1932

Abstract

This review outlines approaches to explanations of crime that incorporate the concept of human mobility—or the patterns of movement throughout space of individuals or populations in the context of everyday routines—with a focus on novel strategies for the collection of geographically referenced data on mobility patterns. We identify three approaches to understanding mobility–crime linkages: () Place and neighborhood approaches characterize local spatial units of analysis of varying size with respect to the intersection in space and time of potential offenders, victims, and guardians; () person-centered approaches emphasize the spatial trajectories of individuals and person–place interactions that influence crime risk; and () ecological network approaches consider links between persons or collectivities based on shared activity locations, capturing influences of broader systems of interconnection on spatial- and individual-level variation in crime. We review data collection strategies for the measurement of mobility across these approaches, considering both the challenges and promise of mobility-based research for criminology.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-criminol-061020-021551
2021-01-13
2024-04-13
Loading full text...

Full text loading...

/deliver/fulltext/criminol/4/1/annurev-criminol-061020-021551.html?itemId=/content/journals/10.1146/annurev-criminol-061020-021551&mimeType=html&fmt=ahah

Literature Cited

  1. Abowd JM, Stephens BE, Vilhuber L, Andersson F, McKinney KL et al. 2005. The LEHD infrastructure files and the creation of the quarterly workforce indicators Tech. Pap TP-2006-01, US Census Bur., Suitland, MD
  2. Anderson E. 1999. Code of the Street: Decency, Violence, and the Moral Life of the Inner City New York: WW Norton
    [Google Scholar]
  3. Bader MDM, Mooney SJ, Bennett B, Rundle AG 2017. The promise, practicalities, and perils of virtually auditing neighborhoods using Google Street View. Ann. Am. Acad. Political Soc. Sci. 669:18–40
    [Google Scholar]
  4. Basta LA, Richmond TS, Wiebe DJ 2010. Neighborhoods, daily activities, and measuring health risks experienced in urban environments. Soc. Sci. Med. 71:1943–50
    [Google Scholar]
  5. Bernasco W, Block R. 2009. Where offenders choose to attack: a discrete choice model of robberies in Chicago. Criminology 47:93–130
    [Google Scholar]
  6. Bernasco W, Block R. 2010. Robberies in Chicago: a block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. J. Res. Crime Delinquency 48:133–57
    [Google Scholar]
  7. Bernasco W, Ruiter S, Bruinsma GJN, Pauwels LJR, Weerman FM 2013. Situational causes of offending: a fixed-effects analysis of space-time budget data. Criminology 51:895–926
    [Google Scholar]
  8. Boettner B, Browning CR, Calder CA 2019. Feasibility and validity of geographically explicit ecological momentary assessment with recall-aided space-time budgets. J. Res. Adolesc. 29:627–45
    [Google Scholar]
  9. Boivin R, Felson M. 2018. Crimes by visitors versus crimes by residents: the influence of visitor inflows. J. Quant. Criminol. 34:465–80
    [Google Scholar]
  10. Brantingham P, Brantingham P. 1995. Criminality of place: crime generators and crime attractors. Eur. J. Crim. Policy Res. 3:5–26
    [Google Scholar]
  11. Brantingham PJ, Brantingham PL. 1981. Environmental Criminology New York: SAGE
