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2014 | nr 16 Przestrzeń publiczna i sektor usług w małych miastach | 169--188
Tytuł artykułu

Zastosowanie metody krigingu Poissona w badaniach rozkładu przestrzennego problemów społecznych na przykładzie Poznania

Treść / Zawartość
Warianty tytułu
Poisson Kriging as a Tool for Social Problems Analysis- Poznań Case Study
Języki publikacji
PL
Abstrakty
Analiza przestrzenna danych społecznych wymaga niejednokrotnie odfiltrowania wpływu nierealnych, odstających danych. Celem pracy jest omówienie podstaw teoretycznych bardzo efektywnej, a mało znanej metody do tego służącej - krigingu Poissona. Ilustrację praktyczną jej zalet przedstawiono na przykładzie identyfikacji obszarów występowania różnych kategorii problemów społecznych na obszarze Poznania. (abstrakt oryginalny)
EN
Planning of social policy it is complicated and multidimensional issue, especially incomplex urban structures characteristic for big cities. However econometric indicators ofspatial dependence provide us some information about spatial autocorrelation, their donot show the differences in local variability. Geostatistics is an answer for this challenge.This method is not only helpful in more accurate determination of the most importantproblems but it also enables identification of their location, scale and possible reasons.This paper presents possibilities given by Poisson Kriging for analysis of socialproblems in urban space. Its was applied in Poznań for identification of neigh bourhoodsor local communities (related to the basic administrative units called "osiedla") in which concentration of people needing social help is bigger than population distribution mightit suggest. The data used in the analysis was taken from urban centre helping families indifficult social situation (MOPR). They concerns people who received financial supportin 2008. The basic information taken into account was their place of residence and thereason for which they received financial aid. MOPR distinguish 13 categories of socialproblems needing support, including poverty, chronic diseases, alcoholism and domesticviolence. 9 473 persons received financial aid in the analysed period of time. Taking intoaccount their families it give us at least 18 264 people struggling with social problems - 3,3% of the city population (545 000 inhabitants).In order to receive comparable measure of issues analysed in urban space, thenumber of people needing social support must be compared with the population distribution.Thus, information about place of residence of people who receives financial aidwas aggregated to bigger areas - 731 regular polygons for which the number of city inhabitants was know. Side length of single polygon was 500 meters. In each polygondata needed also to be age-adjusted. It is very sophisticated task, therefore special scriptdedicated for ArcGIS was created. The age-adjusted data aggregated in the polygonswere bases for main spatial analysis.Application of Poisson Kriging resulted in more precise identification of areasaffected by the major social problems in Poznań. Presence of autocorrelation wasnoticeable in case of majority of analysed social problems. The most common ranges of autocorrelation were 1-1,2 km (which is similar to the spatial range of single local188 Alfred Stach, Patrycja Wysocka communities) and 6-6,5 km (the range of single neighbourhoods). Analysis showed that there are some neighbourhoods in Poznań where occurrence of social problems issignificantly higher than mean occurrence for the whole city. Presented method enabledsmoothing of unreliable, extremely high relative risks values but without loss of the localvariability. (original abstract)
Twórcy
autor
  • Uniwersytet im. Adama Mickiewiczaw Poznaniu
  • Uniwersytet im. Adama Mickiewiczaw Poznaniu
Bibliografia
  • Ali M. i in., 2006, Application of Poisson kriging to the mapping of cholera and dysenteryincidence in an endemic area of Bangladesh, "International Journal of HealthGeographics, 5, s. 45, http://www.pubmedcentral.nih.gov/articlerender.fcgiartid=1617092&tool=pmcentrez&rendertype=abstract.
  • Anderson R.N., Rosenberg H.M., 1998, Age Standardization of Death Rates: Implementationof the Year 2000 Standard, "National Vital Statistics Reports", 47(3).
  • Bevan A., Conolly J., 2009, Modelling Spatial Heterogeneity and Nonstationarity in Artifact-Rich Landscapes, "Journal of Archaeological Science", 36(4), s. 956-964.
  • Buescher P.A., 1997, Problems with rates based on small numbers, [w:] Statistical Primer,North Carolina State Center for Health Statistics, s. 1-7, http://www.schs.state.nc.us/SCHS/pdf/primer12.pdf.
  • Bumpus S., 2012, Analysing and visualising areal crime data. A case study of residential burglary in San Francisco, USA, Master of Science Thesis in Geospatial Technologies, Universidade Nova de Lisboa, http://hdl.handle.net/10362/8316.
  • David K., Higgs G., White S., 2003, Socio-Economic Applications in Geographical Information Science, Taylor & Francis, London.
  • De Oliveira V., 2013, Hierarchical Poisson models for spatial count data, "J. Multivariate Anal.", 122, s. 393-408.
  • De Oliveira V., 2014, Poisson kriging: A closer investigation, "Spatial Statistics", 7, s. 1-20.
