Literature Cited
- Barboza-Salerno, G. E. 2020a. Examining spatial regimes of child maltreatment allegations in a social vulnerability framework. Child Maltreatment 25 (1):70–84. doi: https://doi.org/10.1177/1077559519850340.
- Barboza-Salerno, G. E. 2020b. Variability and stability in child maltreatment risk across time and space and its association with neighborhood social & housing vulnerability in New Mexico: A Bayesian space–time model. Child Abuse & Neglect 104 (June):104472. doi: https://doi.org/10.1016/j.chiabu.2020.104472.
- Brantingham, P., and P. Brantingham. 1995. Criminality of place: Crime generators and crime attractors. European Journal on Criminal Policy and Research 3 (3):5–26. doi: https://doi.org/10.1007/BF02242925.
- Caplan, J. M. 2011. Mapping the spatial influence of crime correlates: A comparison of operationalization schemes and implications for crime analysis and criminal justice practice. Cityscape 13 (3):57–83. doi: https://doi.org/10.2307/41426675.
- Caplan, J. M., L. W. Kennedy, J. D. Barnum, and E. L. Piza. 2015. Risk terrain modeling for spatial risk assessment. Cityscape 17 (1):7–16.
- Coulton, C. J., D. S. Crampton, M. Irwin, J. C. Spilsbury, and J. E. Korbin. 2007. How neighborhoods influence child maltreatment: A review of the literature and alternative pathways. Child Abuse & Neglect 31(11–12):1117–42. doi: https://doi.org/10.1016/j.chiabu.2007.03.023.
- Cunradi, C. B., C. Mair, W. Ponicki, and L. Remer. 2011. Alcohol outlets, neighborhood characteristics, and intimate partner violence: Ecological analysis of a California city. Journal of Urban Health: Bulletin of the New York Academy of Medicine 88 (2):191–200. doi: https://doi.org/10.1007/s11524-011-9549-6.
- Daley, D., M. Bachmann, B. A. Bachmann, C. Pedigo, M.-T. Bui, and J. Coffman. 2016. Risk terrain modeling predicts child maltreatment. Child Abuse & Neglect 62 (December):29–38. doi: https://doi.org/10.1016/j.chiabu.2016.09.014.
- Drawve, G., S. A. Thomas, and J. T. Walker. 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 (March):21–29. doi: https://doi.org/10.1016/j.jcrimjus.2015.12.002.
- Drawve, G., and A. Wooditch. 2019. A research note on the methodological and theoretical considerations for assessing crime forecasting accuracy with the predictive accuracy index. Journal of Criminal Justice 64 (September):101625. doi: https://doi.org/10.1016/j.jcrimjus.2019.101625.
- Eubanks, V. 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. New York: St. Martin’s.
- Freisthler, B., P. J. Gruenewald, and J. P. Wolf. 2015. Examining the relationship between marijuana use, medical marijuana dispensaries, and abusive and neglectful parenting. Child Abuse & Neglect 48 (October):170–78. doi: https://doi.org/10.1016/j.chiabu.2015.07.008.
- Freisthler, B., and C. Kranich. 2020. Medical marijuana dispensaries and referrals for child maltreatment investigations. Journal of Interpersonal Violence. Advance online publication. doi: https://doi.org/10.1177/0886260520912596.
- Freisthler, B., B. Needell, and P. J. Gruenewald. 2005. Is the physical availability of alcohol and illicit drugs related to neighborhood rates of child maltreatment? Child Abuse & Neglect 29 (9):1049–60. doi: https://doi.org/10.1016/j.chiabu.2004.12.014.
- Friedman, J., T. Hastie, and R. Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33 (1):1–22.
- Goodling, E., J. Green, and N. McClintock. 2015. Uneven development of the sustainable city: Shifting capital in Portland, Oregon. Urban Geography 36 (4):504–24. doi: https://doi.org/10.1080/02723638.2015.1010791.
- Griffith, D., Y. Chun, and B. Li. 2019. Spatial regression analysis using eigenvector spatial filtering: Spatial econometrics and spatial statistics. Cambridge, MA: Academic Press.
