Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach

Abstract

Due to the challenges in data acquisition, especially for developing countries and at local levels, spatiotemporal evaluation for SDG11 indicators was still lacking. The availability of big data and earth observation technology can play an important role to facilitate the monitoring of urban sustainable development. Taking Guilin, a sustainable development agenda innovation demonstration area in China as a case study, we developed an assessment framework for SDG indicators 11.2.1, 11.3.1, and 11.7.1 at the neighborhood level using high-resolution (HR) satellite images, gridded population data, and other geospatial big data (e.g., road network and point of interest data). The findings showed that the proportion of the population with convenient access to public transport in the functional urban area gradually improved from 42% in 2013 to 52% in 2020. The increase in built-up land was much faster than the increase in population. The areal proportion of public open space decreased from 56% in 2013 to 24% in 2020, and the proportion of the population within the 400 m service areas of open public space decreased from 73% to 59%. The township-level results indicated that low-density land sprawling should be strictly managed, and open space and transportation facilities should be improved in the three fast-growing towns, Lingui, Lingchuan, and Dingjiang. The evaluation results of this study confirmed the applicability of SDG11 indicators to neighborhood-level assessment and local urban governance and planning practices. The evaluation framework of the SDG11 indicators based on HR satellite images and geospatial big data showed great promise to apply to other cities for targeted planning and assessment.