During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.

Abstract:As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler's first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty's Spatial Entropy model which can only be used in a general scope.

Urban green space has been proven effective in improving public health in the contemporary background of planetary urbanization. There is a growing body of literature investigating the relationship between non-communicable diseases (NCDs) and green space, whereas seldom has the correlation been explored between green space and epidemics, such as dysentery, tuberculosis, and malaria, which still threaten the worldwide situation of public health. Meanwhile, most studies explored healthy issues with the general green space, public green space, and green space coverage, respectively, among which the different relevance has been rarely explored. This study aimed to examine and compare the relevance between these three kinds of green space and incidences of the three types of epidemic diseases based on the Panel Data Model (PDM) with the time series data of 31 Chinese provinces from 2007 to 2016. The results indicated that there exists different, or even opposite, relevance between various kinds of green space and epidemic diseases, which might be associated with the process of urban sprawl in rapid urbanization in China. This paper provides a reference for re-thinking the indices of green space in building healthier and greener cities.

The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents' travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.residents' mobility over time and space can be used for urban geographic mapping [13], epidemiological analysis [14,15], real-time urban monitoring [16], etc. and can also be used for recognition of urban spatial features [17][18][19] or measurement of urban vibrancy [20]. Another important scenario of application is the study of residents' commuting and urban transport, including the identification of commuting areas and commuting distances [21,22], and the acquisition of commuter Origin-Destination (OD) matrices [23][24][25]. Among various sources of location data, mobile phone data have been widely employed in studies such as residents' commuting thanks to its extensive coverage, passive data collection, and the fact that its data acquisition requires no extra equipment. In comparison, alternative data sources, such as smart card data or taxi GPS data, have equivalent difficulties in data coverage but much smaller population coverage [26]. Results of studies based on new data have been shown to have higher accuracy compared to those that are based on statistical data or measured data, proving the effectiveness of big data application in urban studies. In general, most of these studies are at an early stage of describing urban phenomena through data, few studies attempt to go further such as using big data to identify the connection between residents' travel and traffic c...

Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs.