Identification of densely populated-informal settlements and their role in Chinese urban sustainability assessment

ABSTRACT

China’s National Plan on Implementation of the 2030 Agenda for Sustainable Development identified the upgrading of urban shantytowns, urban villages, and dilapidated houses as an important initiative to implement the Sustainable Development Goal (SDG) 11.1. However, informal housing being used as temporary housing by low-income families (especially in megacities) has resulted in informal settlements that are usually densely populated, dilapidated, and disorganized. Identifying targets based on deep learning and single very high-resolution images remains a challenging task. Here, we used multi-source geographic information data and machine-learning methods to identify and compare the distribution of densely populated‒informal settlements (DPISs) and measured population ratios within the urban areas of three Chinese first-tier cities: Beijing, Shanghai, and Guangzhou. Our results indicate that DPISs occupy 1.98%, 0.67%, and 3.95% of the total area in these study regions, with population densities of 42,800 people/km², 7,100 people/km², and 20,800 people/km², respectively. Further, significant variability existed in the distribution of DPISs, population ratios, and composition of the study areas. DPISs reflected the rapid urbanization process in China and the planning of the city, which can be used as an indicator of sustainable urban development in China.

KEY POLICY HIGHLIGHTS

• Localizing SDGs indicators to study areas and incorporating regional variations and specific characteristics enhanced the understanding of their role in regional sustainable development.

• DPISs were introduced in this study; the similarities and differences were made between DPIS and SDG indicators.

• Contrasting distribution patterns of DPISs in Beijing, Shanghai, and Guangzhou revealed distinct regional disparities and urban development dynamics.

• Integration of multi-source remote sensing data enabled comprehensive analysis of complex features and spatial patterns of DPISs.

• Exploration of the intricate relationship between DPISs and urbanization unveiled challenges and potential opportunities for inclusive urban development.

KEYWORDS:

• SDG 11.1
• multi-source geographic information data
• machine learning
• densely populated-informal settlements
• localization indicators

Acknowledgments

We thank many students (including Jingbo Wang, Yi Du, Xun Yu, and Kunqin Liu) from the Department of Geography and Environmental Sciences, Shanghai Normal University for their hard work in processing remote sensing images and extracting densely populated‒informal settlements.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

Datasets used:

1. Gaofen-2: open dataset at http://www.cresda.com/CN/index.shtml

2. Luojia-1: open dataset at http://59.175.109.173:8888/

3. Sentinel-2: open dataset at https://scihub.copernicus.eu/

4. RTUD: open dataset at https://heat.qq.com/index.php

5. Road network data: open dataset at http://www.openstreetmap.org/

6. WorldPop dataset: open dataset at https://www.worldpop.org/

7. China Land Use Remote Sensing Monitoring Data: open dataset at https://www.resdc.cn/-data.aspx?DATAID=335

8. DPIS dataset: https://figshare.com/articles/dataset/Untitled_Item/23805075

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant numbers 42171389 and 41730642.