Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data

ABSTRACT

The differences in the definition of urban areas lead to our contrasting or inconsistent understanding of global urban development and their corresponding socioeconomic and environmental impacts. The existing urban areas were widely identified by the boundaries of built-environment or social-connections, rather than urban entities that are essentially the spatial extents of human activity agglomerations. Thus, this study has attempted to map and evaluate global urban entities (2000–2020) from a perspective of an updated urban concept of urban entities based on the consistent remotely sensed nighttime light data. First, a K-means algorithm was developed to cluster urban and non-urban pixels automatically in consideration of global region division. Then, a post-processing was conducted to enhance the temporal and logical consistency of urban entities during the study period. Rationality assessment indicates that urban entities derived from remotely sensed nighttime light data more effectively reflect the spatial agglomeration extents of human activities than those of physical urban areas. Global urban entities increased from 157,733 km² in 2000 to 470,632 km² in 2020 accompanied by a differentiated urban expansion at global, continental, and national levels. Our study provides long-time series and fine-resolution datasets (500 m) and new research avenues for spatiotemporal analysis of global urban entity expansion with the improvement of the understanding of urbanization and the emergence of effective urban mapping theories and approaches.

KEYWORDS:

• Nighttime light data
• SNPP-VIIRS-like
• Urban entities
• Human activity agglomeration extent

Acknowledgements

We would like to thank Profs. Bailang Yu and Jianping Wu from East China Normal University, China for their helpful training and suggestions on the explorations of remotely sensed nighttime light data. We also want to express our respects and gratitude to the anonymous reviewers and editors for their valuable comments on improving the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data Availability Statement

Global urban entities (2000–2020) can be downloaded https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/79CRQJ. The newly generated SNPP-VIIRS-like data are openly available in Harvard Dataverse at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YGIVCD#. Population was extracted from the LandScan datasets. Artificial impervious extents collected from the data product of MODIS Land Cover Type Version 6 and urban built-up areas proposed by He et al. (2019). Terra land surface temperature, Terra vegetation index, Terra land water, and Landsat 8 OLI, were obtained from the Google Earth Engine platform. The 2010 developed land was acquired from GlobeLand30. Road networks were extracted from OpenStreetMap. Statistical data, including urban population and GDP, were obtained from the United Nations and World Bank, respectively. Administrative boundaries were collected from Natural Earth.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2022.2161199

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (42101345).