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The significance of urban function recognition has stimulated the need for multi-source geospatial data fusion, especially the fusion between remote sensing images and spatiotemporal big data. In previous studies, the natural correspondence across multi-source geospatial data has often been ignored in the description of one object, which would influence the performance of data fusion. Therefore, this study introduces the cross-correlations mechanism to achieve the natural correspondence by taking remote sensing images, point of interest (POI), and real-time social media users as an example. It proposes a new cross-correlations based functional urban land use (CC-FLU) model to infer urban functions. The presented model extracts physical and human semantic features from multi-source geospatial data, then maps them to their common subspaces to obtain their cross-correlations respectively. These semantic features and their cross-correlations are integrated together to classify urban functions. An experiment in Shenzhen, China was implemented to evaluate the performance of the presented model at a fine scale. The results show that the proposed CC-FLU model achieved a better performance than previous methods, yielding OA and Kappa values of 0.851 and 0.812, respectively. The results of the presented approach outperform those of methods using one single source geospatial data. The results demonstrate that the skilled information of each type of geospatial data is fully melted into the data fusion model, and simultaneously achieves the natural correspondence across multi-source geospatial data. Moreover, this study resolves the possible disadvantages of models using one single source data and fusion methods by sequentially concatenating multi-source features. The results will benefit urban planners and urban policy-makers. • Propose a data fusion approach to recognize functional urban land use. • Extract physical and human feature semantics from multi-source geospatial data. • Consider the natural correspondence between physical and human feature semantics through the cross-correlations. • Resolve the potential disadvantage of one single source geospatial data. [ABSTRACT FROM AUTHOR]

Abstract Based on our GEOSS Clearinghouse operating experience, we summarized the three Earth Observation (EO) Big Data access challenges, namely, fast access, accurate service estimation and global access, and two essential research questions: are there any spatiotemporal patterns when end users access EO data, and how can these spatiotemporal patterns be utilized to better facilitate EO Big Data access? To tackle these two research questions, we conducted a two-year pattern analysis with 2+ million user access records. The spatial pattern, temporal pattern and spatiotemporal pattern of user-data interactions were explored. For the second research question, we developed three spatiotemporal optimization strategies to respond to the three access challenges: a) spatiotemporal indexing to accelerate data access, b) spatiotemporal service modeling to improve data access accuracy and c) spatiotemporal cloud computing to enhance global access. This research is a pioneering framework for spatiotemporal optimization of EO Big Data access and valuable for other multidisciplinary geographic data and information research. Highlights • Spatiotemporal patterns of user-data interaction were explored. • Three Earth Observation Big Data access challenges were summarized. • We respond access challenges with a spatiotemporal optimization framework. [ABSTRACT FROM AUTHOR]

Abstract: The spatial patterns of road networks play an important role in spatial recognition, urban structure analysis, spatial data matching, and network analysis. This paper proposes and implements a method for identifying the spatial patterns of road networks. The method first builds a node-edge topology, generates polygons from these edges, and then calculates a set of parameters, specifically consistent arrangement, shape similarity, and a grid shape index of the polygons. Finally, the method identifies spatial patterns via a multi-criteria decision method. Experimental testing of the method showed that it is a very competent feasible means to detect spatial patterns in road networks. [Copyright &y& Elsevier]

Abstract Sensing turn-level or lane-level traffic conditions not only enables navigation systems to provide users with more detailed and finer-grained information, it can also improve the accuracy in the search for the fastest routes and in short-term predictions of traffic conditions. The widespread collection and application of taxis' GPS data enable us to sense urban traffic flow on a large scale. Since current GPS positional accuracy cannot reach the lane level, existing approaches using GPS trajectory data only analyze traffic conditions at the road level. Whereas some studies attempted to detect lane-level traffic conditions using lane-level data, the high cost of data collection considerably limits their practical application. To address this limitation, this article proposes an approach for detecting traffic congestion from taxis' GPS trajectories at the turn level. Based on analyzing features of GPS trajectories and identifying valid trajectory segments, the proposed approach detects congested trajectory segments of three different intensities. It then identifies congestion events in each turning direction through a clustering approach. Finally, congestion intensity, time of the day when congestion occurred and queue length in each turning direction at a road intersection in Wuhan, China are explored and analyzed. The results support the feasibility of this approach for detecting and analyzing traffic congestion at the turn level. Compared with other approaches that detect traffic congestion using GPS trajectory data, the proposed approach analyzes congestion at a finer-grained level (the turn level). Compared with other approaches that detect traffic congestion at the lane level, the proposed approach can sense traffic congestion over a larger area and at a lower cost. Highlights • This study proposes an approach to filter taxis' GPS data that reflect the real traffic condition. • The trajectories that reflect real traffic conditions are differentiated based on machine-learning technique. • This study proposes an approach that can detect turn-level congestions in multiple dimensions. • It is a fine-grained congestion analysis approach using taxi GPS data, which can be applied to other study areas. • Compared with other approaches, it analyzes congestions at a finer-grained level, over a larger area and at a lower cost. [ABSTRACT FROM AUTHOR]

