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

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.

Monitoring of UN sustainable development goal SDG-9.1.1: study of Algerian “Belt and Road” expressways constructed by China

“…This paper aims to calculate SDG indicator 9.1.1 in the proximity of five Algerian expressways. The SDG-9.1.1 indicator represents the proportion of the rural population 0-150 18 0 0 31 0 0 37 0 0 42 0 0 5 150-300 57 4 0 124 6 1 145 11 1 159 11 1 10 300-600 28 3 0 34 3 0 42 3 0 58 3 0 15 >600 17 2 2 22 2 2 28 2 2 33 2 2 20 Total population 1,790 2,785 3, Qiu et al (2019), Mariathasan, Bezuidenhoudt & Olympio (2019) and Xu, Bai & Chen (2019) have proposed different methods. However, most of the above methods use existing population data that is updated slowly, such that the new population cannot be found in time.…”

“…The revised RAI fully considers urban disadvantaged groups and eliminates the dependence of the original indicators on urban-rural boundary data. Qiu et al (2019) used Deqing County in China as an example for evaluating the SDG-9.1.1 indicator. They used the building area and number of floors as weighting factors to establish a classification model.…”

A Comprehensive Measurement of Progress toward Local SDGs with Geospatial Information: Methodology and Lessons Learned

“…Geospatial disaggregation refers to disaggregating and mapping the statistical data into geographical space with the help of ancillary geospatial data. For instance, the original population data was collected for each administrative unit and could be transformed into geospatial grid spaces through a correlation analysis of population and land use and building layers information [39]. Change mapping utilized multitemporal remotely sensed data to extract the changes of wetlands, forest, and built-up areas and generated time-series data, which were the basis for calculating indicators and assessing SDG goal(s) assessment with a geographical location perspective [40,41].…”

“…As there are only 12 towns in Deqing, the spatial variation details of population are largely smoothed, making it difficult to examine its geographic distribution and pattern ( Figure 3a). In this study, population was estimated according to heights and sizes of buildings, and population density at 30-m spatial resolution was then generated [39,43,44]. The results of the geospatially disaggregated population density are given by Figure 3b, where more spatial details about the population distribution pattern can be observed.…”

Peer Review #2 of "Monitoring of UN sustainable development goal SDG-9.1.1: study of Algerian “Belt and Road” expressways constructed by China (v0.2)"

“…At present, researchers in China and elsewhere have used many methods to evaluate the SDG-9.1.1 indicator. Qiu et al (2019), Bezuidenhoudt and Olympio (2019) and Xu, Bai and Chen (2019) have proposed different methods. However, most of the above methods use existing population data that is updated slowly, such that the new population cannot be found in time.…”