Spatial-temporal Patterns Research Articles

A novel spatio-temporal attention mechanism model for car-following in autonomous driving

Car following is critical for the overall safety, efficiency, and smooth operation of autonomous vehicles in traffic. However, existing car-following model primarily focuses on local feature extraction, overlooking the spatial–temporal relationships between vehicles and key information within the time series. This limitation negatively impacts prediction accuracy and generalization capabilities. To address these issues, a novel Spatio-Temporal Car-Following model based on Graph Convolution Network and Attention mechanism is proposed to enhance the safety, efficiency, and comfort of autonomous driving systems. Firstly, the spatial–temporal structure of the car-following model is constructed using the GCN network. This approach efficiently captures the topological relationships between vehicles. Secondly, we interleave spatial and temporal blocks to extract feature information from both spatial and temporal dimensions, which allows us to perceive spatial interactions and temporal motion patterns of the car. Additionally, a self-attention module assigns different attention weights to the various neighbors of a node in the car-following model, which can facilitate the capture of diverse aspects of node relationships and enhance expressive power. Finally, a Multi-Layer Perceptron (MLP) layer predicts the future behaviors of following vehicles. The proposed car-following model (CF) was trained and evaluated using five real-world datasets: HighD, SPMD, Waymo, NGSIM, and Lyft. The results indicate that our approach surpasses current models in terms of Mean Squared Error (MSE) for spacing, while also maintaining a zero collision rate across every dataset. These findings indicate that the Spatio-Temporal Attention Model (GSTAM-CF) proposed in this paper enhances safety, efficiency, and operational comfort compared to existing models.

Enhancing wave energy farm efficiency: Eigen-stacking ensemble framework

The significant wave height (SWH) plays a pivotal role across diverse domains, ranging from the assessment of wave energy potential to the optimization of marine operations and the advancement of ocean engineering techniques. Understanding the spatial and temporal distribution patterns of SWH data among buoy network stations holds utmost importance for various applications. This research introduces a novel methodology for accurate spatiotemporal SWH prediction across multiple stations within buoy monitoring networks. Leveraging eigen SWH time series enables the identification of dataset patterns, feature extraction, and dimensionality reduction. Examining eigen time series provides insights into SWH dynamics, allowing exclusive use for prediction across all stations. By incorporating this eigen SWH time series as the sole input into a stacking ensemble model, we introduce a highly efficient and accurate framework for SWH prediction. Notably, the methodology achieves success in forecasting hourly SWH values along the western United States coastline, utilizing a stacking ensemble technique for enhanced accuracy. Evaluation metrics confirm outstanding performance, with CE and R2 values surpassing 0.95 and 0.93, respectively. Further, a single Long Short-Term Memory (LSTM) model is incorporated as a benchmark to assess the effectiveness of the stacking ensemble model for significant wave height (SWH) prediction, with results demonstrating that the ensemble model outperforms the single LSTM in terms of accuracy and robustness. Consequently, the introduced stacking ensemble model promises autonomy in forecasting spatial-temporal SWH patterns, extending its applicability within ocean engineering and scientific research.

The effect of stress history on the critical shear stress of bedload transport in gravel-bed streams

Critical shear stress is a pivotal parameter that describes the bedload transport and riverbed stability. Recent studies indicate that different interevent periods in gravel-bed streams can lead to a realignment of riverbed structures, thereby disrupting the bedload transport. In this research, a series of flume experiments were conducted to study the effect of stress history on the critical shear stress of bedload transport in a gravel-bed channel. The results reveal the temporal-spatial variation patterns of critical shear stress. The critical shear stress has an increased trend after interevent with low flow and long duration. Conversely, a decreasing trend in critical shear stress observed after low flow and short duration conditions. After interevent durations with medium to high flow condition, no matter the duration, critical shear stress shows a decreasing trend. A significant positive correlation between the flow magnitude of the interevent period and the critical shear stress of the subsequent flood period is shown. Besides, critical shear stress has a significant spatial variation pattern on the gravel bed due to the presence of microtopography as revealed by the correlation analysis results among the spatial density of microtopography with different protrusion heights, riverbed statistical parameters, and critical shear stress. Moreover, the stress history, by adjusting the position of fine particles on the riverbed surface through different magnitudes and durations of the interevent period, affects the spatial density of the microtopography, thereby influencing the critical shear stress. The conventional bedload transport equations are deficit by only considering the riverbed slope or standard deviation of bed surface elevation. An optimization of the critical shear stress calculation method, based on parameter correlation analysis, is proposed, potentially enhancing the calculation accuracy of bedload transport in mountainous rivers.

