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.

There has been general trend to shift the location of warehouses and distribution facilities away from consumer markets (logistics sprawl) in Southern California. This shift has a negative impact on cost and the environment because freight vehicles have to travel longer to reach their destinations. However, during the last decade, this trend has not continued at the same pace, and it may have even reversed. Two main factors potentially explain this phenomenon: the 2008-2009 economic slow-down, and an increase in e-commerce activity. E-commerce impacts are relevant for freight planning because of the changes in vehicle size to distribute smaller shipments at higher frequencies, consumer proximity requirements to improve delivery times, and the redistribution of freight activity and supply chain configurations.

This research conducted spatio-temporal analyses of Caltrans Weigh-in-Motion data to validate some of these assumptions. There is evidence that during 2003-2015, the short-haul ...

An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross-unit averages to extract common factors (viewed as a source of strong cross-sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio-temporal model for the de-factored real house price changes and obtain significant evidence of spatial connections, both positive and negative.

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.

Supplementary tables and figure of paper Spatio-temporal analysis for detection of pre-symptomatic shape changes in neuro-degenerative diseases: initial application to the GENFI cohort, for testing different number of clusters of the spatio-temporal regression. The supplementary materials shows results for 2 4 6 8 12 14 and 16 clusters. The 10 clusters analysis is in the paper.

Yearly citation counts for the publication titled "Spatial, temporal, and spatiotemporal analysis of malaria in Hubei Province, China from 2004–2011".

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.

Concerns over free riding in NATO are widespread. An intuitive approach to analyzing free riding is treating it as a systematic pattern of spatial interdependence between the allies: How does a NATO member’s defense spending react to changes in its allies’ military expenditures? While recent work has found statistically significant free riding (negative spatial interdependence in the outcomes), it suffers from important limitations. First, this research does not adequately account for temporal dependence. Second, it does not quantify the effect of interest. Accounting directly for temporal dependence provides a meaningfully distinct perspective on the within-alliance dynamics, demonstrating that the spatiotemporal effect of free riding is, in fact, more substantial than its short-run effect, challenging inferences of static spatial models. We discuss the relevant practical and theoretical implications.

The nutrient data reconstructed in grid format using Spatiotemporal Kriging. It is stored in '.csv' and '.RData' formats, and includes the validation results of the observation data using 10-fold cross-validation.

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.

This dataset was exctracted from time-lapse microscopy data for spatio-temporal analysis of Cancer Stem Cells in a cell population. Live cells were imaged for 5 days in a atmosphere and temperature controlled top stage incubator. The cell lines were SUM159PT or MDAMB231. The pALDH1a1-mNeptune reporter was used as CSC marker. Highly fluorescent cells are considered as CSC while low fluorescence signal indicate differentiated cell. see Bidan, N., Bailleul‐Dubois, J., Duval, J., Winter, M., Denoulet, M., Hannebicque, K., ... & Lagadec, C. (2019). Transcriptomic analysis of breast cancer stem cells and development of a pALDH1A1: mNeptune reporter system for live tracking. Proteomics, 19(21-22), 1800454. Cells were stained with Hoechst to allow segmenatation and tracking. The proposed dataset comprises : 1) RAW DATA : positions and fluorescent state of all single cell in 7mmx7mm field of view in both .csv and .mat files 2) intermediate analysis in matlab (including analysis scripts) 3) FIGURES : for spatio-temporal analysis of Cancer Stem Cell plasticity in both .csv and .mat files

Cumulative Spatial Impact Layers (CSIL) is a GIS-based tool that summarizes spatio-temporal datasets based on overlapping features and attributes. Applying a recursive quadtree method and multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets simultaneously by calculating data, record, or attribute density. The CSIL tool was designed based on the original approach (Bauer et al. 2015) and more information on the tool overall along with applications can be found in Romeo et al. (in review). Providing an efficient summarization of disparate geospatial data, CSIL bridges the gap between understanding data and analysis.

