This study examines to what extent new and emerging data sources or big data have been empirically used to measure key theoretical concepts within environmental criminology. By means of a scoping review, aimed at studies published between 2005 and 2018, insight is provided into the characteristics of studies that used big data sources within environmental criminology. The type and extent of big data sources used, as well as the strengths and weaknesses of these data sources, are synthesized. After the selection procedure, 84 studies were included for further analysis. Although the number of studies increased each year, there has been a remarkable increase in the number of studies since 2014. The findings suggest that most studies used administrative data or user-generated content as one type of research data. However, innovative data sources (automated and volunteered data) have gained in importance in recent years. Also, most studies are of a descriptive or predictive nature, predominantly conducted by computational (social) scientists. Since these approaches pay little to no attention to mechanisms that bring about social outcomes, an alternative philosophical framework is proposed. We put forward a scientific realist approach as a solution to integrate data-driven and theory-driven research. This approach responds to recent calls to move towards an ‘analytical criminology’. The results are discussed within this framework, and translated into avenues for future research.
ArkseyHO’MalleyL (2005) Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology8(1): 19–32. doi:10.1186/1748-5908-5-69.
2.
BoydDCrawfordK (2012) Critical questions for big data. Information, Communication & Society15(5): 662–679. doi:10.1080/1369118X.2012.678878.
3.
BragaAAClarkeRV (2014) Explaining high-risk concentrations of crime in the city: Social disorganization, crime opportunities, and important next steps. Journal of Research in Crime and Delinquency51: 480–498.
4.
BrantinghamPJBrantinghamPL (1991) Environmental Criminology. Prospect Heights, IL: Waveland.
5.
BrantinghamPLBrantinghamPJ (1993) Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology13: 3–28.
6.
BruinsmaGJN (2016) Proliferation of crime causation theories in an era of fragmentation: Reflections on the current state of criminological theory. European Journal of Criminology13(6): 659–676.
7.
BruinsmaGJNJohnsonSD (2018) Environmental criminology: Scope, history, and state of the art. In: BruinsmaGJNJohnsonSD (eds) The Oxford Handbook of Environmental Criminology. Oxford: Oxford University Press, 1–31.
8.
BungeM (2006) Chasing Reality. Strife Over Realism. Toronto: University of Toronto Press.
9.
BursikRJGrasmickHG (1993) Neighborhoods and Crime. New York: Lexington.
10.
ChanJBennett MosesL (2016) Is big data challenging criminology?Theoretical Criminology20(1): 21–39.
11.
ChenCMaJSusiloYLiuYWangM (2016) The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C68: 285–299.
12.
ColquhounHLLevacDO’BrienKKStrausSTriccoACPerrierLKastnerMMoherD (2014) Scoping reviews: Time for clarity in definition, methods, and reporting. Journal of Clinical Epidemiology67(12): 1291–1294. doi:10.1016/j.jclinepi.2014.03.013.
13.
ConnellyRPlayfordCJGayleVDibbenC (2016) The role of administrative data in the big data revolution in social science research. Social Science Research59: 1–12.
14.
De MauroAGrecoMGrimaldiM (2015) What is big data. A consensual definition and a review of key research topics. AIP Conference Proceedings1644(1): 97–104. doi:10.1063/1.4907823.
15.
DuanLYeXHuTZhuX (2017) Prediction of suspect location based on spatiotemporal semantics. ISPRS International Journal of Geo-Information6(7): 185–212.
16.
FrantzenKKFettersMD (2016) Meta-integration for synthesizing data in a systematic studies review: Insights from research on autism spectrum disorder. Quality & Quantity50(5): 2251–2277.
17.
GebruTKrauseJWangYChenDDengJLieberman AidenEFei-FeiL (2017) Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. PNAS114(50): 13108–13113.
18.
GlaeserELKominersSDLucaMNaikN (2018) Big data and big cities: The promises and limitations of improved measures of urban life. Economic Inquiry56(1): 114–137.
19.
HoxJJ (2017) Computational social science methodology, anyone?Methodology13 (Supplement): 3–12.
20.
HoxJJBoeijeHR (2005) Data collection, primary vs. secondary. In: Kempf-LeonardK (ed.) Encyclopedia of Social Measurement. Atlanta: Elsevier Science, 593–599.
21.
