With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Antarctica plays a significant role in global change studies. As a typical land cover in Antarctica, exposed rock is considered indispensable in many studies, and the mapping of exposed rocks is seen as a key basis of work to meet the demand for more accurate and updated datasets with the consecutive development of satellite technology. Although the normalized difference snow index has been commonly used for differentiating exposed rocks and snow, it often misidentifies clouds as rocks. The British Antarctic Survey has used Landsat 8 data to create a new rock outcrop map for Antarctica, overcoming the limitations of previous techniques and generating Antarctic Digital Database (ADD) New Rock Outcrop with higher accuracy than previously achieved. However, there are still some omission and commission errors apparent in the shaded areas, which affect the accuracy. Widespread shaded areas in Antarctica due to low solar elevation angles and extreme topography cause difficulty in accurately mapping exposed rock. In addition, major differences are present between existing products. Addressing the existing issues about extraction of exposed rock, this study used the near infrared band and shortwave infrared 2 band of Landsat 8 reflectance data to build a specific exposed rock index for the extraction of exposed rocks. A shadow detection method combined with a blue reflectance threshold is used for the shadowed rock identification. Accuracy assessment of these extraction results showed that the accuracy of the new product is higher than all existing exposed rock products. The conversion from DN values to top of atmosphere reflectance and the solar elevation correction for each pixel individually eliminate a variety of errors associated with the different acquisition times of each image. From the statistics of the reflectance related to the training samples, this paper established the threshold of exposed rock extraction so as to ensure the applicability of the same threshold for exposed rock extraction in all images. This method is applied to a total of 1100 high-quality images that were collected for covering the Antarctic continent from November 2013 to February 2014. The results show that 253 of the images contain exposed rocks, and these images were used for mapping work. The map showed that the main exposed rock areas are mainly distributed in four coastal regions: The Antarctic Peninsula, Queen Maud Land, Lambert Glacier basin, and Victoria Land regions. We also compared our results with ADD New Rock Outcrop and Bedrock Mapping Project 2 (Bedmap2) data in the four main regions. Our results were close to the ADD rock outcrops and exhibited remarkable differences with Bedmap2. We explored the possibility of analyzing and explaining these differences. Especially, as using the same data source Landsat 8 but with a different method, the comparison between our results and ADD New Rock Outcrop is discussed and concludes in shadowed rocks extraction, mixed-pixels, and omission disagreement. The results also show that shadowed rocks accounts for nearly 12% of the total exposed rocks and cannot be neglected. The method we developed can be quickly applied for extraction and mapping of large areas of exposed rocks using Landsat 8.

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.