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Redistribution population data across a regular spatial grid according to buildings characteristics

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Języki publikacji
EN
Abstrakty
EN
Population data are generally provided by state census organisations at the predefined census enumeration units. However, these datasets very are often required at userdefined spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a flat. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.
Rocznik
Strony
149--162
Opis fizyczny
Bibliogr. 45 poz., rys., tab.
Twórcy
autor
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, 2 Gen. S. Kaliskiego St. 01-476 Warsaw
autor
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, 2 Gen. S. Kaliskiego St. 01-476 Warsaw
  • Military University of Technology, Faculty of Civil Engineering and Geodesy, Institute of Geodesy, 2 Gen. S. Kaliskiego St. 01-476 Warsaw
Bibliografia
  • [1] Anderson, W., Guikema, S., Zaitchik, B and Pan, W. (2014). Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru. PLoS ONE 9(7): e100037, doi:10.1371/journal.pone.0100037.
  • [2] Azar, D., Graesser, J., Engstrom, R., Comenetz, J., Leddy JR, R.M., Schechtman, N.G. and Andrews, T. (2010). Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti. International Journal of Remote Sensing, 31 (21): 5635–5655, doi:10.1080/01431161.2010.496799.
  • [3] Azar D, Engstrom R, Graesser J, Comenetz J (2013) Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data. Remote Sens Environ, 130:219–232, doi: doi:10.1016/j.rse.2012.11.022.
  • [4] Bakillah, M.,. Liang, S, Mobasheri, A., Arsanjani J.J. and Zipf A. (2014). Fine-resolution population mapping using OpenStreetMap points-of-interest, International Journal of Geographical Information Science, vol. 28(9):1940-1963, doi: 10.1080/13658816.2014.909045.
  • [5] Bielecka, E. (2015). Geographical data sets fitness of use evaluation. Geodetski vestnik Vol. 59 (2015), No. 2:335–348, DOI: 10.15292/geodetski-vestnik.2015.02.335-348.
  • [6] Bielecka, E. (2005). A dasymetric population density map of Poland, Proceedings of the 22nd International Cartographic Conference, 9–15 July 2005, A Coruña, Spain (CD).
  • [7] Bielecka, E. and Bober, A. (2013). Reliability analysis of interpolation methods in travel time maps-the case of Warsaw. Geodetski vestnik, 57.2 (Jun 2013), 299–312.
  • [8] Bielecka, E., Leszczynska, M. and Hall, P. (2014). User perspective on geospatial data quality. Case study of the Polish Topographic Database. The 9th International Conference “ENVIRONMENTAL ENGINEERING” 22–23 May 2014, Vilnius, Lithuania, selected papers, eISSN 2029-7092 / eISBN 978-609-457-640-9. Available online at http://enviro.vgtu.lt, doi: http://dx.doi.org/10.3846/enviro.2014.193.
  • [9] Ciołkosz, A. and Bielecka, E. (2005). Land cover in Poland. CORINE Land Cover databases. Biblioteka Monitoringu Środowiska, Warsaw, pp. 76.
  • [10] de Smith, M.J. Goodchild, M.F. and Longley, P.A. (2015). Geospatial Analysis. A Comprehensive Guide to Principles, Techniques and Software Tools. Fifth Edition, Issue version: 1 (2015).
  • [11] de Smith, M., Longley, P. and P. Goodchild (2016). Geospatial Analysis book online – web version, http://www.spatialanalysisonline.com/.
  • [12] Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D. and Tatem, A.J. (2014). Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. 2014, 111:15888–15893.
  • [13] Eicher, C. L. and Brewer, C. A. (2001). Dasymetric mapping and areal interpolation. Implementation and evaluation, Cartography and Geographic Information Science,28:125–138.
  • [14] Gallego, J. (2010). A population density grid of the European Union. Population and Environment, 31:460–473, doi: 10.1007/s11111-010-0108-y.
  • [15] GUS. (2015a). Gospodarka mieszkaniowa w 2014 r. Informacje i opracowania statystyczne. Główny Urząd Statystyczny, Warszawa, avaiable on-line: http://stat.gov.pl/files/gfx/portalinformacyjny/pl/defaultaktualnosci/5492/7/10/1/gospodarka_mieszkaniowa_2014.pdf.
  • [16] GUS. (2015b). Powierzchnia i ludność w przekroju terytorialnym w 2015 r. [Area and population in the territorial profile in 2015], Główny Urząd Statystyczny, Warszawa.
  • [17] Hazewinkel, M. ed. (2001), “Pareto distribution”, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4.
  • [18] Horanont, T. and Shibasaki, R. (2010). Estimate ambient population density: discovering the current flow of the city. Available on line: https://www.academia.edu/2004297/ESTIMATE_AMBIENT_POPULATION_DENSITY_DISCOVERING_THE_CURRENT_FLOW_OF_THE_CITY.
  • [19] Horanont, T., Phithakkitnukoon, S. and Shibasaki, R. (2015). Sensing Urban Density Using Mobile Phone GPS Locations: A Case Study of Odaiba Area, Japan. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 144:146–155, 10.1007/978-3-319-15392-6_15.
  • [20] INSPIRE. (2010). D2.8.I.2 INSPIRE Specification on Geographical Grid Systems – Guidelines. http://inspire.ec.europa.eu/documents/Data_Specifications/INSPIRE_Specification_GGS_v3.0.1.pdf.
  • [21] Maantay, J.A., Maroko, A.R., and Herrmann, C. (2007). Mapping population distribution in the urban environment: the cadastral-based expert dasymetric system (CEDS). Cartography and Geographic Information Science, 34 (2):77–102, doi:10.1559/152304007781002190.
