Origin-Destination (OD) flow, as an abstract representation of the object's movement or interaction, has been used to reveal the movement patterns of human activities and the coupling process of the human-land system. As a developing spatial analysis method, OD flow clustering can be used to identify the dominant trends and spatial structures of urban mobility. However, urban flow exhibits universal heterogeneity, which is mainly manifested in irregular shapes, uneven distribution, and obvious scale differences. The existing methods are constrained by specific spatial scales and sensitive parameter settings, making it difficult to reveal heterogeneous urban mobility patterns within travel OD data. In this paper, we propose an OD flow analysis method that integrates spatial statistics and density clustering. This method can determine parameter values from datasets without manual intervention and adaptively identify multi-scale mixed OD flow clusters. In the simulation experiment, the proposed method accurately detects all preset OD clusters with less noise. It outperforms the baseline methods in terms of Silhouette Coefficient, V-measure, and Fowlkes Mallows index. As a case study, this method is applied to OD data from Chengdu, China, extracting 63 representative flow clusters and revealing the trends of heterogeneous urban mobility across different lengths and densities for public transit optimization.

Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.

The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results. © 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

With the development of Volunteered Geographical Information (VGI) data, the OpenStreetMap has high research value in terms of project activity, social influence, urban development, application scope, and historical richness and the number of buildings or roads is increasing every day. However, how to evaluate the quality of a large amount OpenStreetMaps efficiently and accurately is still not fully understood. This article presents the development of an approach regarding multilevel stratified spatial sampling based on slope knowledge and official 1:1000 thematic maps as the reference dataset for OpenStreetMap data quality inspection of Hong Kong. This multilevel stratified spatial sampling plan is as follows: (1) The terrain characteristics of Hong Kong are fully considered by dividing grids into quality estimate strata based on the slope information; (2) Spatial sampling for the selection of grids or objects is used; (3) A more reliable sampling subset is made, regarding the representation of the entire OpenStreetMap dataset of Hong Kong. This sampling plan displays a 10% higher sampling accuracy, but without increasing the sample size, particularly as regards building completeness inspection compared with simple random sampling and systematic random sampling. This research promotes further applications of the Open-Street-Map dataset, thus enabling us to have a better understanding of the OpenStreetMap data quality in urban areas. © 2023 John Wiley & Sons Ltd.

Central concern of this article is presenting the high-exactitude analysis on hygro-thermo-mechanical vibration of three-phase multi-scale hybrid composite angle-ply laminated rectangular plate (MHCALRP) for different couples of boundary conditions within the context of twelve-variable refined higher order shear deformation theory (RHOSDT12). Defining the kinematics of the system according to RHOSDT12 is the essential step toward achieving sufficient accuracy in elucidation of vibrational behavior of moderately thick plates. Mechanical properties of MHCALRP are calculated through two-step Halpin–Tsai homogenization process. Numerical solution to the governing differential motion equations is acquired by implementing generalized Differential quadrature method (GDQM). Accuracy of the employed approach is evaluated by a comparative study with the published studies. As a practical conclusion it is revealed that the orientation angle of the macrofibres can effectively compensate the lack of nano reinforcement as well as the absence of extensional load to overcome negative impacts of hygrothermal environment in an affordable cost. The influence of extensional load along two directions, nano/macro scale reinforcements and hygro-thermal environment on the vibrational response of the MHCALRP is completely elucidated through the parametric investigation. © 2021, Springer-Verlag London Ltd., part of Springer Nature.

