Keywords
dasymetric modelling
R
How to Cite
Abstract
Existing resources of population data, provided by national censuses in the form of areal aggregates, have usually insufficient resolution for many practical applications. Dasymetric modelling has been a standard technique to disaggregate census aggregates into finer grids. Although dasymetric modelling of population distribution is well-established, most literature focuses on proposing new variants of the technique, while only few are devoted to developing broad-scale population grids that could be used for real-life applications. This paper reviews literature on construction of broad-scale population grids using dasymetric modelling. It also describes an R implementation of fully automated framework to calculate such grids from aggregated data provided by national censuses. The presented implementation has been used to produce high resolution, multi-year comparable, U.S.-wide population datasets that are the part of the SocScape (Social Landscape) project.
Funding
SocScape project has been developed in the Space Informatics Lab at the University of Cincinnati and it is available on http://sil. uc.edu. All R scripts are available from http:// dmowska.home.amu.edu.pl. Author would like to thank both reviewers for their helpful and insightful comments on the paper.
References
Alegana V.A., Atkinson P.M., Pezzulo, C., Sorichetta A., Weiss D., Bird T., Erbach-Schoenberg E., Tatem A.J., 2015. Fine resolution mapping of population age-structures for health and development applications. Journal of the Royal Society Interface 12(105): 20150073.
Bakillah M., Liang S., Mobasheri A., Jokar Arsanjani J., Zipf A., 2014. Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science 28(9): 1940–1963.
Batista e Silva F., Gallego J., Lavalle C., 2013. A high-resolution population grid map for Europe, Journal of Maps 9(1): 16–28.
Benn H.P., 1995. Synthesis of transit practice 10: bus route evaluation standards. Tech. rep., Transit Cooperative Research Program, Transportation Research Board, National Research Council, Washington, DC.
Berke P., Newman G., Lee J., Combs T., Kolosna C., Salvesen D., 2015. Evaluation of networks of plans and vulnerability to hazards and climate change: A resilience scorecard. Journal of the American Planning Association 81: 287–302.
Bhaduri B., Bright E., Coleman P., Dobson J., 2002. LandScan: Locating people is what matters. Geoinformatics 5(2): 34–37.
Bielecka E., 2005. A dasymetric population density map of Poland. In Proceedings of the 22nd International Cartographic Conference: 9–15.
Bivand R., 2017. rgrass7: Interface Between GRASS 7 Geographical Information System and R. R package version 0.1-10. Online: https://CRAN.R-project.org/package=rgrass7 (accessed 10 February 2019).
Bivand R., Keitt T., Rowlingson B., 2018. rgdal: Bindings for the ’Geospatial’ Data Abstraction Library. R package version 1.3-4. Online: URL https://CRAN.R-project.org/package=rgdal (accessed 10 February 2019).
Briggs D.J., Gulliver J., Fecht D., Vienneau D.M., 2007. Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sensing of Environment 108(4): 451–466.
Calka B., Nowak Da Costa J., Bielecka E., 2017. Fine scale population density data and its application in risk assessment. Geomatics, Natural Hazards and Risk 8(2): 1440–1455.
Chen K., McAneney J., Blong R., Leigh R., Hunter L., Magill C., 2004. Defining area at risk and its effect in catastrophe loss estimation: A dasymetric mapping approach. Applied Geography 24: 97–117.
Deville P., Linard C., Martin S., Gilbert M., Stevens F.R., Gaughan A.E., Blondel V.D., Tatem A. J., 2014. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences 111(45): 15888– 15893.
Dmowska A., Stepinski T.F., 2014. High resolution dasymetric model of U.S. demographics with application to spatial distribution of racial diversity. Applied Geography 53: 417–426.
Dmowska A., Stepinski T.F., 2017a. A high resolution population grid for the conterminous United States: The 2010 edition. Computers, Environment and Urban Systems 61: 13–23.
Dmowska A., Stepinski T.F., 2017b. Mapping changes of racial composition in the United States: 1990–2010. In: Population Association of America 2017, Chicago, April 26– 29, 2017. Online: https://paa.confex.com/paa/2017/meetingapp.cgi/Paper/10564 (accessed 10 February 2019).
Dmowska A., Stepinski T.F., 2019. Mapping racial diversity using grid-based racial dot maps and racial diversity maps. Accepted In: Population Association of America 2019, Austin, April 10–13, 2019. Online: http://paa2019.populationassociation.org/abstracts/191266 (accessed 10 February 2019).
Dmowska A., Stepinski T.F., Netzel P., 2017. Comprehensive framework for visualizing and analyzing spatio-temporal dynamics of racial diversity in the entire United States. PLoS ONE 12(3): e0174993.
Dobson J.E., Bright E.A., Coleman P.R., Worley B.A., 2000. LandScan: a global population database for estimating populations at risk. Photogrammetric engineering and remote sensing 66 (7): 849–857.
