Data files for "Eccles KM, Karmaus AL, Kleinstreuer NC, Parham F, Rider CV, Wambaugh JF, Messier KP. A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target. Sci Total Environ. 2023 Jan 10;855:158905. doi: 10.1016/j.scitotenv.2022.158905. Epub 2022 Sep 21. PMID: 36152849"

The Geospatial Fabric version 1.1 (GFv1.1 or v1_1) is a dataset of spatial modeling units covering the conterminous United States (CONUS) and most major river basins that flow in from Canada. The GFv1.1 is an update to the original Geospatial Fabric (GFv1, Viger and Bock, 2014) for the National Hydrologic Modeling (NHM). Analogous to the GFv1, the GFv1.1 described here includes the following vector feature classes: points of interest (POIs_v1_1), a stream network (nsegment_v1_1), and hydrologic response units (nhru_v1_1), with several additional ancillary tables. These data are contained within the Environmental Systems Research Institute (ESRI) geodatabase format (GFv1.1.gdb).

: RapidLiq is free, simple-to-use Windows software. The only input is a ground-motion raster, downloadable minutes after an earthquake or available for countless future scenarios. The software predicts the probability of liquefaction-induced ground failure at high spatial resolution over the large regional extents impacted by earthquakes. Use of the software is described in a detailed user manual.

Climatic, sociological and environmental conditions are known to affect the spatial distribution of malaria vectors and disease transmission. Intensive use of insecticides in the agricultural and public health sectors exerts a strong selective pressure on resistance genes in malaria vectors. Spatio-temporal models of favorable conditions for Anopheles species’ presence were developed to estimate the probability of presence of malaria vectors and insecticide resistance in Lao PDR. These models were based on environmental and meteorological conditions, and demographic factors. GIS software was used to build and manage a spatial database with data collected from various geographic information providers. GIS was also used to build and run the models. Results showed that potential insecticide use and therefore the probability of resistance to insecticide is greater in the southwestern part of the country, specifically in Champasack province and where malaria incidence is already known to be high. These findings can help national authorities to implement targeted and effective vector control strategies for malaria prevention and elimination among populations most at risk. Results can also be used to focus the insecticide resistance surveillance in Anopheles mosquito populations in more restricted area, reducing the area of surveys, and making the implementation of surveillance system for Anopheles mosquito insecticide resistance possible.

Discover the booming Spatial Analysis Software market! Our in-depth analysis reveals a $5 billion market projected to reach $12.4 billion by 2033, driven by AI, cloud computing, and rising geospatial data. Learn about key trends, regional insights, and leading companies shaping this dynamic sector.

The booming remote sensing software market is projected to reach $5 billion by 2025, growing at a CAGR of 8% until 2033. Driven by advancements in sensor technology and cloud computing, this market caters to various sectors, including environmental monitoring, urban planning, and defense. Learn about key market trends and leading players.

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. For four of the map units – 3-SDF, 4-SDAF, 27-POHV, and 31-LBY – modeling using GIS principles was also employed. Modeling involves using environmental conditions of a map unit, such as elevation, slope, and aspect, which were determined by the field-collected ecological data. Data satisfying these conditions were obtained from ancillary data sources, such as USGS DEM data. These data were fed into a model that will result in locations (pixels) where all the desired conditions exist. For example, if a certain map unit was a shrubland that predominantly occurs above 8000 feet, on slopes of 3-10%, and on west-facing aspects, the correctly-constructed model will output only locations where this combination of conditions will be found. The resulting areas were then examined manually with the traditional photo interpretation process to confirm that they indeed could be accepted as that map unit. If photo interpretation determines that the areas were not acceptable, then they were changed to a more appropriate map unit.

Public Safety GIS Software Market Outlook

According to our latest research, the Public Safety GIS Software market size reached USD 2.86 billion in 2024, demonstrating robust adoption across emergency response and law enforcement sectors worldwide. The market is experiencing significant momentum, driven by the imperative need for real-time geospatial intelligence in public safety operations. With a projected compound annual growth rate (CAGR) of 11.2% from 2025 to 2033, the market is forecasted to reach USD 7.36 billion by 2033. This remarkable expansion is fueled by increasing investments in digital transformation of public safety infrastructure, rising incidences of natural disasters, and the growing complexity of urban environments necessitating advanced GIS solutions.

One of the primary growth factors propelling the Public Safety GIS Software market is the escalating frequency and severity of natural and man-made disasters worldwide. As cities become more densely populated and climate change intensifies, the demand for sophisticated geospatial analysis tools to support disaster preparedness and response has surged. Public safety agencies are leveraging GIS software to map hazard zones, optimize evacuation routes, and allocate resources effectively in real time. This technological evolution enables a more proactive and coordinated approach to emergency management, significantly reducing response times and enhancing situational awareness for first responders. The integration of GIS with IoT sensors, drones, and real-time data feeds further amplifies its value, providing a comprehensive operational picture that is critical for life-saving interventions.

Another significant driver is the increasing adoption of cloud-based deployment models, which are transforming the accessibility and scalability of GIS solutions for public safety. Cloud-based GIS platforms offer cost-effective, flexible, and secure options for agencies of all sizes, enabling seamless data sharing and collaboration across departments and jurisdictions. This democratization of geospatial intelligence is particularly beneficial for smaller municipalities and rural areas that previously lacked the resources for advanced on-premises systems. Enhanced interoperability with other public safety technologies, such as computer-aided dispatch (CAD) and records management systems (RMS), is further accelerating market growth. The ongoing digitalization of public safety processes, coupled with supportive government policies and funding initiatives, is expected to sustain the market’s upward trajectory over the forecast period.

The growing emphasis on crime analysis and predictive policing is also contributing to the expansion of the Public Safety GIS Software market. Law enforcement agencies are increasingly utilizing GIS-driven analytics to identify crime hotspots, forecast criminal activity, and allocate patrol resources more efficiently. The ability to visualize and analyze spatial patterns of crime in conjunction with demographic, socioeconomic, and environmental data enhances strategic decision-making and supports community-oriented policing initiatives. Furthermore, advancements in artificial intelligence and machine learning are enabling more sophisticated geospatial modeling, empowering agencies to anticipate threats and deploy preventive measures proactively. This shift toward data-driven policing is fostering greater trust and transparency between law enforcement and the communities they serve.

From a regional perspective, North America continues to lead the global Public Safety GIS Software market in terms of both adoption and innovation. The presence of technologically advanced public safety infrastructure, high levels of government investment, and a strong ecosystem of GIS vendors and solution providers have made the region a pioneer in integrating geospatial intelligence into emergency response workflows. Europe is also witnessing substantial growth, driven by stringent regulatory mandates and cross-border collaboration on disaster management. Meanwhile, Asia Pacific is emerging as a high-growth market, fueled by rapid urbanization, increasing vulnerability to natural disasters, and ambitious smart city initiatives. Latin America and the Middle East & Africa are gradually catching up, with growing recognition of the value of GIS in enhancing public safety outcomes.

Component Analysis

The Component segment of the Pu

Well-established conservation planning principles and techniques framed by geodesign were used to assess the restorability of areas that historically supported coastal wetlands along the U.S. shore of western Lake Erie. The resulting analysis supported planning efforts to identify, prioritize, and track wetland restoration opportunity and investment in the region. To accomplish this, publicly available data, criteria derived from the regional managers and local stakeholders, and geospatial analysis were used to form an ecological model for spatial prioritization (Western Lake Erie Restoration Assessmente (WLERA)). Within the 192,618 ha study area that was bounded by the mouths of the Detroit River, MI to the north and the Black River, OH to the south, the model identified and prioritized 6,600 hectares of land most suitable for coastal wetland habitat restoration.

Well-established conservation planning principles and techniques framed by geodesign were used to assess the restorability of areas that historically supported coastal wetlands along the U.S. shore of Saginaw Bay. The resulting analysis supported planning efforts to identify, prioritize, and track wetland restoration opportunity and investment in the region. To accomplish this, publicly available data, criteria derived from the regional managers and local stakeholders, and geospatial analysis were used to form an ecological model for spatial prioritization.

This dataset is the output of a python script/ArcGIS model that identifes dikes as having a difference in elevation above a certain threshold. If the elevation difference was below a certain threshold the area was not considered a dike; however, if the difference in elevation between two points was significantly high then the area was marked as a dike. Areas continuous with eachother were considered part of the same dike. Post processing occured. Users examined the data output, comparing the proposed dike locations to aerial imagery, flowline data, and the DEM. Dikes that appeared to be false positives were deleted from the data set.