With the advent of Information and Communication technology (ICT) in modern age, the statement of “death of distance” has received numerous discussions. This article contributes a new empirical study to the debate of “death of distance” by considering the effect of spatial autocorrelation in the estimation of distance decay effect with the incorporation of network autocorrelation in spatial econometric modeling. This work is based on a city-level dataset from China's largest social networking site called Weibo. The findings are shown as following. First, the coefficient value of network autocorrelation term (0.007, significant at 0.01 level) suggests that the city-level online social links are spatially dependent. In other words, these social connections are not randomly distributed across space but tend to form spatial clusters where neighboring links are more similar. Second, controlling spatial autocorrelation in the data, a distance decay effect on the formation of online social links is unveiled with a much smaller scaling exponent of the distances (i.e., 0.276) as compared to those (e.g., 2.0, 1.8, 1.45, 1.06, 1.03, 0.4, and 0.5) in existing studies. This research provides a useful modeling framework to analyze the real-world driving forces that characterize the patterns of social interactions in virtual space and thus advance our understanding in the connection of virtual and real spaces. [ABSTRACT FROM AUTHOR]

This paper studies the space–time properties of locations that are critical to travel activities in an urban environment. Specifically, we analyze locations on the urban street network from the perspective of the distribution of people’s travel trajectories. We identify the intersections of an urban transportation network which are characterized by good connectivity, serving a high density of trip trajectories, and exhibiting multiple traversing patterns of trip trajectories as potential functionally critical network locations (FCNLs). A geospatial method is proposed to extract FCNLs from people’s moving trajectories based on the street network. Two groups of quantitative indices are introduced to measure the evolution of the spatial extent and temporal variation patterns of different criticality levels of FCNLs. A case study using taxi trajectory data from Wuhan, China has been implemented. The results show that the FCNLs are very powerful in uncovering the space–time traveling patterns of a particular population and studying the relationship between urban functional structures and people’s activities. [ABSTRACT FROM AUTHOR]

• Three alternative urban datasets are presented to portray urban vibrancy. • A comparative empirical study is conducted to examine multisource urban big data. • The results demonstrate the commonalities and differences in urban vibrancy proxies. Understanding urban vibrancy aids policy-making to foster urban space and therefore has long been a goal of urban studies. Recently, the emerging urban big data and urban analytic methods have enabled us to portray citywide vibrancy. From the social sensing perspective, this study presents a comprehensive and comparative framework to cross-validate urban vibrancy and uncover associated spatial effects. Spatial patterns of urban vibrancy indicated by multisource urban sensing data (points-of-interest, social media check-ins, and mobile phone records) were investigated. A comprehensive urban vibrancy metric was formed by adaptively weighting these metrics. The association between urban vibrancy and demographic, economic, and built environmental factors was revealed with global regression models and local regression models. An empirical experiment was conducted in Shenzhen. The results demonstrate that four urban vibrancy metrics are all higher in the special economic zone (SEZ) and lower in non-SEZs but with different degrees of spatial aggregation. The influences of employment and road density on all vibrancy metrics are significant and positive. However, the effects of metro stations, land use mix, building footprints, and distance to district center depend on the vibrancy indicator and location. These findings unravel the commonalities and differences in urban vibrancy metrics derived from multisource urban big data and the hidden spatial dynamics of the influences of associated factors. They further suggest that urban policies should be proposed to foster vibrancy in Shenzhen therefore benefit social wellbeing and urban development in the long term. They also provide valuable insights into the reliability of urban big data-driven urban studies. [ABSTRACT FROM AUTHOR]