HsiehYPMurphyJ (2017) Total Twitter Error: Decomposing public opinion measurement on Twitter from a Total Survey Error perspective. In: BiemerPPde LeeuwEEckmanSEdwardsBKreuterFLybergLETuckerNCWestBT (eds) Total Survey Error in Practice. Hoboken, NJ: John Wiley & Sons, 23–46.
22.
JadejaBIssaT (2018) Big Data: A new technology trend and factors affecting the implementation of big data in Australian industries. In: SkourletopoulosGMastorakisGMavromoustakisCXDobreCPallisE (eds) Mobile Big Data: A Roadmap from Models to Technologies (Lecture Notes on Data Engineering and Communications Technologies, Vol. 10). Cham: Springer International Publishing, 259–288.
23.
JohnsonTPSmithTW (2017) Big data and survey research: Supplement or substitute? In: ThakuriahPVTilahunNYZellnerM (eds) Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics. Cham: Springer, 113–126.
24.
JonesRWPridemoreWA (2018) Toward an integrated multilevel theory of crime at place: Routine activities, social disorganization, and the law of crime concentration. Journal of Quantitative Criminology. https://doi.org/10.1007/s10940-018-9397-6.
25.
KeusschniggMLovsjöNHedströmP (2018) Analytical sociology and computational social science. Journal of Computational Social Science1(1): 3–14.
26.
KitchinR (2013) Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography3(3): 262–267.
27.
KitchinR (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society1(1): 1–12. doi:10.1177/2053951714528481.
28.
KitchinR (2015) The opportunities, challenges and risks of big data for official statistics. Statistical Journal of the IAOS31(3): 471–481.
29.
KitchinRMcArdleG (2016) What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data & Society3(1): 1–10.
LeitnerMGlasnerPKounadiO (2018) Laws of geography. In: Oxford Research Encyclopedia of Criminology. Oxford: Oxford University Press Inc. doi:10.1093/acrefore/9780190264079.013.325.
LewinK (1936) Principles of Topological Psychology. New York: McGraw-Hill Book Company.
34.
LouderbackERSen RoyS (2017) Integrating social disorganization and routine activity theories and testing the effectiveness of neighbourhood crime watch programs: Case study of Miami-Dade County, 2007–15. British Journal of Criminology58(4): 968–992. doi:10.1093/bjc/azx062.
35.
LynchK (2018) Not even our own facts: Criminology in the era of big data. Criminology56(3): 437–454. doi:10.1111/1745-9125.12182.
36.
MatsuedaRL (2017) Toward an analytical criminology: The micro-macro problem, causal mechanisms, and public policy. Criminology55(3): 493–519.
37.
Mayer-SchönbergerVCukierK (2013) Big Data: A Revolution That Will Transform How We Live, Work and Think. New York: Houghton Mifflin Harcourt.
38.
MietheTDMeierRF (1994) Crime and Its Social Context: Toward an Integrated Theory of Offenders, Victims, and Situations. New York: State University of New York Press.
OberwittlerDWikströmP-OH (2009) Why smaller is better: Advancing the study of the role of behavioral contexts in crime causation. In: WeisburdDBernascoWBruinsmaGJN (eds) Putting Crime in Its Place: Units of Analysis in Geographic Criminology. New York: Springer Science Business Media, 36–60.
41.
PauwelsLPonsaersPSvenssonR (2010) An analytical perspective on the study of crime at multiple levels. In: PauwelsLHardynsWVan de VeldeM (eds) Social Disorganisation, Offending, Fear and Victimisation: Findings from Belgian Studies on the Urban Context of Crime. The Hague: Boom Juridische Uitgevers, 199–215.
42.
RiceKJSmithWRC (2002) Socioecological models of automotive theft: Integrating routine activity and social disorganization approaches. Journal of Research in Crime and Delinquency39: 304–336.
43.
RidgewayG (2017) Policing in the era of big data. Annual Review of Criminology1(1): 401–419. doi:10.1146/annurev-criminol-062217-114209.
44.
RisteaAKurlandJReschBLeitnerMLangfordC (2018) Estimating the spatial distribution of crime events around a football stadium from georeferenced tweets. ISPRS International Journal of Geo-Information7(2): 1–25. doi:10.3390/ijgi7020043.
45.
RummensAHardynsWPauwelsLJR (2017) The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context. Applied Geography86: 255–261.
46.
SampsonRJ (2012) Great American City. Chicago and the Enduring Neighborhood Effect. Chicago: University of Chicago Press.
47.
