Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.

The continuous expansion of urban land has led to massive encroachment upon cropland. To examine the impacts of urban expansion on the loss and fragmentation of cropland in China’s nine major grain production areas (MGPAs), we used standard deviation ellipse, land use transfer matrix, land use dynamic degree, and landscape metric to explore the spatio-temporal evolutions, mutual transfer, and landscape patterns of cropland and urban land. The results show the following: (1) From 1995 to 2018, the areas of cropland in MGPAs showed a trend of “short-term increase—long-term decrease—short-term increase”, while that of urban land grew continuously; (2) Urban expansion is the main cause of cropland loss. The cropland area converted to urban land accounts for a large proportion (49.26%) of the total transfer of cropland to other land types, especially in the densely populated, rapidly urbanizing and industrializing Taihu Lake Plain, Jianghuai Region, and Pearl River Delta; (3) In most MGPAs, urban expansion has led to fragmentation of cropland, especially in the Pearl River Delta, as indicated by the significant change of patch density. However, in the Sanjiang Plain and Songnen Plain, a less pronounced or even reduced cropland fragmentation was observed due to the significant conversion of other land types to cropland under specific land policies. From these results, we suggest that the government should regulate the encroachment of urban land on cropland and the transfer of natural land to it, and encourage the rural land consolidation to increase the cropland.

Poverty and disasters are globally prevalent in ecologically fragile areas. Ecological migration is regarded as an effective way to address these issues. This paper investigates the spatial pattern of ecological migration and the corresponding spatiotemporal changes in land use and vegetation restoration in Gulang County, located in northwest China, between 2010 and 2018. For this purpose, we calculated three indicators: the transfer matrix of land use, normalized difference vegetation index (NDVI), and vegetation restoration degree (VRD). We found that ecological migrants in Gulang County moved from the Qilian Mountain National Park to the intersection between flat area and desert. The spatial patterns ranged from high-altitude to low-altitude, and the slopes became less steep. The distribution of the resettlements is more clustered and shaped by the traffic conditions and guided by the local governments. Unused land in the whole-village migration area and construction land in the resettlement area were dramatically impacted by ecological migration (625% and 279.3%, respectively). The cropland and construction land in the outmigration areas were mainly replaced with grassland and forest. In contrast, the grassland and unused land in the resettlement area were transferred to cropland and construction land. After ecological migration, the mean NDVI and VRD in Gulang County significantly increased, indicating that the vegetation in the outmigration areas quickly recovered. Moreover, the VRD in the whole-township migration areas was greater than that in the whole-village migration areas (121% > 68%). The main contribution to the increase in NDVI was the conversion of forest to grassland, accounting for 33%. In addition, the transition from other types of land to grassland made a larger contribution to the NDVI than conversions to forest.