Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban vibrancy. This study aims to investigate the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator. This study first integrated application (App) use and mobility patterns from cellphone data to portray residents’ occupations and evaluate the OMI in Guangzhou. Then, the mechanism of OMI distribution was analyzed with the GeoDetector model. Next, an optimized GeoDetector-based index, interactive effect variation ratio (IEVR) was proposed to quantify the variation between individual and interactive effects of factors. The results showed that land use mixture was the dominating factor, and that land use mixture, building density, floor area ratio, road density affected the OMI distribution directly. Some interesting findings were uncovered by IEVR. The influences of cultural inclusiveness and metro accessibility were less important in factor detector result, while they were found to be the most influential in an indirect way interacting with other built environment factors. The results suggested that both “hardware facilities” (land use mixture, accessibility) and “soft facilities” (cultural inclusiveness) should be considered in planning a harmonious urban employment space and sustainable city.

TOD (transit-oriented development) is a planning concept that uses public transportation stations as the center of development, and it aims to integrate land use efficiency and transportation planning linkages to encourage the use of public transportation. The impact of metro TOD projects on urban transportation is multifaceted and complex, and the promotion of metro TOD ridership is an important topic in academic circles. However, the theoretical analysis framework of the impact mechanism of metro TOD ridership is still not perfect. Most studies ignore the TOD characteristics of the stations and the interaction between the station area’s land use and the station area functional linkage. Moreover, a few studies have focused on the mechanisms of the impact of TOD built environment factors on the spatial differentiation of station ridership, and the interactive effects of built environment factors. In this paper, the factors of a metro TOD station built environment were selected based on the node–place–linkage model expanded by the 5D principle of TOD, and a solution is provided for the computable transformation of the 5D principle. The GeoDetector method was used to detect the individual and interactive effects of the TOD built environment factors. The results show that the spatial distribution of the metro TOD station area ridership shows a core–peripheral structure and spatial heterogeneity, both on weekdays and weekends. Moreover, the individual effects of each factor can explain up to 49% and 35% of the traffic distribution on weekdays and weekends, respectively. In addition, the two-factor interactive effect has a stronger influence on metro ridership. The interactive effect can explain up to 72% and 77% of the traffic distribution on weekdays and weekends, respectively. Furthermore, the individual effects of each factor exhibited spatial heterogeneity in the local spaces, showing spatial facilitation and inhibition, respectively. Finally, the main policy recommendations are as follows: One of the important ways to guide the development of cities toward polycentric structure is to promote a TOD model in the peripheral areas of the cities. Building more public open spaces in TOD station areas and improving the collection and distribution capacity of the bus transport systems can effectively stimulate the ridership of metro stations.

Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines K-core decomposition and Louvain algorithms. In this method, author keywords are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined as K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. The results show that the run time of the proposed method is significantly less than that of the traditional method.

A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial–temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial–temporal hidden Markov model with efficient index strategies to capture local spatial–temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial–temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of $87.80\%$ and a recall of $84.28\%$). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day’s data from 4 million users.

With the rapid development of localization techniques and the prevalence of mobile devices, massive amounts of trajectory data have been generated, playing essential roles in areas of user analytics, smart transportation, and public safety. Measuring trajectory similarity is one of the fundamental tasks in trajectory analytics. Although considerable research has been conducted on trajectory similarity, the majority of existing approaches measure the similarity between two trajectories by calculating the distance between aligned locations, leading to challenges related to uncertain trajectories (e.g., low and heterogeneous data sampling rates, as well as location noise). To address these challenges, we propose Contra, a convolution-based similarity measure designed specifically for uncertain trajectories. The main focus of Contra is to identify the similarity of trajectory shapes while disregarding the time/order relevance of each record within the trajectory. To this end, it leverages a series of convolution and pooling operations to extract high-level geo-information from trajectories, and subsequently compares their similarities based on these extracted features. Moreover, we introduce efficient trajectory index strategies to enhance the computational efficiency of our proposed measure. We conduct comprehensive experiments on two trajectory datasets to evaluate the performance of our proposed approach. The experiments on both datasets show the effectiveness and efficiency of our approach. Specifically, the mean rank of Contra is 3 times better than the state-of-the-art approaches, and the precision of Contra surpasses baseline approaches by 20–40%.