SampsonRJRaudenbushSEarlsF (1997) Neighbourhoods and violent crime. A multilevel study of collective efficacy. Science277: 918–924.
48.
ShawCRMcKayH (1969 [1942]) Juvenile Delinquency and Urban Areas: A Study of Rates of Delinquency in Relation to Differential Characteristics of Local Communities in American Cities. Chicago: University of Chicago Press.
49.
ShermanLWGartinPRBuergerME (1989) Hot spots of predatory crime: Routine activities and the criminology of place. Criminology27(1): 27–55.
50.
SkoganWG (1990) Disorder and Decline: Crime and the Spiral of Decay in American Neighborhoods. New York: The Free Press.
51.
SloanLMorganJ (2015) Who tweets their location? Understanding the relationship between demographic characteristics and the use of geoservices and geotagging on Twitter. PLoS ONE10(11): 1–15. doi:10.1371/journal.pone.0142209.
52.
SmithGJDBennett MosesLChanJ (2017) The challenges of doing criminology in the big data era: Towards a digital and data-driven approach. British Journal of Criminology57: 259–274.
53.
SmithWRFrazeeSGDavisonEL (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. Criminology38(2): 489–523.
54.
SolymosiR (2017) Exploring spatial and temporal variation in perception of crime and place using crowdsourced data. PhD thesis, University College London, UK. URL: http://discovery.ucl.ac.uk/1541235/.
55.
SolymosiRBowersK (2018) The role of innovative data collection methods in advancing criminological understanding. In: BruinsmaGJNJohnsonSD (eds) The Oxford Handbook of Environmental Criminology. Oxford: Oxford University Press, 210–237.
56.
SymonsJAlvaradoR (2016) Can we trust big data? Applying philosophy of science to software. Big Data & Society3(2): 1–17.
57.
ThakuriahPVTilahunNYZellnerM (2017) Big data and urban informatics: Innovations and challenges to urban planning and knowledge discovery. In: ThakuriahPVTilahunNYZellnerM (eds) Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics. Cham: Springer, 11–45.
58.
WebbEJCampbellDTSchwarzRDSechrestL (1966) Unobtrusive Measures: Nonreactive Research in the Social Sciences. Chicago: Rand McNally College.
59.
WeisburdD (2015) The law of crime concentration and the criminology of place. Criminology53(2): 133–157. doi:10.1111/1745-9125.12070.
60.
WeisburdDEckJE (2018) Unraveling the Crime–Place Connection: New Directions in Theory and Policy. New York: Routledge.
61.
WeisburdDBruinsmaGJNBernascoW (2009) Units of analysis in geographic criminology: Historical developments, critical issues, and open questions. In: WeisburdDBernascoWBruinsmaGJN (eds) Putting Crime in Its Place: Units of Analysis in Geographic Criminology. New York: Springer Science Business Media, 3–31.
62.
WeisburdDGroffERYangS-M (2012) The Criminology of Place: Street Segments and Our Understanding of the Crime Problem. Oxford: Oxford University Press.
63.
WeisburdDShayMAmramSZamirR (2018) The relationship between social disorganization and crime at the micro geographic level: Findings from Tel Aviv-Yafo using Israeli census data. In: WeisburdDEckJE (eds) Unraveling the Crime–Place Connection: New Directions in Theory and Policy. New York: Routledge, 97–119.
64.
WeisburdDEckJEBragaAATelepCWCaveBBowersKBruinsmaGJNGillCGroffEHibdonJHinkleJCJohnsonSDLawtonBLumCRatcliffeJRengertGTaniguchiTYangS-M (2016) Place Matters: Criminology for the 21st Century. Cambridge: Cambridge University Press.
65.
WikströmP-OH (2012) Individuals’ situational criminal actions. In: LoeberRWelshB (eds) The Future of Criminology. Oxford: Oxford University Press, 55–61.
66.
WikströmP-OHTreiberK (2015) Situational theory: The importance of interactions and action mechanisms in the explanation of crime. In: PiqueroA (ed.) Handbook of Criminological Theory. Oxford: Wiley-Blackwell, 414–444.
67.
WilsonJQKellingGL (1982) The police and neighbourhood safety: Broken windows. The Atlantic Monthly3(2): 29–38.
68.
ZhaoXTangJ (2018) Crime in urban areas: A data mining perspective. ACM SIGKDD Explorations Newsletter20(1): 1–12. doi:10.1145/3229329.3229331.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
