Functional urban land use recognition integrating multi-source geospatial data
ABSTRATThe significance of urban function recognition has stimulated the need for multi-source geospatial data fusion, especially the fusion between r
城市模型 (Urban Modeling)
A human-machine adversarial scoring framework for urban perception assessment
ABSTRATThough global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing loca
使用时序出租车轨迹识别多层次城市功能结构
ABSTRAT地理时空大数据被广泛用于城市功能结构识别，其中功能层次性的研究对于系统理解城市功能的结构特征和分布形态具有重大意义，但相关研究仍处于空缺状态。 基于时序出租车出行数据和感兴趣点数据描述居民出行模式，结合动态时间规整和K-MEDOIDS聚类算法识别城市的功能属性和空间结构。 研究结果表明
卷积神经网络和随机森林的城市房价微观尺度制图方法
ABSTRATChina’s rapid urbanization has caused a large number of migrants to move to the city, which has also led to housing shortages. Rapid access to
Simulating urban expansion and impact on functional connectivity in the Three Gorges Reservoir Area
AbstractUnderstanding the impact of urban expansion on functional connectivity is significant to biodiversity conservation. Particularly, in the Three
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Mapping fine-scale urban housing prices by fusing remote-sensing images and social media data
AbstractThe accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factor
Classifying urban land use by integrating remote sensing and social media data
AbstractUrban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However,
Mapping population distributions at the building level by integrating multisource geospatial data
Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.
Delineating urban functional areas withsocial media data based a dynamic time warping distance
AbstractThis paper presents a novel method for delineating urban functional areas based on building-level social media data. Our method assumes that s
Sensing spatial distribution of urban land use by integrating POI and Google Word2Vec model
AbstractUrban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past