Doxsey-Whitfield E., MacManus K., Adamo S.B., Pistolesi L., Squires J., Borkovska O., Baptista S.R., 2015. Taking advantage of the improved availability of census data: A first look at the gridded population of the world, version 4. Papers in Applied Geography 1 (3): 226–234.
EEA [European Environment Agency], 2019. Data and maps. Online: www.eea.europa.eu/data-and-maps (accessed February 11, 2019).
Eicher C.L., Brewer C.A., 2001. Dasymetric mapping and areal interpolation: Implementation and evaluation. Cartography and Geographic Information Science 28: 125–138.
Flowerdew R., Green M., 1992. Developments in areal interpolation methods and GIS. Annals of Regional Science 26: 67–78.
Gallego F., 2010. A population density grid of the European Union. Population and Environment 31(6): 460–473.
Gallego F., Batista F., Rocha C., Mubareka S., 2011. Disaggregating population density of the European Union with CORINE land cover. International Journal of Geographical Information Science 25: 2051–2069.
Gaughan A.E., Stevens F.R., Linard C., Jia P., Tatem A.J., 2013. High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE 8(2): e55882.
Gleick P.H., 1996. Basic water requirements for human activities: Meeting basic needs. Water international 21(2): 83–92.
Goodchild M., Anselin L., Deichmann U., 1993. A framework for the areal interpolation of socioeconomic data. Environment and Planning, A 25: 383–397.
Goodchild M., Lam N., 1980. Areal interpolation: A variant of the traditional spatial problem. Geo-Processing 1: 297–312.
Hay S.I., Noor A.M., Nelson A., Tatem, A.J., 2005. The accuracy of human population maps for public health application. Tropical Medicine and International Health 10(10): 1073–1086.
Jia P., Gaughan A.E., 2016. Dasymetric modelling: A hybrid approach using land cover and tax parcel data for mapping population in Alachua County, Florida. Applied Geography 66: 100–108.
Jia P., Qiu Y., Gaughan A.E., 2014. A fine-scale spatial population distribution on the High-resolution Gridded Population Surface and application in Alachua County, Florida. Applied Geography 50: 99–107.
Langford M., 2013. An evaluation of small area population estimation techniques using open access ancillary data. Geographical Analysis 45(3): 324–344.
Langford M., Unwin D.J., 1994. Generating and mapping population density surfaces within a geographical information system. The Cartographic Journal 31(1): 21–6.
Linard C., Gilbert M., Tatem A.J., 2011. Assessing the use of global land cover data for guiding large area population distribution modelling. GeoJournal 76(5): 525–538.
Lloyd C. T., Sorichetta A., Tatem A.J., 2017. High resolution global gridded data for use in population studies. Scientific data 4: 170001.
Lu Z., Im J., Quackenbush L., Halligan K., 2010. Population estimation based on multi-sensor data fusion. International Journal of Remote Sensing 31(21): 5587–5604.
Lung T., Lübker T., Ngochoch J. K., Schaab G., 2013. Human population distribution modelling at regional level using very high resolution satellite imagery. Applied Geography 41: 36–45.
Maantay J., Maroko A., 2009. Mapping urban risk: Flood hazards, race, and environmental justice in New York. Applied Geography 29(1): 111–124.
Maantay J.A., Maroko A.R., 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.
McGranahan G., Balk D., Anderson B., 2007. The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and Urbanization 19(1): 17–37.
Mennis J., 2003. Generating surface models of population using dasymetric mapping. The Professional Geographer 55(1): 31–42.
Mennis J., 2009. Dasymetric mapping for estimating population in small areas. Geography Compass 3(2): 727–745.
Mennis J., Hultgren T., 2006. Intelligent dasymetric mapping and its application to areal interpolation. Cartography and Geographic Information Science 33(3): 179–194.
Mileu N., Queirós M., 2018. Development of a QGIS plugin to dasymetric mapping. Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings 18: 9. Online: scholarworks.umass.edu/foss4g/vol18/iss1/9 (accessed 10 February 2019).
Mller K., Wickham H., James D. A., Falcon S., 2018. RSQLite: ’SQLite’ Interface for R. R package version 2.1.1. Online: https://CRAN.R-project.org/package=RSQLite (accessed 10 February 2019).
Mondal P., Tatem A.J., 2012. Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding. PLoS ONE 7(10): 1–7.
MPC [Minnesota Population Center], 2019. National Historical Geographic Information System. Online: www.nhgis. org (accessed February 11, 2019).
Murray A.T., Davis R., Stimson R. J., Ferreira. L., 1998. Public transportation access. Transportation Research Part D: Transport and Environment 3(5): 319–328.
ONRL [Oak Ridge National Laboratory], 2019. LandScan™. Online: landscan.ornl.gov (accessed February 11, 2019).
Patel N.N., Stevens F.R., Huang Z., Gaughan A.E., Elyazar I., Tatem A.J., 2017. Improving large area population mapping using geotweet densities. Transactions in GIS 21(2): 317–331.
