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A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source

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Listed:
  • Jiafang Song

    (Yale University)

  • Joshua L. Warren

    (Yale University)

Abstract

Point sources in spatially referenced data can impact outcomes in surrounding locations (e.g., a factory that emits air pollution). Previous statistical methods have sought to describe the non-stationary correlation induced by the presence of a point source, with fewer attempting to quantify its overall impact. We introduce directionally varying change points (DVCP), a model that aims to estimate the magnitude of the impact of a point source as well as its range of influence across the spatial domain. DVCP allows for a unique change point parameter, describing the range of influence of a point source, at every angle extending from the source and uses a Gaussian process with directionally defined correlation function to facilitate estimation of the parameters. The Gaussian predictive process approximation is used for fitting the model to large datasets. Through simulation, we show that DVCP can easily accommodate a wide range of shapes defining the range of influence. We apply DVCP to better understand spatial patterns of ambient PM $$_{2.5}$$ 2.5 concentrations and issues related to environmental inequity in California and Colorado. The method is available in the R package DVCP. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Jiafang Song & Joshua L. Warren, 2022. "A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 46-62, March.
    • Handle: RePEc:spr:jagbes:v:27:y:2022:i:1:d:10.1007_s13253-021-00466-y
    DOI: 10.1007/s13253-021-00466-y
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    References listed on IDEAS

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    1. Yushim Kim & Yongwan Chun, 2019. "Revisiting environmental inequity in Southern California: Does environmental risk increase in ethnically homogeneous or mixed communities?," Urban Studies, Urban Studies Journal Limited, vol. 56(9), pages 1748-1767, July.
    2. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    3. Lidia Ceriani & Paolo Verme, 2012. "The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(3), pages 421-443, September.
    4. Hughes-Oliver, Jacqueline M. & Gonzalez-Farias, Graciela & Lu, Jye-Chyi & Chen, Di, 1998. "Parametric nonstationary correlation models," Statistics & Probability Letters, Elsevier, vol. 40(3), pages 267-278, October.
    5. Michael Ash & T. Robert Fetter, 2004. "Who Lives on the Wrong Side of the Environmental Tracks? Evidence from the EPA's Risk‐Screening Environmental Indicators Model," Social Science Quarterly, Southwestern Social Science Association, vol. 85(2), pages 441-462, June.
    6. Joshua L. Warren, 2020. "A Nonstationary Spatial Covariance Model for Processes Driven by Point Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 415-430, September.
    7. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
    8. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    9. Veronica J. Berrocal & Alan E. Gelfand & David M. Holland, 2012. "Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality," Biometrics, The International Biometric Society, vol. 68(3), pages 837-848, September.
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