Characterizing urban expansion patterns is of great significance to planning and decision-making for urban agglomeration development. This study examined the urban expansion in the entire Yangtze River Delta Region (YRDR) with its land-use data of six years (1995, 2000, 2005, 2010, 2015, and 2018). On the basis of traditional methods, we comprehensively considered the four aspects of urban agglomeration: expansion speed, expansion difference, expansion direction, and landscape pattern, as well as the interconnection of and difference in the expansion process between each city. The spatiotemporal heterogeneity of urban expansion development in this region was investigated by using the speed and differentiation indices of urban expansion, gravity center migration, landscape indices, and spatial autocorrelations. The results show that: (1) over the 23 years, the expansion of built-up land in the Yangtze River Delta Region was significant, (2) the rapidly expanding cities were mainly located along the Yangtze River and coastal areas, while the slowly expanding cities were mainly located in the inland areas, (3) the expansion direction of each city varied and the gravity center of the urban agglomeration moved toward the southwest, and (4) the spatial structure of the region became more clustered, the shape of built-up land turned simpler, and fragmentation decreased. This study unravels the spatiotemporal change of urban expansion patterns in this large urban agglomeration, and more importantly, can serve as a guide for formulating urban agglomeration development plans.

Carbon emissions (CE) in Anhui Province are closely related to carbon emissions from industrial land (CEIL). In this study, based on industrial land, industrial energy consumption, and related statistical data in Anhui Province from 2000 to 2016, the carbon emissions coefficient method and the standard deviational ellipse were used to measure and analyze the CEIL and their spatial and temporal evolution characteristics, aiming to provide a basis for the relevant government departments to formulate CE policies. The main results showed that: (1) The total amount of CEIL followed an inverted U-shaped trend of rapid increase followed by a decrease, while the overall carbon emission intensity from industrial land (CEIIL) followed a downward trend. (2) The CE had an evident spatial differentiation, with those from resource-based cities being much higher than those of industrial and tourism-based cities; (3) The overall pattern of CEIL in Anhui Province showed that the increase in the north-south direction is significantly higher than that in the east-west direction, and mainly expanded in the north-south direction. The overall industrial growth rate of Southern Anhui, represented by the Wanjiang City Belt, was higher than that of Northern Anhui, although its CEIL center showed to move towards Northern Anhui.

Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R² of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.