Spatio-temporal Clustering Research Articles

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022).

Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide.

Zoonotic spillover and extreme weather events drive the global outbreaks of airborne viral emerging infectious diseases.

Outbreaks of airborne viral emerging infectious diseases (EIDs) cause an increasing burden on global public health, particularly with a backdrop of intensified climate change. However, infection sources and drivers for outbreaks of airborne viral EIDs remain unknown. Here, we aim to explore the driving mechanisms of outbreaks based on the one health perspective. Outbreak information for 20 types of airborne viral EIDs was collected from the Global Infectious Disease and Epidemiology Network database and a systematic literature review. Four statistically significant and high-risk spatiotemporal clusters for airborne viral EID outbreaks were identified globally using multivariate scan statistic tests. There were 112 outbreaks with clear infection sources, and zoonotic spillover was the most common source (95.54%, 107/112). Since 1970, the majority of outbreaks occurred in healthcare facilities (24.82%), followed by schools (17.93%) and animal-related settings (15.93%). Significant associations were detected between the number of earthquakes, storms, duration of floods, and airborne viral EIDs' outbreaks using a case-crossover study design and multivariable conditional logistic regression. These findings implied that zoonotic spillover and extreme weather events are driving global outbreaks of airborne viral EIDs, and targeted prevention and control measures should be made to reduce the airborne viral EIDs burden.

Detecting spatial and spatio-temporal clusters of rape in India, 2011–2020

The study aims to identify the spatial and spatio-temporal patterns of rape in India from 2011 to 2020 using crime data aggregated at the district level. The study also tries to understand persistent patterns in the spatial variation of rape incidence across the country during the study period. The annual rape data from 2011 to 2020 were extracted from the National Crime Records Bureau. Retrospective spatial and temporal cluster analyses were performed using the purely spatial (Kulldorff’s and Mann–Whitney scan statistic) and space–time scan statistic (Kulldorff’s based on the discrete Poisson model). Cluster frequency analysis was performed. Results showed the spatial heterogeneity in the pattern of rape crime across the country as well as its localization among geographically contiguous regions across time and space. The detected primary spatial clusters (P < 0.05) persisted mainly in the Central, North, and North Eastern zones of the country over the ten year study period. The primary spatio-temporal cluster (LLR = 5560.09, P < 0.001) appeared between the time frame 2014 to 2016 and was concentrated mainly in the Northern zone. Thirty-seven districts of India (5.78%) contributed to spatial clusters in all years of the study period. The heterogeneous distribution of rape across India could be due to the spatio-temporal variations in the determinants of rape (such as socioeconomic status, demographics, urbanisation, and gender equality) across the country. Identification of the localized rape clusters could result in understanding the contextual factors of rape and, thereby be beneficial to national and regional rape control strategies.

The Spatial Scale and Spread of Child Victimization.

Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K-function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions.

Clinical Features and Genomic Epidemiology of Bloodstream Infections due to Enterococcal Species Other Than Enterococcus faecalis or E. faecium in Patients With Cancer.

Non-Enterococcus faecium, non-E. faecalis (NFF) enterococci are a heterogeneous group of clinically pathogenic enterococci that include species with intrinsic low-level vancomycin resistance. Patients with cancer are at increased risk for bacteremia with NFF enterococci, but their clinical and molecular epidemiology have not been extensively described. We conducted a retrospective review of all patients (n = 70) with NFF bacteremia from 2016 to 2022 at a major cancer center. The main outcomes assessed were 30-day mortality, microbiological failure (positive blood cultures for ≥4 days), and recurrence of bacteremia (positive blood culture <14 days after clearance). Whole-genome sequencing was performed on all available NFF (n = 65). Patients with hematological malignancies made up 56% of the cohort (77% had leukemia). The majority of solid malignancies (87%) were gastrointestinal in origin. The majority of infections (83%) originated from an intra-abdominal source. The most common NFF species were E. gallinarum (50%) and E. casseliflavus (30%). Most (61%) patients received combination therapy. Bacteremia recurred in 4.3% of patients, there was a 30-day mortality of 23%, and 4.3% had microbiological failure. E. gallinarum and E. casseliflavus isolates were genetically diverse with no spatiotemporal clustering to suggest a single strain. Frequencies of ampicillin resistance (4.3%) and daptomycin resistance (1.9%) were low. Patients with hematologic malignancy had infections with NFF enterococci that harbored more resistance genes than patients with solid malignancy (P = .005). NFF bacteremia is caused by a heterogeneous population of isolates and is associated with significant mortality. Hematological malignancy is an important risk factor for infection with NFF resistant to multiple antibiotics.