  12. Brantingham PL, Brantingham PJ. 1993. Nodes, paths and edges: considerations on the complexity of crime and the physical environment. J. Environ. Psychol. 13:3–28
    [Google Scholar]
  13. Brantingham PL, Brantingham PJ. 1999. Theoretical model of crime hot spot generation. Stud. Crime Crime Prev. 8:7–26
    [Google Scholar]
  14. Brantingham PJ, Brantingham PL, Song J, Spicer V 2020. Crime hot spots, crime corridors and the journey to crime: an expanded theoretical model of the generation of crime concentrations. Geographies of Behavioural Health, Crime, and Disorder: The Intersection of Social Problems and Place KM Lersch, J Chakraborty 61–86 Cham, Switz: Springer
    [Google Scholar]
  15. Brantingham PJ, Tita G. 2008. Offender mobility and crime pattern formation from first principles. Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems L Liu, J Eck 193–208 Hershey, PA: Idea Press
    [Google Scholar]
  16. Browning CR, Byron RA, Calder CA, Krivo LJ, Kwan M-P et al. 2010. Commercial density, residential concentration, and crime: land use patterns and violence in neighborhood context. J. Res. Crime Delinquency 47:329–57
    [Google Scholar]
  17. Browning CR, Calder CA, Soller B, Jackson AL, Dirlam J 2017a. Ecological networks and neighborhood social organization. Am. J. Sociol. 122:1939–88
    [Google Scholar]
  18. Browning CR, Calder CA, Boettner B, Smith A 2017b. Ecological networks and urban crime: the structure of shared routine activity locations and neighborhood-level informal control capacity. Criminology 55:754–78
    [Google Scholar]
  19. Browning CR, Calder CA, Ford JL, Boettner B, Smith AL, Haynie D 2017c. Understanding racial differences in exposure to violent locations: integrating survey, smartphone, and administrative data resources. Ann. Am. Acad. Political Soc. Sci. 669:41–62
    [Google Scholar]
  20. Browning CR, Jackson AL. 2013. The social ecology of public space: active streets and violent crime in urban neighborhoods. Criminology 51:1009–43
    [Google Scholar]
  21. Browning CR, Soller B. 2014. Moving beyond neighborhood: activity spaces and ecological networks as contexts for youth development. Cityscape J. Policy Dev. Res. 16:165–96
    [Google Scholar]
  22. Bursik R, Grasmick HG. 1993. Neighborhoods and Crime: The Dimensions of Effective Community Control Lanham, MD: Lexington Books
    [Google Scholar]
  23. Byrnes HF, Miller BA, Morrison CN, Wiebe DJ, Woychik M, Wiehe SE 2017. Association of environmental indicators with teen alcohol use and problem behavior: teens’ observations versus objectively-measured indicators. Health Place 43:151–57
    [Google Scholar]
  24. Clarke P, Ailshire JA, Bader M, Morenoff JD, House JS 2008. Mobility disability and the urban built environment. Am. J. Epidemiol. 168:506–13
    [Google Scholar]
  25. Clarke RV, Cornish DB. 1985. Modeling offenders’ decisions: a framework for research and policy. Crime Justice 6:147–85
    [Google Scholar]
  26. Cohen LE, Felson M. 1979. Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44:588–608
    [Google Scholar]
  27. Corcoran J, Zahnow R, Wickes R, Hipp J 2018. Neighbourhood land use features, collective efficacy and local civic actions. Urban Stud 55:2372–90
    [Google Scholar]
  28. Czajka JL, Beyler A. 2016. Declining response rates in federal surveys: trends and implications Rep., Math. Policy Res Washington, DC: https://www.mathematica.org/our-publications-and-findings/publications/declining-response-rates-in-federal-surveys-trends-and-implications-background-paper
