Comparing continuity and compactness of choropleth map classes

• Autorzy: Całka B.

Identyfikatory

DOI

10.24425/118704

• Języki publikacji: EN

Abstrakty

EN

Population density varies sharply from place to place on the whole territory of Poland. The largest number of people per 1 km2 is 21,531, while uninhabited areas account for about 48% of the country. Such uneven, non-Gaussian distribution of the data causes some difficulty in choosing the classification method in geometric choropleth maps. A thorough evaluation of a geometric choropleth map of population data is not possible using only traditional indicators such as the Tabular Accuracy Index (TAI). That is why the aim of the article is to develop an innovative index based on distance analysis and neighbour analysis of grid cells. Two indexes have been suggested in this paper: the Spatial Distance Index (SDI) and the Spatial Contiguity Index (SCI). The paper discusses the use of five classification methods to evaluate choropleth maps of population data, like head-tail breaks, natural breaks, equal intervals, quantile, and geometrical intervals. A comprehensive assessment of such geometric choropleth maps is also done. The research was conducted for the whole territory of Poland, using data from the 2011 National Census of Population and Housing. Population data are presented in the 1km grid. The results of the analysis are shown on thematic maps. A compatibility of the choropleth maps with urban-rural typology of the OECD (Organisation for Economic Co-operation and Development) was also checked.

Słowa kluczowe

EN

data classification; choropleth map; neighbourhood analysis; spatial contiguity analysis; head-tail breaks method

PL

klasyfikacja danych; zagospodarowanie przestrzenne; zaludnienie obszaru

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Geodesy and Cartography
• Rocznik: 2018
• Tom: Vol. 67, no. 1
• Strony: 21--34
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Całka B.

• Military University of Technology Faculty of Civil Engineering and Geodesy 2 gen. W. Urbanowicza St. 00-908 Warsaw 46, Poland