Investigating the spatiotemporal patterns and clustering of attendances for mental health services to inform policy and resource allocation in Thailand

BackgroundMental illness poses a substantial global public health challenge, including in Thailand, where exploration of access to mental health services is limited. The spatial and temporal dimensions of mental illness in the country are not extensively studied, despite the recognized association between poor mental health and socioeconomic inequalities. Gaining insights into these dimensions is crucial for effective public health interventions and resource allocation.MethodsThis retrospective study analyzed mental health service utilization data in Thailand from 2015 to 2023. Temporal trends in annual numbers of individuals visiting mental health services by diagnosis were examined, while spatial pattern analysis employed Moran’s I statistics to assess autocorrelation, identify small-area clustering, and hotspots. The implications of our findings for mental health resource allocation and policy were discussed.ResultsBetween 2015 and 2023, mental health facilities documented a total of 13,793,884 visits. The study found anxiety, schizophrenia, and depression emerged as the top three illnesses for mental health visits, with an increase in patient attendance following the onset of the COVID-19 outbreak. Spatial analysis identified areas of significance for various disorders across different regions of Thailand. Positive correlations between certain disorder pairs were found in specific regions, suggesting shared risk factors or comorbidities.ConclusionsThis study highlights spatial and temporal variations in individuals visiting services for different mental disorders in Thailand, shedding light on service gaps and socioeconomic issues. Addressing these disparities requires increased attention to mental health, the development of appropriate interventions, and overcoming barriers to accessibility. The findings provide a baseline for policymakers and stakeholders to allocate resources and implement culturally responsive interventions to improve mental health outcomes.

Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm

In this work, the results of instrumentation over 8 years, including the phases of construction, first impounding, and operation, have been used to analyze the location of the Eyvashan Dam settlement. Mohr–Coulomb behavioral model and numerical model of Plaxis 2D software were used to verify the monitoring results. The results demonstrated that settlement of the dam has increased in the dam's core since the beginning of construction, and they eventually stabilized during the operation phase. After the completion of the construction phase, the maximum settlement of the dam core was recorded as 809 mm, which is equivalent to 1.2% of the height of the dam at the middle level. Also, an approach to interpreting the settlement behavior of earth dams has been presented that is based on spatiotemporal clustering. Also, RF, MARS, and GMDH models were created based on a proposed scenario to predict settlement using points located in a cluster. Therefore, the settlement location of the studied dam was determined using the results of the k-means clustering algorithm in the aforementioned AI models. The high accuracy of the results of the proposed method confirms the proper performance of using AI models in predicting and diagnosing the settlement of earthen dams using the results of k-means spatiotemporal clustering algorithm. The evaluation of the models shows that the ENN model is a more suitable and efficient tool in this field and can be useful in monitoring the settlement of earth dams.

Summary statistics for spatio-temporal point processes on linear networks

We propose novel second/higher-order summary statistics for inhomogeneous spatio-temporal point processes when the spatial locations are limited to a linear network. More specifically, letting the spatial distance between events be measured by a regular distance metric, appropriate forms of K- and J-functions are introduced, and their theoretical relationships are studied. The theoretical forms of our proposed summary statistics are investigated under homogeneity, Poissonness, and independent thinning. Moreover, non-parametric estimators are derived, facilitating the use of our proposed summary statistics to study the spatio-temporal dependence between events. Through simulation studies, we demonstrate that our proposed J-function effectively identifies spatio-temporal clustering, inhibition, and randomness. Finally, we examine spatio-temporal dependencies for street crimes in Valencia, Spain, and traffic accidents in New York, USA.