Assessing the Gap: Resource Efficiency of Institutionalised Elderly Care in Urban and Rural China

ObjectivesThe allocation of resources in elderly care institutions is directly linked to the well-being, quality of life and sustainable development of public health for the elderly. However, a disparity in elderly care resources between urban and rural areas exists. Therefore, this study aimed to explore resource allocation efficiency in China's urban and rural institutional elderly care. Study DesignEmploying a mixed-methods approach, the research integrated quantitative analysis with spatial-temporal assessments to investigate spatial-temporal evolution patterns of resource allocation efficiency. MethodsA Projection Pursuit Evaluation Model (PPM) utilising an Accelerated Genetic Algorithm was applied to measure the input and output of urban and rural elderly care institutions. Data Envelopment Analysis (DEA) was used to visualise the static and dynamic efficiency. Spatial analysis techniques determined the spatial evolution of resources. ResultsSignificant disparities in resource allocation between urban and rural areas were identified, with rural areas facing declining numbers of elderly care institutions despite having a larger ageing population. The efficiency analysis revealed room for improvement in both urban and rural settings, emphasising the need to strengthen management and technology in elderly care institutions. ConclusionsAddressing the challenges of elderly care in China requires policy reform, enhanced government support and stimulation of private investment, particularly in rural areas. A shift towards quality-focused elderly care and strategic investments is crucial for equitable and efficient service provision across China.

The ABC transporter SmABCG1 mediates tanshinones export from the peridermic cells of Salvia miltiorrhiza root.

Plants have mechanisms to transport secondary metabolites from where they are biosynthesized to the sites where they function, or to sites such as the vacuole for detoxification. However, current research has mainly focused on metabolite biosynthesis and regulation, and little is known about their transport. Tanshinone, a class diterpenoid with medicinal properties, is biosynthesized in the periderm of Salvia miltiorrhiza roots. Here, we discovered that tanshinone can be transported out of peridermal cells and secreted into the soil environment and that the ABC transporter SmABCG1 is involved in the efflux of tanshinone ⅡA and tanshinone Ⅰ. The SmABCG1 gene is adjacent to the diterpene biosynthesis gene cluster in the S. miltiorrhiza genome. The temporal-spatial expression pattern of SmABCG1 is consistent with tanshinone accumulation profiles. SmABCG1 is located on the plasma membrane and preferentially accumulates in the peridermal cells of S. miltiorrhiza roots. Heterologous expression in Xenopus laevis oocytes demonstrated that SmABCG1 can export tanshinone ⅡA and tanshinone Ⅰ. CRISPR/Cas9-mediated mutagenesis of SmABCG1 in S. miltiorrhiza hairy roots resulted in a significant decrease in tanshinone contents in both hairy roots and the culture medium, whereas overexpression of this gene resulted in increased tanshinone contents. CYP76AH3 transcript levels increased in hairy roots overexpressing SmABCG1 and decreased in knockout lines, suggesting that SmABCG1 may affect the expression of CYP76AH3, indirectly regulating tanshinone biosynthesis. Finally, tanshinone ⅡA showed cytotoxicity to Arabidopsis roots. These findings offer new perspectives on plant diterpenoid transport and provide a new genetic tool for metabolic engineering and synthetic biology research.