    [Google Scholar]
  29. de Jong E, Bernasco W, Lammers M 2019. Situational correlates of adolescent substance use: an improved test of the routine activity theory of deviant behavior. J. Quant. Criminol. https://doi.org/10.1007/s10940-019-09433-w
    [Crossref] [Google Scholar]
  30. Duany A, Plater-Zyberk E, Speck J 2001. Suburban Nation: The Rise of Sprawl and the Decline of the American Dream New York: North Point Press
    [Google Scholar]
  31. Earls FJ, Brooks-Gunn J, Raudenbush SW, Sampson RJ 2007. Project on human development in Chicago neighborhoods (PHDCN): community survey, 1994–1995 Rep., Natl. Arch. Consort. Political Soc. Res Ann Arbor, MI: http://doi.org/10.3886/ICPSR02766.v3
    [Crossref] [Google Scholar]
  32. Eck J. 2003. Police problems: the complexity of problem theory, research and evaluation. Problem-Oriented Policing: From Innovation to Mainstream J Knutsson 79–113 Monsey, NY: Crim. Justice Press
    [Google Scholar]
  33. Eck JE, Guerette RT. 2012. Place-based crime prevention: theory, evidence, and policy. Oxf. Handb. Crime Prev. https://doi.org/10.1093/oxfordhb/9780195398823.013.0018
    [Crossref] [Google Scholar]
  34. Eck JE, Madensen-Herold TD 2018. Place management, guardianship, and the establishment of order. Deterrence, Choice, and Crime, Vol. 23 DS Nagin, FT Cullen, CL Jonson 269–308 Milton Park, UK: Taylor & Francis
    [Google Scholar]
  35. Eck J, Weisburd DL. 2015. Crime places in crime theory. Crime Place Crime Prev. Stud. 4:1–33
    [Google Scholar]
  36. Felson M, Boba RL. 2010. Crime and Everyday Life Thousand Oaks, CA: SAGE
  37. Felson M, Boivin R. 2015. Daily crime flows within a city. Crime Sci 4:31
    [Google Scholar]
  38. Felson M, Poulsen E. 2003. Simple indicators of crime by time of day. Int. J. Forecast. 19:595–601
    [Google Scholar]
  39. Fowler EP. 1987. Street management and city design. Soc. Forces 66:365–89
    [Google Scholar]
  40. Furstenberg FF, Cook TD, Eccles J, Elder GH Jr 1999. Managing to Make It: Urban Families and Adolescent Success Chicago: Univ. Chicago Press
    [Google Scholar]
  41. Gardiner RA. 1978. Design for safe neighborhoods Rep., Natl. Inst. Law Enforc. & Crim. Law Enforc. Assist. Adm Washington, DC:
  42. Ghandour RM, Jones JR, Lebrun-Harris LA, Minnaert J, Blumberg SJ et al. 2018. The design and implementation of the 2016 National Survey of Children's Health. Matern. Child Health J. 22:1093–102
    [Google Scholar]
  43. Golledge RG, Stimson RJ. 1987. Analytical Behavioural Geography London: Croom Helm
  44. Goodchild MF. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal 69:211–21
    [Google Scholar]
  45. Goodchild MF. 2009. What problem? Spatial autocorrelation and geographic information science. Geogr. Anal. 41:411–17
    [Google Scholar]
  46. Graif C, Freelin BN, Kuo Y-H, Wang H, Li Z, Kifer D 2019. Network spillovers and neighborhood crime: a computational statistics analysis of employment-based networks of neighborhoods. Justice Q https://doi.org/10.1080/07418825.2019.1602160
    [Crossref] [Google Scholar]
  47. Graif C, Lungeanu A, Yetter AM 2017. Neighborhood isolation in Chicago: violent crime effects on structural isolation and homophily in inter-neighborhood commuting networks. Soc. Netw. 51:40–59
