IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/g5r9t.html
   My bibliography  Save this paper

Strategic Point Processes

Author

Listed:
  • Davidson, Marty

Abstract

Spatial point processes provide a way for researchers to describe event-based outcomes in a bounded study window. Commonly used processes, such as the Neyman-Scott processes, however, often lack the behavioral frameworks that social scientists need to analyze strategic interactions between cohorts of actors. Strategic point processes address this issue by framing spatial point processes as a fixed set of players who engage in a countably infinite number of sequential games in a bounded study window. In this setup, each player receives a random function as their spatial strategy and their observed joint strategies generate the point pattern formations researchers record from the real world. To introduce the logic of strategic point processes, this paper describes the basic assumptions of Neyman-Scott (NS) spatial games, which model the clustering tendencies of strategic actors. Next, this paper discusses how researchers can reconstruct NS spatial games using observational datasets. Finally, this paper discusses how researchers can use complete spatial randomness (CSR) as an analytic baseline to draw inferences from NS spatial games.

Suggested Citation

  • Davidson, Marty, 2024. "Strategic Point Processes," OSF Preprints g5r9t, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:g5r9t
    DOI: 10.31219/osf.io/g5r9t
    as

    Download full text from publisher

    File URL: https://osf.io/download/668ca26b196f7d0020bf7919/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/g5r9t?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. Julian Besag & Peter J. Diggle, 1977. "Simple Monte Carlo Tests for Spatial Pattern," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 327-333, November.
    2. Peter J. Diggle & Barry S. Rowlingson, 1994. "A Conditional Approach to Point Process Modelling of Elevated Risk," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 433-440, May.
    3. Rasmus Plenge Waagepetersen, 2007. "An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes," Biometrics, The International Biometric Society, vol. 63(1), pages 252-258, March.
    4. A. Baddeley & R. Turner & J. Møller & M. Hazelton, 2005. "Residual analysis for spatial point processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 617-666, November.
    5. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    6. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    7. J. Pfanzagl, 1969. "On the measurability and consistency of minimum contrast estimates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 14(1), pages 249-272, December.
    8. Luke N. Condra & Jacob N. Shapiro, 2012. "Who Takes the Blame? The Strategic Effects of Collateral Damage," American Journal of Political Science, John Wiley & Sons, vol. 56(1), pages 167-187, January.
    9. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
    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. Nicoletta D’Angelo & Marianna Siino & Antonino D’Alessandro & Giada Adelfio, 2022. "Local spatial log-Gaussian Cox processes for seismic data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 633-671, December.
    2. Nicoletta D’Angelo & Giada Adelfio, 2024. "Minimum contrast for the first-order intensity estimation of spatial and spatio-temporal point processes," Statistical Papers, Springer, vol. 65(6), pages 3651-3679, August.
    3. Jesper Møller & Carlos Díaz‐Avalos, 2010. "Structured Spatio‐Temporal Shot‐Noise Cox Point Process Models, with a View to Modelling Forest Fires," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 2-25, March.
    4. D'Angelo, Nicoletta & Adelfio, Giada & Mateu, Jorge, 2023. "Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    5. Roba Bairakdar & Debbie Dupuis & Melina Mailhot, 2024. "Deviance Voronoi Residuals for Space-Time Point Process Models: An Application to Earthquake Insurance Risk," Papers 2410.04369, arXiv.org.
    6. Isabel Fuentes-Santos & Wenceslao González-Manteiga & Jorge Mateu, 2016. "Consistent Smooth Bootstrap Kernel Intensity Estimation for Inhomogeneous Spatial Poisson Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 416-435, June.
    7. Yu Ryan Yue & Ji Meng Loh, 2011. "Bayesian Semiparametric Intensity Estimation for Inhomogeneous Spatial Point Processes," Biometrics, The International Biometric Society, vol. 67(3), pages 937-946, September.
    8. Giuseppe Espa & Giuseppe Arbia & Diego Giuliani, 2013. "Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan," Journal of Geographical Systems, Springer, vol. 15(1), pages 31-50, January.
    9. Guangshun Bai & Xuemei Yang & Guangxin Bai & Zhigang Kong & Jieyong Zhu & Shitao Zhang, 2024. "Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis," Sustainability, MDPI, vol. 16(16), pages 1-24, August.
    10. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    11. Andrew J Edelman, 2012. "Positive Interactions between Desert Granivores: Localized Facilitation of Harvester Ants by Kangaroo Rats," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-9, February.
    12. Amanda S. Hering & Sean Bair, 2014. "Characterizing spatial and chronological target selection of serial offenders," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 123-140, January.
    13. Coeurjolly, Jean-François & Reynaud-Bouret, Patricia, 2019. "A concentration inequality for inhomogeneous Neyman–Scott point processes," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 30-34.
    14. Diego Giuliani & Giuseppe Arbia & Giuseppe Espa, 2014. "Weighting Ripley’s K-Function to Account for the Firm Dimension in the Analysis of Spatial Concentration," International Regional Science Review, , vol. 37(3), pages 251-272, July.
    15. Christophe Ange Napoléon Biscio & Frédéric Lavancier, 2017. "Contrast Estimation for Parametric Stationary Determinantal Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 204-229, March.
    16. María Cristina Rodríguez Rangel & Marcelino Sánchez Rivero & Julián Ramajo Hernández, 2020. "A Spatial Analysis of Intensity in Tourism Accommodation: An Application for Extremadura (Spain)," Economies, MDPI, vol. 8(2), pages 1-21, April.
    17. Michaela Prokešová & Jiří Dvořák & Eva B. Vedel Jensen, 2017. "Two-step estimation procedures for inhomogeneous shot-noise Cox processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(3), pages 513-542, June.
    18. Jean-François Coeurjolly, 2017. "Median-based estimation of the intensity of a spatial point process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 303-331, April.
    19. Daniel, Jeffrey & Horrocks, Julie & Umphrey, Gary J., 2018. "Penalized composite likelihoods for inhomogeneous Gibbs point process models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 104-116.
    20. Kenneth A. Flagg & Andrew Hoegh & John J. Borkowski, 2020. "Modeling Partially Surveyed Point Process Data: Inferring Spatial Point Intensity of Geomagnetic Anomalies," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 186-205, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:osfxxx:g5r9t. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.