IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v4y2018i1d10.1057_s41599-018-0188-3.html
   My bibliography  Save this article

Comparing apples to apples: an environmental criminology analysis of the effects of heat and rain on violent crimes in Boston

Author

Listed:
  • Alice J. Sommer

    (Harvard University)

  • Mihye Lee

    (St. Luke’s Int’l University)

  • Marie-Abèle C. Bind

    (Harvard University)

Abstract

Weather characteristics have been suggested by many social scientists to influence criminality. According to a recent study, climate change may cause a substantial increase in criminal activities during the twenty-first century. The additional number of crimes due to climate have been estimated by associational models, which are not optimal to quantify causal impacts of weather conditions on criminality. Using the Rubin Causal Model and crime data reported daily between 2012 and 2017, this study examines whether changes in heat index, a proxy for apparent temperature, and rainfall occurrence, influence the number of violent crimes in Boston. On average, more crimes are reported on temperate days compared to extremely cold days, and on dry days compared to rainy days. However, no significant differences in the number of crimes between extremely hot days versus less warm days could be observed. The results suggest that weather forecasts could be integrated into crime prevention programs in Boston. The weather-crime relationship should be taken into account when assessing the economic, sociological, or medical impact of climate change. Researchers and policy makers interested in the effects of environmental exposures or policy interventions on crime should consider a causal inference approach to analyze their data.

Suggested Citation

  • Alice J. Sommer & Mihye Lee & Marie-Abèle C. Bind, 2018. "Comparing apples to apples: an environmental criminology analysis of the effects of heat and rain on violent crimes in Boston," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:4:y:2018:i:1:d:10.1057_s41599-018-0188-3
    DOI: 10.1057/s41599-018-0188-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-018-0188-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-018-0188-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dennis M. Mares & Kenneth W. Moffett, 2016. "Climate change and interpersonal violence: a “global” estimate and regional inequities," Climatic Change, Springer, vol. 135(2), pages 297-310, March.
    2. Ranson, Matthew, 2014. "Crime, weather, and climate change," Journal of Environmental Economics and Management, Elsevier, vol. 67(3), pages 274-302.
    3. Dennis Mares & Kenneth Moffett, 2016. "Climate change and interpersonal violence: a “global” estimate and regional inequities," Climatic Change, Springer, vol. 135(2), pages 297-310, March.
    4. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ujjal Kumar Mukherjee & Benjamin E. Bagozzi & Snigdhansu Chatterjee, 2023. "A Bayesian framework for studying climate anomalies and social conflicts," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    2. Zabrocki, Léo & Leroutier, Marion & Bind, Marie-Abèle, 2021. "Estimating the Local Air Pollution Impacts of Maritime Traffic: A Principled Approach for Observational Data," OSF Preprints v7ctk_v1, Center for Open Science.
    3. Vanessa Azevedo & Mariana Magalhães & Daniela Paulo & Rui Leandro Maia & Gisela M. Oliveira & Maria Simas Guerreiro & Ana Isabel Sani & Laura M. Nunes, 2021. "Temporal Variability of Theft Types in the Historic Centre of Porto," Social Sciences, MDPI, vol. 10(10), pages 1-12, October.
    4. Kimpton, Anthony & Loginova, Julia & Pojani, Dorina & Bean, Richard & Sigler, Thomas & Corcoran, Jonathan, 2022. "Weather to scoot? How weather shapes shared e-scooter ridership patterns," Journal of Transport Geography, Elsevier, vol. 104(C).

    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. Cathy W. S. Chen & Sangyeol Lee, 2017. "Bayesian causality test for integer-valued time series models with applications to climate and crime data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 797-814, August.
    2. Ugur, Mehmet & Trushin, Eshref, 2018. "Asymmetric information and heterogeneous effects of R&D subsidies: evidence on R&D investment and employment of R&D personel," Greenwich Papers in Political Economy 21943, University of Greenwich, Greenwich Political Economy Research Centre.
    3. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
    4. Ian Gazeley & Rose Holmes & Andrew Newell & Kevin Reynolds & Hector Gutierrez Rufrancos, 2023. "Escaping from hunger before WW1: the nutritional transition and living standards in Western Europe and USA in the late nineteenth century," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 17(3), pages 533-565, September.
    5. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.
    6. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    7. Duncan Chaplin & Arif Mamun & Ali Protik & John Schurrer & Divya Vohra & Kristine Bos & Hannah Burak & Laura Meyer & Anca Dumitrescu & Christopher Ksoll & Thomas Cook, "undated". "Grid Electricity Expansion in Tanzania by MCC: Findings from a Rigorous Impact Evaluation, Final Report," Mathematica Policy Research Reports 144768f69008442e96369195e, Mathematica Policy Research.
    8. Jan Stede, 2019. "Do Energy Efficiency Networks Save Energy? Evidence from German Plant-Level Data," Discussion Papers of DIW Berlin 1813, DIW Berlin, German Institute for Economic Research.
    9. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    10. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Jan 2025.
    11. Eva-Maria Egger & Julie Litchfield, 2019. "Following in their footsteps: an analysis of the impact of successive migration on rural household welfare in Ghana," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 9(1), pages 1-22, December.
    12. Koch, Nicolas & Basse Mama, Houdou, 2019. "Does the EU Emissions Trading System induce investment leakage? Evidence from German multinational firms," Energy Economics, Elsevier, vol. 81(C), pages 479-492.
    13. Gerhard Krug, 2017. "Augmenting propensity score equations to avoid misspecification bias – Evidence from a Monte Carlo simulation [Erweiterung der Propensity Score Gleichung zur Vermeidung von Fehlspezifikationen? Ein," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 205-231, December.
    14. Gary King & Christopher Lucas & Richard A. Nielsen, 2017. "The Balance‐Sample Size Frontier in Matching Methods for Causal Inference," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 473-489, April.
    15. Francesco Manaresi & Carlo Menon & Pietro Santoleri, 2021. "Supporting innovative entrepreneurship: an evaluation of the Italian “Start-up Act” [The effects of entry on incumbent innovation and productivity]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(6), pages 1591-1614.
    16. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2020. "A New Quantile Treatment Effect Model for Studying Smoking Effect on Birth Weight During Mother's Pregnancy," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202003, University of Kansas, Department of Economics, revised Feb 2020.
    17. Christian Alemán-Pericón & Alexander Ludwig & Christopher Busch & Raül Santaeulà lia-Llopis, 2022. "A Stage-Based Identification of Policy Effects," Working Papers 1369, Barcelona School of Economics.
    18. Fukui Hideki, 2023. "Evaluating Different Covariate Balancing Methods: A Monte Carlo Simulation," Statistics, Politics and Policy, De Gruyter, vol. 14(2), pages 205-326, June.
    19. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    20. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201905, University of Kansas, Department of Economics, revised Mar 2019.

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:4:y:2018:i:1:d:10.1057_s41599-018-0188-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.