    [Google Scholar]
  48. Greenberg SW, Rohe WM. 1984. Neighborhood design and crime a test of two perspectives. J. Am. Plan. Assoc. 50:48–61
    [Google Scholar]
  49. Groff ER. 2007a. Simulation for theory testing and experimentation: an example using routine activity theory and street robbery. J. Quant. Criminol. 23:75–103
    [Google Scholar]
  50. Groff ER. 2007b. ‘Situating’ simulation to model human spatio-temporal interactions: an example using crime events. Trans. GIS 11:507–30
    [Google Scholar]
  51. Grubesic TH, Pridemore WA. 2011. Alcohol outlets and clusters of violence. Int. J. Health Geogr. 10:30
    [Google Scholar]
  52. Hägerstrand T. 1970. What about people in regional science. Pap. Reg. Sci. Assoc. 24:6–21
    [Google Scholar]
  53. Hardie B. 2019. Why monitoring doesn't always matter: the interaction of personal propensity with physical and psychological parental presence in a situational explanation of adolescent offending. Deviant Behav https://doi.org/10.1080/01639625.2019.1673924
    [Crossref] [Google Scholar]
  54. Hindelang MJ, Gottfredson MR, Garofalo J 1978. Victims of Personal Crime: An Empirical Foundation for a Theory of Personal Victimization Cambridge, MA: Ballinger
    [Google Scholar]
  55. Hipp JR, Bates C, Lichman M, Smyth P 2019. Using social media to measure temporal ambient population: Does it help explain local crime rates. Justice Q 36:718–48
    [Google Scholar]
  56. Hipp JR, Corcoran J, Wickes R, Li T 2014. Examining the social porosity of environmental features on neighborhood sociability and attachment. PLOS ONE 9:e84544
    [Google Scholar]
  57. Hipp JR, Kim Y-A. 2017. Measuring crime concentration across cities of varying sizes: complications based on the spatial and temporal scale employed. J. Quant. Criminol. 33:595–632
    [Google Scholar]
  58. Hipp JR, Kim Y-A. 2019. Explaining the temporal and spatial dimensions of robbery: differences across measures of the physical and social environment. J. Crim. Justice 60:1–12
    [Google Scholar]
  59. Hipp JR, Williams SA. 2020. Advances in spatial criminology: the spatial scale of crime. Annu. Rev. Criminol. 3:75–95
    [Google Scholar]
  60. Hirsch JA, Winters M, Ashe MC, Clarke PJ, McKay HA 2016. Destinations that older adults experience within their GPS activity spaces: relation to objectively measured physical activity. Environ. Behav. 48:55–77
    [Google Scholar]
  61. Hoeben EM, Bernasco W, Weerman FM, Pauwels L, van Halem S 2014. The space-time budget method in criminological research. Crime Sci 3:12
    [Google Scholar]
  62. Jacobs J. 1961. The Death and Life of Great American Cities New York: Random House
  63. Jones M, Pebley AR. 2014. Redefining neighborhoods using common destinations: social characteristics of activity spaces and home census tracts compared. Demography 51:727–52
    [Google Scholar]
  64. Jones M, Pebley AR, Sastry N 2011. Eyes on the block: measuring urban physical disorder through in-person observation. Soc. Sci. Res. 40:523–37
    [Google Scholar]
  65. Kadar C, Pletikosa I. 2018. Mining large-scale human mobility data for long-term crime prediction. EPJ Data Sci 7:26
    [Google Scholar]
  66. Kadar C, Te Y-F, Brüngger RR, Cvijikj IP 2016. Digital neighborhood watch: to share or not to share?. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems2148–55 San Jose, CA: Assoc. Comput. Mach.
