We theoretically discuss why addressing spatial dependence is important and empirically demonstrate its methodological advantages in the context of neighborhood and crime studies. We found that as the uncertainty in measuring neighbors increases, the bias in the coefficient estimates increases. However, importantly, we also observed that even with a high rate of uncertainty in the spatial matrix, the bias is smaller than in the non-spatial models. Likewise, as the uncertainty in defining neighbors increases, models tend to underestimate the standard errors. However, even with higher uncertainty in the spatial weight matrix, underestimation seems smaller than that of the non-spatial model.
AnselinL. (1988). Spatial econometrics: Methods and models. Springer.
2.
AnselinL. (2002). Under the hood issues in the specification and interpretation of spatial regression models. Agricultural Economics, 27(3), 247–267.
3.
AnselinL.CohenJ.CookD.GorrW.TitaG. (2000). Spatial analyses of crime. Criminal Justice, 4(2), 213–262.
4.
BallerR. DAnselinL.MessnerS. F.DeaneG.HawkinsD. F. (2001). Structural covariates of us county homicide rates: Incorporating spatial effects. Criminology, 39, 561–588.
BernascoW.BlockR. (2011). Robberies in Chicago: A block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. Journal of Research in Crime and Delinquency, 48(1), 33–57.
7.
BetzT.CookS. J.HollenbachF. M. (2021). Bias from network misspecification under spatial dependence. Political Analysis, 29(2), 260–266.
8.
BoessenA.HippJ. R. (2015). Close-ups and the scale of ecology: Land use and the geography of social context and crime. Criminology, 53(3), 399–426.
9.
BoydR.RichersonP. J. (1985). Culture and the evolutionary process. The University of Chicago Press.
10.
BrowningC. R.FeinbergS. L.DietzR. D. (2004). The paradox of social organization: Networks, collective efficacy, and violent crime in urban neighborhoods. Social Forces, 83(2), 503–534.
11.
CohenJ.TitaG. (1999). Diffusion in homicide: Exploring a general method for detecting spatial diffusion processes. Journal of Quantitative Criminology, 15(4), 451–493.
12.
CookS. J.HaysJ. C.FranzeseR. J. (2023). STADL up! The spatiotemporal autoregressive distributed lag model for TSCS data analysis. American Political Science Review, 117(1), 59–79.
13.
FelsonM.BobaR. (2010). Crime and everyday life. SAGE Publications.
14.
GriffthD. A. (1996). Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data. Canadian Geographies, 40(4), 351–367.
15.
HabermanC. P.RatcliffeJ. H. (2015). Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology, 53(3), 457–483.
16.
HippJ. R. (2007). Block, tract, and levels of aggregation: Neighborhood structure and crime and disorder as a case in point. American Sociological Review, 72(5), 659–680.
17.
HippJ. R.KubrinE. C. (2017). From bad to worse: How changing inequality in nearby areas impacts local crime. RSF: The Russell Sage Foundation Journal of the Social Sciences, 3(2), 129–151.
18.
HippJ. R.PerrinA. J. (2009). The simultaneous effect of social distance and physical distance on the formation of neighborhood ties. City & Community, 8(1), 5–25. https://doi.org/10.1111/j.1540-6040.2009.01267.x
19.
HippJ. R.RoussellA. (2013). Micro- and macro-environment population and the consequences for crime rates. Social Forces, 92(2), 563–595.
20.
HippJ. R.WoJ. C.KimY.-A. (2017). Studying neighborhood crime across different macro spatial scales: The case of robbery in 4 cities. Social Science Research, 68, 15–29.
21.
HunterA. (1974). Symbolic communities: The persistence and change of Chicago’s local communities (Studies of urban society). University of Chicago Press.
22.
JanowitzM. (1952). The community press in an urban setting (Glencoe, Ill). Free Press, 3, 473–493.
23.
KimY.-A.HippJ. R. (2020). Street egohood: An alternative perspective of measuring neighborhood and spatial patterns of crime. Journal of Quantitative Criminology, 36, 29–66.
24.
LeeJ. Y.KwanM.-P. (2011). Visualisation of socio-spatial isolation based on human activity patterns and social networks in space-time. Tijdschrift voor economische en sociale geografie, 102(4), 468–485.
25.
LeSageJ.PaceR. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC.
26.
LoftinC. (1986). Assaultive violence as a contagious social process. Bulletin of the New York Academy of Medicine, 62(5), 550–555.
27.
LynchJ. P.AddingtonL. A. (2006). Understanding crime statistics: Revisiting the divergence of the NCVS and the UCR. Cambridge University Press.
28.
MayhewB. H.LevingerR. L. (1976). Size and the density of interaction in human aggregates. American Journal of Sociology, 82(1), 86–110. http://www.jstor.org/stable/2777462
29.
MearsD. P.BhatiA. S. (2006). No community is an island: The effects of resource deprivation on urban violence in spatially and socially proximate communities*. Criminology, 44(3), 509–548.
30.
MessnerS. F.AnselinL.BallerR. D.HawkinsD. F.DeaneG.TolnayS. E. (1999). The spatial patterning of county homicide rates: An application of exploratory spatial data analysis. Journal of Quantitative Criminology, 15(4), 423–450.
31.
MorenoffJ. D.SampsonR. J.RaudenbushS. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39(3), 517–559.
32.
PapachristosA. V.WildemanC.RobertoE. (2015). Tragic, but not random: The social contagion of nonfatal gunshot injuries. Social Science & Medicine, 125, 139–150.
33.
PetersonR. D.KrivoL. J. (2010). Divergent social worlds: Neighborhood crime and the racial-spatial divide. Russell Sage Foundation.
34.
RenF.KwanM.-P. (2009). The impact of the Internet on human activity–travel patterns: Analysis of gender differences using multi-group structural equation models. Journal of Transport Geography, 17(6), 440–450.
RickG. (2009). From the ground up: Translating geography into community through neighbor networks. Princeton University Press.
37.
RüttenauerT. (2019). Spatial regression models: A systematic comparison of different model specifications using monte carlo experiments. Sociological Methods & Research, 51(2), 728–759.
38.
SampsonR. J. (2012). Great American city: Chicago and the enduring neighborhood effect. University of Chicago Press.
39.
SampsonR. J.RaudenbushS. W.EarlsF. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924.
40.
WeisburdD. (2015). The law of crime concentration and the criminology of place. Criminology, 53(2), 133–157.
41.
WeisburdD.GroffE. R.YangS.-M. (2012). The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press.
42.
WoJ. C.HippJ. R.BoessenA. (2016). Voluntary organizations and neighborhood crime: A dynamic perspective. Criminology, 54(2), 212–241.
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.
