:588–608

  14. Copes H (1999) Routine activities and motor vehicle theft: a crime specific approach. J Crime Justice 22(2):125–146

    Article  Google Scholar 

  15. Corcoran JJ, Wilson ID, Ware JA (2003) Predicting the geo-temporal variations of crime and disorder. Int J Forecast 19(4):623–634

    Article  Google Scholar 

  16. Elonheimo H, Gyllenberg D, Huttunen J, Ristkari T, Sillanmäki L, Sourander A (2014) Criminal offending among males and females between ages 15 and 30 in a population-based nationwide 1981 birth cohort: results from the finncrime study. J Adolesc 37(8):1269–1279

    Article  Google Scholar 

  17. Fu C, Zhang P, Jiang J, Yang K, Lv Z (2015) A bayesian approach for sleep and wake classification based on dynamic time warping method. Multimedia Tools and Applications:1–20

  18. Gorr W, Harries R (2003) Introduction to crime forecasting. Int J Forecast 19(4):551–555

    Article  Google Scholar 

  19. Gorr W, Olligschlaeger A, Thompson Y (2003) Short-term forecasting of crime. Int J Forecast 19(4):579–594

    Article  Google Scholar 

  20. Groot W, Van Den Brink HM (2010) The effects of education on crime. Appl Econ 42(3):279–289

    Article  Google Scholar 

  21. Gruenewald PJ, Freisthler B, Remer L, LaScala EA, Treno A (2006) Ecological models of alcohol outlets and violent assaults: crime potentials and geospatial analysis. Addiction 101(5):666–677

    Article  Google Scholar 

  22. Jiang D, Xu Z, Lv Z (2015) A multicast delivery approach with minimum energy consumption for wireless multi-hop networks. Telecommun Syst:1–12

  23. Kaikhah K, Doddameti S (2006) Discovering trends in large datasets using neural networks. Appl Intell 24(1):51–60

    Article  Google Scholar 

  24. Kianmehr K, Alhajj R (2008) Effectiveness of support vector machine for crime hot-spots prediction. Appl Artif Intell 22(5):433–458

    Article  Google Scholar 

  25. Li ST, Kuo SC, Tsai FC (2010) An intelligent decision-support model using fsom and rule extraction for crime prevention. Expert Syst Appl 37(10):7108–7119

    Article  Google Scholar 

  26. Li X, Lv Z, Zheng Z, Zhong C, Hijazi IH, Cheng S (2015) Assessment of lively street network based on geographic information system and space syntax. Multimedia Tools and Applications:1–19

  27. Li X, Lv Z, Wang W, Zhang B, Hu J, Yin L, Feng S (2016) Webvrgis based traffic analysis and visualization system. Adv Eng Softw 93:1–8

    Article  Google Scholar 

  28. Lu Y, Chen X (2007) On the false alarm of planar k-function when analyzing urban crime distributed along streets. Soc Sci Res 36(2):611–632

    Article  Google Scholar 

  29. Lu Z, Rehman SU, Chen G (2013) Webvrgis: webgis based interactive online 3d virtual community. In: Virtual Reality and Visualization (ICVRV), vol 2013. International Conference on IEEE, pp 94–99

  30. Lv Z, Yin T, Han Y, Chen Y, Chen G et al (2011) Webvr web virtual reality engine based on p2p network. J Netw 6(7):990–998

    Google Scholar 

  31. Lv Z, Li X, Hu J, Yin L, Zhang B, Feng S (2015) Virtual geographic environment based coach passenger flow forecasting. arXiv preprint arXiv:150401057

  32. Mohler GO, Short MB, Brantingham PJ (2012) Self-exciting point process modeling of crime. J Am Stat Assoc

  33. Piza EL, Caplan JM, Kennedy LW (2014) Analyzing the influence of micro-level factors on cctv camera effect. J Quant Criminol 30(2):237–264

    Article  Google Scholar 

  34. Rephann T J (2009) Rental housing and crime: the role of property ownership and management. Ann Reg Sci 43(2):435–451

    Article  Google Scholar 

  35. Salleh SA, Mansor NS, Yusoff Z, Nasir RA (2012) The crime ecology: ambient temperature vs. spatial setting of crime (burglary). Procedia-Social Behav Sci 42:212–222

    Article  Google Scholar 

  36. Sherman LW, Gartin PR, Buerger ME (1989) Hot spots of predatory crime: Routine activities and the criminology of place*. Criminology 27(1):27–56

    Article  Google Scholar 

  37. Shoesmith GL (2013) Space–time autoregressive models and forecasting national, regional and state crime rates. Int J Forecast 29(1):191–201

    Article  Google Scholar 

  38. Short M, D’Orsogna M, Brantingham P, Tita G (2009) Measuring and modeling repeat and near-repeat burglary effects. J Quant Criminol 25(3):325–339

    Article  Google Scholar 

  39. Spicer V, Reid AA, Ginther J, Seifi H, Dabbaghian V (2012) Bars on blocks: a cellular automata model of crime and liquor licensed establishment density. Comput Environ Urban Syst 36(5):412–422

    Article  Google Scholar 

  40. Tekeli S, Günsoy G (2013) The relation between education and economic crime: an assessment for turkey. Procedia-Social Behav Sci 106:3012–3025

    Article  Google Scholar 

  41. Tobler W R (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr:234–240

  42. Toomey TL, Erickson DJ, Carlin BP, Lenk KM, Quick HS, Jones AM, Harwood EM (2012) The association between density of alcohol establishments and violent crime within urban neighborhoods. Alcohol Clin Exp Res 36(8):1468–1473

    Article  Google Scholar 

  43. Townsley M (2009) Spatial autocorrelation and impacts on criminology. Geogr Anal 41(4):452–461

    Article  Google Scholar 

  44. Wang JJ, Wang JZ, Zhang ZG, Guo SP (2012) Stock index forecasting based on a hybrid model. Omega 40(6):758–766

    Article  Google Scholar 

  45. Wang ST (2014) Optimized light guide plate optical brightness parameter: integrating back-propagation neural network (bpn) and revised genetic algorithm (ga). Mater Manuf Process 29(1):1–8

    Article  Google Scholar 

  46. Xu Chong ZS, Liu L (2015) Patterns of near-repeat street robbery in dp peninsula. Geogr Res 34:384–394

    Google Scholar 

  47. Yang J, He S, Lin Y, Lv Z (2015) Multimedia cloud transmission and storage system based on internet of things. Multimedia Tools and Applications:1–16

  48. Yu F, Xu X (2014) A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved bp neural network. Appl Energy 134:102–113

    Article  Google Scholar 

  49. Yu H, Liu F, Zou K (2012) Improved fuzzy bp neural network and its application in crime prediction [j]. J Liaoning Technical University (Natural Science) 2:025

    Google Scholar 

  50. Zhang Y, Liu J (2002) Improvement of sensor’s performance by using bp neutral network and it’s new algorithms [j]. J Transcluction Technology 3

Download references

Author information

Authors and Affiliations

  1. School of Geography, South China Normal University, Guangzhou, 510631, China

    Li Weihong, Wen Lei & Chen Yebin

Authors
  1. Li Weihong
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wen Lei
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chen Yebin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Li Weihong.

Additional information

Li Weihong, Associate Professor, specializing in criminal geographic research and spatial−temporal data mining.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weihong, L., Lei, W. & Yebin, C. Spatial−temporal forecast research of property crime under the driven of urban traffic factors. Multimed Tools Appl 75, 17669–17687 (2016). https://doi.org/10.1007/s11042-016-3467-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3467-2

Keywords