    [Google Scholar]
  67. Kinney JB, Brantingham PL, Wuschke K, Kirk MG, Brantingham PJ 2008. Crime attractors, generators and detractors: land use and urban crime opportunities. Built Environ 34:62–74
    [Google Scholar]
  68. Kirchner TR, Shiffman S. 2016. Spatio-temporal determinants of mental health and well-being: advances in geographically-explicit ecological momentary assessment (GEMA). Soc. Psychiatry Psychiatr. Epidemiol. 51:1211–23
    [Google Scholar]
  69. Kounadi O, Ristea A, Leitner M, Langford C 2018. Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies. Cartogr. Geogr. Inf. Sci. 45:205–20
    [Google Scholar]
  70. Kubrin CE, Hipp JR. 2016. Do fringe banks create fringe neighborhoods? Examining the spatial relationship between fringe banking and neighborhood crime rates. Justice Q 33:755–84
    [Google Scholar]
  71. LaGrange TC. 2016. The impact of neighborhoods, schools, and malls on the spatial distribution of property damage. J. Res. Crime Delinquency 36:4393–422
    [Google Scholar]
  72. Land KC, Felson M. 1976. A general framework for building dynamic macro social indicator models: including an analysis of changes in crime rates and police expenditures. Am. J. Sociol. 82:565–604
    [Google Scholar]
  73. Lenormand M, Picornell M, Cantú-Ros OG, Tugores A, Louail T et al. 2014. Cross-checking different sources of mobility information. PLOS ONE 9:e105184
    [Google Scholar]
  74. Leverentz A. 2020. Beyond neighborhoods: activity spaces of returning prisoners. Soc. Probl. 67:150–170
    [Google Scholar]
  75. Malleson N, Andresen MA. 2015a. Spatio-temporal crime hotspots and the ambient population. Crime Sci 4:10
    [Google Scholar]
  76. Malleson N, Andresen MA. 2015b. The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartogr. Geogr. Inf. Sci. 42:112–21
    [Google Scholar]
  77. Malleson N, Andresen MA. 2016. Exploring the impact of ambient population measures on London crime hotspots. J. Crim. Justice 46:52–63
    [Google Scholar]
  78. Mastrofski S, Parks R, McCluskey J 2010. Systematic social observation in criminology. Handbook of Quantitative Criminology AR Piquero, D Weisburd 225–47 Dordrecht, Neth: Springer
    [Google Scholar]
  79. Matthews SA, Yang T-C. 2013. Spatial polygamy and contextual exposures (SPACEs): promoting activity space approaches in research on place and health. Am. Behav. Sci. 57:1057–81
    [Google Scholar]
  80. Mazerolle LG, Kadleck C, Roehl J 1998. Controlling drug and disorder problems: the role of place managers. Criminology 36:371–404
    [Google Scholar]
  81. McCord ES, Ratcliffe JH. 2007. A micro-spatial analysis of the demographic and criminogenic environment of drug markets in Philadelphia. Aust. N. Z. J. Criminol. 40:143–63
    [Google Scholar]
  82. McCormick TH, Lee H, Cesare N, Shojaie A, Spiro ES 2017. Using Twitter for demographic and social science research: tools for data collection and processing. Sociol. Methods Res. 46:390–421
    [Google Scholar]
  83. Menting B, Lammers M, Ruiter S, Bernasco W 2019. The influence of activity space and visiting frequency on crime location choice: findings from an online self-report survey. Br. J. Criminol. 60:2303–22
    [Google Scholar]
  84. Mooney SJ, Bader MDM, Lovasi GS, Teitler JO, Koenen KC et al. 2017. Street audits to measure neighborhood disorder: virtual or in-person. Am. J. Epidemiol. 186:265–73
    [Google Scholar]
  85. Morenoff JD, Sampson RJ, Raudenbush SW 2001. Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology 39:517–58
    [Google Scholar]
  86. Newman O. 1972. Defensible Space New York: MacMillan Press
  87. Odgers CL, Caspi A, Bates CJ, Sampson RJ, Moffitt TE 2012. Systematic social observation of children's neighborhoods using Google Street View: a reliable and cost-effective method. J. Child Psychol. Psychiatry 53:1009–17
    [Google Scholar]
