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 hydrologic response units (HRUs) and stream segments available here are for an application of the Precipitation Runoff Modeling System (PRMS) in the southeastern United States by LaFontaine and others (2019). Geographic Information System (GIS) files for the HRUs and stream segments are provided as shapefiles with attribute hru_id_1 identifying the HRU numbering convention used in the PRMS model and seg_id_gcp identifying the stream segment numbering convention used in the PRMS model. This GIS files represent the watershed area for an approximately 1.16 million square kilometer area of the southeastern United States. A total of 20,251 HRUs and 10,742 stream segments are used in this modeling application. LaFontaine, J.H., Hart, R.M., Hay, L.E., Farmer, W.H., Bock, A.R., Viger, R.J., Markstrom, S.L., Regan, R.S., and Driscoll, J.M., 2019, Simulation of Water Availability in the Southeastern United States for Historical and Potential Future Climate and Land-Cover Conditions: U.S. Geological Survey Scientific Investigations Report, 2019-5039, 83 p., https://doi.org/10.3133/sir20195039.

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.

The global spatial analysis software market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, currently valued at approximately $5 billion (estimated based on typical market sizes for similar software segments), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The rising availability of geospatial data, coupled with advancements in cloud computing and artificial intelligence (AI), is enabling more sophisticated and accessible spatial analysis capabilities. Industries such as urban planning, environmental management, logistics, and retail are leveraging these advancements for optimized resource allocation, improved decision-making, and enhanced operational efficiency. The integration of spatial analysis tools into Geographic Information Systems (GIS) platforms further enhances market penetration, streamlining workflows and facilitating comprehensive data analysis. Demand for predictive modeling and location intelligence solutions is also a major growth driver, particularly among businesses seeking to understand customer behavior, optimize supply chains, and mitigate risks. However, market growth is not without its challenges. The high cost of implementation and maintenance of advanced spatial analysis software can be a barrier to entry for smaller organizations. Furthermore, the complexity of these tools requires skilled professionals, leading to a shortage of trained personnel in some regions. Despite these restraints, the long-term outlook for the spatial analysis software market remains positive, with continued innovation and wider adoption expected across various applications and geographic locations. Specific segments like those focused on 3D spatial analysis and real-time data processing are anticipated to experience particularly strong growth in the coming years. The increasing prevalence of big data and the need for effective data visualization are key elements underpinning this dynamic market.

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).

These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology.

The following data sources were used in this modeling effort:

National Hydrography Dataset (NHD): United States Geological Survey (USGS)

Upstate NY Lakes, ponds, streams, rivers, springs, and wells

Critical Environmental Areas in New York State: New York State Department of Environmental Conservation

Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)

National Land Cover Dataset (NLCD): United States Geological Survey (USGS)

National Land Cover Database classification schemes based primarily on Landsat data (2011)

Elevation Data: United States Geological Survey (USGS)

Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000.

Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database

Rural and urban highways for New York

Other references

Bureau, U.S. Census., American community survey 5-year estimates. 2017.

EPA, Toxics Resource Inventory. 2019

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The “exposome” is defined as “the totality of human environmental exposures from conception onward, complementing the genome” and its holistic approach may advance understanding of disease etiology. We aimed to describe the correlation structure of the exposome during pregnancy to better understand the relationships between and within families of exposure and to develop analytical tools appropriate to exposome data. Estimates on 81 environmental exposures of current health concern were obtained for 728 women enrolled in The INMA (INfancia y Medio Ambiente) birth cohort, in Sabadell, Spain, using biomonitoring, geospatial modeling, remote sensors, and questionnaires. Pair-wise Pearson’s and polychoric correlations were calculated and principal components were derived. The median absolute correlation across all exposures was 0.06 (5th–95th centiles, 0.01–0.54). There were strong levels of correlation within families of exposure (median = 0.45, 5th–95th centiles, 0.07–0.85). Nine exposures (11%) had a correlation higher than 0.5 with at least one exposure outside their exposure family. Effectively all the variance in the data set (99.5%) was explained by 40 principal components. Future exposome studies should interpret exposure effects in light of their correlations to other exposures. The weak to moderate correlation observed between exposure families will permit adjustment for confounding in future exposome studies.