  • [22] Marczak, S. (2015). Assessment of society involvement in creation of spatial data in Poland on the example of OpenStreetMap project [in Polish: Ocena zaangażowania społeczeństwa w tworzenie danych przestrzennych w Polsce na przykładzie projektu OpenStreetMap]. Annals of Gematics, t. XIII, 3(69), 239–253.
  • [23] Medyńska-Gulij, B. (2015). Kartografia. Zasady i zastosowania geowizualizacji, PWN Warszawa, ISBN: 9788301183288.
  • [24] Medyńska-Gulj, B. (2014). Cartographic sign as a core of multimedia map prepared by non-cartographers in free map services. Geodesy and Cartography. Vol. 63:55–64, doi: 10.2478/geocart-2014-0004.
  • [25] Mennis, J. and Hultgren, T. (2006). Intelligent Dasymetric Mapping and Its Application to Areal Interpolation. Cartography and Geographic Information Science, 33:179–194.
  • [26] Mennis, J. (2003). Generating Surface Models of Population Using Dasymetric Mapping. The Professional Geographer., 55:1–42.
  • [27] Mennis, J. (2009). Dasymetric mapping for estimating population in small areas. Geography Compass, 3:727–745.
  • [28] Mennis, J. (2015). Increasing the accuracy of urban population analysis with dasymetric mapping. Cityscape, 17(1):115–126.
  • [29] Murshed, S. M. (2009). Disaggregation of regional population data for residential hot water demand assessment, Proceedings of 24th ICC/ICA cartographic Conference, Santiago de Chile, Santiago de Chile, Chile, 15–21 November 2009.
  • [30] Nowak Da Costa, J. (2016a). Novel tool to examine data completeness based on comparative studof VGI data and official building datasets. Geodetskij vestnik, 60(3): 495–508. DOI: 10.15292/geodetskivestnik.2016.03.495-508).
  • [31] Nowak Da Costa, J. (2016b). Towards building data semantic similarity analysis: OpenStreetMap and the Polish Database of Topographic Objects. 2016 Baltic Geodetic Congress (BGC Geomatics), Gdansk, 2016, pp. 269–275. DOI: 10.1109/BGC.Geomatics.2016.55.
  • [32] Langford, M. (2013). An Evaluation of Small Area Population Estimation Techniques Using Open Access Ancillary Data. Geographical Analysis, 45:324–344.
  • [33] Olszewski, R. and Gotlib. D. (red.). (2013). Rola bazy danych obiektów topograficznych w tworzeniu infrastruktury informacji przestrzennej w Polsce. Joint publication, GUGiK, ISBN 978-83-254-1975-2.
  • [34] Regulation. (2011). Regulation of Ministry of Internal Affairs and Administration of 11 November 2011 as regards topographic objects database and database of general topographic objects as well as standard cartographic products. Dz. U. 2011 No. 279, entry 1642, Poland, 2011.
  • [35] Silva, M. and Pereira, S. (2014). Assessment of physical vulnerability and potential losses of buildings due to shallow slides, NatHazards, 72(2), 1029–1050, doi 10.1007/s11069-014-1052-4, 2014.
  • [36] Sridharan, H. and Qiu, F. (2013). A Spatially Disaggregated Areal Interpolation Model Using Light Detection and Ranging-Derived Building Volumes. Geographical Analysis, 45:238–258
  • [37] Smith, A., Newing, A., Quinn, N., Martin, D.; Cockings, S. and Neal, J. (2015). Assessing the Impact of Seasonal Population Fluctuation on Regional Flood Risk Management. ISPRS Int. J. Geo-Inf. 2015, 4:1118–1141, DOI:10.3390/ijgi4031118.
  • [38] Stevens, F.R., Gaughan A., E. and Tatem, A. J. (2015). Disaggregation census data for population mapping using Random Forest with remotely-sensed and ancillary data. PLoS One, 2015, 10(2), e0107042.
  • [39] Su, M.D., Lin, M.C., Hsieh, H.I., Tsai, B.W. and Lin, C.H. (2010). Multi-layer multi-class dasymetric mapping to estimate population distribution. Science of the Total Environment, 408 (20):4807–4816, doi:10.1016/j.scitotenv.2010.06.032.
  • [40] Tobler, W. R., Deichmann, U, Gottsgen J. and Maloy, K. (1995). The global demography project. National Center for Geographic Information and Analysis (NCGIA), University of California; Santa Barbara, California: 1995. Technical Report TR-6-95.
  • [41] Wright, J K. (1936). A Method of Mapping Densities of Population: With Cape Cod as an Example. Geographical Review, 26:104–15.
  • [42] Wu, S.-S., Qiu, X. and Wang, L. (2005). Population Estimation Methods in GIS and Remote Sensing : a review. GIScience and Remote sensing, 42 (1):58–74; doi: 10.2747/1548-1603.42.1.80.
  • [43] Wu, S-S., Wang, L., and Qiu, X. (2008). Incorporating GIS building data and census housing statistics for subblock-level population estimation. The Professional Geographer, 60 (1):121–135, doi: 10.1080/00330120701724251.
  • [44] Wu, C. and Murray, A.T. (2005). A cokriging method for estimating population density in urban areas. Computers, Environment and Urban Systems, 29 (5), 558–579, doi:10.1016/j.compenvurbsys.2005.01.006.
  • [45] Zandbergen, P. and Ignizio, D. (2010). Comparison of dasymetric mapping techniques for small-area population estimates. Cartography and Geographic Information Science, 37 (3):199–214, doi:10.1559/152304010792194985.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6ba87bd0-dcc0-4e52-b570-201ee5774432
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