Partly due to global climate change, extreme weather and natural hazards have increased dramatically during the recent decades. Those sudden environmental changes often cause significant impacts on the living species on the planet via directly affecting the population structures or indirectly causing habitat loss or fragmentations. In August - October 2020, tremendous mortalities of avian species were reported in the western and central US, likely resulting from winter storms and wildfires based on previous evidence. However, the differences of how different species might respond to the environmental changes were still poorly understood. In this study, we focused on three species that have been recorded with the highest death observations collected by citizen scientists (i.e., Wilson’s warbler, barn owl, and common murre) and employed the random forest model to disentangle their responses to the two environmental changes. We found the mortalities of Wilson’s warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average of maximum daily snowfalls. Barn owl responded to both wildfire effects and winter storms but with more deaths identified in places with high wildfire-induced air pollution. Both events had mild effects on common murre. Mortalities of common murre may be related to high water temperature. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations.

This record serves as a specification of a pre-configured subset of the tables in the Geospatial Fabric Attribute Tables for PRMS (Preliminary)data set(https://www.sciencebase.gov/catalog/folder/537a3ec1e4b0efa8af08150a). This subset provides an initial set of values corresponding to many of the spatial parameters needed for application of the USGS PRMS watershed model. This subset should be considered provisional. Users should carry out quality assurance and calibration according to their needs. soils parameters: http://dx.doi.org/doi:10.5066/F7RX9937 land cover parameters: http://dx.doi.org/doi:10.5066/F7N58JD8 topographic parameters: http://dx.doi.org/doi:10.5066/F70C4ST6 geography parameters: http://dx.doi.org/doi:10.5066/F7HD7SPJ subsurface flux parameters: http://dx.doi.org/doi:10.5066/F7CN71XR surface depression parameters: http://dx.doi.org/doi:10.5066/F7445JHG

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

Well-established conservation planning principles and techniques framed by geodesign were used to assess the restorability of areas that historically supported coastal wetlands along the U.S. shore of the connecting rivers (Detroit River and St. Clair River). The resulting analysis supported planning efforts to identify, prioritize, and track wetland restoration opportunity and investment in the region. To accomplish this, publicly available data, criteria derived from the regional managers and local stakeholders, and geospatial analysis were used to form an ecological model for spatial prioritization.

The Surface Earth System Analysis and Modeling Environment (SESAME) Human-Earth Atlas includes hundreds of variables capturing both human and non-human aspects of the Earth system on two common spatial grids of 1- and 0.25-degree resolution. The Atlas is structured by common spheres, and many variables resolve changes over time. Many of the national-level tabular human system variables are downscaled to spatial grids using dasymetric mapping, accounting for country boundary changes over time. An associated software toolbox allows users to add raster, point, line, polygon, and tabular datasets, transforming them onto a standardized spatial grid at the desired resolution as well as to work conveniently with jurisdictional (e.g. country) data.

File Description: atlas: Contains netCDF files at 1-degree resolution in netCDF format. atlas_p25: Contains selected netCDF files at 0.25-degree resolution. genscripts: Original Jupyter notebook scripts used to generate the atlas. SESAME_Atlas_Documentation_v1.pdf: Documentation file for the SESAME Human-Earth Atlas. SESAME_Human-Earth_Atlas_v1.xlsx: Comprehensive summary and documentation for the SESAME Human-Earth Atlas, including details on pre- and post-processing steps.

The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 2 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.0: http://worldclim.org/version2 Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals.The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.

This dataset contains a 30-year rolling average of annual average minimum and maximum temperatures from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099.The year identified is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are:

HadGEM2-ES (warm/dry),CanESM2 (average), CNRM-CM5 (cooler/wetter), and MIROC5 the model least like the others to improve coverage of the range of outcomes.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

So much metadata. So little time.

IN the world of met data, gridded datasets at continental or regional scales which need to be corrected to seasonal averages based on observations. e.g. match average 30 January precipitation and compare that to your gridded product at that location.
If temperature is not a good fit, add or subtract the monthly difference to shift the monthly average values to the monthly average of the dataset you trust. It addresses scale mismatch issues of downscaling general circuluatoin models (100 km) to a local monthly average at a 5 km grid. This structure of the bias correction can be applied to future datasets downscaled with the same structure as the historic gridded products.