Origin-Destination Spatial-Temporal Patterns of Dockless Shared Bikes Based on Shopping Activities and Its Application in Urban Planning: The Case of Nanjing

The utilization of dockless shared bikes for shopping purposes has become increasingly prevalent. This research seeks to optimize the configuration of facilities and transportation policies for shared bike travel by analyzing the spatiotemporal patterns of shopping trips from the perspectives of destination (D), origin (O), and O-D correlation in Nanjing’s main city area. As the second-largest commercial center in East China, Nanjing offers a significant context for this research. First, we introduce the “cycling intensity” indicator to analyze the patterns of shared bicycle trips with shopping facilities as destinations at both the subdistrict and road section scales. Second, we utilize spatial autocorrelation analysis and k-means clustering to explore the outflow patterns of shared bicycle trips originating from shopping facilities. Finally, we employ grey correlation analysis to investigate the dynamic flow correlations of shared bicycle O-D trips around various grades of shopping facilities at both subdistrict and road section levels. Concurrently, we endeavored to delineate the practical transformation and application of the research findings. Our results indicate the following: (1) There is a high concentration of cycling intensity around shopping facilities on east–west and north–south roads, with community shopping facilities primarily associated with north–south roads. (2) The outflow of shared bikes from shopping areas can be categorized into four distinct modes. (3) The inflow and outflow of shopping trips exhibit significant synchronicity, particularly on the branch routes. These findings can provide valuable insights for zoning planning, construction of bicycle infrastructure, and formulation of sustainable urban transportation policies.

Spatial–Temporal Patterns and the Driving Mechanism for the Gross Ecosystem Product of Wetlands in the Middle Reaches of the Yellow River

Wetlands are crucial for sustainable development, and the evaluation of their GEP is a key focus for governments and scientists. This study created a dynamic accounting model for wetland GEP and assessed the GEP of 39 wetlands in the middle reaches of the Yellow River in Ningxia province. The results indicate that Ningxia province’s wetlands have an average annual GEP of CNY 5.24 billion. Haba wetland contributes the most at 0.52, while Qingtongxia, Sha, and Tenggeli wetlands follow with 0.12, 0.04, and 0.03, respectively. Climate regulation is the most valuable function at 38.24%, with species conservation and scientific research/tourism at 24.93% and 15.11%, respectively. Ningxia’s northern wetlands are vast and shaped by the Yellow River, while the smaller, seasonal southern wetlands are more affected by rainfall and mountain groundwater. Southern wetlands show a strong correlation between GEP and precipitation (0.82), whereas northern wetlands have a moderate correlation between GEP and evapotranspiration (0.52). The effective conservation and management of these wetlands require consideration of their locations and weather patterns, along with customized strategies. To maintain the stability of wetland habitats and provide a suitable environment for various species, it is essential to preserve wetlands within a certain size range. Our study found a strong correlation of 0.85 between the wetland area and the GEP value, indicating that the size of wetlands is a key factor in conserving their GEP. The results provide accurate insights for creating a wetland ecological benefit compensation mechanism.

Methods of Calculation of Daily Flooded Areas in the Volga Delta During the Flood Periods Based on the Remote Sensing Data

We developed methods for calculating the daily flooded areas in the Volga delta (VD) for the entire flood period using satellite data. The methodology is based on the construction of dependences of the flooding areas of the hydrographic network and interchannel spaces of VD (Ftotal) on the means of daily water levels in the channels of watercourses (НР). Ftotal for individual flood dates were determined using satellite images. Data on НР were taken at hydrological stations (h/s) on the same dates from the State Water Cadastre. These dependencies were used to calculate the daily flooding areas of DV with high accuracy for flood periods of different water contents (high-water, low-water and medium-water contents). This method was developed in two modifications: 1 – modification for the case of the sufficient number of satellite images to cover all the main changes in the course of each phase of the flood, 2 – modification for the case of the insufficient number of satellite images to cover all the main changes in the course of each phase of the flood. We conducted a comparative analysis of daily Ftotal obtained with high accuracy using modification 1 for floods of different water contents (high-water, low-water, medium-water contents). We revealed how floods of different water contents differ in characteristics including timing of passage, amplitude, area of maximum flooding area, duration of the rise phase, decline phase and flood plateau phase. Such calculations have never been conducted before. The results of calculations by this method allow us to identify the spatial-temporal patterns of the VD flooding under different types of water contents. Our method enables to predict the dynamics of floods and to calculate the water balance of the Volga delta.