Middle to lower crustal earthquakes in the western East Sea (Sea of Japan) and their implications for neotectonic evolution

The western East Sea (Sea of Japan) is a seismically active region. A major earthquake in the western East Sea may cause damage in the eastern Korean Peninsula. The nature of seismicity in the western East Sea is important information for mitigation of seismic hazards. The seismicity in the western East Sea occur around the paleo-rifting structures where seismic and geophysical properties apparently change. Both reverse faulting and strike slip earthquakes occur in the region. Earthquake locations and focal depths are refined. The maximum focal depth generally increases from ∼10 km in the coast to ∼32 km in the continental slope of the western East Sea that is located in distance of ∼60 km from the coast. Mid to lower crust earthquakes continue around the paleo-rifting structures that may extend to the lower crust and Moho. Offshore earthquakes rarely occur in oceanic basins of bathymetry with >2 km. Clustered episodic earthquakes occur in four local offshore areas including the stress-induced areas by the 12 September 2016 ML5.8MW5.4 Gyeongju and 15 November 2017 ML5.4MW5.5 Pohang earthquakes. The spatiotemporal clustering of earthquakes suggests stress accumulation at localized paleo-rifting structures and episodic stress release. The mid to lower crustal earthquakes are confirmed from phase arrival time analyses with collocated vertically-separated sensors. The laterally progressive focal depth changes suggest neotectonic evolution of thrust across the crust in the paleo-rifting structure.

Developing Complex Disaster Response Scenarios Using Social Network Analysis

Given the escalating frequency and severity of complex disasters, it is imperative to transition from isolated disaster-specific systems to integrated disaster response frameworks. The development of complex disaster scenarios represents a crucial initial step, facilitating an in-depth comprehension of extant vulnerabilities and the forecasting of prospective challenges. This study reinterprets individual disasters that occurred within the same spatiotemporal domain as complex disasters. Complex spatiotemporal disaster clusters were explored, a social network analysis on individual disasters within these identified clusters was performed, and a regional disaster response scenario was proposed. This approach, which integrates spatiotemporal disaster big data with social network analysis, enables statistical measurement of inter-disaster relationships. Visual representations of these relationships through network graphs offer a quantitative lens to analyze relationships that may be difficult to describe narratively. Developing such scenarios facilitates a clear and swift understanding of the relational dynamics among disasters, particularly in situations where there is an inadequate policy and institutional framework for responding to complex disasters.

Safety Monitoring Method for the Uplift Pressure of Concrete Dams Based on Optimized Spatiotemporal Clustering and the Bayesian Panel Vector Autoregressive Model

To establish a safety monitoring method for the uplift pressure of concrete dams, spatiotemporal information from monitoring data is needed. In the present study, the method of ordering points to identify the clustering structure is employed to spatially cluster the uplift pressure measuring points at different locations on the dam; three distance indexes and two clustering evaluation indexes are used to realize clustering optimization and select the optimal clustering results. The Bayesian panel vector autoregressive model is used to establish the uplift stress safety monitoring model for each category of monitoring point. For a nonstationary sequence, the difference method is selected to ensure that the sequence is stable, and the prediction is carried out according to the presence or absence of exogenous variables. The result is that the addition of exogenous variables increases the accuracy of the model’s forecast. Engineering examples show that the uplift pressure measurement points on the dam are divided into seven categories, and classification is based mainly on location and influencing factors. The multiple correlation coefficients of the training set and test set data of the BPVAR model are more than 0.80, and the prediction error of the validation set is lower than that of the Back Propagation neural network, XGBoost algorithm, and Support Vector Machines. The research in this paper provides some reference for seepage monitoring of concrete dams.

Genome analysis of Streptococcus spp. isolates from animals in pre-antibiotic era with respect to antibiotic susceptibility and virulence gene profiles.

Lyophilized Streptococcus spp. isolates(n = 50) from animal samples submitted to the diagnostic laboratory at the University of Connecticut in the 1940s were revivified to investigate the genetic characteristics using whole-genome sequencing (WGS). The Streptococcus spp. isolateswere identified as follows; S. agalactiae (n = 14), S. dysgalactiae subsp. dysgalactiae (n = 10), S. dysgalactiae subsp. equisimils (n = 5), S. uberis (n = 8), S. pyogenes (n = 7), S. equi subsp. zooepidemicus (n = 4), S. oralis (n = 1), and S. pseudoporcinus (n = 1). We identified sequence types (ST) of S. agalactiae, S. dysgalactiae, S. uberis, S. pyogenes, and S. equi subsp. zooepidemicus and reported ten novel sequence types of those species. WGS analysis revealed that none of Streptococcus spp. carried antibiotic resistance genes. However, tetracycline resistance was observed in four out of 15 S. dysgalactiae isolates and in one out of four S. equi subsp. zooepidemicus isolate. This data highlights that antimicrobial resistance is pre-existed in nature before the use of antibiotics. The draft genome sequences of isolates from this study and 426 complete genome sequences of Streptococcus spp. downloaded from BV-BRC and NCBI GenBank database were analyzed for virulence gene profiles and phylogenetic relationships. Different Streptococcus species demonstrated distinct virulence gene profiles, with no time-related variations observed. Phylogenetic analysis revealed high genetic diversity of Streptococcus spp. isolates from the 1940s, and no clear spatio-temporal clustering patterns were observed among Streptococcus spp. analyzed in this study. This study provides an invaluable resource for studying the evolutionary aspects of antibiotic resistance acquisition and virulence in Streptococcus spp.

Scaling law of real traffic jams under varying travel demand

The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation.

Community-Scale Classification and Governance Policy Implications for Demographic, Economic, and Land-Use Linkages in Mega-Cities

Analyzing the spatio-temporal relationship between socio-economics and land-use structure at the micro-scale is crucial for effective spatial governance in large cities. This paper focuses on Beijing, utilizing long time-series remote sensing images and multi-source data spanning 30 years. We employ spatio-temporal clustering based on kilometer grid cells and a community-scale multi-factor aggregation method to categorize the linkages and spatio-temporal matching of population, GDP, land development, and ecological protection at the community level in a problem-oriented approach. Results indicate significant changes in Beijing’s population, GDP, and land use, with a 11.53% increase in land development intensity. We identify significant temporal and spatial disparities between population–GDP dynamics, population–land development trends, and GDP–land development patterns, underscoring the multifaceted challenges inherent in urban governance. Areas characterized by lagging population concentration, sluggish economic growth, rampant land development, and ecological fragility collectively encapsulate notable portions of Beijing’s expansive urban terrain. Mismatches pose governance risks, with medium to high-risk communities comprising 18.08% of community units and high-risk types representing 4.27% in Beijing. These discrepancies pose formidable governance risks, with communities ranging from moderate to high-risk categories, necessitating tailored interventions to address their unique challenges. This systematic exploration of comprehensive governance issues within mega-cities promises to furnish decision-makers with invaluable insights, facilitating nuanced and strategic urban governance approaches tailored to the intricacies of urban dynamics and challenges.

The Spatiotemporal Impact of Digital Economy on High-Quality Agricultural Development: Evidence from China

China’s high-quality economic development is strongly supported by the high-quality development of agriculture, and the digital economy has emerged as a key driver for promoting shared prosperity and high-quality economic development. Against this backdrop, investigating the connection between high-quality agricultural development and the digital economy holds significant importance. This study utilized the entropy-weighted TOPSIS model to evaluate comprehensive evaluation indicators of the two according to panel data from 30 provinces in China between 2011 and 2021. Subsequently, GIS spatial analysis and exploratory spatial data analysis (ESDA) were employed to investigate the spatiotemporal evolution features and spatial correlations. Finally, the spatiotemporal geographically weighted regression (GTWR) model was constructed to examine the spatiotemporal impact of the digital economy on the advancement of high-quality agricultural growth. The results indicate that: (1) from 2011 to 2021, China’s high-quality agricultural development and digital economy both demonstrated a general increasing trend. In terms of spatial distribution, there were significant spatial variations, with a general trend of “Southeast is higher, whereas the Northwest is lower”. The regions with significant value were primarily clustered in the coastal areas in the east and several provincial capitals. (2) Both of the two exhibited significant global spatial self-correlation, and there were also significant spatiotemporal clustering effects in high-quality agricultural growth, gradually forming a high-value cluster centered around Shanghai and a low-value cluster centered around western provinces. (3) The digital economy positively influences the enhancement of high-quality agricultural development, demonstrating notable spatial and temporal heterogeneity. In contrast to the southeastern areas, the influence is more pronounced in the northern and central-western areas.

Web-based spatial decision support system for precision agriculture: A tool for delineating dynamic management unit zones (MUZs)

Precision agriculture (PA) is a farming management concept that aims to provide agronomic, economic, and environmental benefits. One of the fields of research in PA is the delineation of Management Unit Zones (MUZs). MUZs are the sub-division of fields featuring an inter-zonal variation delineated by agronomists for on-field PA operations. To develop MUZs, three factors typically need to be considered: input multi-dimensional data, procedures to process the information, and the optimal number of zones a field should be divided into. PA uses digital technologies to collect and analyze a large amount of data, outline MUZs, monitor crops, and carry out site-specific crop management. Web-based spatial decision support systems (WB-SDSS) can provide users with tools that ease the complex procedures for PA. The objective of this study is twofold: on the one hand, we developed a free and open source (FOSS) WB-SDSS to facilitate the implementation and use of such tools for delineating MUZs and monitoring crops; on the other, a MUZs outline procedure was developed based on Sentinel-2 and Planetscope time series data, and spatio-temporal dynamic clustering model using fuzzy c-means. Our study highlighted that the WB-SDSS might be a helpful solution for harmonizing data collected from different sources, easing the implementation and use of complex geospatial procedures for PA, and delineating MUZs. We tested the system on a particularly representative farm in the Emilia Romagna region (Northern Italy), with 512 hectares of durum wheat crops. Using the WB-SDSS, we quickly delineated homogeneous zones for 27 fields in the study area during the phenological cycle of durum wheat (November 2018-June 2019).

Intelligent Caching for Vehicular Dew Computing in Poor Network Connectivity Environments

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

Spatiotemporal clustering of dam settlement monitoring using instrumentation data (case study: Eyvashan Earth Dam)

The integrity of monitoring data is important for studying the amount and location of deformation in earth dams. In this research, the meeting of the Eyvashan Earth Dam was analyzed using the results of precision instruments over a period of eight years during three periods of construction, first impoundment, and exploitation. To verify the accuracy of the results, the numerical model of the largest section of the dam was constructed with Plaxis software and analyzed according to the Mohr–Coulomb behavior model. The results showed that since the beginning of construction, the occurrence of settlements in the core of the dam has been increasing, and with the passage of time, settlements reach a constant value in the operation phase. The maximum settlement of the dam core at the end of the construction stage was 809 mm, which was equal to 1.2% of the dam height in the middle level. Additionally, an approach based on spatiotemporal clustering has been presented to improve the interpretability of the settlement behavior of earth dams. Clustering analysis of spatiotemporal data is used as a measure to determine the similarity of settlements and to select relevant data. Therefore, k-means clustering algorithms and two-step clustering were used to determine the displacement of earth dams. By comparing the correlation results, the proposed method of detecting settlement areas using a spatiotemporal clustering model can be considered a reliable method for monitoring the settlement of earth dams.

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh.

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.