Understanding the different types of urban renewal implementation processes can inform ways to improve residents’ quality of life and optimize sustainable development strategies. Existing research has primarily focused on detecting pixel-level or object-level changes in urban physical space, but it frequently overlooks the semantic complexity inherent in urban renewal. This complexity involves an integration of what changed, where the change occurred, and how it occurred, and is important to distinguish the different types of renewal. To address this gap, this study provides a multi-type urban renewal identification (MTURI) framework using street view images (SVIs) to combine multi-source information at the street level. We constructed an SVI benchmark dataset and developed a comprehensive indicator system comprising built features, semantic attributes and street-level environmental factors. Using a two-stage recognition algorithm, the MTURI model identifies various types of urban renewal activities. We also applied the model to four renewal community cases to evaluate the potential applications of the method. The findings demonstrate that our model can effectively identify various types of renewal and provide evidence-based assessments of the effectiveness and benefits of urban policy interventions. Our paradigm introduces new tools and insights into research and practice in SVI identification.

A spatial cluster of trajectories refers to objects that follow similar paths, revealing shared movement trends and aiding in anomaly detection. However, detecting clusters in trajectory data becomes challenging when the cluster-to-noise density ratio (CNDR) is low. For example, clusters in free-range sheep movements are easily seen due to their group behaviour, whereas the diversity of human movement introduces significant noise, making clustering difficult. The L-function, widely used for clustering detection in various data types (e.g. point or OD flow data), captures aggregation changes across scales without relying on predefined thresholds, offering potential for low CNDR trajectory data. Thus, we define a trajectory space to derive the Trajectory L (TL)-function for multipoint trajectories. Then we use the second derivative of the TL-function and the local TL-function to identify cluster sizes and extract clusters. Inflection points in the second derivatives enable the detection of subtle changes in aggregation, allowing for precise and sensitive cluster identification. Simulation experiments show that our method outperforms four state-of-the-art approaches in detecting clusters under low CNDR conditions while avoiding parameter dependency. We validated the generality and robustness of our method using both taxi GPS trajectories and mobile phone signalling trajectories. Furthermore, our work lays a rigorous and extensible foundation for the future formulation of spatiotemporal statistical frameworks tailored to trajectory data.

In spatial point data, a hotspot is defined as a group of points with a significantly higher density within an arbitrarily shaped area. Among existing hotspot identification methods, spatial scan statistic, known for its simple mechanism in locating hotspots, has been extensively studied and applied in various fields. However, existing methods rely on pre-defined scanning window shapes, e.g. generic geometries like circles or pre-divided regions, like administrative divisions, and thereby may not accurately capture the irregular shapes of hotspots. This study enhances the spatial scan statistic by introducing a tightened window, which is defined as the window tightened to align with the hotspot’s shape. In our method, without the necessity of outlining the exact geometry, the area of the tightened window, estimated using the nearest distance statistics, is used for calculating the objective function. Experiments with simulated data demonstrate that our method outperforms existing methods in terms of testing hotspots’ significance, identifying arbitrarily shaped hotspots, estimating hotspots’ spatial extent, and reducing subjectivity in parameter selection. An empirical study using taxi pick-up point data shows our method can identify regions with high taxi demand and potential traffic congestion, including subway exits and commercial streets.

The mobility of scientists, manifested by movements to new academic institutions, grows with globalization and plays a crucial role in individual careers, institutional productivity, and knowledge dissemination. Current research on scientists’ mobility focuses on aggregated levels such as inter-country mobility, with little attention paid to fine-grained institutional level, leading to a simplified spatial portrayal of the mobility. To fill the gap, we take scientists in geography as examples, and reconstructed their dynamic mobility network among institutions from 1970 to 2020 based on massive literature metadata. Our findings reveal the spatial mobility pattern that is now dominated by North America, Western and Northern Europe, East Asia, and Oceania, with the trend of intensification, multipolarity, and inequality over time. Specifically, the mobility network exhibits clear community structure largely constrained by spatial proximity and national borders. We also uncovered a universal downward mobility pattern embedded in the hierarchical structure. Our quantitative analysis further suggest that mobility is facilitated by multiple realities, including spatial, cultural, and scientific proximity, institutional rankings and national economic levels, cooperation, and visa-free policies, with varying dynamics. These results contribute to spatiotemporal insights into the mechanisms of scientific development in theory, and the basis for talent policymaking in practice.