Bauer, J. R., Nelson, J., Romeo, L., Eynard, J., Sim, L., Halama, J., Rose, K., & Graham, J. (2015). A spatio-temporal approach to analyze broad risks and potential impacts associated with uncontrolled hydrocarbon release events in the offshore Gulf of Mexico (NETL-TRS-2-2015 EPAct Technical Report Series). U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV; p 60.

Romeo, L., Nelson, J., Wingo, P., Bauer, J.R., Justman, D., & Rose, K. (in review). Cumulative Spatial Impact Layers: A Novel Multivariate Spatio-Temporal Analytical Summarization Tool. Transactions in GIS.

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

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 8th, 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 24th, 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 24th 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.

This dataset is part of a study investigating brain development in infants using a 0.35T low-field MRI system, specifically optimized for infants. The goal is to enhance our understanding of early brain development and identify potential neurological conditions that may affect long-term cognitive and behavioral outcomes.Participants were recruited from the Children’s Hospital of Zhejiang University School of Medicine. They underwent the 0.35T MRI as part of a differential diagnosis to rule out cerebral complications. Sedation was used during imaging for clinical assessment purposes. This dataset includes scans of 53 female and 47 male Chinese infants without visible abnormalities, conducted between November 9, 2022, and September 28, 2023. The subjects ranged in age from 1 to 70 days post delivery(mean age: 35.66 ± 19.80 days). The dataset includes high-resolution 2D axial T2-weighted images acquired using an optimized fast spin echo technique.Additionally, the dataset includes manual brain mask segmentations for each image, as well as whole-brain segmentations based on an atlas-based method using the MCRIB template. All data are structured according to the BIDS specification. The participants’ age and gender information is provided in the accompanying .tsv file.

Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate Abstract Climate change has well-documented, yet variable, influences on the annual movements of migratory birds. The effects of climate change on fall migration remains understudied compared to spring, but appears to be less consistent among species, regions, and years. Changes in the pattern and timing of waterfowl migration in particular may result in cascading effects on ecosystem function, and socioeconomic and cultural outcomes. We investigated changes in the migration of 15 waterfowl species along a major flyway corridor of continental importance in northeastern North America using 43 years of community-science data. We built spatially- and temporally-explicit hierarchical generative additive models for each species and demonstrated that climate, specifically the interaction between minimum temperature and precipitation, significantly influences migration phenology for most species. Certain species’ migratory movements responded to specific temperature thresholds (climate migrants) and others reacted more to the interaction of temperature and precipitation (extreme event migrants). There are already significant changes in the fall migration phenology of common waterfowl species with high ecological and economic importance, which may simply increase in the context of a changing climate. If not addressed, climate change could induce mismatches in management, regulations, and population surveys which would negatively impact the hunting industry. Our findings highlight the importance of considering species-specific spatiotemporal scales of effect on climate on migration and our methods can be widely adapted to quantify and forecast climate-driven changes in wildlife migration. 1. Author List: Barbara Frei, Amelia R. Cox, Ana Morales, and Christian Roy 2. Date of data collection (single date, range, approximate date): 1970-2012 3. Geographic location of data collection: Quebec, Canada 4. Information about funding sources that supported the collection of the data: Environment and Climate Change Canada ## SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 2. Links to publications that cite or use the data: Barbara Frei, Amelia R. Cox, Ana Morales, and Christian Roy 2024. Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate. Journal of Animal Ecology. 1. Links to other publicly accessible locations of the data: None 2. Links/relationships to ancillary data sets: None 3. Was data derived from another source? No A. If yes, list source(s): NA 4. Recommended citation for this dataset: Barbara Frei, Amelia R. Cox, Ana Morales, and Christian Roy. 2024. Data from: Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate. Journal of Animal Ecology. Dryad Digital Repository. 1. Description of data source Raw bird observation data is hosted by QuébecOiseaux and can only be requested directly. Raw climatic data is hosted by Natural Resources Canada and can only be requested directly. To run the climate models, rank models, and perform all other analyses and create the figures we have included the transformed and joined data file "Fall DOY EPOQ with pendads.csv" ## CODE OVERVIEW 1. File list for code All analysis was conducted in R version 4.2.3 (2023-03-15). A) JAE_code.R : This code runs 24 GAM models for each species, ranks the models, creates the predicted abundance plots, extracts the peak migration and passage period for each species, runs LM to identify changes in peak migration data and passage period length over time, and creates plots to visualize the findings. 1. Relationship between code files, if important: None. ## DATA OVERVIEW 1. File list for data A) Fall DOY EPOQ with pendads.csv 1. Relationship between files, if important: None 2. Additional related data collected that was not included in the current data package: None 3. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA ######################################################################### DATA-SPECIFIC INFORMATION FOR: Fall DOY EPOQ with pendads.csv 1. Number of variables: 72 2. Number of cases/rows: 548,744 3. Variable List: * Species - Four letter species code (ABDU - American Black Duck, AMWI - American Wigeon, BAGO - Barrow's Goldeneye, BWTE - Blue-winged Tea, CANG - Canada Goose, COEI - Common Eider, COGO - Common Goldeneye, GWTE - Green-winged Teal, HOME - Hooded Merganser, LTDU - Long-tailed Duck, MALL - Mallard, NOPI - Northern Pintail, RNDU - Ring-necked Duck, SNGO - Snow Goose, SUSC - Surf Scoter) * Year - Year of observation * DOY - Day of Year (1-365) * Period - This vari...

