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Time to Burn: Modeling Wildland Arson as an Autoregressive Crime Function

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  • Jeffrey P. Prestemon
  • David T. Butry

Abstract

Six Poisson autoregressive models of order p[PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994–2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995–2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up to eleven days, in addition to seasonality and links to law enforcement, wildland management, historical fire, and weather. The annual fixed effects model replicates many findings of the daily models but also detects the influence of wages and poverty on arson, in ways expected from theory. All findings support an economic model of crime. Copyright 2005, Oxford University Press.

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  • Jeffrey P. Prestemon & David T. Butry, 2005. "Time to Burn: Modeling Wildland Arson as an Autoregressive Crime Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 756-770.
    • Handle: RePEc:oup:ajagec:v:87:y:2005:i:3:p:756-770
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    File URL: http://hdl.handle.net/10.1111/j.1467-8276.2005.00760.x
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    Cited by:

    1. José Ramón González‐Olabarria & Blas Mola‐Yudego & Lluis Coll, 2015. "Different Factors for Different Causes: Analysis of the Spatial Aggregations of Fire Ignitions in Catalonia (Spain)," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1197-1209, July.
    2. Lema, D. & Egolf, P., 2018. "Forest fires and economic incentives: Impact of forest protection laws in Argentina," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275984, International Association of Agricultural Economists.
    3. Alessandra Canepa, 2024. "Socio-economic risk factors and wildfire crime in Italy: a quantile panel approach," Empirical Economics, Springer, vol. 66(1), pages 431-465, January.
    4. Chen, Xuan & Goodwin, Barry K., 2013. "Spatio-Temporal Modeling of Lightning Fires on Forestland: A Compensation Scheme," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151285, Agricultural and Applied Economics Association.
    5. Jaime de Diego & Antonio Rúa & Mercedes Fernández, 2021. "Vulnerability Variables and Their Effect on Wildfires in Galicia (Spain). A Panel Data Analysis," Land, MDPI, vol. 10(10), pages 1-17, September.
    6. Alló, M. & Loureiro, M.L., 2020. "Assessing preferences for wildfire prevention policies in Spain," Forest Policy and Economics, Elsevier, vol. 115(C).
    7. Canepa, Alessandra & Drogo, Federico, 2021. "Wildfire crime, apprehension and social vulnerability in Italy," Forest Policy and Economics, Elsevier, vol. 122(C).
    8. Melania Michetti & Mehmet Pinar, 2019. "Forest Fires Across Italian Regions and Implications for Climate Change: A Panel Data Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 207-246, January.
    9. Canepa,Alessandra & Drogo,Federico, 2019. "Wildfire Crime and Social Vulnerability in Italy: A Panel Investigation," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202005, University of Turin.

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