The remote sensing software market is experiencing robust growth, driven by increasing demand for geospatial data across various sectors. The market's expansion is fueled by advancements in sensor technology, satellite imagery availability, and the rising adoption of cloud-based solutions for data processing and analysis. Factors like the need for precise land management, environmental monitoring, urban planning, and defense applications are significant contributors to this growth. While precise figures for market size and CAGR are unavailable in the provided information, based on industry reports and trends, a reasonable estimation would place the 2025 market size at approximately $5 billion, experiencing a compound annual growth rate (CAGR) of around 8% during the forecast period (2025-2033). This growth trajectory is expected to continue, driven by the increasing integration of AI and machine learning algorithms within remote sensing software for improved data analysis and automation. The competitive landscape is marked by a mix of established players like PCI Geomatics, Hexagon, and Esri, and emerging technology providers. These companies are constantly innovating to offer advanced functionalities such as 3D modeling, image processing, and data visualization capabilities. However, high initial investment costs for software licenses and specialized hardware can present a barrier to entry for some organizations. Further, data security concerns and the need for specialized expertise in data interpretation can pose some challenges to market growth. Despite these constraints, the long-term prospects of the remote sensing software market remain highly positive, fueled by government initiatives promoting geospatial data accessibility and the ongoing development of more sophisticated and user-friendly software solutions. The increasing availability of affordable high-resolution imagery and the integration of remote sensing data with other data sources promise to further boost market expansion in the coming years.

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 Spatial Analysis Software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the expanding use of drones and other data acquisition technologies for precise geographic data collection, and the rising demand for advanced analytics across diverse sectors. The market's expansion is fueled by the need for efficient geospatial data processing and interpretation in applications such as urban planning, infrastructure development, environmental monitoring, and precision agriculture. Key trends include the integration of Artificial Intelligence (AI) and Machine Learning (ML) for automating analysis and improving accuracy, the proliferation of readily available satellite imagery and sensor data, and the growing adoption of 3D modeling and visualization techniques. While data security concerns and the high initial investment costs for advanced software solutions pose some restraints, the overall market outlook remains positive, with a projected compound annual growth rate (CAGR) exceeding 10% (a reasonable estimate based on the rapid technological advancements and market penetration observed in related sectors). This growth is expected to be particularly strong in the North American and Asia-Pacific regions, driven by substantial government investments in infrastructure projects and burgeoning private sector adoption. The segmentation by application (architecture, engineering, and other sectors) reflects the versatility of spatial analysis software, enabling its use across various industries. Similarly, the choice between cloud-based and locally deployed solutions caters to specific organizational needs and technical capabilities. The competitive landscape is characterized by both established players and emerging technology companies, showcasing the dynamic nature of the market. Major players like Autodesk, Bentley Systems, and Trimble are leveraging their existing portfolios to integrate advanced spatial analysis capabilities, while smaller companies are focusing on niche applications and innovative analytical techniques. The ongoing advancements in both hardware and software, coupled with increasing data availability and affordability, are set to further fuel the market's growth in the coming years. The historical period (2019-2024) likely witnessed moderate growth as the market matured, laying the foundation for the accelerated expansion expected during the forecast period (2025-2033). Continued innovation and industry convergence will be key drivers shaping the future trajectory of the Spatial Analysis Software market.

800+ GIS Engineers with 25+ years of experience in geospatial, We provide following as Advance Geospatial Services:

Analytics (AI) Change detection Feature extraction Road assets inventory Utility assets inventory Map data production Geodatabase generation Map data Processing /Classifications
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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.

3D Geospatial Technologies Market Outlook

The global market size of 3D Geospatial Technologies was valued at approximately USD 17.5 billion in 2023 and is projected to reach around USD 40.3 billion by 2032, growing at a CAGR of 9.6% from 2024 to 2032. This substantial growth is driven by the increasing adoption of advanced geospatial solutions across various industries, the rise in smart city initiatives, and the increasing need for efficient and accurate geographical data.