  • Ebenezer B., Ebenezer O.S., Linda O., 2013, Application of Area to Point Kriging to Buruli Ulcer Incidence in Ashanti and BrongAhafo Regions of Ghana, Geoinfor. Geostat: An Overview, 1:1.
  • Goodchild M.F., Janelle D.G., 2004, Spatialy integrated social science, Oxford University Press, Inc., New York.
  • Goovaerts P., 1997, Geostatistics for Natural Resources Evaluation., Oxford University Press, Inc., New York.
  • Goovaerts P., 2005, Geostatistical analysis of disease data: estimation of cancermortality risk from empirical frequencies using Poisson kriging, "International Journal of Health Geographics", 4(1), s. 31, http://www.pubmedcentral.nih.govarticlerender.fcgi?artid=1360096&tool=pmcentrez&rendertype=abstract.
  • Goovaerts P., 2006, Geostatistical Analysis of Disease Data: Accounting for Spatial Support and Population Density in the Isopleth Mapping of Cancer Mortality Risk Using Area to Point Poisson Kriging, "International Journal of Health Geographics", 5(52).
  • Goovaerts P., 2008, Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps, "Environmental and Ecological Statistics", 15, s. 421-446.
  • Goovaerts P., Gebreab S., 2008, How does Poisson kriging compare to the popular BYM model for mapping disease risks?, "International Journal of Health Geographics", 7(1), s. 6, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2276482&tool=pmcentrez&rendertype=abstract.
  • Isaaks E.H., Srivastava R.M., 1989, Applied Geostatistics, Oxford University Press, Inc., New York.
  • Jargowsky P.A., Kim J., 2005, A Measure of Spatial Segregation: The Generalized Neighborhood Sorting Index A Measure of Spatial Segregation, National Poverty Center Working Paper Series, http://www.nationalpovertycenter.net/publications/working_papers/.
  • Jenks G.F., 1967, The Data Model Concept in Statistical Mapping, "International Yearbookof Cartography", 7, s. 186-190.
  • Kafadar K., 1994, Choosing among two-dimensional smoothers in practice, "Computational Statistics and Data Analysis", 18, s. 419-439.
  • Kerry R. i in., 2010, Applying Geostatistical Analysis to Crime Data: Car-Related Theftsin the Baltic States, "Geographical Analysis", 42(1), s. 53-77, http://doi.wiley.com/10.1111/j.1538-4632.2010.00782.x.
  • Kerry R. i in., 2012, A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in KrugerNational Park, South Africa, "International Journal of Geographical Information Science".
  • Kober K. i in., 2010, An analysis of the numbers and distribution of seabirds within the British Fishery Limit aimed at identifying areas that qualify as possible marine SPAs.
  • Krivoruchko K., Gribov A., Krause E., 2011, Multivariate areal interpolation for continuou sand count data, "Procedia Environ. Sci.", 3, s. 14-19.
  • Monestiez P., Dubroca L., Bonnin E., Durbec J.P., Guinet C., 2005, Comparison ofModel Based Geostatistical Methods in Ecology: Application to Fin Whale SpatialDistribution in Northwestern Mediterranean Sea, [w:] Leuanghton O., Deutsch C.V.(red.), "Geostatistics Banff 2004", 2, s. 777-786.
  • Naeimeh S.A. i in., 2013, Area-to-Area Poisson Kriging Analysis of Mapping of County-Level Esophageal Cancer Incidence Rates in Iran, "Asian Pacific Journal of Cancer Prevention", 14, s. 11-13.
  • Oliver M.A. i in., 1998, Binomial cokriging for estimating and mapping the risk of childhoodcancer, "IMA Journal of Mathematics Applied in Medicine and Biology", 15, s. 79-297.
  • Pardo-Igúzquiza E., 1999, VARFIT: a Fortran-77 program for fitting variogram models by weighted least squares, "Computers and Geosciences", 25, s. 251-261.
  • Parker R.N., Asencio E.K., 2008, GIS and Spatial Analysis for the Social Sciences Coding, Mapping and Modeling, Taylor & Francis, New York.
  • Parysek J.J., Mierzejewska L., 2006, City Profile Poznań, "Cities", 23(4), s. 291-305, http://linkinghub.elsevier.com/retrieve/pii/S0264275106000291.
  • Pfeiffer D.U. i in., 2008, Spatial Analysis in Epidemiology, Oxford University Press, Oxford, UK.
  • Reardon S.F., Sullivan D.O., 2004, Measure of spatial segregation, "Sociological Methodology", 34(1), s. 121-162.
  • Shao C.Y., Mueller U., Cross J., 2009, Area-to-point Poisson kriging analysis for lungcancer incidence in Perth areas, [w:] 18th World IMACS/MODSIM Congress 13-17July 2009, Cairns, Australia, s. 1-7.
  • Talbot T.O. i in., 2000, Evaluation of spatial filters to create smoothed maps of healthdata, "Statistics in Medicine", 19, s. 2399-2408.
  • Waller L.A., Gotway C.A., 2004, Applied Spatial Statistics for Public Health Data, John Wiley & Sons, Inc., Hoboken, NJ, USA.
  • Wang F., 2004, Geographic Information Systems and Crime Analysis, [w:] Wang F.(red.), IGI Global, http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-59140-453-8.
  • Wilson W.J., 1987, The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy, Bridge G., Watson S. (red.), University of Chicago Press, http://www.amazon.com/Truly-Disadvantaged-Underclass-Public-Policy/dp/0226901319.
  • Wilson W.J., 2012, The Truly Disadvantaged: The Inner City, the Underclass, and PublicPolicy, Second Edition II, University of Chicago Press, USA.
  • Wong D.W.S., 1999, Geostatistics as measures of spatial segregation, "Urban Geography",20(7), s. 635-647, http://bellwether.metapress.com/openurl.asp?genre=article&id=doi:10.2747/0272-3638.20.7.635.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171386725

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