Spatial Analysis Of Foreign Migration In Poland In 2012 Using Geographically Weighted Regression

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Abstract

Migration has a principal influence on countries’ population changes. Thus, the issues connected with the causes, effects and directions of people’s movements are a common topic of political and academic discussions.

The aim of this paper is to analyse the spatial distribution of officially registered foreign migration in Poland in 2012. GIS tools are implemented for data visualization and statistical analysis. Geographically weighted regression (GWR) is used to estimate the impact of unemployment, wages and other socioeconomic variables on the foreign emigration and immigration measure. GWR provides spatially varying estimates of model parameters that can be presented on a map, giving a useful graphical representation of spatially varying relationships.

Benson T., Chamberlin J., Rhinehart I. (2005), An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi, Food Policy no. 30.

Bonifazi C., Okólski M., Schoorl J., Simon P. (2008), International Migration in Europe. New Trends and New Methods of Analysis, ʻIMISCOE Researchʼ, Amsterdam University Press.

Brunsdon C., Fotheringham S., Charlton M. (1996), Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity, ʻGeographical Analysisʼ, vol. 28, no. 4.

Brunsdon C., Fotheringham A., Chambers M. (1997), Measuring spatial variations in relationships with geographically weighted regression, [in:] Fischer M., Getis A. (eds.) Recent developments in Spatial Analysis, Springer-Verlag, London.

Brunsdon C., Fotheringham A., Chambers M. (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Wiley, United Kingdom.

Byrne G., Pezić A. (2004), Modelling internal migration drivers with geographically weighted regression, Australian Population Association, Proceedings of the 12th Biennial Conference of the Australian Population Association, 15-17 September 2004, Canberra.

Cellmer R. (2013), The Use of the Geographically Weighted Regression for the Real Estate Market Analysis, ʻFolia Oeconomica Stetinensiaʼ no. 11 (1).

Charlton M., Fotheringham S. (2009), Geographically weighted regression. White paper, National Centre for Geocomputation National University of Ireland Maynooth, http://gwr.nuim.ie/ downloads/GWR_WhitePaper.pdf (Accessed on 24 May 2014).

Comber A.J., Brunsdon C., Radburn R. (2011), A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions, ʻInternational Journal of Health Geographicsʼ, no. 10 (44).

Foody G.M. (2003), Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI-rainfall relationship, Remote Sensing of the Environment no. 88 (3).

Gilbert A., Chakraborty J. (2011), Using geographically weighted regression for environmental justice analysis: Cumulative cancer risks from air toxics in Florida, Social Science Research no. 40 (1).

Główny Urząd Statystyczny (2013), Informacja o rozmiarach i kierunkach emigracji z Polski w latach 2004-2012, GUS, Departament Badań Demograficznych i Rynku Pracy, Warszawa.

Jensen T., Deller S. (2007), Spatial Modeling of the Migration of Older People with a Focus on Amenities, ʻThe Review of Regional Studiesʼ, no. 37 (3).

Jivraj S., Brown M., Finney N. (2013), Modelling Spatial Variation in the Determinants of Neighbourhood Family Migration in England with Geographically Weighted Regression, ʻApplied Spatial Analysis and Policyʼ, no. 6 (4).

Kisiala W. (2013), Wykorzystanie geograficznie ważonej regresji do analizy czynników kształtujących zapotrzebowanie na świadczenia przedszpitalnego ratownictwa medycznego, ʻPrzegląd Geograficznyʼ, no. 85 (2).

Lo C.P. (2008), Population Estimation Using Geographically Weighted Regression, GIScience & Remote Sensing no. 45(2).

Matthews S.A., Yang T.C. (2012), Mapping the results of local statistics: Using geographically weighted regression, ʻDemographic Researchʼ, no.26/6.

McMillen D. (1996), One hundred fifty years of land values in Chicago: A nonparametric approach, ʻJournal of Urban Economicsʼ, no. 40.

Mennis J. (2006), Mapping the Results of Geographically Weighted Regression, ʻThe Cartographic Journalʼ, vol. 43, no. 2.

Nakaya T., Fotheringham A.S., Brunsdon C., Charlton M. (2005), Geographically weighted Poisson regression for disease association mapping, ʻStatistics in Medicineʼ, no. 24(17).

Partridge M.D., Rickman D.S. (2007), Persistent pockets of extreme American Poverty and Job Growth: Is There a Place-Based Policy Role?, ʻJournal of Agricultural and Resource Economicsʼ, no. 32 (1).

Young L.J., Gotway C.A. (2010), Using geostatistical methods in the analysis of public health data: The final frontier? geoENV VII - Geostatistics for Environmental Applications no. 16.

Yu D-L, Wei Y.D., Wu C. (2007), Modeling spatial dimensions of housing prices in Milwaukee, ʻWI. Environment and Planning B: Planning and Designʼ, no. 34 (6).

Journal Information


CiteScore 2018: 0.46

SCImago Journal Rank (SJR) 2018: 0.241
Source Normalized Impact per Paper (SNIP) 2018: 0.366

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