The Importance of Importance Sampling: Exploring Methods of Sampling from Alternatives in Discrete Choice Models of Crime Location Choice
Tóm tắt
The burgeoning field of individual level crime location choice research has required increasingly large datasets to model complex relationships between the attributes of potential crime locations and offenders’ choices. This study tests methods of sampling aiming to overcome computational challenges involved in the use of such large datasets. Using police data on 38,120 residential and non-residential burglary, commercial and personal robbery and extra-familial sex offense locations and the offenders’ pre-offense activity locations (e.g., home, family members’ homes and prior crime locations), and in the context of the conditional logit formulation of the discrete spatial choice model, we tested a novel method for importance sampling of alternatives. The method over-samples potential crime locations near to offenders’ activity locations that are more likely to be chosen for crime. We compared variants of this method with simple random sampling. Importance sampling produced results more consistent with those produced without sampling compared with simple random sampling, and provided considerable computational savings. There were strong relationships between the locations of offenders’ prior criminal and non-criminal activities and their crime locations. Importance sampling from alternatives is a relatively simple and effective method that enables future studies to use larger datasets (e.g., with more variables, wider study areas, or more granular spatial or spatio-temporal units) to yield greater insights into crime location choice. By examining non-residential burglary and sexual offenses, in New Zealand, the substantive results represent a novel contribution to the growing literature on offenders’ spatial decision making.
Tài liệu tham khảo
Altizio A, York D (2007) Robbery of convenience stores. U.S. Department of Justice, Office of Community Oriented Policing Services, Washington, DC
Ben-Akiva ME, Bowman JL (1998) Integration of an activity-based model system and a residential location model. Urban Stud 35:1131–1153. https://doi.org/10.1080/0042098984529
Ben-Akiva ME, Lerman SR (1985) Discrete choice analysis: Theory and application to travel demand. MIT Press, Cambridge, MA
Bernasco W (2006) Co-offending and the choice of target areas in burglary. J Investig Psych Offender Profil 3:139–155. https://doi.org/10.1002/jip.49
Bernasco W (2010) Modeling micro-level crime location choice: application of the discrete choice framework to crime at places. J Quant Criminol 26:113–138. https://doi.org/10.1007/s10940-009-9086-6
Bernasco W (2017) Modeling offender decision making with secondary data. In: Bernasco W, Van Gelder J-L, Elffers H (eds) The Oxford handbook on offender decision making. Oxford University Press, Oxford, England, pp 569–586
Bernasco W (2019) Adolescent offenders’ current whereabouts predict locations of their future crimes. PLoS ONE 14:e0210733. https://doi.org/10.1371/journal.pone.0210733
Bernasco W, Jacques S (2015) Where do dealers solicit customers and sell them drugs? a micro-level multiple method study. J Contemp Crim Justice 31:376–408. https://doi.org/10.1177/1043986215608535
Bernasco W, Nieuwbeerta P (2005) How do residential burglars select target areas? a new approach to the analysis of criminal location choice. Br J Criminol 45:296–315. https://doi.org/10.1093/bjc/azh070
Bernasco W, Block R, Ruiter S (2013) Go where the money is: modeling street robbers’ location choices. J Econ Geogr 13:119–143. https://doi.org/10.1093/jeg/lbs005
Bernasco W, Johnson SD, Ruiter S (2015) Learning where to offend: effects of past on future burglary locations. Appl Geogr 60:120–129. https://doi.org/10.1016/j.apgeog.2015.03.014
Bernasco W, Ruiter S, Block R (2017) Do street robbery location choices vary over time of day or day of week? a test in Chicago. J Res Crime Delinq 54:244–275. https://doi.org/10.1177/0022427816680681
Bhat C, Govindarajan A, Pulugurta V (1998) Disaggregate attraction-end choice modeling formulation and empirical analysis. Transp Res Rec 1645:60–68. https://doi.org/10.3141/1645-08
Bichler G, Malm A, Christie-Merrall J (2012) Urban backcloth and regional mobility patterns as indicators of juvenile crime. In: Andresen MA, Kinney JB (eds) Patterns, prevention, and geometry of crime. Routledge, London, England, pp 118–136
Bowman JL, Ben-Akiva ME (2001) Activity-based disaggregate travel demand model system with activity schedules. Transp Res Part A 35:1–28. https://doi.org/10.1016/S0965-8564(99)00043-9
Brantingham PL, Brantingham PJ (1991) Notes on the geometry of crime. In: Brantingham PJ, Brantingham PL (eds) Environmental criminology, 2nd edn. Waveland Press, Prospect Heights, IL, pp 27–54
