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As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization. [ABSTRACT FROM AUTHOR]

Functional zones are basic spatial units of urban management and urban planning and they provide the basic space where human activities occur. The concept of urban functional zones is closely related to two basic factors: urban landscape environment and human activities. The former provides the basic space for human activity and influences urban land use at a coarse scale, and the latter indicates the differentiation of functions in local urban areas. In previous studies, the hierarchical correspondence and interaction between urban landscape and human activities have not been given full consideration in the cognition of urban functional zones, which would influence the accuracy and interpretability of the results. Therefore, a hierarchical fusion method considering urban landscape and human activity patterns based on multi‐source data is proposed in this article. We introduce a new cognition framework. First, a land use classification based on urban landscape features from remote sensing images is extracted and used as the fundamental results of urban land uses. Then, fine‐grained functional semantics of local urban areas are recognized based on human activity patterns extracted from crowdsourced data (i.e., points of interest and taxi trajectories). Finally, the above results at different scales are fused with hierarchical constraints, which means that the information from both urban landscape and human activities can be fully utilized to obtain a refined and fine‐grained map of urban functional zones. Wuhan, China was chosen as an example to evaluate the performance of the proposed method. Results show that the overall accuracy of the proposed method is 82.51% (accuracies of the mixed functional zones and single functional zones are 77.93 and 87.96%, respectively) and compared with state‐of‐the‐art methods, the proposed method performed better for the recognition of mixed functional zones. [ABSTRACT FROM AUTHOR]

Spatial point data is an important data source that represents the locations of spatial events (such as crime, disease cases, and earthquakes). Detecting clusters I n spatial point data plays a key role in exploratory spatial data analysis. Although much attention has been paid to the clustering of spatial points, how to automatically and efficiently discover the statistically significant clusters with irregular shapes is still a challenging work. On that account, an automatic method to detect the statistically significant high-density clusters in spatial point data with noise is proposed in this paper. First, the Voronoi diagram of the spatial points is constructed, and the densities of spatial points are defined by the areas of the Voronoi cells. Then, high-density points are automatically detected using spatial hotspot statistics analysis, and a density-based clustering strategy is further adapted to combine the neighboring high-density points into candidate clusters. Finally, a statistical significance test is proposed to evaluate the significance of the candidate clusters under the spatially homogeneous or heterogeneous distribution assumption. We tested the proposed method with the simulated data sets and the real-world taxi trajectory data for detecting the pick-up hotspot regions in Wuhan, China. Results show that the proposed method can successfully find arbitrarily shaped significant clusters that existing state-of-the-art clustering algorithms may fail to find in spatial point data with noise. • A method to detect statistically significant spatial point clusters is proposed. • Spatial cold spot statistics are applied to detect spatial point clusters. • Statistic constrained clustering method is proposed to detect candidate clusters. • Monte-Carlo permutation test is applied to evaluate the significance of clusters. • The proposed method has good performances and great potential in cluster detection. [ABSTRACT FROM AUTHOR]