Abstract
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.
Highlights
In order to promote the coordinated development of the economy, society, and environment, leaders around the world adopted the 2030 Agenda for Sustainable Development at the United Nations (UN) Summit in September 2015 [1], which covers 17 Sustainable Development Goals (SDGs) with 169 targets and 342 indicators
The geospatial disaggregation of population data at a fine scale is of great significance to support the measurement and monitoring of the SDGs
After analyzing the advantages and disadvantages of the current spatial disaggregation methods of population data, and considering work requirements and data availability, in order to ensure that the statistical population value was equal to the total population after disaggregation, the dasymetric mapping method was selected to achieve fine-scale population spatialization
Summary
In order to promote the coordinated development of the economy, society, and environment, leaders around the world adopted the 2030 Agenda for Sustainable Development at the United Nations (UN) Summit in September 2015 [1], which covers 17 Sustainable Development Goals (SDGs) with 169 targets and 342 indicators. Golding et al [15] calculated under-five and neonatal mortality (SDG target 3.2) at a 5 × 5-km resolution in Africa for 2000, 2005, 2010, and 2015 based on the Bayesian geostatistical analytical method These results showed that detailed population data were conducive to improving the accuracy of development and health metrics assessments and optimizing interventions. Some example studies include Fisher et al [25], Fan et al [26], and Martin [27] This method is simple and suitable for depicting the patterns of population distribution on a large scale but cannot meet the needs of high-resolution mapping. The principle of the dasymetric mapping method is to subdivide the population distribution space into small areas that can reflect the spatial variation with the aid of auxiliary information and apply the interpolation technique to generate fine-scale population distribution data. We used the disaggregated population data with a resolution of 30 m to support the quantitative, qualitative, and positional assessment of Deqing’s progress toward achieving the SDGs
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