IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i18p11416-d911954.html
   My bibliography  Save this article

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis

Author

Listed:
  • Danlin Yu

    (Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA)

  • Chuanglin Fang

    (Center for Urban and Regional Planning Design and Research, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100045, China)

Abstract

Urban crimes are a severe threat to livable and sustainable urban environments. Many studies have investigated the patterns, causes, and strategies for curbing the occurrence of urban crimes. It is found that neighborhood socioeconomic status, physical environment, and ethnic composition all might play a role in the occurrence of urban crimes. Inspired by the recent interest in exploring urban crime patterns with spatial data analysis techniques and the development of Bayesian hierarchical analytical approaches, we attempt to explore the inherently intricate relationships between urban assaultive violent crimes and the neighborhood socioeconomic status, physical environment, and ethnic composition in Paterson, NJ, using census data of the American Community Survey, alcohol and tobacco sales outlet data, and abandoned property listing data from 2013. Analyses are set at the census block group level. Urban crime data are obtained from the Paterson Police Department. Instead of examining relationships at a global level with both non-spatial and spatial analyses, we examine in depth the potential locally varying relationships at the local level through a Bayesian hierarchical spatially varying coefficient model. At both the global and local analysis levels, it is found that median household income is decisively negatively related to urban crime occurrence. Percentage of African Americans and Hispanics, number of tobacco sales outlets, and number of abandoned properties are all positively related with urban crimes. At the local level of analysis, however, the different factors have varying influence on crime occurrence throughout the city of Paterson, with median household income having the broadest influence across the city. The practice of applying a Bayesian hierarchical spatial analysis framework to understand urban crime occurrence and urban neighborhood characteristics enables urban planners, stakeholders, and public safety officials to engage in more active and targeted crime-reduction strategies.

