Abstract
Objectives
To examine the causal impact of small businesses on street theft and the underlying mechanisms.
Methods
The “Cleanup Holes in the Wall” campaign in Beijing, China, provides a rare opportunity for a natural experiment. Drawing on street view images processed by deep learning algorithms and other big data sources such as court judgments and location-based service (LBS) population, we use difference-in-difference (DID) models to investigate how the disappearance of small businesses leads to changes in the occurrence of theft. We further examine the mechanisms by introducing mediators, including ambient population and social activity.
Results
The treatment units that experienced a mass loss of small businesses showed a significant reduction in street theft compared to the control units that were less affected by the cleanup campaign. Ambient population and social activity played a mediating role in promoting and deterring crime, respectively, with the former dominating. The results remain robust after including covariates in the models, balancing covariates using the propensity score matching method, and adopting alternative thresholds to classify the treatment group.
Conclusions
There are two competing yet coexisting mechanisms through which small businesses influence street theft. On the one hand, commercial premises provide large numbers of criminal opportunities for potential offenders; on the other hand, they are central to local social control and order. While small businesses exercise a certain amount of natural surveillance power, as a whole, they function primarily as crime generators. Implications for implementing targeted policies tailored to the nature of small businesses are discussed.
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Notes
News media reports such as South China Morning Post (https://www.scmp.com/culture/arts-entertainment/article/2107970/beijing-hutong-clean-threatens-independent-art-space), China Daily (https://www.chinadailyhk.com/articles/220/111/152/1505700836140.html/), and City Monitor (https://citymonitor.ai/environment/bricking-beijing-could-end-hutong-mean-end-street-life-chinese-capital-3287) have documented more details about small businesses at the time.
More information can be found in media reports such as Caixin (https://www.caixinglobal.com/2017-08-25/street-stores-vanish-in-beijing-as-government-seals-up-holes-in-wall-101135578.html), ThePaper (https://www.thepaper.cn/newsDetail_forward_2160157), New York Times (https://www.nytimes.com/2017/07/17/world/asia/beijing-china-reconstruction-hutong.html), and Foreign Policy Magazine (https://foreignpolicy.com/2017/05/31/how-to-destroy-the-heart-of-a-chinese-city-beijing).
We compute the commercial signage index using the street view images and a semantic segmentation approach described in detail in Sect. "Natural Experiment Design". The formula is signage indexi = \(\left( {\mathop \sum \limits_{j}^{4} T_{j} /4 + \mathop \sum \limits_{j}^{4} SB_{j} /4} \right)/\left( {\mathop \sum \limits_{j}^{4} B_{j} /4 + \mathop \sum \limits_{j}^{4} H_{j} /4 + \mathop \sum \limits_{j}^{4} SS_{j} /4} \right)\), where Tj, SBj, Bj, Hj, SSj denote the proportion of trade name, signboard, building, house, and skyscraper pixels, respectively, to all pixels in the jth heading angle of sampling point i. According to the paired-samples t test, the average commercial signage index of the treatment group, where the cleanup campaign is concentrated, shows a statistically significant decrease in the post-intervention period (2017–2019) compared to the pre-intervention period (2014–2016). However, for the control group, which is less affected by the cleanup campaign, the average signage index does not change significantly between the two periods. This suggests that the cleanup campaign does lead to the demise of small businesses.