The kriging model can accommodate various spatial supports and has been extensively applied in hydrology, meteorology, soil science, and other domains. With the expansion of applications, it is essential to extend the kriging model for new spatial support of high-dimensional data. Geographical flows can depict the movements of geographical objects and imply the underlying mobility patterns in geographical phenomena. However, due to the bias, sparsity, and uneven quality of flow data in the real world, research about flows remains hindered by the lack of complete flow data and effective flow interpolation methods. In this study, we design a kriging interpolation model for flows based on several flow-related concepts and the autocorrelation of flows. We also analyze the second-order stationarity and anisotropy in the flow spatial random field. To illustrate the effectiveness and applicability of our method, we conduct two case studies. The former case study compares several experiments of flow density interpolation using Beijing mobile signaling data and illustrates the conditions of applicable areas. The latter case study extends our model to other flow attributes, such as travel time uncertainty, using Beijing taxi origin-destination flow data. The results of these cases demonstrate the effectiveness and high accuracy of our model.

A geographical flow (hereafter flow) is defined as a movement between locations at two different times. A group of spatiotemporal flows can be viewed as a cluster if their origins and destinations are both spatiotemporally concentrated. Identifying spatiotemporal flow clusters may help reveal underlying spatiotemporal mobility trends or intensive relationships between regions. Despite recent advances in flow clustering methods, most only consider spatial attributes and ignore temporal information, and may fail to differentiate space-close but time-separated clusters. To this end, we derive global and local versions of the Spatiotemporal Flow L-function, extended from the classical L-function for points, and thereby construct a clustering method. First, the global version is utilized to check whether flow data contain clusters and estimate the spatial and temporal scales of the clusters. The local version is then employed to extract the clusters with the estimated scales. Experiments of simulated data demonstrate that our method outperforms three state-of-the-art methods in identifying spatiotemporal flow clusters with arbitrary shapes and different densities and reducing subjectivity in the parameter selection process. A case study with taxi data shows that our method reveals residents’ spatiotemporal moving patterns, including rush-hour commuting and whole-daytime transferring among railway stations.

Geographical flows reflect the movements, spatial interactions or connections among locations and are generally abstracted as origin-destination (OD) flows. In this context, clustering is a spatial pattern describing a group of flows with adjacent O and D points. For data composed of two types of flows (bivariate-flow data), a bivariate-flow cluster is a cluster comprising two types of flows, at least one of which exhibits a clustering pattern. In a bivariate-flow cluster, varying flow density combinations imply different meanings. For instance, a cluster with high-density travel flows on both weekdays (type A) and weekends (type B) may be associated with entertainment, whereas high-density flows on weekdays and sparse flows on weekends may reveal work-related travel. However, identifying bivariate-flow clusters with different flow density combinations is still an unsolved problem. To this end, we extend a bivariate-point clustering method and propose a density-based clustering method for bivariate flows. The simulation experiments verify model robustness. In a case study, we apply this method to extract clusters of bivariate-flow data comprising Beijing taxi OD flows of different periods, and identify clusters of work-related, entertainment, tourism, or egress and return travels. These results demonstrate the capability of our method in detecting bivariate-flow clusters.

Geographical flow (hereafter flow) can be modeled as an orderly connected point pair composed of an origin (O) and a destination (D). Aggregation is the most common form of spatial heterogeneity of flows, which we define as their deviation from complete spatial randomness (CSR), and the aggregation scale is an important indicator for its perception. Nevertheless, quantifying the aggregation scale of flows is still an unsolved problem. In this paper, we propose the L-function for flows as a solution, derive theoretical null models of the K-function and L-function in a flow space. We conduct simulation experiments to validate the L-function and its capability to detect aggregation scales. Finally, we apply the solution in a case study with taxi data in Beijing and identify nine aggregation scales of taxi OD flows, ranging from 170 m to 22.1 km. These scales correspond to three classes: less than 300 m, from 600 m to 700 m and more than 1500 m. The classes are related to the sizes of the urban facilities where the dominant flow clusters occur, indicating that the L-function in flow space can detect the aggregation scale of flows at the building scale, the block scale and the district scale.

The dynamic nature of urban population distribution plays a key role in urban planning, emergency management and public travel information services. Currently, the widespread use of mobile phone data provides the opportunity to support fine-scale population studies. However, the data sparsity problem of mobile phone data has been a huge handicap. To overcome this, we proposed a comprehensive approach to achieve fine-grained dynamic population distribution and high-resolution population map based on large-scale sparse mobile phone data. First, we developed an anchor-point-based trajectory reconstruction method to improve the spatiotemporal granularity of mobile phone trajectories. Then, a rapid and efficient automation population mapping method was proposed with the support of reconstructed high spatiotemporal resolution of human movements. Finally, we analyze spatiotemporal characteristics of population distribution and spatial-temporal interaction of human movement. Using a real mobile phone dataset in the city of Shanghai as a case study, we evaluated the performance of our method. Results indicated that our method improved the precision and reliability of population distribution estimation and could be utilized for quantitatively analyzing the spatiotemporal characteristics of population distribution and migration. We argue that this study is useful for understanding the highly dynamic human movement states and supporting advanced urban applications.

The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.

In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.