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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 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

Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time. Two tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.

These data describe the spatio-temporal distribution of dabbling duck species across the continental United States during four biologically relevant seasons. This dataset contains two types of distribution models: (1) probability of presence, and (2) abundance. The model type, species, and season depicted in a raster are defined in the file name. File names begin with either abun (indicating that it is an abundance model) or prob (indicating a probability of occurrence model). Following model type is species, for which there are 10 provided: ABDU (American Black Duck), AMEW (American Wigeon), BWTE (Blue-winged Teal), CITE (Cinnamon Teal), GADW (Gadwall), AGWT (Green-winged Teal), MALL (Mallard), MODU (Mottled Duck), NOPI (Northern Pintail), and NSHO (Northern Shoveler). Finally, season is indicated as either Winter, Spring, Summer, or Fall.

Digital tracking technologies have considerably increased the amount and quality of animal trajectories, enabling the study of habitat use and habitat selection at a fine spatial and temporal scale. However, current approaches do not yet explicitly account for a key aspect of habitat use, namely the sequential variation in the use of different habitat features. To overcome this limitation, we propose a tree-based approach that makes use of sequence analysis methods, derived from molecular biology, to explore and identify ecologically relevant sequential patterns in habitat use by animals. We applied this approach to ecological data consisting of simulated and real trajectories from a roe deer population (Capreolus capreolus), expressed as ordered sequences of habitat use. We show that our approach effectively captured spatio-temporal patterns of sequential habitat use by roe deer. In our case study, individual sequences were clustered according to the sequential use of the elevation gradient (first order) and of open/closed habitats (second order). We provided evidence for several behavioural processes, such as migration and daily alternating habitat use. Some unexpected patterns, such as homogeneous sequences of use of open habitat, could also be identified. Our findings advocate the importance of dealing with the sequential nature of movement data. Approaches based on sequence analysis methods are particularly useful and effective since they allow exploring temporal patterns of habitat use in a synthetic and visually captive manner. The proposed approach represents a useful and effective way to classify individual movement behaviour across populations and species. Ultimately, this method can be applied to explore the temporal scale of ecological processes based on movement.

Visual Analytics for Spatio-Temporal Data

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.

This data set allows to systematically evaluate approaches for the task of identifying extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.

Magnetic field and electric field data products from the Cluster Spatio-Temporal Analysis of Field Fluctuations (STAFF) available from the Cluster Active Archive include: Level 2 calibrated data -- STAFF Waveform Analyzer (STAFF-SC) Complex Spectra of the magnetic field in GSE coordinates (normal mode, up to 12.5 Hz and burst mode up to 225 Hz) with a time resolution of 10 second and a frequency resolution of 0.1 Hz; STAFF-SC Calibrated Magnetic Field WaveForm in ISR2 coordinates (normal mode, 25 Hz sampling and burst mode, 450 Hz sampling); STAFF Spectrum Analyzer (STAFF-SA) Spectral Matrix (8 Hz to 4 KHz) which is the cross-product of the magnetic and electric fileds values computed on-board the spacecraft with time resolutions of 4 seconds and 1 second; and Power Spectral Density (8 Hz to 4 KHz) with time resolutions of 1, 0.125 and 0.25 second. Level 3 value-added products include STAFF-SA Polarization and Propagation Parameters (8 Hz to 4 KHz). In the base mode in normal bit rate the auto-spectra are averaged over 1 s, and the complete 25-component matrix is averaged over 4 s for five components. In high bit rate, only the two highest frequency bands are analyzed. In the fast modes, time resolution is 1 s for the cross-spectra, and either 0.125 s or 0.25 s for the auto-spectra. The physical units for magnetic field spectral power are nT^2 Hz^-1, and electric field spectral power, V^2 m^-2 Hz^-1. Ancillary data included preliminary spectra, uncalibrated waveform, caveats, and calibration information. For more details, see "The Cluster Active Archive: Studying the Earth's Space Plasma Environment", edited by Dr. Harri Laakso, Matthew G. T. T. Taylor, C. Philippe Escoubet, from which this information was obtained.

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.

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.

The proposed framework for analyzing medium dense crowd videos at various levels.

