The data was collected from Caltrans Weigh-in-Motion (WIM) database. The California Department of Transportation (Caltrans) has installed WIM devices in about 150 sites. Several stations are at PrePass locations, but the majority are spread throughout the transportation network as WIM data stations. The Caltrans WIM database contains the information of the WIM data stations in 13 years (2003-2015). California has twelve districts, but this analysis was filtered the data base with the four districts in southern California and their 39 stations, it also incluedes analysis of raw_wim and vehicle class using those vehicles with maxgvw>0 and gvw>0. The database also contains information of twelve vehicle classes (VC), from VC 4 to VC 15. Vehicle class 4 is bus service and vehicle class 15 is unclassified. Therefore, the analysis excludes these two classes (Class 7 is another particular case). Finally, the variables recorded are Gross vehicle weight (GVW) and Maximum gr...

We used matched filter detection and multiple-event relocation techniques to characterize the spatiotemporal evolution of the sequence. Our analysis is from the 14 closest seismic stations to the earthquake sequence, which included seven permanent stations from the Montana Regional Seismic Network, one permanent station from the ANSS backbone network and three temporary seismic stations deployed by the USGS within four days after the mainshock. A catalog of 685 well-located earthquakes larger than M 1 occurring Between 5 July and 15 October 2017 were relocated using a hypocentroid decomposition (HD) multiple-event relocation approach. The resulting dataset had an average epicentral and depth uncertainties (90% confidence) on the order of 1 km. Using match filtering of the station waveforms for each of these relocated events we were able to detect four foreshocks in the three days prior to, and 3005 aftershocks in the three weeks following the mainshock.

This dataset comprises the source code to perform fully automatic spatiotemporal segmentation in time series of topographic surface change data (Python scripts). Further provided is the validation material of the resulting extraction of 4D objects-by-change at the study site of a sandy beach in The Netherlands, together with results of the validation as aggregated expert evaluations. Details on the method and workflow are given in the corresponding paper: Geographic observation benefits from the increasing availability of time series of 3D geospatial data, which allow analysis of change processes at high temporal detail and over extensive periods. In this context, the demand for advanced methods to detect and extract topographic surface changes from these 4D geospatial data emerges. Changes in natural scenes occur with varying magnitude, duration, spatial extent, and change rate, and the timing of their occurrence is not known. Standard pairwise change detection requires the selection of fixed analysis periods and the specification of magnitude thresholds to determine accumulation or erosion forms. In settings with continuous surface morphology and dynamic changes to the surface due to material transport, such change forms are typically temporary and may be missed or aggregated if they occur with spatial and/or temporal overlap. This is overcome with the extraction of 4D objects-by-change (4D-OBCs). These objects are obtained by firstly detecting surface changes in the temporal domain at locations in the scene. Subsequently, they are spatially delineated by considering the full history of surface change during region growing from the seed location of a detected change. To perform this spatio¬temporal segmentation systematically for entire 3D time series, we develop a fully automatic approach of seed detection and selection, combined with locally adaptive thresholding for region growing of individual objects with varying change properties. We apply our workflow to a five-months hourly time series of around 3,000 terrestrial laser scanning point clouds acquired for coastal monitoring at a sandy beach in The Netherlands. This provides 2,021 4D-OBCs as extracted accumulation or erosion forms. Results are validated through majority agreement of six expert analysts, who evaluate the segmentation performance at sample locations throughout the scene. Accordingly, our method extracts surface changes with an error of omission of 4.7 % and an error of commission of 16.6 %. We examine the results and provide considerations how postprocessing of segments can further improve the change analysis workflow. The developed approach thereby provides a powerful tool for automatic change analysis in 4D geospatial data, namely to detect and delineate natural surface changes across space and time.

Crime is a significant social, economic, and legal issue. This paper presents an open-access spatiotemporal repository of street and neighborhood crime data, comprising approximately one million records of crimes in China, with specific geographic coordinates (latitude and longitude) and timestamps for each incident. The dataset is based on publicly available law court judgment documents. Artificial intelligence (AI) technologies are employed to extract crime events at the neighborhood or even building level from vast amounts of unstructured judicial text. This dataset enables more precise spatial analysis of crime incidents, offering valuable insights across interdisciplinary fields such as economics, sociology, and geography. It contributes significantly to the achievement of the United Nations Sustainable Development Goals (SDGs), particularly in fostering sustainable cities and communities, and plays a crucial role in advancing efforts to reduce all forms of violence and related mortality rates.

Attached file provides supplementary data for linked article.

Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

This dataset is about book series, has 1 rows. and is filtered where the books is A two stage approach to spatiotemporal analysis with strong and weak cross-sectional dependence. It features 10 columns including book series, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

BNE: Brazil NortheastBSE: Brazil Southeast (except São Paulo)BSP: São PauloBSU: Brazil SouthEU: EuropeNA: North America.Most common region trajectory patterns.

