IDEAS home Printed from https://ideas.repec.org/a/adp/jbboaj/v1y2017i5p99-103.html
   My bibliography  Save this article

PSpatial Point Pattern Analyses and its Use in Geographical Epidemiology

Author

Listed:
  • Murat Yazici

    (Data Scientist& Researcher, Turkey)

Abstract

Spatial epidemiology is a subfield of health geography focused on the study of the spatial distribution of health outcomes. Point pattern analysis is the evaluation of the pattern, or distribution, of a set of points on a surface. It can refer to the actual spatial or temporal location of these points or also include data from point sources. It is one of the most fundamental concepts in geography and spatial analysis. In this study, geographical epidemiology, data for spatial analysis, disease clustering, disease mapping are introduced.

Suggested Citation

  • Murat Yazici, 2017. "PSpatial Point Pattern Analyses and its Use in Geographical Epidemiology," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(5), pages 99-103, May.
  • Handle: RePEc:adp:jbboaj:v:1:y:2017:i:5:p:99-103
    DOI: 10.19080/BBOAJ.2017.01.555573
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/bboaj/pdf/BBOAJ.MS.ID.555573.pdf
    Download Restriction: no

    File URL: https://juniperpublishers.com/bboaj/BBOAJ.MS.ID.555573.php
    Download Restriction: no

    File URL: https://libkey.io/10.19080/BBOAJ.2017.01.555573?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 & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, 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. Marvin M. Smith & Tony E. Smith & John Wackes, 2007. "Alternative financial service providers and the spatial void hypothesis," Community Affairs Discussion Paper 07-01, Federal Reserve Bank of Philadelphia.
    2. Zhang, Tonglin & Lin, Ge, 2016. "On Moran’s I coefficient under heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 83-94.
    3. Costa, Marcelo Azevedo & Assunção, Renato Martins & Kulldorff, Martin, 2012. "Constrained spanning tree algorithms for irregularly-shaped spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1771-1783.
    4. HAEDO, Christian & MOUCHART , Michel & ,, 2013. "Specialized agglomerations with areal data: model and detection," LIDAM Discussion Papers CORE 2013060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Mohammad Meysami & Joshua P. French & Ettie M. Lipner, 2023. "Flexible-Elliptical Spatial Scan Method," Mathematics, MDPI, vol. 11(17), pages 1-22, August.
    6. Johnston, Robert J. & Ramachandran, Mahesh & Schultz, Eric T. & Segerson, Kathleen & Besedin, Elena Y., 2011. "Characterizing Spatial Pattern in Ecosystem Service Values when Distance Decay Doesn’t Apply: Choice Experiments and Local Indicators of Spatial Association," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103374, Agricultural and Applied Economics Association.
    7. Tomoya Mori & Tony E. Smith, 2011. "An Industrial Agglomeration Approach To Central Place And City Size Regularities," Journal of Regional Science, Wiley Blackwell, vol. 51(4), pages 694-731, October.
    8. Kulldorff, Martin & Tango, Toshiro & Park, Peter J., 2003. "Power comparisons for disease clustering tests," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 665-684, April.
    9. Tomoya Mori & Tony E. Smith, 2014. "A probabilistic modeling approach to the detection of industrial agglomerations," Journal of Economic Geography, Oxford University Press, vol. 14(3), pages 547-588.
    10. Ikuho Yamada & Peter Rogerson & Gyoungju Lee, 2009. "GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters," Journal of Geographical Systems, Springer, vol. 11(2), pages 155-173, June.
    11. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
    12. Wan, You & Pei, Tao & Zhou, Chenghu & Jiang, Yong & Qu, Chenxu & Qiao, Youlin, 2012. "ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 283-296.
    13. Gerald A. Carlino & Jake Carr & Robert M. Hunt & Tony E. Smith, 2010. "The agglomeration of R&D labs," Working Papers 10-33, Federal Reserve Bank of Philadelphia.
    14. Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    15. repec:rri:wpaper:200506 is not listed on IDEAS
    16. Ben Said FOUED, 2015. "Tunisian Coastal Cities Attractiveness And Amenities," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 10(3), pages 49-70, August.
    17. Peter Congdon, 2000. "Monitoring Suicide Mortality: A Bayesian Approach," European Journal of Population, Springer;European Association for Population Studies, vol. 16(3), pages 251-284, September.
    18. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    19. Katarzyna Kopczewska, 2022. "Spatial machine learning: new opportunities for regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 713-755, June.
    20. Porter, Michael D. & Brown, Donald E., 2007. "Detecting local regions of change in high-dimensional criminal or terrorist point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2753-2768, February.
    21. Ronald E. Gangnon & Murray K. Clayton, 2000. "Bayesian Detection and Modeling of Spatial Disease Clustering," Biometrics, The International Biometric Society, vol. 56(3), pages 922-935, September.

    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:adp:jbboaj:v:1:y:2017:i:5:p:99-103. 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: Robert Thomas (email available below). General contact details of provider: .

    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.