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Abstract: In this paper, Shanghai city, the biggest city in China, was selected as the case for quantifying the impact of land-use/land cover (LULC) change on patterns of surface urban heat island (UHI). Time series of LULC maps of Shanghai were produced from Landsat TM/ETM+ images between 1997 and 2008, during which this city experienced unprecedented urban growth. The results show that dramatic changes in LULC have occurred, with loss of cropland, forest and shrub to urban use. The built-up land increased by 219.50%. In contrast, bare land, cropland, fallow land, forest and shrub decreased by 79.38%, 50.50%, 43.35%, and 20.90%, respectively. Consequently, these drastically altered the land surface characteristics and spatiotemporal patterns of UHI. According to temporal analysis on seasonal and inter-annual variations of intensity of the UHI (UHII), both the mean UHII between the city center (UHIIC-R) and UHII between the peri-urban and surrounding rural areas (UHIIP-R) reached maximum during summer period, followed by UHIIC-R and UHIIP-R during spring period. UHIIC-R and UHIIP-R were both relatively weak during winter period. In contrast, UHIIC-R and UHIIP-R were both slight during autumn period. Spatially, there were significant LSTs gradients from the city center to surrounding rural areas. Furthermore, the overall relationships between surface UHI pattern and pixel-based biophysical features as well as population density and road density were quantitatively explored. Thus, based on temporal–spatial analysis of land use dynamics, patterns of LST, and socioeconomic driving forces, our findings highlight the urgent demands for planers and decision-makers to deliberately take urban expansion, UHI effects, and their impact on local climate change into account in future planning. [Copyright &y& Elsevier]

Based on the significant hotspots analysis method (Getis-Ord Gi* significance statistics), space-time cube model (STC) and the Mann–Kendall trend test method, this paper proposes a G-STC-M spatio-temporal analysis method based on Archaeological Sites. This method can integrate spatio-temporal data variable analysis and the space-time cube model to explore the spatio-temporal distribution of Archaeological Sites. The G-STC-M method was used to conduct time slice analysis on the data of Archaeological Sites in the study area, and the spatio-temporal variation characteristics of Archaeological Sites in East China from the Tang Dynasty to the Qing Dynasty were discussed. The distribution of Archaeological Sites has temporal hotspots and spatial hotspots. Temporally, the distribution of Archaeological Sites showed a gradual increasing trend, and the number of Archaeological Sites reached the maximum in the Qing Dynasty. Spatially, the hotspots of Archaeological Sites are mainly distributed in Jiangsu (30°~33° N, 118°~121° E) and Anhui (29°~31° N, 117°~119° E) and the central region of Zhejiang (28°~31° N, 118°~121° E). Temporally and spatially, the distribution of Archaeological Sites is mainly centered in Shanghai (30°~32° N, 121°~122° E), spreading to the southern region. [ABSTRACT FROM AUTHOR]

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver's perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver's revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making. [ABSTRACT FROM AUTHOR]

Analyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and corresponds to understanding how people use places. With the help of big geospatial data which contain affluent information about human mobility and activity, we propose a novel multiple subspaces-based model to interpret the urban functional regions. This model is based on the assumption that the temporal activity patterns of places lie in a high-dimensional space and can be represented by a union of low-dimensional subspaces. These subspaces are obtained through finding sparse representations using the data science method known as sparse subspace clustering (SSC). The paper details how to use this method in the context of detecting functional regions. With these subspaces, we can detect the functionality of urban regions in a designated study area and further explore the characteristics of functional regions. We conducted experiments using real data in Shanghai. The experimental results and outperformance of our proposed model against the single subspace-based method prove the efficacy and feasibility of our model. [ABSTRACT FROM AUTHOR]