Spatial-temporal patterns and driving mechanism of rural vulnerability at county level：A case study of 117 counties in Heilongjiang Province, China

Rural vulnerability is used to understand the potential multi-hazard threats and describe the fragile state of rural areas, which engage decision-makers in developing policies and strategies to reduce vulnerability. Existing studies about rural vulnerability focused on the single exogenous disturbance but paid inadequate attention to the multi-disturbances of rural coupled human-environment system. In addition, current studies mainly analyzed the spatial differentiation of vulnerability degree, ignoring the temporal evolution and the internal elements’ relationships of rural systems. In this study, we structured the cognition of rural vulnerability with a framework for understanding coupled human-environment system, evaluated rural vulnerability with the dimensions of exposure, sensitivity and adaptability, and analyzed the driving mechanism based on spatial-temporal heterogeneity. Taking 117 county units in Heilongjiang Province as study cases, we found that (1) rural vulnerability was indeed significant, as the area of county units with extreme or high vulnerability levels accounts for 50.4% of the total area, indicating a trend of high vulnerability in the counties on the north and south sides and low vulnerability in the center. (2) The spatial-temporal heterogeneity of rural vulnerability presented a clustering trend, shifting from a relatively balanced spatial distribution from 2010 to 2013 to a state of vulnerability aggregation at all levels from 2016 to 2019. (3) Rural vulnerability was mainly affected by changes in the principal factors of sensitivity and adaptability, and driving sources mainly generated by human activities, which was largely derived from rural construction activities and government policy guidance on rural regulation. Based on the results, we classified county units into different rural vulnerability types, put forward a rural resilience planning mode of “General + Special” with planning strategies for each type, which can be used as a reference for rural planning positioning of county and township level land spatial planning in the national territory spatial planning.

Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data

Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m2 aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R2 coefficients ranging from 0.879 to 0.997 with the majority exceeding an R2 of 0.95, all with p-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience.

Analysis of the Spatial-Temporal Characteristics and Driving Factors of Cultivated Land Fragmentation Under the Expansion of Urban and Rural Construction Land: A Case Study of Ezhou City

A systematic understanding of the spatial-temporal evolution patterns of cultivated land fragmentation (CLF), its driving factors, and its relationship with the expansion of urban and rural construction land is essential for identifying strategies to mitigate CLF in rapidly urbanizing regions. This study combined landscape fragmentation with ownership fragmentation, analyzing CLF through three dimensions: resource endowment, spatial concentration, and convenience of utilization, with eight selected indicators. By comparing village-level data from 2013 to 2022, we explored the key drivers of CLF and its conflicts with urban and rural construction land expansion. The findings indicate a clear spatial variation in village-level CLF in Ezhou, characterized by low fragmentation in the northwest and northeast, and high fragmentation in the southwest and central regions. This pattern is in contrast to Ezhou’s economic development, which decreased progressively from east to north and south. Over the study period, village-level CLF in Ezhou evolved from being primarily moderately and relatively severely fragmented to predominantly severely and relatively severely fragmented, with an overall declining trend and more pronounced polarization. At the same time, the CLF within the village region demonstrated notable spatial clustering features, with a rapid increase observed between 2013 and 2022. It was also discovered that CLF is driven by various factors, with the main influences being the proportion of construction land, land use intensity, and population density. Cultivated land is the main source of both urban construction land (UCL) and rural construction land (RCL), with average contribution rates of 46.47% and 62.62%, respectively. This research offers empirical evidence for rapid urbanization and serves as a critical reference for rural revitalization and coordinated urban–rural development, with potential guidance for future policy formulation and implementation.

Capturing signals of road traffic safety risk: based on the spatial-temporal correlation between traffic violations and crashes

