1,604
Views
1
CrossRef citations to date
0
Altmetric
Articles

Mapping population distribution from open address data: application to mainland Portugal

ORCID Icon, ORCID Icon & ORCID Icon
Pages 585-593 | Received 21 Mar 2022, Accepted 15 Aug 2022, Published online: 07 Sep 2022

References

  • 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. https://doi.org/10.1080/13658816.2014.909045
  • Biljecki, F., Arroyo Ohori, K., Ledoux, H., Peters, R., & Stoter, J. (2016). Population estimation using a 3D city model: A multi-scale country-wide study in the Netherlands. PLoS One, 11(6), e0156808. https://doi.org/10.1371/journal.pone.0156808
  • Calka, B., 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. https://doi.org/10.1080/19475705.2017.1345792
  • Deng, C., Wu, C., & Wang, L. 2010. Improving the housing–unit method for small–area population estimation using remotesensing and GIS information. International Journal of Remote Sensing, 31, 5673–5688.
  • Gallego, F. (2010). A population density grid of the European Union. Population and Environment, 31(6), 460–473. https://doi.org/10.1007/s11111-010-0108-y
  • Gaspar, J. (1987). Portugal: os próximos 20 anos. Ocupação e organização do espaço – Retrospectiva e Tendências (Vol. 1). Fundação Calouste Gulbenkian.
  • GEOSTAT 1A. (2011). ESSnet project GEOSTAT – Representing census data in a European population grid (1. Final report for A 2010–2011). European Statistical System.
  • Lloyd, C., Catney, G., Williamson, P., & Bearman, N. (2017). Exploring the utility of grids for analysing long term population change. Computers, Environment and Urban Systems, 66, 1–12. https://doi.org/10.1016/j.compenvurbsys.2017.07.003
  • Lloyd, C., Sorichetta, A., & Tatem, A. (2017). High resolution global gridded data for use in population studies. Scientific Data, 4(1), 170001. https://doi.org/10.1038/sdata.2017.1
  • Martin, D., Lloyd, C., & Shuttleworth, I. (2011). Evaluation of gridded population models using 2001 Northern Ireland census data. Environment and Planning A, 43(8), 1965–1980. https://doi.org/10.1068/a43485
  • Norman, P., Rees, P., & Boyle, P. (2003). Achieving data compatibility over space and time: Creating consistent geographical zones. International Journal of Population Geography, 9(5), 365–386. https://doi.org/10.1002/ijpg.294
  • Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth observation data using census disaggregation and bottom-up estimates. PLoS One, 16(3), e0249044. https://doi.org/10.1371/journal.pone.0249044
  • Smith, S., & Cody, S. (1994). Evaluating the housing unit method: A case study of 1990 population estimates in Florida. Journal of the American Planning Association, 60(2), 209–221. https://doi.org/10.1080/01944369408975574
  • Smith, S., Nogle, J., & Cody, S. (2002). A regression approach to estimating the average number of persons per household. Demography, 39(4), 697–712. https://doi.org/10.1353/dem.2002.0040
  • Tu, W., Liu, Z., Du, Y., Yi, J., Liang, F., Wang, N., Qian, J., Huang, S., & Wang, H. (2022). An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data. International Journal of Applied Earth Observation and Geoinformation, 107, 102709. ISSN 1569-8432. https://doi.org/10.1016/j.jag.2022.102709
  • Wardrop, N., Jochem, W., Bird, T., Chamberlain, H., Clarke, D., Kerr, D., Bengtsson, L., Juran, S., Seaman, V., & Tatem, A. (2018). Spatially disaggregated population estimates in the absence of national population and housing census data. Proceedings of the National Academy of Sciences, 115, 3529–3537. https://doi.org/10.1073/pnas.1715305115