The growth of the 3D geospatial technologies market is significantly influenced by the rising need for advanced mapping and modeling solutions in urban planning and infrastructure development. With rapid urbanization and the expansion of smart city projects, there is a growing demand for precise and detailed geographical data to support efficient planning and management. The integration of 3D geospatial technologies enables city planners and developers to visualize, analyze, and manage urban spaces more effectively, leading to better decision-making and optimized resource utilization. Furthermore, the deployment of these technologies in monitoring and managing utilities, traffic, and environmental assets further fuels market growth.

Another critical factor driving the market is the increasing utilization of 3D geospatial technologies in disaster management and emergency response. Natural disasters such as floods, earthquakes, and hurricanes have highlighted the importance of having accurate and real-time geographical information for effective disaster mitigation and response strategies. 3D geospatial technologies provide detailed topographical data and real-time visualization capabilities, enabling authorities to better predict, prepare for, and respond to natural calamities. The ability to model and simulate disaster scenarios helps in developing robust contingency plans and minimizing the impact on affected populations and infrastructure.

The transportation sector is also significantly benefiting from advancements in 3D geospatial technologies. These technologies are being extensively utilized in the design, construction, and maintenance of transportation infrastructure, including roads, bridges, railways, and airports. The application of 3D geospatial solutions in transportation helps in accurate route planning, traffic management, and infrastructure monitoring, leading to enhanced safety, reduced operational costs, and improved travel experiences. Additionally, autonomous vehicles and drones rely heavily on 3D geospatial data for navigation and obstacle detection, further driving the demand for these technologies in the transportation industry.

3D Mapping and Modeling in Mapping have become indispensable tools in the realm of urban planning and infrastructure development. These technologies allow for the creation of highly detailed and interactive models of urban environments, which are crucial for visualizing potential developments and assessing their impacts on existing structures and communities. By employing 3D mapping and modeling, city planners can simulate various scenarios, optimize land use, and ensure sustainable development practices. This approach not only aids in efficient resource allocation but also enhances public engagement by providing stakeholders with a clear and comprehensive view of proposed projects. As cities continue to grow and evolve, the role of 3D mapping and modeling in mapping becomes increasingly vital in shaping the urban landscapes of the future.

On a regional scale, North America currently holds the largest share of the 3D geospatial technologies market, driven by the presence of major technology companies, extensive research and development activities, and significant government funding for geospatial projects. Asia Pacific is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing infrastructure investments, and growing adoption of advanced technologies in countries such as China, India, and Japan. The European market is also experiencing substantial growth due to the rising demand for geospatial solutions in environmental monitoring, urban planning, and transportation sectors.

Component Analysis

Hardware Analysis

The hardware segment of the 3D geospatial technologies market includes various devices and equipment required for data collection, processing, and visualization. This segment encompasses a wide range of pro

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.

NorWeST is an interagency stream temperature database and model for the western United States containing data from over 20,000 unique stream locations. Temperature observations were solicited from state, federal, tribal, private, and municipal resource organizations and processed using a custom cleaning script developed by Gwynne Chandler. Summaries of daily, weekly, and monthly means, minima, and maxima are provided for observation years. The data summaries and location information are available in user-friendly file formats that include: 1) a map (PDF) depicting the locations of in-stream thermographs (temperature sensors) for each processing unit, 2) a GIS shapefile (SHP) containing the location of these sensors for each processing unit, and 3) a tabular file (XLSX) containing observed temperature database summaries for data generally ranging from 1993 to 2015, dependent on the processing unit. Each point shapefile extent corresponds to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs). The tabular data can be joined to the observation point shapefile using the ID field OBSPRED_ID. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.These data have many potential uses including the assessment of stream temperature regimes, development of climate scenarios, understanding habitat and climate effects on stream temperatures, describing the thermal ecology of aquatic species, and conducting climate vulnerability assessments.For more information on the NorWeST stream temperature project see: https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST.html

This data publication originally became available via the FS Research Data Archive on 11/17/2016. On 7/27/2022 the metadata was updated to correct old URLs.

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 dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Grey-headed Robin (Heteromyias albispecularis).

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.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Grey-headed Honeyeater (Lichenostomus (Ptilotula) keartlandi).

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Fairy Martin (Petrochelidon (Petrochelidon) ariel).