Objective The paper aims to explore the possibility of using traffic violations as indicators for spatial-temporal risk of traffic safety within road network constraints, identify key types of traffic violations that indicate spatial-temporal risks in road traffic safety, and investigate their distribution patterns at the road section level. Methods Firstly, we employ the Ripley’s K function with network constraints and utilize rigorous statistical inference to thoroughly examine the spatial-temporal correlation between various types of traffic violations and crashes, identifying key types that exhibit significant correlation with crashes. Secondly, we combine Ripley’s K function with network constraints, Network Kernel Density Estimation, and Local Moran’s Index, to identify high-incidence road sections of these violations. Building upon this foundation, we introduce the concept of Influence Intensity for Land Use Type, which leverages Point of Interest information to analyze the land use characteristics at the road section level, revealing the distribution patterns of these key traffic violations. Results Analysis of actual data from Shenzhen, China reveals a total of 17 key traffic violations significantly correlated with crashes of varying severity across different time scenarios in the spatial ranges of 2.1–3.8 kilometers. These include types that are typically considered to have a relatively low likelihood of directly causing crashes that deserve more attention. These key traffic violations tend to aggregate in road sections categorized as “Business & Finance” and “Public Transport Infrastructure.” Furthermore, in contrast to weekdays, weekends witness a higher number of key traffic violation types with more pronounced spatial aggregation characteristics, and the land use type of aggregation areas shifts from “Public Administration & Services” to “Public Green Spaces & Attractions” and “Residence & Living.” Conclusions This study demonstrates that particular traffic violations can serve as signals for road traffic safety risk within specific space-time scopes, and the spatial-temporal aggregation patterns of these key traffic violations are closely linked to the urban land use. This finding can offer theoretical support for utilizing key traffic violations in real-time monitoring and early warning of road traffic crashes, while also providing inspiration for exploring the causes of these traffic violations from a land use perspective.

Effect of ocean dynamic processes on the temporal-spatial pattern of persistent organic pollutants (PCB-153 and BDE-47) in the shelf seas

Field observations of persistent organic pollutants (POPs) in shelf seas presented abnormal phenomena such as high-concentration patches in offshore areas and different vertical profiles of POPs at the same location. We assumed that these phenomena were associated with the presence of bottom cold water mass (BCWM) in shelf seas and used a hydrodynamic-ecosystem-POP coupled model to confirm this hypothesis. Based on model results, with the formation of BCWM during summer, POPs accumulated inside BCWM due to their transport across the thermocline by the sorption to sinking particles. With the intensification of vertical mixing during winter, the release of POPs from BCWM induced high-concentration patches of POPs on the surface layer. Meanwhile, the low water temperature in winter was favorable for the gas deposition of POPs, which led to high surface concentrations of POPs. Because the accumulation of POPs in BCWM depended on the sorption of dissolved POPs by particles, not all types of POPs accumulated in BCWM. Some POPs even accumulated at the sea surface above the BCWM due to large gaseous deposition and weak sorption by particles. Using this feature, we proposed a prediction function for the accumulation of POPs in BCWM.

A Comparison of Uncertainty Estimation Methods for Building Footprint Change Detection from Sentinel-2 Imagery

Abstract. This manuscript investigates the effects of uncertainty methods applied to the problem of deep learning-based semantic segmentation of building footprints on moderate-resolution satellite imagery. While the recent efforts of big corporations to add information about building locations and sizes on a global or continental scale are generally valuable, still the overall challenge persists in identifying the spatial-temporal patterns of growing urbanization. In this work, we extend UNet-type architectures to perform binary building footprint classification based on Sentinel-2 imagery resulting in five different models. While previous studies focused on urban areas in the Western world we conduct all training and evaluation in India. All models are trained on Microsoft building footprint products while for evaluation purposes high-quality reference data is manually selected from regions with especially good open-street-map coverage. Quantitative and qualitative experiments are conducted where a significant performance gain is found for a model trained with a mixture of aleatoric and epistemic uncertainty measures. The performance gain is even more pronounced for subsequent quantitative multi-temporal change detection experiments.

Nonlinear Radiative Response to Patterned Global Warming due to Convection Aggregation and Nonlinear Tropical Dynamics

Abstract Climate responses to global warming exhibit large dependence on the spatial pattern of sea surface temperature (SST) changes. A Green’s function (GF) approach—predicting the global climate response to a complex SST pattern change as the linear superposition of the response to finite-area SST perturbations evaluated in isolation—has been used to systematically evaluate and understand the top of atmosphere (TOA) radiation response to patterned warming. However, in general, the linear GF approach fails for TOA radiation reconstruction under future global warming scenarios in coupled models. Here, we show that the linear superposition of global mean TOA radiation responses to large SST warming perturbations at multiple patches (the GF approach prediction) overestimates the actual response to simultaneous multipatch perturbation. This is because the linear superposition overestimates tropical large-scale convection aggregation strengthening upon localized heating that enhances longwave radiative cooling, which is further explained by the overestimation of circulation response and associated horizontal water vapor transport. The nonadditivity of TOA radiation response is caused by the nonadditivity of convection aggregation, ultimately rooted in nonlinear tropical dynamics. We also demonstrate that the prediction error of the GF approach grows with decreasing patch size (equivalently, increasing patch number). We conclude that using the GF approach to predict future climate change could overestimate longwave radiative cooling and underestimate (effective) climate sensitivity. Numerical experiments may be used to identify specific perturbation patterns where the errors are smaller than the signal. Our research also highlights that an increase in the degree of large-scale convection aggregation has a nonlinear and negative feedback for mean warming.

Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records

Intelligent diagnosis of individual crop diseases has matured. How to understand the evolution patterns and predict regional disease trends remains a significant challenge. Plant Electronic Medical Records (PEMRs) offer valuable spatial-temporal characteristics about crop diseases, presenting a new opportunity for predicting the occurrence of regional diseases. In this study, we used a large prescription database from Beijing (2018–2021) to reframe regional disease prediction as a time series forecasting task. Firstly, to analyze spatial-temporal evolution patterns, we use ArcGIS to extract key information and identify potential connections between different disease occurrence points. Then, we developed a novel deep learning combined model SV-CBA, which combines Seasonal and Trend decomposition (STL) with Variational Mode Decomposition (VMD) to identify trend, seasonal, and residual components, and re-decomposes the residuals. STL-VMD can capture long-term trends and periodic variations while managing nonlinear and volatile characteristics. The CNN-BiLSTM-Attention model calculates disease trends by linearly integrating predictions of each sub-series. To reduce computational complexity while maintaining predictive performance, we propose an improved simplified attention mechanism. Our model demonstrates superior performance in both comparative and ablation experiments using the PEMRs dataset, outperforming numerous other models. This study provides accurate disease trend predictions, aiding farmers and regional managers in agricultural production management.

Emergent behaviors of buckling-driven elasto-active structures

Active systems of self-propelled agents, e.g., birds, fish, and bacteria, can organize their collective motion into myriad autonomous behaviors. Ubiquitous in nature and across length scales, such phenomena are also amenable to artificial settings, e.g., where brainless self-propelled robots orchestrate their movements into spatial-temporal patterns via the application of external cues or when confined within flexible boundaries. Like their natural counterparts, these approaches typically require many units to initiate collective motion, so controlling the ensuing dynamics is challenging. Here, we demonstrate a simple mechanism that leverages nonlinear elasticity to tame near-diffusive motile particles in forming structures capable of directed motion and other emergent behaviors. Our elasto-active system comprises two centimeter-sized self-propelled microbots connected with elastic beams. These microbots exert forces that suffice to buckle the beam and set the structure in motion. We first rationalize the physics of the interaction between the beam and the microbots. Then we use reduced-order models to predict the interactions of our elasto-active structures with boundaries, e.g., walls and constrictions, and demonstrate how they can exhibit remarkable emergent behaviors such as maze navigation. These findings demonstrate that allowing and understanding changes in body morphology can enhance the capabilities of active matter systems and enable the design of robotic materials capable of space exploration, adaptation, and complex interactions with their surrounding environment.

Urban Exposome and Health

Abstract Background The environment in which we live is thought to explain 70% of the non-communicable disease burden. The aim of EXPANSE project (funded from the European Union Horizon 2020 research and innovation programme under grant agreement No 874627) is to evaluate the optimal strategies for maintaining health in the context of contemporary urban environments. As a component of the project, Urban Labs allow deep phenotyping of the urban exposome and assessing its use and perception. The Urban Labs aim to improve exposure assessment, identify mediating and modifying lifestyle factors, and link the urban exposome to intermediate endpoints relevant for cardiometabolic and pulmonary health. Methods By monitoring the activities of 4,000 individuals in five distinct urban areas, Urban Labs provide a comprehensive understanding of the urban exposome, which can be compared with residential address-based model estimates. The participants complete a baseline questionnaire, repeated bi-monthly questionnaires and exposure-specific questionnaires over a period of 2 years using mobile phone application (ExpanSeeker). Two-week personalized measurements including spatial-temporal activity patterns, sensor-based environmental and behavioral measurements, and minimally invasive biological sample collection are performed covering different seasons. Results The Baseline Questionnaire (BQ) and two-weeks measurement campaigns (MC) were completed in European cities as follows: Athens BQ = 379, MC = 232; Barcelona BQ = 1040, MC = 303; Basel BQ = 731, MC = 655; Lodz BQ = 634, MC = 100; The Netherlands (multicity) BQ = 1033, MC = 326. Data collection process is ongoing. Conclusions The collection of novel data on individuals activities and perceptions within temporal and geographical contexts in Urban Labs will contribute to a more comprehensive understanding of how the urban dwellers are exposed to and interact with their environment, thereby supporting healthier urban engineering and policy making. Key messages • Urban Labs will provide a more comprehensive understanding of the impact of the urban environment on individual’s behavior, their exposome, enabling more accurate prediction of non-communicable risk. • Urban Labs will provide a base to support healthier urban engineering and policy making.