  88. Palmer JRB, Espenshade TJ, Bartumeus F, Chung CY, Ozgencil NE, Li K 2013. New approaches to human mobility: using mobile phones for demographic research. Demography 50:1105–28
    [Google Scholar]
  89. Perkins DD, Meeks JW, Taylor RB 1992. The physical environment of street blocks and resident perceptions of crime and disorder: implications for theory and measurement. J. Environ. Psychol. 12:21–34
    [Google Scholar]
  90. Phillips NE, Levy BL, Sampson RJ, Small ML, Wang RQ 2019. The social integration of American cities: network measures of connectedness based on everyday mobility across neighborhoods. Sociol. Methods Res. https://doi.org/10.1177/0049124119852386
    [Crossref] [Google Scholar]
  91. Pratt M, King M, Burash J, Tompsett CJ 2019. What differences do they see? Using mixed methods to capture adolescent perceptions of neighborhood contexts. Am. J. Community Psychol. 65:3–4320–31
    [Google Scholar]
  92. Reiss AJ. 1971. Systematic observation of natural social phenomena. Sociol. Methodol. 3:3–33
    [Google Scholar]
  93. Rengert GF, Wasilchick J. 1985. Suburban Burglary: A Time and a Place for Everything Springfield, IL: CC Thomas
    [Google Scholar]
  94. Roncek DW, Bell R. 1981. Bars, blocks, and crimes. J. Environ. Syst. 11:35–47
    [Google Scholar]
  95. Roncek DW, Faggiani D. 1985. High schools and crime: a replication. Sociol. Q. 26:491–505
    [Google Scholar]
  96. Roncek DW, LoBosco A. 1983. The effect of high schools on crime in their neighborhoods. Soc. Sci. Q. 64:598–613
    [Google Scholar]
  97. Roncek DW, Maier PA. 1991. Bars, blocks, and crimes revisited: linking the theory of routine activities to the empiricism of hot spots. Criminology 29:725–53
    [Google Scholar]
  98. Roncek DW, Pravatiner MA. 1989. Additional evidence that taverns enhance nearby crime. Sociol. Soc. Res. 73:185–88
    [Google Scholar]
  99. Rosés R, Kadar C, Gerritsen C, Rouly C 2018. Agent-based simulation of offender mobility: integrating activity nodes from location-based social networks. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems804–12 Stockholm: Int. Found. Auton. Agents Multiagent Syst.
    [Google Scholar]
  100. Rosés Brüngger R, Bader R, Kadar C, Pletikosa I 2017. Towards simulating criminal offender movement based on insights from human dynamics and location-based social networks. Social Informatics: Lecture Notes in Computer Science GL Ciampaglia, A Mashhadi, T Yasseri 458–65 Cham, Switz: Springer
    [Google Scholar]
  101. Sampson RJ. 2012. Great American City: Chicago and the Enduring Neighborhood Effect Chicago: Univ. Chicago Press
    [Google Scholar]
  102. Sampson RJ. 2018. Neighbourhood effects and beyond: explaining the paradoxes of inequality in the changing American metropolis. Urban Stud 56:13–32
    [Google Scholar]
  103. Sampson RJ, Levy BL. 2020. Beyond residential segregation: mobility-based connectedness and rates of violence in large cities. Race Soc. Probl. 12:77–86
    [Google Scholar]
  104. Sampson RJ, Raudenbush SW. 1999. Systematic social observation of public spaces: a new look at disorder in urban neighborhoods. Am. J. Sociol. 105:603–51
    [Google Scholar]
  105. Sampson RJ, Raudenbush SW. 2004. Seeing disorder: neighborhood stigma and the social construction of broken windows. Soc. Psychol. Q. 67:319–42
    [Google Scholar]
  106. Sastry N, Ghosh-Dastidar B, Adams J, Pebley AR 2006. The design of a multilevel survey of children, families, and communities: the Los Angeles Family and Neighborhood Survey. Soc. Sci. Res. 35:1000–24
    [Google Scholar]
  107. Sastry N, Pebley AR, Zonta M 2002. Neighborhood definitions and the spatial dimension of daily life in Los Angeles Work. Pap. CCPR-033-04, Calif. Cent. Popul. Res UCLA:
  108. Saxon J. 2019. The local structures of human mobility in Chicago Res. Pap. 13, Mansueto Inst. Urban Innov., Univ Chicago:
