IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v71y2022i1p175-193.html
   My bibliography  Save this article

A discrete kernel stick‐breaking model for detecting spatial boundaries in hydraulic fracturing wastewater disposal well placement across Ohio

Author

Listed:
  • Joshua L. Warren
  • Jiachen Cai
  • Nicholaus P. Johnson
  • Nicole C. Deziel

Abstract

Detecting sharp differences, or boundaries, in areal data can uncover important biological, physical and/or social differences between spatial regions. We introduce a new discrete areal data kernel function for use in the kernel stick‐breaking process framework that is shown to yield improved (i) detection of spatial boundaries, (ii) estimation of regression parameters and (iii) model fit through a simulation study and comparison with existing approaches. We use the model to analyse county‐level hydraulic fracturing Class II injection well counts in Ohio, where interesting boundary patterns may exist due to the close connection between hydraulic fracturing and shale rock formations. Class II injection wells are used for disposing hydraulic fracturing liquid waste and may pose an environmental risk for surrounding communities. Counties located on the Devonian shale with increased poverty, less income equality, smaller proportion of the population that is white, and increased population density are found to contain more wells, with the relationship reversed for counties off the shale. Results suggest that the new method provides improved model fit and is robust to the exclusion of an important spatially varying covariate, while also detecting boundaries surrounding different shale rock formations. The method is implemented in the R package KSBound.

Suggested Citation

  • Joshua L. Warren & Jiachen Cai & Nicholaus P. Johnson & Nicole C. Deziel, 2022. "A discrete kernel stick‐breaking model for detecting spatial boundaries in hydraulic fracturing wastewater disposal well placement across Ohio," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 175-193, January.
  • Handle: RePEc:bla:jorssc:v:71:y:2022:i:1:p:175-193
    DOI: 10.1111/rssc.12527
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12527
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssc.12527?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
    ---><---

    References listed on IDEAS

    as
    1. Lucija Muehlenbachs & Elisheba Spiller & Christopher Timmins, 2015. "The Housing Market Impacts of Shale Gas Development," American Economic Review, American Economic Association, vol. 105(12), pages 3633-3659, December.
    2. G. M. Jacquez & S. Maruca & M.-J. Fortin, 2000. "From fields to objects: A review of geographic boundary analysis," Journal of Geographical Systems, Springer, vol. 2(3), pages 221-241, September.
    3. Johnston, J.E. & Werder, E. & Sebastian, D., 2016. "Wastewater disposal wells, fracking, and environmental injustice in Southern Texas," American Journal of Public Health, American Public Health Association, vol. 106(3), pages 550-556.
    4. Samuel I. Berchuck & Jean-Claude Mwanza & Joshua L. Warren, 2019. "Diagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1063-1074, July.
    5. Haijun Ma & Bradley P. Carlin & Sudipto Banerjee, 2010. "Hierarchical and Joint Site-Edge Methods for Medicare Hospice Service Region Boundary Analysis," Biometrics, The International Biometric Society, vol. 66(2), pages 355-364, June.
    6. 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.
    7. Kirsten Hardy & Timothy W. Kelsey, 2015. "Local income related to Marcellus shale activity in Pennsylvania," Community Development, Taylor & Francis Journals, vol. 46(4), pages 329-340, October.
    8. Eddelbuettel, Dirk & Francois, Romain, 2011. "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i08).
    9. Duncan Lee & Alastair Rushworth & Sujit K. Sahu, 2014. "A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution," Biometrics, The International Biometric Society, vol. 70(2), pages 419-429, June.
    10. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    11. Alastair Rushworth & Duncan Lee & Christophe Sarran, 2017. "An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 141-157, January.
    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. 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.
    2. Alastair Rushworth & Duncan Lee & Christophe Sarran, 2017. "An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 141-157, January.
    3. Fleming, David & Komarek, Timothy & Partridge, Mark & Measham, Thomas, 2015. "The Booming Socioeconomic Impacts of Shale: A Review of Findings and Methods in the Empirical Literature," MPRA Paper 68487, University Library of Munich, Germany.
    4. Duncan Lee & Richard Mitchell, 2013. "Locally adaptive spatial smoothing using conditional auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 593-608, August.
    5. Hoy, Kyle A. & Xiarchos, Irene M. & Kelsey, Timothy W. & Brasier, Kathryn J. & Glenna, Leland L., 2018. "Marcellus Shale Gas Development and Farming," Agricultural and Resource Economics Review, Cambridge University Press, vol. 47(3), pages 634-664, December.
    6. Bello Musa Zango & Sanni Mohammed Lekan & Mohammed Jibrin Katun, 2020. "Conventional Methods in Housing Market Analysis: A Review of Literature," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 8(1), pages 227-241, January.
    7. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    8. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    9. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    10. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2001. "Comparing dynamic equilibrium economies to data," FRB Atlanta Working Paper 2001-23, Federal Reserve Bank of Atlanta.
    11. Oded Stark & Wiktor Budzinski, 2021. "A social‐psychological reconstruction of Amartya Sen’s measures of inequality and social welfare," Kyklos, Wiley Blackwell, vol. 74(4), pages 552-566, November.
    12. Brown, Jason P. & Fitzgerald, Timothy & Weber, Jeremy G., 2016. "Capturing rents from natural resource abundance: Private royalties from U.S. onshore oil & gas production," Resource and Energy Economics, Elsevier, vol. 46(C), pages 23-38.
    13. Atahan Afsar; José Elías Gallegos; Richard Jaimes; Edgar Silgado Gómez & José Elías Gallegos & Richard Jaimes & Edgar Silgado Gómez, 2020. "Reconciling Empirics and Theory: The Behavioral Hybrid New Keynesian Model," Vniversitas Económica 18560, Universidad Javeriana - Bogotá.
    14. Guignet, Dennis & Jenkins, Robin R. & Belke, James & Mason, Henry, 2023. "The property value impacts of industrial chemical accidents," Journal of Environmental Economics and Management, Elsevier, vol. 120(C).
    15. Katie Jo Black & Shawn J. McCoy & Jeremy G. Weber, 2018. "When Externalities Are Taxed: The Effects and Incidence of Pennsylvania’s Impact Fee on Shale Gas Wells," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 5(1), pages 107-153.
    16. Bai, Yizhou & Xue, Cheng, 2021. "An empirical study on the regulated Chinese agricultural commodity futures market based on skew Ornstein-Uhlenbeck model," Research in International Business and Finance, Elsevier, vol. 57(C).
    17. Hashida, Yukiko & Dundas, Steven J., 2023. "The effects of a voluntary property buyout and acquisition program on coastal housing markets: Evidence from New York," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
    18. Backstrom, Jesse, 2019. "Strategic Reporting and the Effects of Water Use in Hydraulic Fracturing on Local Groundwater Levels in Texas," Center for Growth and Opportunity at Utah State University 307177, Center for Growth and Opportunity.
    19. Fernández de Marcos Giménez de los Galanes, Alberto, 2022. "Data-driven stabilizations of goodness-of-fit tests," DES - Working Papers. Statistics and Econometrics. WS 35324, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Xiaofeng Lv & Gupeng Zhang & Guangyu Ren, 2017. "Gini index estimation for lifetime data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 275-304, April.

    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:bla:jorssc:v:71:y:2022:i:1:p:175-193. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.