Exploring the Spatial-Temporal Patterns, Drivers, and Response Strategies of Desertification in the Mu Us Desert from Multiple Regional Perspectives

Desertification poses a serious threat to the global ecological environment and challenges the achievement of an ecological civilization. Understanding the spatial and temporal evolution of desertification in the Mu Us Desert, a key area in northern China, is crucial for predicting regional trends and analyzing causes. This study employs quantitative methods, including remote sensing data from Landsat satellites (2000–2020), combined with multi-scale analysis and statistical models, to systematically analyze desertification trends. The analysis reveals that desertification improved significantly after 2005 due to effective human intervention and governance efforts. In particular, the eastern regions (Shaanxi Province and Inner Mongolia) showed marked improvement, while the western regions exhibited limited change. The greatest progress was seen in the reduction in high-desertification areas to moderate levels. Quantitatively, human activities contributed to a 17.3% reduction in desertification (p < 0.05), while meteorological factors were responsible for a 45.8% reduction (p < 0.05). Conversely, desertification in Ningxia worsened by 41.8% due to unsustainable land use. Additionally, spatial correlation analysis highlighted that those areas of severe desertification became more uniformly distributed over time. The key drivers influencing desertification were agricultural development, urbanization, climate warming, and vegetation coverage, with human activities playing a substantial role. Initially, agricultural factors had the strongest correlation with desertification, but over time, population growth, rising temperatures, and vegetation cover (NDVI) became more prominent. These findings offer scientific support for desertification control in the Mu Us Desert and provide methodological insights for other severely desertified regions, contributing to sustainable ecological development.

The spatial-temporal evolution patterns of landslide-oriented resilience in mountainous city: A case study of Chongqing, China

Cities, as complex systems with multi-interconnected subsystems, face significant challenges from both rapid urbanization and climate change. Ensuring high resilience in urban areas is essential for managing these dynamic risks effectively. This study introduces an innovative, data-driven approach to quantitatively analyze the spatial-temporal evolution patterns of urban resilience, validated through a case study of Chongqing, a representative mountainous city in China. Based on historical landslide data from Chongqing (2010–2020), which includes 4464 events, along with indicator data from the Chongqing Statistical Yearbook, we developed a comprehensive assessment framework. This framework incorporates 33 variables, covering indicators of physical-environmental resilience (PER) and socio-economic resilience (SER). The model integrates the Random Forest (RF) algorithm, Analytic Hierarchy Process (AHP), and Coupling Coordination Degree (CCD) model. Key findings include: (1) Social development in mountainous cities like Chongqing follows a point-to-area pattern. Although there is an overall increase in SER, the CCD in more developed areas (Chongqing urban circle) was generally higher than in less developed areas (northeastern and southeastern Chongqing) (2) The PER model demonstrated exceptional performance (AUC values consistently above 0.95). Spatiotemporal evolution models reveal that Chongqing maintains a high overall PER. Notably, from 2019 to 2020, the proportion of administrative units classified as highly resilient peaked at 24.5%, marking a historical high. (3) Multi-year average rainfall primarily impacts PER (ranked first), while Gross Domestic Product (GDP) significantly affect SER. The development of multi-dimensional recovery indicators provides a robust framework for assessing resilience against landslides in mountainous cities. The CCD model illustrates the importance of regional dynamic coordinated development in resilience trajectories. This study provides a detailed blueprint for the scientific development of resilient mountainous cities, emphasizing the need for a spatial-temporal perspective on resilience and the benefits of coordinated regional development.