Abstract
The current study examines the social, racial, and economic determinants of homicide rates in the City of St. Louis using census tracts and 1000-m raster grids as geographic units for analysis. We used a meso and micro-level geographic scale to evaluate potential impacts associated with the modifiable areal unit problem. Using spatial interpolation, we generated redistributed values, based on area weighting, for explanatory variables. We applied spatial dependence regression models to assess the relationship with social, economic, and racial determinants in the analysis of homicide rates. At the census tract level, we do not find significant relationships between social, racial, and economic explanatory variables and homicide rates. Significant relationships are observed at the 1000-m grid level with income inequality, public assistance, and racial diversity and homicide rates. The results indicate a trade-off between the explanatory power of the census tract and the precision of the 1000-m raster grid.
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Notes
Ordinary Least Squares (OLS) regression models for census tracts and 1000-m raster grids, and for the race and racial diversity indicated the presence of spatial autocorrelation.
References
Andresen, M. A. (2011). The ambient population and crime analysis. The Professional Geographer, 63(2), 193–212.
Anselin, L., & Bera, A. (1998). Spatial dependence in linear regression models with an introduction spatial econometrics. In A. Ullah & D. Giles (Eds.), Handbook of applied economic statistics (pp. 237–289). New York: Marcel Dekker.
Arnio, A. N., & Baumer, E. P. (2012). Demography, foreclosure, and crime. Demographic Research, 26, 449–488. https://doi.org/10.4054/DemRes.2012.26.18.
Barnum, J. D., Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2017). The crime kaleidoscope: A cross-jurisdictional analysis of place features and crime in three urban environments. Applied Geography, 79, 203–211. https://doi.org/10.1016/j.apgeog.2016.12.011.
Boessen, A., & Hipp, J. R. (2015). Close-ups and the scale of ecology: Land uses and the geography of social context and crime. Criminology, 53(3), 399–426. https://doi.org/10.1111/1745-9125.12074.
Boggess, L. N. (2017). Disentangling the reciprocal relationship between change in crime and racial/ethnic change. Social Science Research, 68, 30–42. https://doi.org/10.1016/j.ssresearch.2017.08.011.
Boggess, L. N., & Hipp, J. R. (2010). Violent crime, residential instability and mobility: Does the relationship differ in minority neighborhoods? Journal of Quantitative Criminology, 26(3), 351–370. https://doi.org/10.1007/s10940-010-9093-7.
Caplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381. https://doi.org/10.1080/07418825.2010.486037.
Caplan, J. M., Kennedy, L. W., & Piza, E. L. (2012). Joint utility of event-dependent and environmental crime analysis techniques for violent crime forecasting. Crime & Delinquency, 59(2), 243–270. https://doi.org/10.1177/0011128712461901.
Chamberlain, A. W., & Hipp, J. R. (2015). It’s all relative: Concentrated disadvantage within and across neighborhoods and communities, and the consequences for neighborhood crime. Journal of Criminal Justice, 43(6), 431–443. https://doi.org/10.1016/j.jcrimjus.2015.08.004.
Charles-Coll, J. A. (2011). Understanding income inequality: Concept, causes, and measurement. International Journal of Economics and Management Sciences, 1(3), 17–28.
Chon, D. S. (2012). The impact of population heterogeneity and income inequality on homicide rates: A cross-national assessment. International Journal of Offender Therapy and Comparative Criminology, 56(5), 730–748. https://doi.org/10.1177/0306624X11414813.
Conceição, P., & Ferreira, P. (2000). Constructing long and dense time-series of inequality using the Theil index. Eastern Economic Journal, 26, 61–74.
Dmowska, A., & Stepinski, T. F. (2014). High resolution dasymetric model of U.S. demographics with application to spatial distribution of racial diversity. Applied Geography, 53, 417–426. https://doi.org/10.1016/j.apgeog.2014.07.003.
Drawve, G., Thomas, S. A., & Walker, J. T. (2016). Bringing the physical environment back into neighborhood research: The utility of RTM for developing an aggregate neighborhood risk of crime measure. Journal of Criminal Justice, 44, 21–29. https://doi.org/10.1016/j.jcrimjus.2015.12.002.
Environmental Systems Research Institute (Esri). (2016). Understanding interpolation analysis. http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/understanding-interpolation-analysis.htm Accessed 11 December 2017.
Federal Bureau of Investigation (FBI). (2016). Uniform crime reports. https://ucr.fbi.gov/crime-in-the-u.s/2016/ Accessed 30 Decmber 2017.
Gehlke, C. E., & Biehl, K. (1934). Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association, 29(185), 169–170. https://doi.org/10.2307/2277827.
Gerell, M. (2016). Smallest is better? The spatial distribution of arson and the modifiable areal unit problem. Journal of Quantitative Criminology, 33(2), 293–318. https://doi.org/10.1007/s10940-016-9297-6.
Groff, E. R., Weisburd, D., & Yang, S. M. (2010). Is it important to examine crime trends at a local “micro” level? A longitudinal analysis of street to street variability in crime trajectories. Journal of Quantitative Criminology, 26(1), 7–32. https://doi.org/10.1007/s10940-009-9081-y.
