Next Article in Journal
Planning Sustainable Economic Development in the Russian Arctic
Previous Article in Journal
An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation
Open AccessArticle

Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China

College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 356;
Received: 12 June 2019 / Revised: 8 August 2019 / Accepted: 11 August 2019 / Published: 13 August 2019
PDF [5460 KB, uploaded 13 August 2019]


Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method. View Full-Text
Keywords: Deqing county; population; disaggregation; SDG indicators; fine scale Deqing county; population; disaggregation; SDG indicators; fine scale

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Qiu, Y.; Zhao, X.; Fan, D.; Li, S. Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China. ISPRS Int. J. Geo-Inf. 2019, 8, 356.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top