Gridded Population Maps Informed by Different Built Settlement Products

The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.

[1]  Alexander Zipf,et al.  Fine-resolution population mapping using OpenStreetMap points-of-interest , 2014, Int. J. Geogr. Inf. Sci..

[2]  A. Tatem,et al.  Population Distribution, Settlement Patterns and Accessibility across Africa in 2010 , 2012, PloS one.

[3]  T. Esch,et al.  Breaking new ground in mapping human settlements from space – The Global Urban Footprint , 2017, 1706.04862.

[4]  Worldpop.  Effectiveness of Remotely Sensed Built Areas for Constraining and Modelling Gridded Population Estimates - Script Samples and Data , 2018 .

[5]  Roland R. Draxler,et al.  Root mean square error (RMSE) or mean absolute error (MAE) , 2014 .

[6]  Catherine Linard,et al.  Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data , 2015, PloS one.

[7]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[8]  Jeremy Mennis,et al.  Dasymetric Mapping for Estimating Population in Small Areas , 2009 .

[9]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[10]  C. Elvidge,et al.  Why VIIRS data are superior to DMSP for mapping nighttime lights , 2013 .

[11]  Jeremy Mennis,et al.  Intelligent Dasymetric Mapping and Its Application to Areal Interpolation , 2006 .

[12]  Kenji Matsuura,et al.  Smart Interpolation of Annually Averaged Air Temperature in the United States , 1995 .

[13]  Heather Zeppel.  Climate change initiatives in the workplace , 2011 .

[14]  Xianming Liu,et al.  Mapping the world population one building at a time , 2017, ArXiv.

[15]  A. Tatem,et al.  The effects of spatial population dataset choice on estimates of population at risk of disease , 2011, Population health metrics.

[16]  Catherine Linard,et al.  High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020 , 2015, Scientific Data.

[17]  Sanjeev V Thomas.  Millennium development goals: Where are we? , 2007 .

[18]  A. Tatem,et al.  The accuracy of human population maps for public health application , 2005, Tropical medicine & international health : TM & IH.

[19]  W. Tobler.  Smooth pycnophylactic interpolation for geographical regions. , 1979, Journal of the American Statistical Association.

[20]  Michael F. Goodchild,et al.  Areal interpolation: A variant of the traditional spatial problem , 1980 .

[21]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[22]  Catherine Linard,et al.  Exploring nationally and regionally defined models for large area population mapping , 2015, Int. J. Digit. Earth.

[23]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[24]  B. Bhaduri,et al.  LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics , 2007 .

[25]  S I Hay,et al.  Determining global population distribution: methods, applications and data. , 2006, Advances in parasitology.

[26]  Peter A. Rogerson,et al.  GIS and Spatial Analytical Problems , 1993, Int. J. Geogr. Inf. Sci..

[27]  B. Anderson,et al.  The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones , 2007 .

[28]  Catherine Linard,et al.  Large-scale spatial population databases in infectious disease research , 2012, International Journal of Health Geographics.