Distribution of dermatophytes according to the age group of the diseased investigated population.

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AA: AsiaAF: AfricaBMI: Brazil MidwestBNE: Brazil NortheastBSE: Brazil Southeast (except São Paulo)BSP: São PauloBSU: Brazil SouthEU: EuropeLA: Latin AmericaNA: North America.Origin and destination trajectory analysis–from undergraduate to employment institution.

The MEANS-ST1.0 dataset consists of a "Data File" and a "Readme File". The "Data File" serve as the core file, while the "Readme File" provides explanations of abbreviations and units, along with a list of key parameters. Within the data files, we offer three different formats of anthropogenic pollutant discharge datasets. The first format is stored as GeoTIFF files, which can be used in conjunction with GIS software for overall characterization and spatial distribution analysis. The spatial resolution is 1 km, covering three representative years (1980, 2000, and 2020) and providing data on total anthropogenic nitrogen discharges, as well as discharges from five types of anthropogenic pollutant sources: urban residential, rural residential, industry, crop farming and livestock farming. The second format comprises ten NetCDF files, suitable for constructing two-dimensional or multi-dimensional models and conducting data visualization analysis. These files have a spatial resolution of 1 km and contain monthly data for different years (1980, 2000, and 2020) on total TN and TP discharges and five types of anthropogenic pollutant sources. The third format of the dataset is Excel files, supporting the construction of a national integrated model and providing yearly data on anthropogenic pollutant discharges for provincial administrative units, including both total and categorized discharges. The MEANS-ST1.0 dataset incorporates the most comprehensive spatiotemporal dynamic parameters, enabling a fine-grained analysis of the long-term dynamics for China's anthropogenic nutrient discharges from both spatial and temporal perspectives.

Acoustic interactions are important for understanding intra- and interspecific communication in songbird communities from the viewpoint of soundscape ecology. It has been suggested that birds may divide up sound space to increase communication efficiency in such a manner that they tend to avoid overlap with other birds when they sing. We are interested in clarifying the dynamics underlying the process as an example of complex systems based on short-term behavioral plasticity. However, it is very problematic to manually collect spatiotemporal patterns of acoustic events in natural habitats using data derived from a standard single-channel recording of several species singing simultaneously. Our purpose here is to investigate fine-scale spatiotemporal acoustic interactions of the great reed warbler. We surveyed spatial and temporal patterns of several vocalizing color-banded great reed warblers (Acrocephalus arundinaceus) using an open source software for robot audition HARK (Honda Research Institute Japan Audition for Robots with Kyoto University) and three new 16-channel, stand-alone, and water-resistant microphone arrays, named DACHO spread out in the bird's habitat. We first show that our system estimated the location of two color-banded individuals' song posts with mean error distance of 5.5 ± 4.5 m from the location of observed song posts. We then evaluated the interdigitation of the temporal pattern of localized songs by comparing the duration of localized songs with those annotated by human observers, with an accuracy score of average 0.89% for one bird that stayed at one song post. We found significant temporal overlap avoidance and an asymmetric relationship between songs of the two singing individuals, using transfer entropy. We believe that our system and analytical approach contribute to a better understanding of fine-scale acoustic interactions in time and space in bird communities.

This data release makes available three data tables supporting a spatiotemporal analysis of riverine conductivity and streamflow trends within the Delaware River Basin. The listed datasets include baseflow and total flow time series for selected gaged basins, watershed attributes, water quality information and trend analysis results.

We performed replicated, repeated-measures data of height, diameter and vitality at tree level to allow analysis of the spatial and temporal structure and diversity of a semi-natural mixed floodplain forest in Italy. Three inventories were performed in 1995, 2005 and 2016 in three ~1 ha plots with varying soil moisture regimes. The use of replicated, repeated-measures data rather than chronosequences allows the examination of true changes in spatial pattern processes through time in this forest type.

Example data frame of class-level metrics for a spatiotemporal analysis.

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

The global spatiotemporal big data platform market, currently valued at approximately $23.83 billion (2025), is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 9.2% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and the need for efficient city management are fueling demand for centralized platforms capable of handling vast amounts of geospatial data from various sources like sensors, IoT devices, and satellite imagery. Simultaneously, the growing focus on environmental monitoring and natural resource management is driving adoption of distributed platforms tailored for applications in ecology, climate change research, and disaster response. Government initiatives promoting smart cities and digital infrastructure are further stimulating market growth, alongside the increasing availability of affordable cloud-based solutions from major players like Microsoft and AWS. The market segmentation reveals strong potential in both government and enterprise applications, with centralized platforms for cities currently dominating, yet distributed platforms for natural environments showing significant growth potential in the coming years. Competition is fierce, with established tech giants alongside specialized firms like Piesat and Geovis vying for market share. Regional analysis suggests North America and Asia Pacific (particularly China) are major market hubs, though growth is anticipated across all regions due to increased data generation and technological advancements. The continued development of advanced analytics capabilities, including AI and machine learning, will further enhance the value proposition of spatiotemporal big data platforms. Integration with existing GIS systems and improvements in data processing speeds are critical factors contributing to market expansion. However, challenges remain. Data security and privacy concerns, alongside the need for skilled professionals to manage and interpret complex datasets, pose potential restraints. The high initial investment costs associated with implementing these platforms can also limit adoption in certain sectors, particularly within smaller enterprises. Overcoming these obstacles through strategic partnerships, robust security protocols, and accessible training programs will be crucial for sustaining the projected growth trajectory of the spatiotemporal big data platform market.