Community shuttle services have the potential to alleviate traffic congestion and reduce traffic pollution caused by massive short-distance taxi-hailing trips. However, few studies have evaluated and quantified the impact of community shuttle services on urban traffic and traffic-related air pollution. In this paper, we propose a complete framework to quantitatively assess the positive impacts of community shuttle services, including route design, traffic congestion alleviation, and air pollution reduction. During the design of community shuttle services, we developed a novel method to adaptively generate shuttle stops with maximum service capacity based on residents' origin-destination (OD) data, and designed shuttle routes with minimum mileage by genetic algorithm. For traffic congestion alleviation, we identified trips that can be shifted to shuttle services and their potential changes in traffic flow. The decrease in traffic flow can alleviate traffic congestion and indirectly reduce unnecessary pollutant emissions. In terms of environmental protection, we utilized the COPERT III model and the spatial kernel density estimation method to finely analyze the reduction in traffic emissions by eco-friendly transportation modes to support detailed policymaking regarding transportation environmental issues. Taking Chengdu, China as the study area, the results indicate that: (1) the adaptively generated shuttle stops are more responsive to the travel demands of crowds compared with the existing bus stops; (2) shuttle services can replace 30.36% of private trips and provide convenience for 50.2% of commuters; (3) such eco-friendly transportation can reduce traffic emissions by 28.01% overall, and approximately 42% within residential areas.

Uncontrolled, large-scale human mobility can amplify a localized disease into a pandemic. Tracking changes in human travel behavior, exploring the relationship between epidemic events and intercity travel generation and attraction under policies will contribute to epidemic prevention efforts, as well as deepen understanding of the essential changes of intercity interactions in the post-epidemic era. To explore the dynamic impact of small-scale localized epidemic events and related policies on intercity travel, a spatial lag model and improved gravity models are developed by using intercity travel data. Taking the localized COVID-19 epidemic in Xi'an, China as an example, the study constructs the travel interaction characterization before or after the pandemic as well as under constraints of regular epidemic prevention policies, whereby significant impacts of epidemic events are explored. Moreover, indexes of the quantified policies are refined to the city level in China to analyze their effects on travel volumes. We highlight the non-negligible impacts of city events and related policies on intercity interaction, which can serve as a reference for travel management in case of such severe events.

An origin-destination (OD) flow is the movement of objects from an origin to a destination. Determining how the flows vary across geographic locations helps understand the mechanism of flow distributions; however, it has rarely been studied. Here, we propose a trend surface model with polynomial functions to quantify the flow distribution with coordinates in the flow space. This model assumes that an observed data-record is composed of the trend value and the residual, and is represented by the orthogonal polynomial with O and D coordinates as independent variables and flow properties as dependent variables. The simulation experiments based on the linear and quadratic models indicated that the trend surface function could reflect the increasing/decreasing variation of flows with OD locations (i.e. flow trends) in different patterns. Applying this model to a case study of taxi OD flows in the broad Central Business District of Beijing, we found that the flows exhibited a rising trend toward the southwest. The trend surface characteristics are associated with the distributions of urban functional patches, where the workplaces and residences increased toward the southwest in the study area. Notably, the spatial deviations of trend surface model can help in identifying site pairs that attract flows at a high density (e.g. commerce centers and big communities), facilitating the planning of public transportation to mitigate the congestion.

Water and energy fluxes at the interface between the surface of the earth and the atmosphere depend strongly on soil moisture. Surface soil moisture (SSM) participates in natural phenomena such as evaporation, infiltration and runoff. From 2019 to 2020, bushfires in Australia caused changes in the runoff of surface water, the transpiration of vegetation, and evapotranspiration of the soil, subsequently affecting the soil water cycle. However, the dynamics of surface soil water content during large-scale bushfires are still not completely understood. Here, we report on patterns of seasonal variation in SSM and vegetation leaf phenology in Australia, identified using L-band radiometric measurements of soil moisture taken by the soil moisture and ocean salinity mission from 2011 to 2020. This study reveals a decrease in the leaf area index in affected regions of New South Wales, Victoria, Queensland, Western Australia, Australian Capital Territory, Northern Territory, and Tasmania. The observed seasonal variations in SSM are highly synchronous with those of leaf phenology in northern and northeastern Australia. However, the development of the leaf area lags behind that of SSM for up to 90 days in the southeastern subtropical woodlands. The time lag between leaf area development and SSM in the southwest is about 60 days. Bushfires can lead to the decrease of evapotranspiration hence shortening the time lag between SSM and leaf phenology. Our results offer insights into the variations in SSM during bushfires, the relationship between SSM and leaf phenology in Australia, and lay a foundation for improving models of ecological hydrology during bushfires.