Pattnaik S.B., Mohan S., Tom V.M., 1998. Urban bus transit route network design using genetic algorithm. Journal of transportation engineering 124(4): 368–375.
Pebesma E.J., Bivand R.S., 2005. Classes and methods for spatial data in R. R News 5 (2), 9–13. Online: https:// CRAN.R-project.org/doc/Rnews/ (accessed 10 February 2019)
Petrov A., 2012. One hundred years of dasymetric mapping: back to the origin. Cartographic Journal 49(3): 256–264.
R Core Team, 2018. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Online https://www.R-project.org/ (accessed 10 February 2019).
Reibel M., Bufalino M.E., 2005. Street-weighted interpolation techniques for demographic count estimation in incompatible zone systems. Environment and Planning A 37(1): 127–139.
Schroeder J.P., 2007. Target-density weighting interpolation and uncertainty evaluation for temporal analysis of census data. Geographical Analysis 39(3): 311–335.
SEDAC [Socioeconomic Data and Applications Center], 2019. Gridded Population of the World (GPW), v4. Online: sedac.ciesin.columbia.edu/data/collection/gpw-v4 (accessed February 11, 2019).
Sleeter R., Gould M., 2007. Geographic Information System software to remodel population data using dasymetric mapping methods.
Sridharan H., Qiu F., 2013. A spatially disaggregated areal interpolation model using light detection and ranging-derived building volumes. Geographical Analysis 45(3): 238–258.
Stevens F.R., Gaughan A.E., Linard C., Tatem A.J., 2015. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE 10(2): 1–22.
Tapp A.F., 2010. Areal interpolation and dasymetric mapping methods using local ancillary data sources. Cartography and Geographic Information Science 37 (3): 215–228.
Tatem A.J., Adamo S., Bharti N., Burgert C.R., Castro M., Dorelien A., Fink G., Linard C., John M., Montana L., Montgomery M.R., Nelson A., Noor A.M., Pindolia D., Yetman G., Balk D., 2012. Mapping populations at risk: improving spatial demographic data for infectious disease modelling and metric derivation. Population Health Metrics 10(1): 8.
Tatem A.J., Campbell J., Guerra-Arias M., De Bernis L., Moran A., Matthews Z., 2014. Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. International Journal of Health Geographic 13(1): 2.
Tatem A.J., Campiz N., Gething P.W., Snow R. W., Linard C., 2011. The effects of spatial population dataset choice on estimates of population at risk of disease. Population Health Metrics 9(1): 4.
Tatem A.J., Gaughan A.E., Stevens F.R., Patel N.N., Jia P., Pandey A., Linard C., 2013. Quantifying the effects of using detailed spatial demographic data on health metrics: a systematic analysis for the AfriPop, AsiaPop, and AmeriPop projects. The Lancet 381: S142.
Tatem A.J., Guerra C.A., Kabaria C.W., Noor A.M., Hay S.I., 2008. Human population, urban settlement patterns and their impact on plasmodium falciparum malaria endemicity. Malaria Journal 7(1): 218.
Tatem A.J., Noor A.M., von Hagen C., Di Gregorio A., Hay S.I., 2007. High resolution population maps for low income nations: Combining land cover and census in East Africa. PLoS ONE 2(12): 1–8.
Tenerelli P., Gallego J.F., Ehrlich D., 2015. Population density modelling in support of disaster risk assessment. International Journal of Disaster Risk Reduction 13: 334–341.
The WorldPop project, 2019. The WorldPop project. Online: http://www.worldpop.org.uk/ (accessed 11 February 2019).
Thieken A.H., Müller M., Kleist L., Seifert I., Borst D., Werner U., 2006. Regionalisation of asset values for risk analyses. Natural Hazards and Earth System Science 6(2): 167–178.
Tralli D.M., Blom R.G., Zlotnicki V., Donnellan A., Evans D., 2005. Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing 59(4): 185–198.
Ural S., Hussain E., Shan J., 2011. Building population mapping with aerial imagery and GIS data. International Journal of Applied Earth Observation and Geoinformation 13(6): 841–852.
Vinkx K., Visee T., 2008. Usefulness of population files for estimation of noise hindrance effects. In: ICAO Committee on Aviation Environmental Protection. CAEP/8 Modelling and Database Task Force (MODTF). 4th Meeting. Sunnyvale, USA. pp. 20–22.
Weber N., Christophersen T., 2002. The influence of non-governmental organisations on the creation of Natura 2000 during the European Policy process. Forest policy and economics 4(1): 1–12.
Wright J.K., 1936. A method of mapping densities of population with Cape Cod as an example. Geographical Review 26 (1): 103–110.
Wu S., Qiu X., Wang L., 2005 Population Estimation Methods in GIS and Remote Sensing: A Review. GIScience & Remote Sensing 42(1): 80–96.
Zandbergen P.A., 2011. Dasymetric mapping using high resolution address point datasets. Transactions in GIS 15: 5–27.