References
Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6):1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Bernasco W, Block R (2011) Robberies in Chicago: a block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. J Res Crime Delinq 48(1):33–57. https://doi.org/10.1177/0022427810384135
Boehme HM (2022) Are coffeeshops really third places? A street segment approach to investigate the influence of coffee shops on property and violent crime. Crim Justice Stud 35(3):219–234. https://doi.org/10.1080/1478601X.2022.2087649
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. https://doi.org/10.1016/s0272-4944(05)80212-9
Brantingham PL, Brantingham PJ (1995) Criminality of place: crime generators and crime attractors. Eur J Crim Policy Res 3(3):5–26. https://doi.org/10.1007/BF02242925
Cervero R, Kockelman K (1997) Travel demand and the 3Ds: density, diversity, and design. Transp Res Part D Transp Environ 2(3):199–219. https://doi.org/10.1016/s1361-9209(97)00009-6
Clarke I, Banga S (2010) The economic and social role of small stores: a review of UK evidence. Int Rev Retail Distrib Consum Res 20(2):187–215. https://doi.org/10.1080/09593961003701783
Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44(4):588–608. https://doi.org/10.2307/2094589
Connealy NT (2021) Understanding the predictors of street robbery hot spots: a matched pairs analysis and systematic social observation. Crime Delinq 67(9):1319–1352. https://doi.org/10.1177/0011128720926116
Cook PJ, MacDonald J (2011) Public safety through private action: an economic assessment of BIDs. Econ J 121(552):445–462. https://doi.org/10.1111/j.1468-0297.2011.02419.x
Deryol R, Wilcox P, Logan M, Wooldredge J (2016) Crime places in context: an illustration of the multilevel nature of hot spot development. J Quant Criminol 32(2):305–325. https://doi.org/10.1007/s10940-015-9278-1
Eck JE, Weisburd D (1995) Crime and places crime prevention studies. Willow Tree Press, New York
Eck JE, Clarke RV, Guerette RT (2007) Risky facilities: Crime concentration in homogeneous sets of establishments and facilities. Crime Prev Stud 21:225–264
Eck JE, Linning SJ, Herold TD (2023) Place management and crime: ownership and property rights as a source of social control. Springer, Berlin
Feldmeyer B (2010) The effects of racial/ethnic segregation on Latino and Black homicide. Sociol Q 51(4):600–623. https://doi.org/10.1111/j.1533-8525.2010.01185.x
Francis J, Giles-Corti B, Wood L, Knuiman M (2012) Creating sense of community: the role of public space. J Environ Psychol 32(4):401–409. https://doi.org/10.1016/j.jenvp.2012.07.002
Hadavi S, Rigolon A, Gobster PH, Stewart WP (2021) Resident-led vacant lot greening and crime: Do ownership and visual condition-care matter? Landsc Urban Plan 211:104096. https://doi.org/10.1016/j.landurbplan.2021.104096
He Z, Wang Z, Xie Z, Wu L, Chen Z (2022) Multiscale analysis of the influence of street built environment on crime occurrence using street-view images. Comput Environ Urban Syst 97(3):101865. https://doi.org/10.1016/j.compenvurbsys.2022.101865
Hickman P (2013) “Third places” and social interaction in deprived neighbourhoods in Great Britain. J Housing Built Environ 28(2):221–236. https://doi.org/10.1007/s10901-012-9306-5
Hu A, Hibel J (2014) Changes in college attainment and the economic returns to a college degree in Urban China, 2003–2010: implications for social equality. Soc Sci Res 44:173–186. https://doi.org/10.1016/j.ssresearch.2013.12.001
Jacobs J (1961) The death and life of great American cities. Random House, New York
Kim Y-A, Hipp JR (2021) Density, diversity, and design: three measures of the built environment and the spatial patterns of crime in street segments. J Crim Just 77:101864. https://doi.org/10.1016/j.jcrimjus.2021.101864
Kim Y-A, Hipp JR (2022) Small local versus non-local: examining the relationship between locally owned small businesses and spatial patterns of crime. Justice Q 39(5):983–1008. https://doi.org/10.1080/07418825.2021.1879899
Kim Y-A, Wo JC, Hipp JR (2023) Estimating age-graded effects of businesses on crime in place. Justice Q 40(3):403–426. https://doi.org/10.1080/07418825.2022.2107943