Hannon, L. E. (2005). Extremely poor neighborhoods and homicide. Social Science Quarterly, 86, 1418–1434. https://doi.org/10.1111/j.0038-4941.2005.00353.x.
Hipp, J. R. (2007). Income inequality, race, and place: Does the distribution of race and class within neighborhoods affect crime rates? Criminology, 45, 665–698.
Hipp, J. R. (2011). Spreading the wealth: The effect of the distribution of income and race/ethnicity across households and neighborhoods on city crime trajectories. Criminology, 49(3), 631–665. https://doi.org/10.1111/j.1745-9125.2011.00238.x.
Hipp, J. R., Wo, J. C., & Kim, Y. A. (2017). Studying neighborhood crime across different macro spatial scales: The case of robbery in 4 cities. Social Science Research, 68, 15–29. https://doi.org/10.1016/j.ssresearch.2017.08.007.
Hipp, J. R., & Yates, D. (2011). Ghettos, thresholds, and crime: Does concentrated poverty really have an accelerating increasing effect on crime? Criminology, 49(4), 955–990.
Kang, S. (2015). Inequality and crime revisited: Effects of local inequality and economic segregation on crime. Journal of Population Economics, 29(2), 593–626. https://doi.org/10.1007/s00148-015-0579-3.
Kennedy, L. W., Caplan, J. M., & Piza, E. (2010). Risk clusters, hotspots, and spatial intelligence: Risk Terrain Modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27(3), 339–362. https://doi.org/10.1007/s10940-010-9126-2.
Kennedy, L. W., Caplan, J. M., Piza, E. L., & Buccine-Schraeder, H. (2015). Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis and Policy, 9(4), 529–548. https://doi.org/10.1007/s12061-015-9165-z.
Krivo, L. J., & Peterson, R. D. (1996). Extremely disadvantaged neighborhoods and urban crime. Social Forces, 75(2), 619–648. https://doi.org/10.1093/sf/75.2.619.
Lauritsen, J. L., & White, N. A. (2001). Putting violence in its place: The influence of race, ethnicity, gender, and place on the risk for violence. Criminology & Public Policy, 1(1), 37–60. https://doi.org/10.1111/j.1745-9133.2001.tb00076.x.
Lee, M. T., & Martinez, R. (2009). Immigration reduces crime: An emerging scholarly consensus. In E. Mcdonald (Ed.), Immigration, crime and justice (pp. 3–16). Bingley: Emerald Group Publishing Limited.
Light, M. T., & Harris, C. T. (2012). Race, space, and violence: Exploring spatial dependence in structural covariates of white and black violent crime in US Counties. Journal of Quantitative Criminology, 28(4), 559–586. https://doi.org/10.1007/s10940-011-9162-6.
MacDonald, J. M., Hipp, J. R., & Gill, C. (2012). The effects of immigrant concentration on changes in neighborhood crime rates. Journal of Quantitative Criminology, 29(2), 191–215. https://doi.org/10.1007/s10940-012-9176-8.
Maimaitijiang, M., Ghulam, A., Sandoval, J. S. O., & Maimaitiyiming, M. (2015). Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geoinformation, 35, 161–174. https://doi.org/10.1016/j.jag.2014.08.020.
Malleson, N., & Andresen, M. A. (2015). Spatio-temporal crime hotspots and the ambient population. Crime Science. https://doi.org/10.1186/s40163-015-0023-8.
Martinez, R., Stowell, J. I., & Lee, M. T. (2010). Immigration and crime in an era of transformation: A longitudinal analysis of homicides in San Diego neighborhoods, 1980–2000. Criminology, 48(3), 799–829.
McCall, P. L., Land, K. C., & Parker, K. F. (2011). Heterogeneity in the rise and decline of city-level homicide rates, 1976–2005: A latent trajectory analysis. Social Science Research, 40(1), 363–378. https://doi.org/10.1016/j.ssresearch.2010.09.007.
Openshaw, S., & Taylor, P. J. (1979). A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. In N. Wrigley & R. J. Bennet (Eds.), Statistical applications in the spatial sciences (pp. 127–144). London: Pion.
Parker, K., & Stansfield, R. (2015). The changing urban landscape: Interconnections between racial/ethnic segregation and exposure in the study of race-specific violence over time. American Journal of Public Health, 105(9), 1796–1805.
Patterson, L. A., & Doyle, M. W. (2009). Assessing effectiveness of national flood policy through spatiotemporal monitoring of socioeconomic exposure. JAWRA Journal of the American Water Resources Association, 45(1), 237–252. https://doi.org/10.1111/j.1752-1688.2008.00275.x.
Payne, T., & Gallagher, K. (2016). The importance of small units of aggregation: Trajectories of crime at addresses in Cincinnati, Ohio, 1998–2012. Criminology, Criminal Justice Law, and Society, 17(1), 20–36.
