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.

Vegetation is one of the most important indicators of climate change, as it can show regional change in the environment. Vegetation health is affected by various factors, including drought, which has cumulative and time-lag effects on vegetation response. However, the cumulative and time-lag effects of drought on different terrestrial vegetation in China are still unclear. To address this issue, this study examined the cumulative and time-lag effects of drought on vegetation from 2001 to 2020 using the Standardized Precipitation Evapotranspiration Index (SPEI) in the Global SPEI database and the Normalized Difference Vegetation Index (NDVI) in MOD13A3. Based on Sen-Median trend analysis and the Mann–Kendall test, the change trend and significance of the NDVI from 2001 to 2020 were explored. The Pearson correlation coefficient was used to analyze the correlation between the SPEI and NDVI at each cumulative scale and time-lag scale and to further analyze the cumulative and time-lag effects of drought on vegetation. The results show the following: (1) The NDVI value increased at a rate of 0.019/10 years, and the increased area of the NDVI accounted for 80.53% of mainland China, with a spatial trend of low values in the west and high values in the east. (2) The average SPEI cumulative time scale most relevant to the NDVI was 7.3 months, and the cumulative effect demonstrated a high correlation at the scale of 9–12 months and revealed different distributions in different areas. The cumulative effect was widely distributed at the 9-month scale, followed by the 12-month scale. The correlation coefficients of cumulative effects between the SPEI and NDVI for cropland, woodland and grassland peaked at 9 months. (3) The average SPEI time-lag scale for the NDVI was 6.9 months, and the time-lag effect had the highest correlation coefficient at the 7-month scale. The strongest time-lag effect for cropland and grassland was seen at 7 months, while the strongest time-lag effect for woodland was seen at 6 months. Woodland had a lower time-lag effect than grassland at different scales. The research results are significant for their use in aiding the scientific response to drought disasters and making decisions for climate change precautions.

For the sustainable development of marine fishery resources, it is essential to comprehensively, accurately, and objectively obtain the spatial characteristics and evolution law of fishing intensity. However, previous studies have focused more on the use of single data sources, such as AIS (Automatic Information System) and VBD (VIIRS boat detection), to obtain fishing intensity information and, as such, have encountered some problems, such as insufficient comprehensive data coverage for ships, non-uniform spatial distribution of data signal acquisition, and insufficient accuracy in obtaining fishing intensity information. The development of big data and remote sensing Earth observation technology has provided abundant data sources and technical support for the acquisition of fishing intensity data for marine fisheries. Based on this situation, this paper proposes a framework that integrates the data of fishing vessels from two sources (AIS, with high space-time granularity, and VBD, with short revisit cycle and high sensitivity), in order to obtain such information based on closely matching and fusing the vector point data of ship positions. With the help of this framework and the strategy of indirectly representing fishing intensity by data point density after fusion, the spatial characteristics and rules of fishing intensity in typical seasons (February, April, September, and November) in the northern South China Sea in 2018 were systematically analyzed and investigated. The results revealed the following: (1) Matching and fusing AIS and VBD data can provide a better perspective to produce robust and accurate marine fishery intensity data. The two types of data have a low proximity match rate (approximately 1.89% and 6.73% of their respective inputs) and the matching success for fishing vessels in the data was 49.42%. (2) Single AIS data can be used for nearshore (50 to 70 km) marine fishery analysis research, while VBD data reflect the objective marine fishing in space, showing obvious complementarity with AIS. (3) The fishing intensity grid data obtained from the integrated data show that high-intensity fishing in the study area was concentrated in the coastal area of Maoming City, Guangdong (0–50 km); the coastal area of Guangxi Beihai (10–70 km); around Hainan Island in Zhangzhou (10–30 km); and the Sanya nearshore area (0–50 km). However, it did not decay with increasing offshore distance, such as at the Trans-Vietnamese boundary in the Beibu Gulf, near the China–Vietnam Common Fisheries Area (50 km) and high-intensity fishing areas. (4) The obtained fishing intensity data (AIS, VBD, and AIS + VBD) were quantitatively analyzed, showing that the CV (Coefficient of Variation) of the average for each month (after fusing the two types of data) was 0.995, indicating that the distribution of the combined data was better than that before fusion (before fusion: AIS = 0.879, VBD = 1.642). Therefore, the integration of AIS and VBD can meet the need for a more effective, comprehensive, and accurate fishing intensity analysis in marine fishery resources.

The outcomes for emergency medical services (EMS) are highly dependent on space-time accessibility. Prior research describes the location of EMS needs with low accuracy and has not integrated a temporal analysis of the road network, which accounts for varying mobility in a dynamic transportation network. In this study, we formulated a network-based location-allocation model (NLAM) and analyzed the spatial characteristics of emergency medical facilities within the fifth ring road in Beijing by considering time, traffic, and population characteristics. The conclusions are as follows: (1) The high demand area for EMS is concentrated in the areas in middle, north, and east during the daytime (8:00–20:00) and in the middle and north during the nighttime (20:00–8:00). From day to night, the centroid of the potential demand distribution shifts in the Western and Southern areas. (2) The road traffic data is sampled 20 times throughout the week, and variations in the average driving speed affect a higher mean driving speed on the weekend. This primarily impacts the main roads, due to these roads experiencing the greatest fluctuation in speed throughout the week of any roadway in the study area. (3) Finally, the 15-min coverage of emergency medical facilities are sampled 20 times in one week and analyzed. Fortunately, there is 100% coverage at night; however, due to traffic congestion, there were a few blind coverage areas in the daytime. The blind area is prevalent in Shijingshan South Station and the Jingxian Bridge in the South fifth ring.