Wildlife roadkill hotspots are frequently used to identify priority locations for implementing mitigation measures. However, understanding the landscape-context and the spatial and temporal dynamics of these hotspots is challenging. Here, we investigate the factors that drive the spatiotemporal variation of bat mortality hotspots on roads along three years. We hypothesize that hotspot locations occur where bat activity is higher and that this activity is related to vegetation density and productivity, probably because this is associated with food availability. Statistically significant clusters of bat-vehicle collisions for each year were identified using the Kernel Density Estimation (KDE) approach. Additionally, we used a spatiotemporal analysis and generalized linear mixed models to evaluate the effect of local spatiotemporal variation of environmental indices and bat activity to predict the variation on roadkill hotspot locations and to asses hotspot strength over time. Between 2009 and 2011 we conducted daily surveys of bat casualties along a 51-km-long transect that incorporates different types of roads in southern Portugal. We found 509 casualties and we identified 86 statistically significant roadkill hotspots, which comprised 12% of the road network length and contained 61% of the casualties. Hotspots tended to be located in areas with higher accumulation of vegetation productivity along the three-year period, high bat activity and low temperature. Furthermore, we found that only 17% of the road network length was consistently classified as hotspots across all years; while 43% of hotspots vanished in consecutive years and 40% of new road segments were classified as hotspots. Thus, non-persistent hotspots were the most frequent category. Spatiotemporal changes in hotspot location are associated with decreasing vegetation production and increasing water stress on road surroundings. This supports our hypothesis that a decline on overall vegetation productivity and increase of roadside water deficit, and the presumed lower abundance of prey, have a significant effect on the decrease of bat roadkills. To our knowledge, this is the first study demonstrating that freely available remote sensing data can be a powerful tool to quantify bat roadkill risk and assess its spatiotemporal dynamics.

ObjectiveThis study aimed to evaluate the spatiotemporal distribution of patients with hepatitis C virus (HCV) and the factors influencing this distribution in Jiangsu Province, China, from 2011 to 2020.MethodsThe incidence of reported HCV in Jiangsu Province from 2011 to 2020 was obtained from the Chinese Information System for Disease Control and Prevention (CISDCP). R and GeoDa software were used to visualize the spatiotemporal distribution and the spatial autocorrelation of HCV. A Bayesian spatiotemporal model was constructed to explore the spatiotemporal distribution of HCV in Jiangsu Province and to further analyze the factors related to HCV.ResultsA total of 31,778 HCV patients were registered in Jiangsu Province. The registered incidence rate of HCV increased from 2.60/100,000 people in 2011 to 4.96/100,000 people in 2020, an increase of 190.77%. Moran's I ranged from 0.099 to 0.354 (P < 0.05) from 2011 to 2019, indicating a positive spatial correlation overall. The relative risk (RR) of the urbanization rate, the most important factor affecting the spread of HCV in Jiangsu Province, was 1.254 (95% confidence interval: 1.141–1.376), while other factors had no significance.ConclusionThe reported HCV incidence rate integrally increased in the whole Jiangsu Province, whereas the spatial aggregation of HCV incidence was gradually weakening. Our study highlighted the importance of health education for the floating population and reasonable allocation of medical resources in the future health work.

The Cordillera Administrative Region (CAR) in the Philippines is among the last forest frontiers in the country and is also home to 13 major watersheds in Northern Luzon that supply irrigation and hydroelectricity to other regions. However, it is faced with the deterioration of the quality of its watersheds due to forest loss driven mainly by agricultural expansion and illegal logging. Thus, this study was conducted to analyze the spatial and temporal patterns of forest loss that could serve as a basis for policy decisions. Also, this paper determined the strength of relationships using Pearson’s correlation coefficient (r) between forest loss and seven independent variables, which includes forest cover, agricultural areas, built-up, road network, and socio-economic data. This study utilized the Hansen Global Forest Change (HGFC), a Landsat-derived dataset from 2001 to 2019. Results revealed that 70,925 hectares (ha) of forest loss were detected with an annual deforestation rate of 3,74...