Suggested Citation

  • Danlin Yu & Chuanglin Fang, 2022. "How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11416-:d:911954
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/18/11416/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/18/11416/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fei Huo & Li Xu & Yanping Li & James S. Famiglietti & Zhenhua Li & Yuya Kajikawa & Fei Chen, 2021. "Using big data analytics to synthesize research domains and identify emerging fields in urban climatology," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 12(1), January.
    2. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    3. David Wheeler & Lance Waller, 2009. "Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests," Journal of Geographical Systems, Springer, vol. 11(1), pages 1-22, March.
    4. Stuart G McIntyre, 2017. "Personal indebtedness, community characteristics and theft crimes," Urban Studies, Urban Studies Journal Limited, vol. 54(10), pages 2395-2419, August.
    5. Ganguly, Prasangsha & Mukherjee, Sayanti, 2021. "A multifaceted risk assessment approach using statistical learning to evaluate socio-environmental factors associated with regional felony and misdemeanor rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    6. Dan-Lin Yu, 2006. "Spatially varying development mechanisms in the Greater Beijing Area: a geographically weighted regression investigation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 40(1), pages 173-190, March.
    7. Weber, Bryan S., 2019. "Uber and urban crime," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 496-506.
    8. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    9. Niki T. Dickerson, 2007. "Black Employment, Segregation, and the Social Organization of Metropolitan Labor Markets," Economic Geography, Taylor & Francis Journals, vol. 83(3), pages 283-307, July.
    10. Richelle L. Winkler & Kenneth M. Johnson, 2016. "Moving Toward Integration? Effects of Migration on Ethnoracial Segregation Across the Rural-Urban Continuum," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 1027-1049, August.
    11. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    12. Warner, Barbara D., 2007. "Robberies with guns: Neighborhood factors and the nature of crime," Journal of Criminal Justice, Elsevier, vol. 35(1), pages 39-50.
    13. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    14. Yu, Danlin & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Li, Guangdong, 2021. "The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis," Land Use Policy, Elsevier, vol. 100(C).
    15. Bahram Zikirya & Xiong He & Ming Li & Chunshan Zhou, 2021. "Urban Food Takeaway Vitality: A New Technique to Assess Urban Vitality," IJERPH, MDPI, vol. 18(7), pages 1-18, March.
    16. Wheaton, William C., 2006. "Metropolitan fragmentation, law enforcement effort and urban crime," Journal of Urban Economics, Elsevier, vol. 60(1), pages 1-14, July.
    17. Masi, Christopher M. & Hawkley, Louise C. & Harry Piotrowski, Z. & Pickett, Kate E., 2007. "Neighborhood economic disadvantage, violent crime, group density, and pregnancy outcomes in a diverse, urban population," Social Science & Medicine, Elsevier, vol. 65(12), pages 2440-2457, December.
    18. Danlin Yu & Chuanglin Fang & Dan Xue & Jingyuan Yin, 2014. "Assessing Urban Public Safety via Indicator-Based Evaluating Method: A Systemic View of Shanghai," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(1), pages 89-104, May.
    19. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, Fall.
    20. Shino Shiode & Narushige Shiode, 2020. "Crime Geosurveillance in Microscale Urban Environments: NetSurveillance," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(5), pages 1386-1406, September.
    21. Fu, Bo & Yu, Danlin & Zhang, Yaojun, 2019. "The livable urban landscape: GIS and remote sensing extracted land use assessment for urban livability in Changchun Proper, China," Land Use Policy, Elsevier, vol. 87(C).
    22. Alves, Luiz G.A. & Ribeiro, Haroldo V. & Rodrigues, Francisco A., 2018. "Crime prediction through urban metrics and statistical learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 435-443.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Feichtinger & Klaus Salhofer, 2016. "The Fischler Reform of the Common Agricultural Policy and Agricultural Land Prices," Land Economics, University of Wisconsin Press, vol. 92(3), pages 411-432.
    2. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    3. Xiaoxi Wang & Yaojun Zhang & Danlin Yu & Xiwei Wu & Ding Li, 2022. "Changes in Demographic Factors’ Influence on Regional Productivity Growth: Empirical Evidence from China, 2000–2010," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    4. Tibor Sipos & Anteneh Afework Mekonnen & Zsombor Szabó, 2021. "Spatial Econometric Analysis of Road Traffic Crashes," Sustainability, MDPI, vol. 13(5), pages 1-16, February.
    5. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    6. Yu, Danlin & Murakami, Daisuke & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Wang, Xiaoxi & Li, Guangdong, 2020. "Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 21-37.
    7. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    8. repec:asg:wpaper:1006 is not listed on IDEAS
    9. Chakir, Raja & Lungarska, Anna, 2015. "Agricultural land rents in land use models: a spatial econometric analysis," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212641, European Association of Agricultural Economists.
    10. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    11. David Maddison, 2009. "A Spatio‐temporal Model of Farmland Values," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 171-189, February.
    12. Kent Kovacs & Thomas Holmes & Jeffrey Englin & Janice Alexander, 2011. "The Dynamic Response of Housing Values to a Forest Invasive Disease: Evidence from a Sudden Oak Death Infestation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 49(3), pages 445-471, July.
    13. Jesus Mur & Ana Angulo, 2005. "A closer look at the Spatial Durbin Model," ERSA conference papers ersa05p392, European Regional Science Association.
    14. Keller, Wolfgang & Shiue, Carol H., 2007. "The origin of spatial interaction," Journal of Econometrics, Elsevier, vol. 140(1), pages 304-332, September.
    15. Ahmad, Mahyudin & Siong Hook, Law, 2022. "Financial development, institutions, and economic growth nexus: A spatial econometrics analysis using geographical and institutional proximities," MPRA Paper 114471, University Library of Munich, Germany.
    16. Herrera Gómez, Marcos & Cid, Juan Carlos & Paz, Jorge Augusto, 2012. "Introducción a la econometría espacial: Una aplicación al estudio de la fecundidad en la Argentina usando R [Introduction to Spatial Econometrics: An application to the study of fertility in Argent," MPRA Paper 41138, University Library of Munich, Germany.
    17. Ozgun, Burcu & Broekel, Tom, 2021. "The geography of innovation and technology news - An empirical study of the German news media," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    18. Bernd Jost, 2012. "Price Dispersion, Search Costs and Spatial Competition: Evidence from the Austrian Retail Gasoline Market," NEURUS papers neurusp166, NEURUS - Network of European and US Regional and Urban Studies.
    19. Diana Mitsova & Ann-Margaret Esnard & Alka Sapat & Betty S. Lai, 2018. "Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 689-709, November.
    20. Pinto, Allan & Griffin, Terry W., 2022. "Detecting bubbles via single time-series variable: applying spatial specification tests to farmland values," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322534, Agricultural and Applied Economics Association.
    21. Márcio Poletti Laurini, 2017. "A spatial error model with continuous random effects and an application to growth convergence," Journal of Geographical Systems, Springer, vol. 19(4), pages 371-398, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11416-:d:911954. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.