Disaggregating population data for assessing progress of SDGs: methods and applications

Figures & data

Figure 1. Illustrations of the population disaggregation methods. (a) Choropleth map. (b) Areal weighting. (c) Pycnophylactic. (d) Binary dasymetric. (e) Multi-class dasymetric. (f) Intelligent dasymetric.

Table 1. Disaggregated population products and their main characteristics.

Dataset	Coverage	Resolution	Year(s) represented	Method	Ancillary data used	Population concept	Distribution policy	Source
Gridded Population of the World (GPWv4) (Doxsey-Whitfield et al. 2015)	Global	30 arc-seconds (∼1 km)	2000, 2005, 2010, 2015, 2020	Areal weighting	None	Residential	Open	https://sedac.ciesin.columbia.edu/data/collection/gpw-v4
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) (Balk et al. 2005; Salvatore et al. 2005; Balk et al. 2006 )	Global	30 arc-seconds (∼1 km)	1990, 1995, 2000	Binary dasymetric mapping	Nighttime lights, settlement points	Residential	Open	https://sedac.ciesin.columbia.edu/data/collection/grump-v1
LandScan Global Population (Dobson et al. 2000; Bhaduri et al. 2007)	Global	30 arc-seconds (∼1 km)	1998, annual release from 2000 to 2019	Multivariate dasymetric mappingc(or smart interpolation)	Nighttime lights, roads, slope, elevation, land cover, urban boundaries, high resolution imagery	Ambient	Free for research/ commercial use at cost	https://landscan.ornl.gov/
WorldPop (Tatem 2017)	Africa, Asia, Central and South America	3 arc-seconds (∼100 m)	2000–2020	Semi-automated dasymetric mapping	Settlement, roads, land cover, urban areas, nighttime lights, infrastructure, climate, vegetation, elevation, slope, protected areas, water bodies, building maps	Residential	Open	http://www.worldpop.org/
Global Human Settlement Layer-Population (GHS-POP) (Freire et al. 2016; Freire et al. 2020)	Global	9 arc-seconds (250 m) and 30 arc-seconds (1 km)	1975, 1990, 2000, 2015	Dasymetric mapping	Global Human Settlement Layer – built-up areas (GHS-BUILT), GPWv4	Residential	Open	https://ghsl.jrc.ec.europa.eu/ghs_pop2019.php
UNEP/GRID-Sioux Falls (Deichmann 1996b; Hyman et al. 2004; Nelson 2004)	Africa, Asia and Latin America and Caribbean	2.5 arc-minutes (∼5 km)	1960-2000, decade interval (Africa, Asia and Latin America); 1995 (Asia)	Areal weighting	Transportation network, urban centers, location and size	Residential	Open	https://na.unep.net/siouxfalls/datasets/datalist.php
World Population Estimate (WPE) (Frye et al. 2018)	Global	150 m (2016), 250 m (earlier)	2013, 2015, and 2016	Dasymetric mapping	Land cover, roads, water bodies, cities or urban areas	Residential	Commercial / Free to ArcGIS Users	https://livingatlas.arcgis.com/en/browse/#d=2&q=World%20Population%20Estimate
High Resolution Settlement Layer (HRSL) (Tiecke et al. 2017)	Countries	1 arc-second (∼30   m)	2015	Binary dasymetric mapping	Digital Globe 0.5 m imagery	Residential	Open	https://www.ciesin.columbia.edu/data/hrsl/
OpenPopGrid (Murdock et al. 2015)	United Kingdom	10 m	2011	Binary dasymetric mapping	Postcode centroids, building footprints	Residential	Open	http://openpopgrid.geodata.soton.ac.uk/
PopGrid (Huang et al. 2021a)	the Contiguous United States	100 m	between 2013 and 2017	Dasymetric mapping	Microsoft building footprints, OSM	Residential	Open	https://doi.org/10.7910/DVN/DLGP7Y

Table 2. SDGs Global Indicator Framework (as of 2020) and indicators that can be supported by disaggregated population data.

Note: Blue signifies indicators directly supporting SDGs assessment and belonging to Tier I, and the use of population spatial disaggregation data can optimize the calculation results derived from statistical data. Yellow and orange both signify that direct support and that the indicators belong to Tier II. In the case of a lack of data, the former can realize the calculation of indicators by integrating population disaggregation data and other data, while the latter can play an important role in SDGs assessment only when the metadata required for the indicator calculation are available. Green means indirect support. Grey signifies that the indicators have nothing to do with the disaggregated population data. The symbol in the upper right corner of the indicator indicates that this indicator is repeated. For example, indicators 7.b.1 and 12.a.1 have the same symbol, ▴, indicating that they are the same and repeat. All repeat indicators: 1.5.1/11.5.1/13.1.1, 1.5.3/11.b.1/13.1.2, 1.5.4/11.b.2/13.1.3, 4.7.1/12.8.1/13.3.1, 7.b.1/12.a.1, 8.4.1/12.2.1, 8.4.2/12.2.2, 10.3.1/16.b.1, 10.6.1/16.8.1, 13.2.1/13.b.1, 15.7.1/15.c.1, 15.a.1/15.b.1.

Figure 2. Illustration of disaggregated population data applications.

Figure 3. Accessibility of health centers in Deqing County (from Qiu et al. 2019).

Table 3. Percentage and cumulative percentage of the population who could reach a health center within each time interval based on population statistical data and disaggregated population data (cite from Qiu et al. 2019).

Time (min)	Population statistical data		Disaggregated population data
	Percentage (Health Center)	Cumulative Percentage (Health Center)	Percentage (Health Center)	Cumulative Percentage (Health Center)
0–5	23.32	23.32	46.16	46.16
5–10	49.42	72.74	44.10	90.26
10–15	19.34	92.07	8.68	98.94
15–20	5.66	97.73	0.96	99.90
20–25	1.90	99.63	0.10	100
25–30	0.33	99.97
30–35	0.03	100

Figure 4. The frequency of use of disaggregated population data with different spatial resolutions in indicator evaluation applications at global, national, and regional levels.

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Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.