  109. Schnell C, Braga AA, Piza EL 2017. The influence of community areas, neighborhood clusters, and street segments on the spatial variability of violent crime in Chicago. J. Quant. Criminol. 33:469–96
    [Google Scholar]
  110. Sharp G, Denney JT, Kimbro RT 2015. Multiple contexts of exposure: activity spaces, residential neighborhoods, and self-rated health. Soc. Sci. Med. 146:204–13
    [Google Scholar]
  111. Sharp G, Warner C. 2018. Neighborhood structure, community social organization, and residential mobility. Socius Soc. Res. Dyn. World. https://doi.org/10.1177/2378023118797861
    [Crossref] [Google Scholar]
  112. Sherman LW, Gartin PR, Buerger ME 1989. Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27:27–56
    [Google Scholar]
  113. Small ML. 2009. Unanticipated Gains: Origins of Network Inequality in Everyday Life Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  114. Smith WR, Frazee SG, Davison EL 2000. Furthering the integration of routine activity and social disorganization theories: small units of analysis and the study of street robbery as a diffusion process. Criminology 38:489–524
    [Google Scholar]
  115. Song G, Bernasco W, Liu L, Xiao L, Zhou S, Liao W 2019. Crime feeds on legal activities: daily mobility flows help to explain thieves’ target location choices. J. Quant. Criminol. 35:831–54
    [Google Scholar]
  116. Sperling J. 2012. The tyranny of census geography: small-area data and neighborhood statistics. Cityscape 14:219–23
    [Google Scholar]
  117. St. Jean PKB 2007. Pockets of Crime: Broken Windows, Collective Efficacy, and the Criminal Point of View Chicago: Univ. Chicago Press
    [Google Scholar]
  118. Steenbeek W, Weisburd D. 2016. Where the action is in crime? An examination of variability of crime across different spatial units in The Hague, 2001–2009. J. Quant. Criminol. 32:449–69
    [Google Scholar]
  119. Stucky TD, Ottensmann JR. 2009. Land use and violent crime. Criminology 47:1223–64
    [Google Scholar]
  120. Sugie NF. 2018a. Utilizing smartphones to study disadvantaged and hard-to-reach groups. Sociol. Methods Res. 47:458–91
    [Google Scholar]
  121. Sugie NF. 2018b. Work as foraging: a smartphone study of job search and employment after prison. Am. J. Sociol. 123:1453–91
    [Google Scholar]
  122. Sugie NF, Lens MC. 2017. Daytime locations in spatial mismatch: job accessibility and employment at reentry from prison. Demography 54:775–800
    [Google Scholar]
  123. Taylor RB. 1988. Human Territorial Functioning: An Empirical, Evolutionary Perspective on Individual and Small Group Territorial Cognitions, Behaviors, and Consequences Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  124. Taylor RB, Gottfredson SD, Brower S 1984. Block crime and fear: defensible space, local social ties, and territorial functioning. J. Res. Crime Delinquency 21:4303–31
    [Google Scholar]
  125. Taylor RB, Koons BA, Kurtz EM, Greene JR, Perkins DD 1995. Street blocks with more nonresidential land use have more physical deterioration: evidence from Baltimore and Philadelphia. Urban Aff. Rev. 31:20–136
    [Google Scholar]
  126. Taylor RB, Shumaker SA, Gottfredson SD 1985. Neighborhood-level links between physical features and local sentiments: deterioration, fear of crime, and confidence. J. Archit. Plan. Res. 2:261–75
    [Google Scholar]
  127. Theall KP, Felker-Kantor E, Wallace M, Zhang X, Morrison CN, Wiebe DJ 2018. Considering high alcohol and violence neighborhood context using daily diaries and GPS: a pilot study among people living with HIV. Drug Alcohol Depend 187:236–41
    [Google Scholar]
  128. Tillyer MS, Wilcox P, Walter RJ 2020. Crime generators in context: examining ‘place in neighborhood’ propositions. J. Quant. Criminol. https://doi.org/10.1007/s10940-019-09446-5
    [Crossref] [Google Scholar]
