Spatio-temporal Model Research Articles

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example

ObjectiveIn order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing.MethodsThis study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model.ResultsWe have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas.ConclusionThe model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks

Predicting the trajectory of geographical events, such as wildfire spread, presents a formidable task due to the dynamic associations among influential biophysical factors. Geo-events like wildfires frequently display short and long-range spatial and temporal correlations. Short-range effects are the direct contact and near-contact spread of the fire front. Long-range effects are represented by processes such as spotting, where firebrands carried by the wind ignite fires distant from the flaming front, altering the shape and speed of an advancing fire front. This study addresses these modeling challenges by clearly defining and analyzing the scale-dependent spatiotemporal dynamics that influence wildfire spread, focusing on the interplay between biophysical factors and fire behavior. We propose two unique attention-based spatiotemporal models using Convolutional Long Short-Term Memory (ConvLSTM) networks. These models are designed to learn and capture a range of local to global and short and long-range spatiotemporal correlations. The proposed models were tested on two datasets: a high-resolution wildfire spread dataset produced with a semi-empirical percolation model and a satellite observed wildfire spread data in California 2012–2021. Results indicate that attention-based models accurately predict fire front movements that are consistent with known wildfire spread-biophysical dynamics. Our research suggests there is considerable potential for attention mechanisms to capture the spatiotemporal behavior of wildfire spread, with model transferability, that can guide rapid deployment of wildfire management operations. We also highlight directions for future studies that focus on how the self-attention mechanism could enhance model performance for a range of geospatial applications.

STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting

As a renewable and clean energy source, wind energy has caught worldwide attention. To ensure the reliability and stability of wind energy production, wind speed forecasting is of great importance. Although existing forecasting models have shown promising results, they remain relatively small in scale and are incapable of learning comprehensive sequential representations from large-scale data to reason intricate spatio-temporal patterns obscured in entangled wind series. Recently, pre-trained large models have demonstrated impressive performance in natural language processing (NLP) and computer vision (CV). However, large models are rarely applied to wind speed forecasting due to the lack of large-scale wind data for training. To resolve this issue, we propose Spatio-Temporal Enhanced Pre-Trained Large Language Model, namely STELLM, innovatively leveraging the powerful reasoning ability of Large Language Models (LLMs) for accurate wind speed forecasting. Specifically, we first perform the series decomposition to separate wind series data into seasonal and trend components, capturing the distinct properties from disentangled patterns. Then, to empower LLMs with the spatio-temporal modeling capability, we introduce the spatial and temporal prompts, which fuse the geographic distribution information and short-term temporal patterns of wind turbines to enrich the input wind series. Moreover, we leverage an autoregressive fine-tuning strategy to align LLMs with wind series data and learn patch-level representations. Afterwards these representations are fed into a localized spatial module to capture the spatial dependencies between wind turbines. Extensive experiments on four public wind datasets demonstrate the performance superiority of STELLM.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.

Factors associated with the spatiotemporal distribution of dog rabies in Tunisia.

Despite continuous efforts of veterinary services to control rabies in dogs since 1982, rabies remains a cause of death in Tunisia, with more than five reported human cases in 2022. As little is known on the determinants of transmission of rabies in dogs, better understand which factors contribute to its spatial heterogeneity in Tunisia is critical for developing bespoke mitigation activities. In this context, we developed Bayesian Poisson mixed-effect spatio-temporal model upon all cases of rabid dogs reported in each delegation during the period from 2019 to 2021. The best fitting model highlighted the association between the risk of rabies and the mean average monthly temperature, the density of markets and the density of dogs in delegations. Interestingly, no relationship was found between intensity of vaccination in dogs and the risk of rabies. Our results provided insights into the spatio-temporal dynamics of dog rabies transmission and highlighted specific geographic locations where the risk of infection was high despite correction for associated explanatory variables. Such an improved understanding represent key information to design bespoke, cost-efficient, rabies prevention and control strategies to support veterinary services activities and policymaking.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Rural-urban difference in the association between particulate matters and stroke incidence: The evidence from a multi-city perspective cohort study