Kitchen T (2005) New urbanism and the CPTED in the British planning system: some critical reflections. J Arch Plan Res 22(4):342–357. https://doi.org/10.2307/43030752
Kurland J, Johnson SD, Tilley N (2014) Offenses around stadiums: a natural experiment on crime attraction and generation. J Res Crime Delinq 51(1):5–28. https://doi.org/10.1177/0022427812471349
Landman K (2009) Boundaries, bars, and barricades: reconsidering two approaches to crime prevention in the built environment. J Arch Plan Res 26(3):213–227. https://doi.org/10.2307/43030870
Leatherdale ST (2019) Natural experiment methodology for research: a review of how different methods can support real-world research. Int J Soc Res Methodol 22(1):19–35. https://doi.org/10.1080/13645579.2018.1488449
Lee MR, Thomas SA (2010) Civic community, population change, and violent crime in rural communities. J Res Crime Delinq 47(1):118–147. https://doi.org/10.1177/0022427809348907
Linning SJ, Eck JE (2021) Whose ‘eyes on the street’ control crime? Expanding place management into neighborhoods. Cambridge University Press, Cambridge
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. https://arxiv.org/abs/2103.14030. https://doi.org/10.48550/arXiv.2103.14030
Lv Y, Lan Z, Kan C, Zheng X (2021) Polycentric urban development and its determinants in China: a geospatial big data perspective. Geogr Anal 53(3):520–542. https://doi.org/10.1111/gean.12236
MacKinnon D (2008) Introduction to statistical mediation analysis. Routledge, New York. https://doi.org/10.4324/9780203809556
Newman O (1972) Defensible space: crime prevention through urban design. Macmillan, New York
Oldenburg R (1999) The great good place: cafes, coffee shops, bookstores, bars, hair salons, and other hangouts at the heart of a community. Da Capo Press, Cambridge
Palmer EJ, Holmes A, Hollin CR (2002) Investigating burglars’ decisions: factors influencing target choice, method of entry, reasons for offending, repeat victimisation of a property and victim awareness. Secur J 15(1):7–18. https://doi.org/10.1057/palgrave.sj.8340101
Papachristos AV, Smith CM, Scherer ML, Fugiero MA (2011) More coffee, less crime? The relationship between gentrification and neighborhood crime rates in Chicago, 1991 to 2005. City Commun 10(3):215–240. https://doi.org/10.1111/j.1540-6040.2011.01371.x
Peterson RD, Krivo LJ, Harris MA (2000) Disadvantage and neighborhood violent crime: Do local institutions matter? J Res Crime Delinq 37(1):31–63. https://doi.org/10.1177/0022427800037001002
Piza EL, Wheeler AP, Connealy NT, Feng SQ (2020) Crime control effects of a police substation within a business improvement district: a quasi-experimental synthetic control evaluation. Criminol Public Policy 19(2):653–684. https://doi.org/10.1111/1745-9133.12488
Ratcliffe JH (2004) Geocoding crime and a first estimate of a minimum acceptable hit rate. Int J Geogr Inf Sci 18(1):61–72. https://doi.org/10.1080/13658810310001596076
Reynald DM, Elffers H (2009) The future of Newman’s defensible space theory: linking defensible space and the routine activities of place. Eur J Criminol 6(1):25–46. https://doi.org/10.1177/1477370808098103
Ridgeway G, MacDonald JM (2017) Effect of rail transit on crime: a study of Los Angeles from 1988 to 2014. J Quant Criminol 33:277–291. https://doi.org/10.1007/s10940-016-9296-7
Ridgeway G, Grogger J, Moyer RA, MacDonald JM (2019) Effect of gang injunctions on crime: a study of Los Angeles from 1988–2014. J Quant Criminol 35:517–541. https://doi.org/10.1007/s10940-018-9396-7
Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(4):41–55. https://doi.org/10.1093/biomet/70.1.41
Sampson RJ, Groves WB (1989) Community structure and crime: testing social-disorganization theory. Am J Sociol 94(4):774–802. https://doi.org/10.2307/2780858
Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277(5328):918–924. https://doi.org/10.2307/2892902
Shaw CR, McKay HD (1942) Juvenile delinquency and urban areas. University of Chicago Press, Chicago
Shihadeh ES, Flynn N (1996) Segregation and crime: the effect of black social isolation on the rates of black urban violence. Soc Forces 74(4):1325–1352. https://doi.org/10.1093/sf/74.4.1325