- Hastie, T., J. Qian, and K. Tay. 2021. An introduction to Glmnet. R Package, Version 4.1-2. https://cran.r-project.org/web/packages/glmnet/vignettes/glmnet.pdf.
- Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference and prediction. New York: Springer.
- Hengl, T., M. Nussbaum, M. N. Wright, G. B. M. Heuvelink, and B. Gräler. 2018. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. Peerj 6 (August):e5518. doi: https://doi.org/10.7717/peerj.5518.
- Kennedy, L. W., J. M. Caplan, E. L. Piza, and H. Buccine-Schraeder. 2016. Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis and Policy 9 (4):529–48. doi: https://doi.org/10.1007/s12061-015-9165-z.
- Kim, H., C. Wildeman, M. Jonson-Reid, and B. Drake. 2017. Lifetime prevalence of investigating child maltreatment among U.S. children. American Journal of Public Health 107 (2):274–80. doi: https://doi.org/10.2105/AJPH.2016.303545.
- Kuhn, M. 2008. Building predictive models in R using the caret package. Journal of Statistical Software 28 (5):26. doi: https://doi.org/10.18637/jss.v028.i05.
- Messer, L. C., B. A. Laraia, J. S. Kaufman, J. Eyster, C. Holzman, J. Culhane, I. Elo, J. G. Burke, and P. O’Campo. 2006. The development of a standardized neighborhood deprivation index. Journal of Urban Health: Bulletin of the New York Academy of Medicine 83 (6):1041–62. doi: https://doi.org/10.1007/s11524-006-9094-x.
- Morton, C. M. 2013. The moderating effect of substance abuse service accessibility on the relationship between child maltreatment and neighborhood alcohol availability. Children and Youth Services Review 35 (12):1933–40. doi: https://doi.org/10.1016/j.childyouth.2013.09.019.
- Peterson, C., C. Florence, and J. Klevens. 2018. The economic burden of child maltreatment in the United States, 2015. Child Abuse & Neglect 86 (December):178–83. doi: https://doi.org/10.1016/j.chiabu.2018.09.018.
- Pinchevsky, G. M., and E. M. Wright. 2012. The impact of neighborhoods on intimate partner violence and victimization. Trauma, Violence & Abuse 13 (2):112–32. doi: https://doi.org/10.1177/1524838012445641.
- Predict Align Prevent. 2019. Richmond, Virginia technical report: Developed for the Virginia Department of Social Services Division of Family Services. Virginia Department of Social Services, Family Services Division, Richmond.
- Purdy, J., and B. Glass. 2020. The pursuit of algorithmic fairness: On “correcting” algorithmic unfairness in a child welfare reunification success classifier. arXiv:2010.12089 [Cs, Stat].
- R Core Team. 2020. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
- Rovi, S., P.-H. Chen, and M. S. Johnson. 2004. The economic burden of hospitalizations associated with child abuse and neglect. American Journal of Public Health 94 (4):586–90. doi: https://doi.org/10.2105/ajph.94.4.586.
- Sampson, R. J., and W. B. Groves. 1989. Community structure and crime: Testing social-disorganization theory. American Journal of Sociology 94 (4):774–802. doi: https://doi.org/10.1086/229068.
- Sandner, M., and S. L. Thomsen. 2020. Preventing child maltreatment: Beneficial side effects of public childcare provision. Hannover Economic Papers (HEP), No. 669, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, Hannover.
- Schaible, L., L. Dwight, and N. Heckler. 2021. The influence of spatial density of nonprofits on crime. Urban Affairs Review 57 (2):460–91. doi: https://doi.org/10.1177/1078087420908944.
- Thurston, H., B. Freisthler, J. Bell, D. Tancredi, P. S. Romano, S. Miyamoto, and J. G. Joseph. 2017. Environmental and individual attributes associated with child maltreatment resulting in hospitalization or death. Child Abuse & Neglect 67 (May):119–36. doi: https://doi.org/10.1016/j.chiabu.2017.02.024.
- Wheeler, A. P., and W. Steenbeek. 2021. Mapping the risk terrain for crime using machine learning. Journal of Quantitative Criminology 37 (2):445–80. doi: https://doi.org/10.1007/s10940-020-09457-7.