Brantingham PL, Brantingham PJ (1993) Environment, routine, and situation: Toward a pattern theory of crime. In: Clarke RV, Felson M (eds) Routine activity and rational choice. Transaction Publishers, Piscataway, NJ, pp 259–294
Bright D, Whelan C, Morselli C (2020) Understanding the structure and composition of co-offending networks in Australia. Australian Institute of Criminology Australia
Buil-Gil D, Moretti A, Langton SH (2021) The accuracy of crime statistics: assessing the impact of police data bias on geographic crime analysis. J Exp Criminol. https://doi.org/10.1007/s11292-021-09457-y
Chiu Y-N, Leclerc B (2020) Predictors and Contexts of Unsolved and Solved Sexual Offenses. Crime Delinq 66:1268–1295. https://doi.org/10.1177/0011128719879027
Clare J, Fernandez J, Morgan F (2009) Formal evaluation of the impact of barriers and connectors on residential burglars’ macro-level offending location choices. Aust N Z J Criminol 42:139–158. https://doi.org/10.1375/acri.42.2.139
Curtis-Ham S, Bernasco W, Medvedev ON, Polaschek DLL (2020) A framework for estimating crime location choice based on awareness space. Crime Sci 9:1–14. https://doi.org/10.1186/s40163-020-00132-7
Curtis-Ham S, Bernasco W, Medvedev ON, Polaschek DLL (2021) A national examination of the spatial extent and similarity of offenders’ activity spaces using police data. ISPRS Int J Geo-Inf 10(2):47. https://doi.org/10.3390/ijgi10020047
Duncombe W, Robbins M, Wolf DA (2001) Retire to where? a discrete choice model of residential location. Int J Popul Geogr 7:281–293. https://doi.org/10.1002/ijpg.227
Frejinger E, Bierlaire M, Ben-Akiva M (2009) Sampling of alternatives for route choice modeling. Transportation Research Part B: Methodological 43:984–994. https://doi.org/10.1016/j.trb.2009.03.001
Frith MJ (2019) Modelling taste heterogeneity regarding offence location choices. J Choice Modell 33:100187. https://doi.org/10.1016/j.jocm.2019.100187
Frith MJ, Johnson SD, Fry HM (2017) Role of the street network in burglars’ spatial decision-making. Criminology 55:344–376. https://doi.org/10.1111/1745-9125.12133
Golledge R (1999) Human wayfinding and cognitive maps. In: Golledge R (ed) Wayfinding behavior: Cognitive mapping and other spatial processes. Johns Hopkins University Press, Baltimore, MD, pp 5–45
Guevara CA, Ben-Akiva ME (2013a) Sampling of alternatives in multivariate extreme value (MEV) models. Transportation Research Part B: Methodological 48:31–52. https://doi.org/10.1016/j.trb.2012.11.001
Guevara CA, Ben-Akiva ME (2013b) Sampling of alternatives in logit mixture models. Transportation Research Part B: Methodological 58:185–198. https://doi.org/10.1016/j.trb.2013.08.011
Guevara CA, Chorus CG, Ben-Akiva ME (2016) Sampling of alternatives in random regret minimization models. Transp Sci 50:306–321. https://doi.org/10.1287/trsc.2014.0573
Hanayama A, Haginoya S, Kuraishi H, Kobayashi M (2018) The usefulness of past crime data as an attractiveness index for residential burglars. J Investigative Psychology and Offender Profiling 15:257–270. https://doi.org/10.1002/jip.1507
Hassan MN, Rashidi TH, Nassir N (2019) Consideration of different travel strategies and choice set sizes in transit path choice modelling. Transportation (dordrecht). https://doi.org/10.1007/s11116-019-10075-x
Huybers T (2005) Destination choice modelling: what’s in a name? Tour Econ 11:329–350. https://doi.org/10.5367/000000005774352999
Jonnalagadda N, Freedman J, Davidson WA, Hunt JD (2001) Development of microsimulation activity-based model for San Francisco: destination and mode choice models. Transp Res Rec 1777:25–35. https://doi.org/10.3141/1777-03
Kim J, Lee S (2017) Comparative analysis of traveler destination choice models by method of sampling alternatives. Transp Plan Technol 40:465–478. https://doi.org/10.1080/03081060.2017.1300242
King G, Honaker J, Joseph A, Scheve K (2000) Analyzing incomplete political science data: an alternative algorithm for multiple imputation. American Political Science Review 95:49–69
Lammers M (2014) Are arrested and non-arrested serial offenders different? a test of spatial offending patterns using DNA found at crime scenes. J Res Crime Delinq 51:143–167. https://doi.org/10.1177/0022427813504097
Lammers M (2018) Co-offenders’ crime location choice: do co-offending groups commit crimes in their shared awareness space? Br J Criminol 58:1193–1211. https://doi.org/10.1093/bjc/azx069
Lammers M, Menting B, Ruiter S, Bernasco W (2015) Biting once, twice: the influence of prior on subsequent crime location choice. Criminology 53:309–329. https://doi.org/10.1111/1745-9125.12071
Lemp JD, Kockelman KM (2012) Strategic sampling for large choice sets in estimation and application. Transp Res Part A 46:602–613. https://doi.org/10.1016/j.tra.2011.11.004