  129. Tompsett CJ, Veits GM, Amrhein KE 2016. Peer delinquency and where adolescents spend time with peers: mediation and moderation of home neighborhood effects on self-reported delinquency. J. Community Psychol. 44:263–70
    [Google Scholar]
  130. Turanovic JJ, Pratt TC, Piquero AR 2018. Structural constraints, risky lifestyles, and repeat victimization. J. Quant. Criminol. 34:251–74
    [Google Scholar]
  131. Vandeviver C, Bernasco W. 2019. “Location, location, location”: effects of neighborhood and house attributes on burglars’ target selection. J. Quant. Criminol. https://doi.org/10.1007/s10940-019-09431-y
    [Crossref] [Google Scholar]
  132. Wang Q, Phillips NE, Small ML, Sampson RJ 2018. Urban mobility and neighborhood isolation in America's 50 largest cities. PNAS 115:307735–40
    [Google Scholar]
  133. Weerman FM, Bernasco W, Bruinsma GJN, Pauwels LJR 2015. When is spending time with peers related to delinquency? The importance of where, what, and with whom. Crime Delinquency 61:101386–413
    [Google Scholar]
  134. Weisburd D, Eck JE, Braga AA, Telep CW, Cave B et al. 2016. Place Matters: Criminology for the Twenty-First Century Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  135. Weisburd D, Groff ER, Yang S-M 2012. The Criminology of Place: Street Segments and Our Understanding of the Crime Problem New York: Oxford Univ. Press
    [Google Scholar]
  136. White R, Renk K. 2012. Externalizing behavior problems during adolescence: an ecological perspective. J. Child Fam. Stud. 21:158–71
    [Google Scholar]
  137. Wikstrom P-O. 1991. Urban Crime, Criminals, and Victims: The Swedish Experience in an Anglo-American Comparative Perspective New York: Springer-Verlag
    [Google Scholar]
  138. Wikstrom P-OH, Butterworth DA. 2013. Adolescent Crime Abingdon, UK: Routledge
  139. Wikström P-OH, Ceccato V, Hardie B, Treiber K 2010. Activity fields and the dynamics of crime: advancing knowledge about the role of the environment in crime causation. J. Quant. Criminol. 26:55–87
    [Google Scholar]
  140. Wikström P-OH, Loeber R. 2000. Do disadvantaged neighborhoods cause well-adjusted children to become adolescent delinquents? A study of male juvenile serious offending, individual risk and protective factors, and neighborhood context. Criminology 38:1109–42
    [Google Scholar]
  141. Wikström P-OH, Mann RP, Hardie B 2018. Young people's differential vulnerability to criminogenic exposure: bridging the gap between people- and place-oriented approaches in the study of crime causation. Eur. J. Criminol. 15:10–31
    [Google Scholar]
  142. Wikström P-OH, Oberwittler D, Treiber K, Hardie B 2012. Breaking Rules: The Social and Situational Dynamics of Young People's Urban Crime Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  143. Wilcox P, Cullen FT. 2018. Situational opportunity theories of crime. Annu. Rev. Criminol. 1:123–48
    [Google Scholar]
  144. Wilcox P, Cullen FT, Feldmeyer B 2018. Communities and Crime: An Enduring American Challenge Philadelphia: Temple Univ. Press
    [Google Scholar]
  145. Wilcox P, Land KC, Hunt SA 2003. Criminal Circumstance: A Dynamic Multi-Contextual Criminal Opportunity Theory Piscataway, NJ: Transaction Publ.
    [Google Scholar]
  146. Wilcox P, Quisenberry N, Cabrera DT, Jones S 2004. Busy places and broken windows? Toward defining the role of physical structure and process in community crime models. Sociol. Q. 45:185–207
    [Google Scholar]
  147. Xi W, Calder CA, Browning CR 2019. Beyond activity space: detecting communities in ecological networks. arXiv:1903.07649 [q-bio, stat]
  148. Yu H, Shaw S-L. 2011. GIS designs for studying human activities in a space-time context. The SAGE Handbook of GIS and Society TL Nyerges, H Couclelis, R McMaster 251–68 London: SAGE
    [Google Scholar]
/content/journals/10.1146/annurev-criminol-061020-021551
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error