Available evidence suggests that air pollutants can cause stroke, but little research has investigated the confounding effects of urban-rural differences. Here, we investigated the urban-rural difference in the correlation between particulate matter (PM2.5 and PM10) exposure and stroke. This cohort study was based on a prospective multi-city community-based cohort (Guizhou Population Health Cohort Study (GPHCS)) in Guizhou Province, China. A total of 7988 eligible individuals (≥18 years) were enrolled with baseline assessments from November 2010 to December 2012, and follow-up was completed by June 2020. Two major particulate matters (PMs, including PM2.5 and PM10) were assessed monthly from 2000 by using satellite-based spatiotemporal models. The risk of stroke was estimated using a Cox proportional hazard regression model. The association between particulate matters' exposure and stroke in different areas (total, urban, and rural) and the potential modification effect of comorbidities (hypertension, diabetes, and dyslipidemia) and age (≤65/>65 years) were examined using stratified analyses. The risk of stroke increased for every 10 μg/m3 increase in mean PMs’ concentrations during the previous 1 year at the residential address (HR: 1.26, 95%CI: 1.24, 1.29 (PM2.5); HR: 1.13, 95%CI: 1.11, 1.15 (PM10)). The presence of diabetes and dyslipidemia increased the risk of PM10-induced stroke in whole, urban, and rural areas. Specifically, people living in rural areas were more likely to experience the effects of PMs in causing a stroke. The risk of stroke due to PMs was statistically increased in the young and older populations living in rural areas. In conclusion, long-term exposure to PMs increased the risk of stroke and such association was more pronounced in people living in rural areas with lower income levels. Diabetes and dyslipidemia seemed to strengthen the association between PMs and stroke.

Association between air temperature and self-perceived health status in Southern Germany: Results from KORA FIT study

BackgroundShort-term exposure to low and high air temperatures can cause serious harmful effects on human health. Existing literature has mostly focused on associations of ambient air temperature with mortality and the need for health care in population-level studies. Studies that have considered self-perceived health status as an outcome when examining the effects of air temperature on health are scarce. In this study, we explored the short-term association of daily mean air temperature with various measures of self-perceived health status. MethodsThis cross-sectional analysis is based on the Cooperative Health Research in the Region of Augsburg (KORA) FIT study conducted in 2018/2019 and included participants from the Augsburg region of Southern Germany. Health-related quality of life (HRQOL) was evaluated by using the 5-level EuroQol Five Dimension (EQ-5D-5L) questionnaire, including the EuroQol visual analog scale (EQ-VAS). Self-rated health (SRH) and comparative self-rated health (CSRH) were each assessed using a single question. Daily mean air temperature data was estimated using a spatiotemporal model and assigned to participants' home addresses at a resolution of 1 × 1 km. Regression models with a Distributed Lag Non-linear Modeling (DLNM) approach were used to investigate the associations between daily mean air temperature and self-perceived health measures. ResultsWe found no association of heat or cold with the HRQOL, SRH or CSRH. Nevertheless, there was a significant protective association of low air temperature with the EQ-5D-5L dimension “usual activities.” ConclusionThere was no evidence of daily mean air temperature adversely affecting participants' self-perceived health status.

Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM

The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD–GAT–BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

Association between air pollution, meteorological factors and hand, foot, and mouth disease incidence in Zhejiang Province: A Bayesian spatio-temporal modelling study

Temperature and humidity are known to influence seasonal patterns of hand, foot and mouth disease (HFMD) transmission. However, the effect of air pollution and climate-air pollution interaction on the intensity of HFMD epidemics is poorly understood. This study aims to quantify the exposure-response effects of air pollution on HFMD risk by level of meteorological indicators in Zhejiang Province. A spatiotemporal Bayesian hierarchical model was used to determine the exposure-response association between air pollution, meteorological factors and the relative risk (RR) of HFMD. The model was fitted to monthly HFMD case data for the 92 counties in Zhejiang Province between January 2013, and December 2021. We assessed both linear interaction effects between air pollutants and meteorological factors on HFMD, and the modification role of extreme weather conditions on the nonlinear air pollutant-HFMD association. A 10 μg/m³ increase in PM10 and NO2 concentration raised the HFMD risk by 6.6% (RR = 1.066, 95% CI: 1.042, 1.092) and 20.8% (RR = 1.208, 95% CI: 1.162, 1.256) respectively. HFMD was negatively associated with O3 (RR = 0.980, 95% CI: 0.968, 0.993), SO2 (RR = 0.820, 95% CI: 0.776, 0.868) and CO (RR = 0.770, 95% CI: 0.621, 0.956). Temperature, humidity, and precipitation positively interacted with most air pollutants on HFMD, while wind speed had a negative interaction effect. In higher temperature conditions, the HFMD risk associated with PM10 increased. Elevated humidity levels amplified the risk associated with PM10, as did CO concentrations within a certain range. Heavy rainfall intensified the risk of HFMD associated with PM10, SO2, and CO. Results of this study showed that air pollution was associated with HFMD incidence, with different climate factors playing various modification roles in the associations and primarily leading to increased risk under extreme conditions. These findings provide a theoretical basis for personal protection and government prevention and control measures.

