Developing computable sustainable urbanization science: interdisciplinary perspective.

In this era of drastic global change, the Anthropocene, carbon neutrality and sustainable development have become common twenty-first century human challenges and goals. Large-scale urbanization is indicative of human activities and provides an important impetus for environmental changes; therefore, cities have become an important stage in which to promote a more sustainable future development of human society. However, current researchers study urbanization issues based on the perspectives and tools of their respective disciplines; therefore, a holistic and comprehensive understanding of urbanization is lacking due to the insufficient integration of multidisciplinary study perspectives. We explored the construction of interdisciplinary computable sustainable urbanization and introduces a conceptual framework for interdisciplinary urbanization, as scientific computing supports and integrates the natural sciences and humanities to simulate urban evolution and further observe, explain, and optimize human and environment interactions in urban areas. We advocated for the establishment of major international research programs and organizations in the field of sustainable urbanization, and the cultivation of talented young professionals with broad-ranging interdisciplinary interests. Expectantly, we hope a livable planet in the Anthropocene era could be created by developing sustainable urbanization and achieving carbon neutrality.

Fine-scale population spatialization data of China in 2018 based on real location-based big data.

Accurate location-based big data has a high resolution and a direct interaction with human activities, allowing for fine-scale population spatial data to be realized. We take the average of Tencent user location big data as a measure of ambient population. The county-level statistical population data in 2018 was used as the assigned input data. The log linear spatially weighted regression model was used to establish the relationship between location data and statistical data to allocate the latter to a 0.01° grid, and the ambient population data of mainland China was obtained. Extracting street-level (lower than county-level) statistics for accuracy testing, we found that POP2018 has the best fit with the actual permanent population (R2 = 0.91), and the error is the smallest (MSEPOP2018 = 22.48