Li M-T, Chow L-F, Zhao F, Li S-C (2005) Geographically stratified importance sampling for the calibration of aggregated destination choice models for trip distribution. Transp Res Rec 1935:85–92. https://doi.org/10.3141/1935-10
Long D, Liu L, Feng J, Zhou S (2018) Assessing the influence of prior on subsequent street robbery location choices: A case study in ZG city. China Sustain 10:1818. https://doi.org/10.3390/su10061818
McFadden D (1977) Modelling the choice of residential location. Yale University, Cowles Foundation for Research in Economics
McFadden D (1984) Econometric analysis of qualitative response models. In: Griliches P, Intriligator MD (eds) Handbook of econometrics. Elsevier, Amsterdam, The Netherlands, pp 105–142
Menting B (2018) Awareness x opportunity: testing interactions between activity nodes and criminal opportunity in predicting crime location choice. Br J Criminol 58:1171–1192. https://doi.org/10.1093/bjc/azx049
Menting B, Lammers M, Ruiter S, Bernasco W (2016) Family matters: effects of family members’ residential areas on crime location choice. Criminology 54:413–433. https://doi.org/10.1111/1745-9125.12109
Menting B, Lammers M, Ruiter S, Bernasco W (2020) The influence of activity space and visiting frequency on crime location choice: findings from an online self-report survey. Br J Criminol 60:303–322. https://doi.org/10.1093/bjc/azz044
Mersman O (2019) microbenchmark: Accurate timing functions. Version 1.4–7URL https://CRAN.R-project.org/package=microbenchmark
Nerella S, Bhat CR (2004) Numerical analysis of effect of sampling of alternatives in discrete choice models. Transp Res Rec 1894:11–19. https://doi.org/10.3141/1894-02
Nevo A (2001) Measuring market power in the ready-to-eat cereal industry. Econometrica 69:307–342
Nguyen HTA, Chikaraishi M, Fujiwara A, Zhang J (2017) Mediation effects of income on travel mode choice: Analysis of short-distance trips based on path analysis with multiple discrete outcomes. Transp Res Rec 2664:23–30. https://doi.org/10.3141/2664-03
Park H, Park D, Kim C et al (2013) A comparative study on sampling strategies for truck destination choice model: case of Seoul metropolitan area. Can J Civ Eng 40:19–26. https://doi.org/10.1139/cjce-2012-0433
R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Rossmo DK (2000) Geographic profiling. CRC Press, Boca Raton, FL
Rubin D (1987) Multiple Imputation for Nonresponse in Surveys, 1st edn. Wiley, NY
Ruiter S (2017) Crime location choice. In: Bernasco W, Van Gelder J-L, Elffers H (eds) The Oxford handbook of offender decision making. Oxford University Press, Oxford, pp 398–420
Schönfelder S, Axhausen KW (2002) Measuring the size and structure of human activity spaces: The longitudinal perspective. ETH, Zurich
Shiftan Y (1998) Practical approach to model trip chaining. Transp Res Rec 1645:17–23. https://doi.org/10.3141/1645-03
Song G, Bernasco W, Liu L et al (2019) Crime feeds on legal activities: Daily mobility flows help to explain thieves’ target location choices. J Quant Criminol. https://doi.org/10.1007/s10940-019-09406-z
Taylor N (2002) Robbery against service stations and pharmacies: recent trends. Australian Institute of Criminology, Canberra, Australia
Therneau T (2020) A Package for Survival Analysis in R. Version 3.1–12URL https://CRAN.R-project.org/package=survival
Townsley M (2016) Offender mobility. In: Wortley R, Townsley M (eds) Environmental criminology and crime analysis. Routledge, London, England, pp 142–161
Townsley M, Birks D, Bernasco W et al (2015) Burglar target selection: a cross-national comparison. J Res Crime Delinq 52:3–31. https://doi.org/10.1177/0022427814541447
Townsley M, Birks D, Ruiter S et al (2016) Target selection models with preference variation between offenders. J Quant Criminol 32:283–304. https://doi.org/10.1007/s10940-015-9264-7
van Daele S, Vander Beken T (2010) Journey to crime of “itinerant crime groups.” Policing Int J. 33:339–353. https://doi.org/10.1108/13639511011044920
van Daele S, Vander Beken T, Bruinsma GJN (2012) Does the mobility of foreign offenders fit the general pattern of mobility? Eur J Criminol 9:290–308. https://doi.org/10.1177/1477370812440065
van Sleeuwen SEM, Ruiter S, Menting B (2018) A time for a crime: temporal aspects of repeat offenders’ crime location choices. J Res Crime Delinq 55:538–568. https://doi.org/10.1177/0022427818766395
Vandeviver C, Bernasco W (2020) “Location, location, location”: effects of neighborhood and house attributes on burglars’ target selection. J Quant Criminol 36:779–821. https://doi.org/10.1007/s10940-019-09431-y
Vandeviver C, Neutens T, van Daele S et al (2015) A discrete spatial choice model of burglary target selection at the house-level. Appl Geogr 64:24–34. https://doi.org/10.1016/j.apgeog.2015.08.004
von Haefen RH, Domanski A (2013) Estimating mixed logit models with large choice sets. In: International Choice Modelling Conference. Sydney