Peterson, R. D., & Krivo, L. J. (2009). Segregated spatial locations, race-ethnic composition, and neighborhood violent crime. The ANNALS of the American Academy of Political and Social Science, 623(1), 93–107. https://doi.org/10.1177/0002716208330490.
Prouse, V., Ramos, H., Grant, J., & Radice, M. (2014). How and when scale matters: The modifiable areal unit problem and income inequality in Halifax. Canadian Journal of Urban Research, 23(1), 61–82.
Ratcliffe, J., & McCullagh, M. (1999). Burglary, victimisation, and social deprivation. Crime Prevention and Community Safety, 1(2), 37–46. https://doi.org/10.1057/palgrave.cpcs.8140014.
Roberts, A., & Willits, D. (2013). Income inequality and homicide in the United States: Consistency across different income inequality measures and disaggregated homicide types. Homicide Studies, 19(1), 28–57. https://doi.org/10.1177/1088767914557811.
Schnell, C., Braga, A. A., & Piza, E. L. (2016). The influence of community areas, neighborhood clusters, and street segments on the spatial variability of violent crime in Chicago. Journal of Quantitative Criminology, 33(3), 469–496. https://doi.org/10.1007/s10940-016-9313-x.
Stowell, J. I., Messner, S. F., McGeever, K. F., & Raffalovich, L. E. (2009). Immigration and the recent violent crime drop in the United States: A pooled, cross-sectional time-series analysis of metrolpolitan areas. Criminology, 47(3), 889–928. https://doi.org/10.1111/j.1745-9125.2009.00162.x.
Stucky, T. D., & Ottensmann, J. R. (2009). Land use and violent crime. Criminology, 47(4), 1223–1264. https://doi.org/10.1111/j.1745-9125.2009.00174.x.
Stucky, T. D., Payton, S. B., & Ottensmann, J. R. (2015). Intra- and inter-neighborhood income inequality and crime. Journal of Crime and Justice, 39(3), 345–362. https://doi.org/10.1080/0735648x.2015.1004551.
Tcherni, M. (2011). Structural determinants of homicide: The big three. Journal of Quantitative Criminology, 27(4), 475–496. https://doi.org/10.1007/s10940-011-9134-x.
Theil, H. (1967). Economics and information theory. Chicago, IL: Rand McNally.
Theil, H. (1972). Statistical decomposition analysis: With applications in the social and administrative sciences. Amsterdam: North-Holland Publishing Company.
Theil, H., & Finizza, A. J. (1971). A note on the measurement of racial integration of schools by means of informational concepts. The Journal of Mathematical Sociology, 1(2), 187–193. https://doi.org/10.1080/0022250X.1971.9989795.
Tighe, J. R., & Ganning, J. P. (2015). The divergent city: Unequal and uneven development in St. Louis. Urban Geography, 36(5), 654–673. https://doi.org/10.1080/02723638.2015.1014673.
Ulmer, J. T., Harris, C. T., & Steffensmeier, D. (2012). Racial and ethnic disparities in structural disadvantage and crime: White, black, and hispanic comparisons. Social Science Quarterly, 93(3), 799–819. https://doi.org/10.1111/j.1540-6237.2012.00868.x.
Vogel, M. (2015). The modifiable areal unit problem in person–context research. Journal of Research in Crime and Delinquency, 53(1), 112–135. https://doi.org/10.1177/0022427815597039.
Wadsworth, T. (2010). Is immigration responsible for the crime drop? An assessment of the influence of immigration on changes in violent crime between 1990 and 2000. Social Science Quarterly, 91(2), 531–553. https://doi.org/10.1111/j.1540-6237.2010.00706.x.
Wang, F., & Arnold, M. T. (2008). Localized income inequality, concentrated disadvantage and homicide. Applied Geography, 28(4), 259–270. https://doi.org/10.1016/j.apgeog.2008.07.004.
Warr, M. (2000). Fear of crime in the United States: Avenues for research and policy. In D. Duffee (Ed.), Criminal justice 2000: Measurement and analysis of crime and justice (pp. 451–489). Washington, DC: U.S. Department of Justice.
Wieczorek, W. F., Delmerico, A. M., Rogerson, P. A., & Wong, D. W. (2012). Clusters in irregular areas and lattices. Wiley Interdisciplinary Reviews: Computational Statistics. https://doi.org/10.1002/wics.196.
Wong, D. (2009). The modifiable areal unit problem (MAUP). In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis (pp. 105–123). London: SAGE Publications Ltd. https://doi.org/10.4135/9780857020130.
Xie, M. (2009). The effects of multiple dimensions of residential segregation on black and hispanic homicide victimization. Journal of Quantitative Criminology, 26(2), 237–268. https://doi.org/10.1007/s10940-009-9078-6.
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Smith, T.A., Sandoval, J.S.O. A Spatial Analysis of Homicides in Saint Louis: The Importance of Scale. Spat Demogr 7, 57–82 (2019). https://doi.org/10.1007/s40980-018-00046-8
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DOI: https://doi.org/10.1007/s40980-018-00046-8