A deep learning-based convolutional spatiotemporal network proxy model for reservoir production prediction

Accurate production prediction is crucial in the field of reservoir management and production optimization. Traditional models often struggle with the complexities of nonlinear relationships and high-dimensional data, which hinders their ability to capture the variability of the production process efficiently and results in time-consuming calculations. To overcome these limitations, this paper introduces an innovative proxy modeling technique employing a convolutional spatiotemporal neural network. This method utilizes convolutional neural networks to extract spatial features from high-dimensional data, while the Transformer is used to model and predict complex temporal dynamics in production. To validate the effectiveness of the proposed proxy model, two case studies involving four injection and nine production wells within two-dimensional (2D) and three-dimensional (3D) non-homogeneous reservoirs were conducted, with the R2 coefficient serving as the primary evaluation metric. As the number of training iterations and data volume increase, the proxy model demonstrates rapid convergence. In tests conducted on the 2D and 3D datasets, the average R2 value exceeded 0.96 and 0.94. These results confirm the accuracy and stability of the proxy model. It also shows that the proxy model can accurately describe the geological and fluid seepage characteristics of the reservoir, which in turn can achieve a highly accurate match with the real data. In addition, the computational time is reduced by two orders of magnitude compared to traditional models. Compared with the long short-term memory method, the accuracy of the prediction results is increased by 30%, which greatly enhances efficiency and accuracy. To some extent, the presented proxy model can provide some guidance for the efficient history match of production data.

Method for constructing typical operation scenarios in power systems considering spatiotemporal correlation of renewable energy generation

Abstract The proposed article focuses on developing a methodology for constructing typical operational scenarios in power systems considering the spatiotemporal characteristics of new energy output. The integration of new energy sources on a large scale into power systems can lead to issues such as line congestion and supply-demand imbalances. To support the planning and scheduling of future high-capacity power systems, this paper introduces a method that accounts for the spatiotemporal characteristics of new energy outputs. The method begins by proposing a spatiotemporal fusion-based multi-head attention model for generating new energy output scenarios. This model employs multi-head attention mechanisms to extract the spatiotemporal characteristics of new energy outputs, eliminating redundancy in the feature data. The processed spatiotemporal feature information is then transferred to a gated feature fusion model for integration, using an encoder structure based on Long Short-Term Memory (LSTM) to generate new energy output scenarios. The paper, by means of an advanced Fuzzy C-Means (FCM) clustering algorithm, identifies typical scenarios of net energy output. Subsequently, the proposed method’s efficacy and precision are confirmed through simulation analysis with real data from a certain area.

A new attention-based CNN_GRU model for spatial-temporal PM2.5 prediction.

Accurately predicting thespatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection.

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data

Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. To approximate the latent solutions of partial differential equations (PDEs) in the specific steps of pressurization, adsorption, heavy reflux, counter-current depressurization, and light reflux, the system's network representation is decomposed into five lightweight sub-networks. On this basis, we propose a parameter-based transfer learning (TL) combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process. Moreover, to tackle challenges related to sharp adsorption fronts, our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function. The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method, effectively simulating all steps and cyclic behavior within the PSA processes.

Spatiotemporal and weather effects on the reproductive success of piping plovers on Prince Edward Island, Canada.