Smith CM (2014) The influence of gentrification on gang homicides in Chicago neighborhoods, 1994 to 2005. Crime Delinq 60(4):569–591. https://doi.org/10.1177/0011128712446052
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(4):831–854. https://doi.org/10.1007/s10940-019-09406-z
Song G, Zhang Y, Bernasco W, Cai L, Liu L, Qin B, Chen P (2023) Residents, employees and visitors: effects of three types of ambient population on theft on weekdays and weekends in Beijing, China. J Quant Criminol 39:385–423. https://doi.org/10.1007/s10940-021-09538-1
Sung H, Lee S, Cheon S (2015) Operationalizing Jane Jacobs’s urban design theory: empirical verification from the great city of Seoul, Korea. J Plan Educ Res 35(2):117–130. https://doi.org/10.1177/0739456x14568021
Tillyer MS, Wilcox P, Walter RJ (2021) Crime generators in context: examining ‘place in neighborhood’ propositions. J Quant Criminol 37(2):517–546. https://doi.org/10.1007/s10940-019-09446-5
van den Berg P, Arentze T, Timmermans H (2015) A multilevel analysis of factors influencing local social interaction. Transportation 42(5):807–826. https://doi.org/10.1007/s11116-015-9648-4
Williams SA, Hipp JR (2019) How great and how good?: Third places, neighbor interaction, and cohesion in the neighborhood context. Soc Sci Res 77:68–78. https://doi.org/10.1016/j.ssresearch.2018.10.008
Wo JC (2016) Community context of crime: a longitudinal examination of the effects of local institutions on neighborhood crime. Crime Delinq 62(10):1286–1312. https://doi.org/10.1177/0011128714542501
Xiao Y, Bian Y (2018) The influence of hukou and college education in China’s labour market. Urban Stud 55(7):1504–1524. https://doi.org/10.1177/0042098017690471
Xin Y, Cai T (2020) Paying money for freedom: effects of monetary compensation on sentencing for criminal traffic offenses in China. J Quant Criminol 36(1):1–28. https://doi.org/10.1007/s10940-019-09409-w
Yin L, Cheng Q, Wang Z, Shao Z (2015) ‘Big data’ for pedestrian volume: exploring the use of Google Street View images for pedestrian counts. Appl Geogr 63:337–345. https://doi.org/10.1016/j.apgeog.2015.07.010
Yue H, Liu L, Xiao L (2023) Investigating the effect of people on the street and streetscape physical environment on the location choice of street theft crime offenders using street view images and a discrete spatial choice model. Appl Geogr 157:103025. https://doi.org/10.1016/j.apgeog.2023.103025
Zhang Y, Cai L, Song G, Liu L, Zhu C (2022) From residential neighborhood to activity space: the effects of educational segregation on crime and their moderation by social context. Ann Am Assoc Geogr 112(8):2393–2412. https://doi.org/10.1080/24694452.2022.2060793
Zhang Y, Cai L, Song G, Zhu C (2023) The long-term theft prediction in Beijing using machine learning algorithms: comparison and interpretation. Crime Delinq. https://doi.org/10.1177/00111287231180102
Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A (2017) Scene parsing through ADE20K dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 633–641. https://doi.org/10.1109/CVPR.2017.544
Zhou B, Zhao H, Puig X, Xiao T, Fidler S, Barriuso A, Torralba A (2019) Semantic understanding of scenes through the ADE20K dataset. Int J Comput Vision 127:302–321. https://doi.org/10.1007/s11263-018-1140-0
Zhuo Y, Messner SF, Zhang L (2008) Criminal victimization in contemporary China: a review of the evidence and challenges for future research. Crime Law Soc Chang 50(3):197–209. https://doi.org/10.1007/s10611-008-9138-0
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This article was completed with support from the Youth Program of National Social Science Foundation of China (21CSH006).
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Zhang, Y., Cai, L., Song, G. et al. Causal Effect of Small Businesses on Street Theft: Evidence from a Natural Experiment of the Beijing Cleanup Campaign. J Quant Criminol (2024). https://doi.org/10.1007/s10940-024-09586-3
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DOI: https://doi.org/10.1007/s10940-024-09586-3