We conducted a standardized spatial thinking ability test (STAT) to examine the spatial thinking abilities of a group of Chinese undergraduates with a focus on their spatial reasoning, which is a very important component of critical spatial thinking. The college subject of human geography in China is often geared toward the preparation of students for government consultancy and policy-making tasks, known as the “tasks leading disciplines” (renwu dai xueke), where the pedagogies are problem-solving based and sustainability centered. Geographic Information Systems (GIS) has become the universal tool for problem solving in geography and other areas, thus human geography in this situation gives us a context to test and investigate whether and to what extent GIS implementation is able to improve undergraduate spatial thinking levels. Our comparative analysis reported the marginally significant difference of STAT test scores between GIS application (geoinformation group) and its control group (geography group without GIS training). It was also found that the Chinese students performed the spatial reasoning better in this test than American participants as reported in prior study, displaying their higher spatial cognition in terms of problem solving and Boolean logics. Futhermore, a strong negative correlation was reported between STAT test scores and final exam rank. It is possible that the higher geography education in a context of China may not fully embrace the spatial thinking capacity as the strategic goal. The results can help us to better understand the Oriental and Western gaps in higher geography education. Policy suggestions are given in the conclusion.

Global warming has accelerated climate and weather changes, impacting the regional water cycle. This study assesses the temporal trends of seasonal and annual runoff in the Chitral River Basin (CRB) and its responses to regional climatic factors (i.e., temperature, precipitation, and Normalized Difference Vegetation Index (NDVI)) and oceanic indices at large scales (i.e., El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO)). The non-parametric Mann–Kendall (MK) test, the Sequential Mann–Kendall test (SQMK) and Sen Slope (SS) is used to evaluate trends and magnitude. In contrast, wavelet analysis is used to assess the coherence. In general, precipitation increases in winter, summer and autumn, whereas it decreases in spring. The temperature increased significantly in winter and spring, while a significant increase in seasonal and annual runoff was evident. Annual NDVI increased, whereas the Normalized Difference Water Index (NDWI) and the Normalized Difference Snow Index (NDSI) decreased. Generally, runoff has significant inter-annual coherences with regional environmental factors, and a significant coherence with NDVI. Monthly runoff has a positive coherence with temperature and NDVI, whereas it has a negative correlation with precipitation, NDWI, and NDSI. In general, ENSO, IOD and PDO show a positive correlation with runoff. The MWC findings indicate that annual runoff prevailed interannual signals with local environmental factors and with the Pacific Ocean, whereas interannual and interdecadal coherences are obvious with the Atlantic Ocean. The results have significant implications for decision-makers seeking to enhance water resource planning, disaster prevention, and mitigation, especially in global warming and the intensification of human activities that influence hydroclimatic changes at high altitudes.

The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors.

As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy efficiency. Inhabitants’ occupancy in buildings and open spaces on campus is critical to optimize campus management and services. Information about spatial occupancy of campus inhabitants can be produced based on various location-based solutions, such as global navigation satellite systems (GNSS), campus cameras, Bluetooth, and Wi-Fi. As an essential component in campus information infrastructure, Wi-Fi network covers almost the entire university campus and has advantages in collecting locations of campus inhabitants. In this paper, geo-visualization of spatial occupancy of campus inhabitants is designed and implemented using anonymized Wi-Fi network log data. First, 3-dimension building models are reconstructed based on LiDAR point clouds and construction drawings. Then, the Wi-Fi network log data are cleaned and preprocessed. Campus inhabitants’ locations are extracted from structural Wi-Fi data. Geo-visualization at room, floor, and building levels is designed and implemented. On a temporal dimension, spatial occupancy can be visualized by each second, minute, hour, or day of the week in 3D buildings. The implementation of the geo-visualization is based on CesiumJS, which offers an interface for 3D-animated visualization and interaction. The research can be used to support university management and educators to implement the smart campus and optimize pedagogical research.

Vector data compression can significantly improve efficiency of geospatial data management, visualization and data transmission over internet. Existing compression methods are either based on information theory for lossless compression mainly or based on map generalization methods for lossy compression. Coordinate values of vector spatial data are mostly represented using floating-point type in which data redundancy is small and compression ratio using lossy algorithms is generally better than that of lossless compression algorithms. The purpose of paper is to implement a new algorithm for efficient compression of vector data. The algorithm, named space division based compression (SDC), employs the basic idea of linear Morton and Geohash encoding to convert floating-point type values to strings of binary chain with flexible accuracy level. Morton encoding performs multiresolution regular spatial division to geographic space. Each level of regular grid splits space horizontally and vertically. Row and column numbers in binary forms are bit interleaved to generate one integer representing the location of each grid cell. The integer values of adjacent grid cells are proximal to each other on one dimension. The algorithm can set the number of divisions according to accuracy requirements. Higher accuracy can be achieved with more levels of divisions. In this way, multiresolution vector data compression can be achieved accordingly. The compression efficiency is further improved by grid filtering and binary offset for linear and point geometries. The vector spatial data compression takes visual lossless distance on screen display as accuracy requirement. Experiments and comparisons with available algorithms show that this algorithm produces a higher data rate saving and is more adaptable to different application scenarios.