Piping plovers (Charadrius melodus sp.) rank among North America's most endangered shorebird species, facing compounding environmental challenges that reduce habitat availability and suppress recruitment and survival rates. Despite these challenges, research on the direct effects of climate variability and extremes on their breeding ecology remains limited. Here, we employ a spatiotemporal modelling approach to investigate how location, nest timing and weather conditions influence reproductive success rates in a small breeding population of C. m. melodus in Prince Edward Island (PEI), Canada from 2011 to 2023. Analysis of 40 years of monitoring records from a subset of nesting sites revealed that flooding and predation have been persistent sources of reproductive failures in this population, with unexplained losses increasing in recent years. Contrary to our hypotheses, our modelled results did not support a negative impact of extreme high temperatures and strong precipitation events on reproductive outcomes. Instead, we identified a positive effect of T MAX and no effect of strong precipitation, perhaps due to limited exposure to extreme high temperatures (>32°C) and context-specific risks associated with precipitation-induced flooding. However, trends in regional climate change are likely to increase exposure to-and the influence of-such factors in the near future. Our models also identified spatiotemporal variability in apparent hatch success over the study period, as well as worse hatch outcomes across popular beachgoing regions and for delayed nesting attempts. While our results offer preliminary insights into factors affecting breeding success in this population, further research will be imperative to enhance understanding of constraints on recruitment. To this end, we encourage the collection and analysis of additional time-series data of prey populations, human activities, fine-scale weather data and predator/flood risks associated with each nest on PEI.

Spatiotemporal Characteristics and Factors Influencing the Cycling Behavior of Shared Electric Bike Use in Urban Plateau Regions

At present, most of the research on shared electric bikes mostly focuses on the scheduling, operation and maintenance of shared electric bikes, while insufficient attention has been paid to the behavioral characteristics and influencing factors of shared cycling in plateau cities. This paper takes Kunming as a research case. According to the user’s cycling behavior, the spatiotemporal cube model and emerging hotspot analysis are used to explore the spatiotemporal characteristics of the citizens’ cycling in the plateau city represented by Kunming, and the method of geographical detectors is used to study the specific factors affecting the shared travel of citizens in Kunming and conduct interactive detection. The findings are as follows: ① the use of shared electric bikes in Kunming varies greatly on weekdays, showing a bimodal feature. In space, the overall distribution of cycling presents a “multi-center” agglomeration feature with distance decay from the center of the main urban area. ② The geographic detector factor detection model quantitatively analyzes the interactive influence between factors, providing a good supplement to the independent influence results of each factor. Through the dual factor interactive detection model, we found that the overall spatiotemporal distribution of cycling during each time period is most significantly affected by the distribution of service facilities, followed by transportation accessibility, land use, and the natural environment. The research results can assist relevant departments in governance of urban shared transportation and provide a reference basis, and they also have certain reference value in urban pattern planning.

An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Context The research explores the benefits of real time tracking of soil moisture for various land management contexts and the importance of spatio-temporal modelling and mapping to gain clear and visual understanding of soil moisture fluxes across a farm. Aims This research aims to outline the key processes required for building an operational on-farm soil moisture monitoring system where the product is highly granular daily soil moisture maps depicting variations temporally, spatially and vertically. Methods We describe processes of capacitance soil moisture probe installation, data collection infrastructure, sensor calibration, spatio-temporal modelling, and mapping. Key results An out-of-bag soil moisture evaluation modelling system was tested for nearly 2 years. We found a model accuracy (RMSE) estimate of 0.002 cm−3 cm−3 and concordance of 0.96 were found. This result is averaged over this period but fluctuated daily, and related to rainfall patterns across the target farm, which were not directly incorporated into the modelling framework. As expected, incorporating prior estimates of soil moisture into the modelling framework contributed to very accurate estimates of real time available soil moisture. Conclusions This research promotes the importance of iterative improvements to the soil moisture monitoring system, particularly in areas of sensor recalibration and spatio-temporal modelling. We stress the need for a longer-term view and plan for ongoing maintenance and improvement of such systems in the emerging digital farming ecosystem. Implications The results of this research will be useful for researchers and practitioners involved in the design and implementation of on-farm soil monitoring systems.

Settlement estimation during foundation excavation using pattern analysis and explainable AI modeling

With the rapid expansion of underground engineering, accurate settlement estimation during foundation excavation using monitoring data has gained prominence. Previous studies have typically overlooked data patterns, relying solely on time-series models, which yielded limited accuracy and short-term predictability. To address these issues, this paper performs a thorough pattern analysis covering both the temporal and spatial properties, and proposes a spatiotemporal modeling method grounded in explainable artificial intelligence technique. A real-world engineering case study is conducted to validate the effectiveness of the proposed method. Counterintuitively, the results reveal a weak temporal effect within the settlement data, with only the last step playing a dominant role, whereas the spatial correlation among measuring points is notably more significant. Compared to conventional models, the proposed method consistently outperforms, achieving significantly higher R2 scores and excelling in long-term estimation with reductions of at least 78% in RMSE, 80% in MAE, and 72% in MAPE.