ABSTRACTOBJECTIVEDespite significant advancements in understanding risk factors and treatment strategies, ischemic heart disease (IHD) remains the leading cause of mortality worldwide, particularly within specific regions in Brazil where the disease is a burden. Therefore, the aim of this study was to estimate the risk of hospitalization and mortality from IHD in Paraná State, Brazil, using spatial analysis.METHODSThis is an ecological study based on secondary and historical mortality data obtained from the Brazilian Mortality Information System and Hospitalization Information System during 2010-2021 period, stratified by Health Regions in Paraná State. To assess the spatial patterns of the disease and identify relative risk (RR) areas, we constructed a risk model by Bayesian inference using the R-INLA and SpatialEpi packages in R software. RESULTSA total of 333,229 hospitalizations and 73,221 deaths with elevated RR of 27.42 of hospitalizations and 15.68 of mortality for IHD in small-sized municipalities. Hospitalization and death rates were higher in men and whites aged 40-59. Regions 2 and 6 had the highest hospitalization risks, while all other regions presented RR of death >1. The RR median of hospitalizations remained constant, but mortality median increased over time.CONCLUSION Areas with increased risk of hospitalization and mortality of IHD was found between small and medium municipalities in Parana State, Brazil. These results suggested that there is a deficit of care in health attention for IHD cases in small and medium-sized municipalities, which can be justified by a low distribution of healthcare resources.

Overview Datasets describing the time-varying properties of spatially embedded public transport networks. Networks Four public transport networks are provided. London Underground: Timetabled London Underground journeys for two days. Specifically, 2015-02-09 00:00 to 2015-02-11 00:00 (GMT).Paris Metro: Timetabled Paris Metro journeys for two days. Specifically, 2015-11-30 00:00 to 2015-12-02 00:00 (CET).New York Subway: Timetabled New York Subway journeys for two days. Specifically, 2015-11-30 00:00 to 2015-12-02 00:00 (EST). US Domestic Flights: Actual flights among 299 US airports over 10 days. Specifically, 2014-02-03 00:00 to 2014-02-13 00:00 (EST). This is derived from the Airline On-Time Performance data collected by the US Bureau of Transportation Statistics. Format Each dataset consits of two files. The "network" CSV file (_stnet.csv.gz) describes the time-evolution of the network's topology. This includes the time-varying transit speed between two stations. This file is in the LMF Aggregate Format, a non-proprietary format for multiplex networks. The "coordinates" CSV file (_coords.csv.gz) provides the geographic locations of each node. Additional information Additional detail specific to these transport networks is available here: http://mattjw.net/pub/stnets/

This submission contains 167 full moment tensor (MT) solutions for the seismicity observed two years prior and three years post start of injection activities. Also included are the azimuth and plunge angles for the three main stress directions sigma1, sigma 2 and sigma 3 at the Prati32 EGS demonstration site in the northwest Geysers geothermal reservoir. The data are divided into 15 time periods spanning a range of five years, including two years prior to start of injection until three years post start of injection activities.

Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world.

XLSX sheet with exposure data to be used for the practical Assignment in Aggregated exposure Pathway (AEP) development. The data set contains one xlsx file, with several data sheets:

README: contain informtion about the data set and columns.

EXPOSURE_DATA: Spatio-temporal exposure data (266 rows) from a 6-location monitoring study in Norway (2019)

SITES: Data for geopositioning of the the sample locations/Sites (SITE_CODE)

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The number of sequences by genotype, country and date of collection can be checked by filtering data in each column header. (XLSX)

This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled "Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 Data". Specifically, this package include the artifacts used to conduct spatial-temporal analysis with space time kernel density estimation (STKDE) using COVID-19 data, which should help readers to reproduce some of the analysis and learn about the methods that were conducted in the associated book chapter. ## What’s inside - A quick explanation of the components of the zip file * Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19.ipynb is a jupyter notebook for this project. It contains codes for preprocessing, space time kernel density estimation, postprocessing, and visualization. * data is a folder containing all data needed for the notebook * data/county.txt: US counties information and fip code from Natural Resources Conservation Service. * data/us-counties.txt: County-level COVID-19 data collected from New York Times COVID-19 github repository on August 9th, 2020. * data/covid_death.txt: COVID-19 death information derived after preprocessing step, preparing the input data for STKDE. Each record is if the following format (fips, spatial_x, spatial_y, date, number of death ). * data/stkdefinal.txt: result obtained by conducting STKDE. * wolfram_mathmatica is a folder for 3D visulization code. * wolfram_mathmatica/Visualization.nb: code for visulization of STKDE result via weolfram mathmatica. * img is a folder for figures. * img/above.png: result of 3-D visulization result, above view. * img/side.png: result of 3-D visulization, side view.