Bibliografia

• [1] Angel, S., Parent, J. and Civco, D.L. (2010). Ten compactness properties of circles: measuring shape in geography. Canadian Geographer-Geographe Canadien, 54 (4), 441–461.
• [2] Andrienko, G., Andrienko, N. and Savinov, A. (2001). Choropleth maps: classification revisited. Proceedings of ICA, 6–10 August 2001, Beijing, China, 2: 1209–1219.
• [3] Armstrong, M.P., Xiao, N., Bennett, D.A. (2003). Using genetic algorithms to create multicriteria class intervals for choropleth maps. Annals, Association of American Geographers, 93 (3), 595–623. DOI: 10.1111/1467-8306.9303005.
• [4] Bregt, A.K. and Wopereis, M.C.S. (1990). Comparison of complexity measures for choropleth maps. The Cartographic Journal, 27, 85–91.
• [5] Brewer, C.A. and Pickle, L. (2002). Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series. Annals of the Association of American Geographers, 92 (4), 662–681.
• [6] Brezzi, M., Dijkstra L. and Ruiz, V. (2011). OECD Extended Regional Typology: The Economic Performance of Remote Rural Regions, OECD Regional Development Working Papers, 2011/06, OECD Publishing. 10.1787/5kg6z83tw7f4-en.
• [7] Calka, B., Bielecka, E. and Figurski, M. (2017). The spatial pattern of ASG-EUPOS sites. Open Geosciences, 9 (1), 613–621. DOI: 10.1515/geo-2017-0046.
• [8] Calka, B., Nowak Da Costa, J. and Bielecka E. (2017). Fine scale population density. Geomatics, Natural Hazard and Risk, 8 (2), 1440–1455. 10.1080/19475705.2017.1345792.
• [9] Clark, P.J. and Evans, F.C. (1954). Distance to nearest neighbour as a measure of spatial relationships in populations. Ecology, 35, 445–453. 10.2307/1931034.
• [10] Coulson, M.R.C. (1987). In the matter of class intervals for choropleth maps: with particular reference to the work of George F. Jenks. Cartographica, 24 (2), 16–39.
• [11] Coulson, M.R.C. (1987). In the matter of class intervals for choropleth maps: with particular reference to the work of George F. Jenks. Cartographica, 24 (2), 16–40.
• [12] Cromley, R.G. (1995). Classed versus Unclassed Choropleth Maps: A Question of How Many Classes. The International Journal for Geographic Information and Geovisualization, 32 (4), 15–27. DOI: 10.3138/J610-13NU-5537-0483.
• [13] Cromley, R.G., Zhang, S. and Vorotyntseva, N. (2015). A concentration-based approach to data classification for choropleth mapping. International Journal of Geographical Information Science, 29 (10), 1845–1863. 10.1080/13658816.2015.1058388.
• [14] Diggle, P.J. (1983). Statistical analysis of spatial point patterns. Academic Press, London.
• [15] Foss, S., Korshunov, D. and Zachary, S. (2013). An Introduction to Heavy-Tailed and Subexponential Distributions. Springer.
• [16] Goodchild, M. (1992). Geographical information science. International Journal of Geographic Information Systems, 6 (1), 31–45.
• [17] Jenks, G. and Coulson., M. (1963). Class Intervals for Statistical Maps. International Yearbook of Cartography, 3, 119–134.
• [18] Jenks, G. (1977). Optimal Data Classification for Choropleth Maps. Occasional paper No. 2, Department of Geography, University of Kansas.
• [19] Jenks, G.F. and Caspall, F.C. (1971). Error on choroplethic maps: Definition, measurement, reduction. Annals of the Association of American Geographers, 61, 217–244, DOI:10.1111/j.1467-8306.1971.tb00779.x.
• [20] Jiang, B. (2013). Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution. The Professional Geographer, 65 (3), 482–494.
• [21] Lai, P., So F., and Chan, K. (2009). Spatial Epidemiological Approaches in Disease Mapping and Analysis. Taylor & Francis Group.
• [22] Li, F. and Zhang, L. (2007). Comparison of point pattern analysis methods for classifying the spatial distributions of spruce-fir stands in the north-east USA. Forestry: An International Journal of Forest Research, 80 (3), 337–349. 10.1093/forestry/cpm010.
• [23] Lin, Y. (2013). A Comparison on Natural and Head/tail Breaks Involving Digital Elevation Models. Available on-line: http://www.diva-portal.org/smash/get/diva2:658963/FULLTEXT02.pdf.
• [24] Medyńska-Gulij, B. (2010). Map compiling, map reading and cartographic design in Pragmatic pyramid of thematic mapping. Quaestiones Geographicae, 29 (1), 5763. DOI: 10.2478/v10117-010-0006-5.
• [25] Medyńska-Gulij, B. (2011). Kartografia i geowizualizacja, PWN Warszawa.
• [26] Mitchell, A. (2005). The ESRI Guide to GIS Analysis, Volume 2. ESRI Press.
• [27] Murray, A.T. and Shyy, T.K. (2000). Integrating attribute and space characteristics in choropleth display and spatial data mining. International Journal of Geographical Information Science, 14, 649–667.
• [28] Müller, J.C. (1975). Definition, measurement and comparison of map attribute in choropleth mapping. In Proceedings of the Association of American Geographers, 7, 160–164.
• [29] Müller, J.C. (1976). Objective and Subjective Comparison in Choroplethic Mapping. The Cartographic Journal, 13, 156–166.
• [30] Olson, J. (1972). Autocorrelation as a measure of map complexity. In Proceedings of the American Congress on Surveying and Mapping, 111–119.
• [31] Pasławski, J. (1984). In search of a general method of class selection for choropleth maps. International Yearbook of Cartography, 34, 159–69.
• [32] Pasławski, J. (2003). Jak opracować kartogram. Uniwersytet Warszawski, Warszawa.
• [33] Robinson, A.H., Morrison, J., Muehrcke, P.C., Kimerling, A. and Guptill, S. (1995). Elements of cartography. New York, USA,John Wiley & Sons.
• [34] Robinson, A.H., Sale, R.D., Morrison, J.L. and Muehrcke, P.C. (1984). Elements of cartography. New York, John Wiley and Sons.
• [35] Wei, R., Tong, D., and Phillips, J.M. (2017). An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey. Computers, Environment and Urban Systems, 63, 95–103. DOI: 10.1016/j.compenvurbsys.2016.04.003.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)