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.

fractures

The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.

gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;

data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);

Codes of files indicate e.g.:

"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;

"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;

The dataset is described in detail in the following publication:

Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021. Submitted to Parasites & Vectors (in review).

The model summary shows:

Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE)

Deviance Residuals: Min 1Q Median 3Q Max
-1.4749 -0.0557 -0.0471 -0.0430 3.7611

Coefficients: Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 ***

classif.glmnet 140.24208 5.05375 27.750 < 2e-16 ***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 170604 on 7303013 degrees of freedom

Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579

Number of Fisher Scoring iterations: 9

Acknowledgements: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: 10.15454/1.5572390655343293E12) for providing computing resources. We are also grateful for the help and support provided by SAVSNET team members Bethaney Brant, Susan Bolan and Steven Smyth. This study was funded mainly by a grant from the Biotechnology and Biological Sciences Research Council, BB/NO19547/1 and British Small Animal Veterinary Association (BSAVA). The research was partly funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at the University of Liverpool in partnership with Public Health England (PHE) and Liverpool School of Tropical Medicine (LSTM). This work has been partially funded by the “Monitoring outbreak events for disease surveillance in a data science context" (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (https://mood-h2020.eu/). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.

Abstract

The dataset was compiled by the Geological and Bioregional Assessment Program from source data referenced within the dataset and/or metadata. The Geological and Bioregional Assessment Program seeks to understand the potential impacts associated with developing unconventional energy resources in key Australian basins. As a potential source of land use change, future seismic data acquisition may cause cumulative land change that may need to be quantified for such a holistic impact assessment. Given the extent of seismic exploration for conventional resources within the Cooper Basin, existing data could provide insight into survey parameters that may be representative of future surveys in the region. We perform an analysis of the historical seismic survey occurrence and acquisition parameters for the existing data in the Cooper GBA region. The spatio-temporal data analysis is used to derive a simple model that could be used as a forecasting tool for seismic survey parameters of interest (e.g. line kilometers) in a forward looking risk assessment.

Attribution

Geological and Bioregional Assessment Program

History

This presents a data analysis of the datasets: \r •\tSeismic survey 3D - QLD\r •\tSeismic survey 2D - QLD\r •\tSouth Australian 2D Seismic Lines \r •\tSouth Australia 3D Seismic Survey Areas\r •\tPetroleum well locations – Queensland\r •\tPetroleum well locations - South Australia