Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

This research constructs a 1 km × 1 km Shanghai energy consumption and carbon emission spatial grid through a bottom-up approach. First, we locate all energy consumption locations in Shanghai via GIS. Second, we calculate energy consumption and associated CO₂ emissions by energy type, by usage type, and by facilities. Finally, we use a spatial grid to represent the energy consumption and CO₂ emissions. The grid shows CO₂ emissions in Shanghai are highly spatially correlated with energy types and volumes of consumption. This research also finds out that high energy consumption and carbon emission locations in Shanghai display significant spatial aggregation. In 7209 spatial energy consumption cells, the top 10 grids of emissions account for 52.8% of total CO₂ emissions in Shanghai; the top 20 grids account for 64.5% and the top 50 grids account for 76.5%. The most critical point emission sources are coal-fired power plants and iron and steel plants. The most important line emission sources are the Yan’an Road and Inner Ring viaducts. The area emission sources that account for the most future-projected growth are commercial and residential natural gas. After this spatial analysis, this paper makes policy suggestions and solutions to conserve energy consumption and mitigate carbon emissions in Shanghai.

Historical cities are important types of cultural heritage sites, and their preservation conditions and conservation status require efficient dynamic monitoring. As the national strategy of China has been vigorously developed in recent years, high-resolution remote sensing technology provides new opportunities for dynamic monitoring of historical and cultural heritage. Based on demand analysis of the historical city conservation and support capacities of high-resolution remote sensing technology, this paper developed a general framework of historical cities conservation monitoring supported by high-resolution remote sensing technology, combined with specific case studies. The framework includes three processes: remote sensing image interpolation for historical cities, temporal and spatial change analysis of conservation elements, and quantitative evaluation of the conservation status. The aims of this study are to explore high-resolution remote sensing technology for conservation monitoring of historical cities and promote the conservation and legacy of historical cities.

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO₂. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO₂ levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO₂ anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.