The importance of microhabitat traits such as floral availability is well known; however, forest bee spatial dynamics have been variably studied across local to broad geographic scales. Past literature suggests that landscape factors from proximate to distal are important in determining forest bee community metrics, including richness, abundance, and taxonomic composition. Leveraging the interest and assistance of citizen science volunteers, we employed standard bee bowl trap transects across Maryland, Delaware, northern Virginia, and the District of Columbia and identified correlations between bee community composition, local and regional landcover, and broader geospatial patterns. We also identified the partial contributions of both specific species and sampling sites to total beta diversity. Various landcover metrics were significantly related to bee community structure, with bee abundance positively and negatively correlated with forest and wetland cover, respectively. In general, l..., Data were collected by citizen science volunteers using bee bowl transects across 99 forest sites in Maryland, Delaware, Washington, D.C., and northern Virginia, USA, in 2014. Characteristics of the forest bee community, including nesting and trophic groups, were correlated with landcover at varying spatial scales (200- and 1000-m buffer from transect origin) and broader biogeographic parameters., , # Geographic drivers more important than landscape composition in predicting bee beta diversity and community structure

Ecosphere

Data were collected by citizen science volunteers using bee bowl transects across 99 forest sites in Maryland, Delaware, Washington, D.C., and northern Virginia, USA, in 2014. Characteristics of the forest bee community, including nesting and trophic groups, were correlated with landcover at varying spatial scales (200- and 1000-m buffer from transect origin) and broader biogeographic parameters.

Site_Data

Data table includes site name (Site), geographic coordinates in decimal degrees (Latitude, Longitude), the sampled State (State) and county (County), and geographic coordinates in meters north (Northing) and east (Easting) of UTM Zone 18 origin. Remaining variables include proportion of land cover types (open water, developed, forest, agriculture and early successional, wetlands) identified within 200- or 1000-m buffer of sampling transect origin. ...

Mapping Layer Data Released: 06/15/2017, | Last Updated 04/20/2024Data Currency: This data is checked semi-annually from it's enterprise federal source fo 2010 CENSUS Data and will support mapping, analysis, data exports and the Open Geospatial Consortium (OGC) Application Programming Interface (API).Data Update Frequency: Twice, YearlyData Cycle | History (as required below)QA/QC Performed: December, 2024Next Scheduled Data QA/QC: July, 2024CDC PLACES (2010 CENSUS) FEATURE LAYERData Requester: Rhode Island Executive Office of Health and Human Service (OHHS) via Health Equity Institute (HEI).Data Requester: Rhode Island Department of Health, Maternal Child Health via Health Equity Institute (HEI).Data Request: Provide a database deliverable via download that contains both US CENSUS tracts and USPS Zip Code Tabulation Areas (ZCTA).HEALTH EQUITY INSTITUTE DATA CONNECT RI Using Modern GIS (Mapping)🡅 Click IT 🡅Facilitate transformative mapping visualizations that engage constituents and measure the impact of real-world solutions.Instructions to Join Your Data Provided Below STEP 1: Video (Pending)STEP 2: Video (Pending)STEP 3: Video (Pending)There are twenty-two U.S. CENSUS fields (download here) that you can join to your datasets. For additional insight, please contact the Center for Health Data and Analysis (CHDA) Rhode Island Department of Health (GIS) Mapping Department for assistance.Database Enhancement: This database contains two (2) additional data fields for consideration to be added to the existing 2020 State of Rhode Island Health Equity Map.Zip Code Tabulation Area (ZCTA)ZCTA/Tract Relationship (Singular ZCTAs per Tract, versus Multiple ZCTAs per Tract)Additional Information: While ZCTAs can be useful for certain qualitative purposes, such as broad or general high level analysis, they may not provide the level of granularity and accuracy required for in-depth demographic research which is required for policy mapping. ZCTAs can change frequently as the US Postal Service (USPS) adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.RIDOH GIS encourages analysts to make the appropriate choice of using census based data, with their consistent boundaries readily available for suitability for spatial analysis when conducting detailed demographic research.Here are a few reasons why you might want to consider using census based data (tracts, block groups, and blocks) instead of ZCTAs:1. Inaccurate Representations: ZCTAs are not designed for statistical analysis or demographic research. They are created by the United States Postal Service (USPS) for efficient mail delivery and can often span multiple cities, counties, or even states. As a result, ZCTAs may not accurately represent the actual geographic boundaries or demographic characteristics of a specific area.2. Lack of Granularity: ZCTAs are typically larger than census tracts, which are smaller, more homogeneous geographic units defined by the U.S. Census Bureau. Census tracts are designed to be relatively consistent in terms of population size, allowing for more detailed analysis at a local level. ZCTAs, on the other hand, can vary significantly in terms of population size, making it challenging to draw precise conclusions about specific neighborhoods or communities.3. Data Availability and Compatibility: Census tracts are used by the U.S. Census Bureau to collect and report demographic data. Consequently, a wide range of demographic information, such as population counts, age distribution, income levels, and education levels, is readily available at the census tract level. In contrast, data specifically tailored to ZCTAs may be more limited, making it difficult to obtain comprehensive and consistent data for demographic analysis.4. Changes Over Time: Census tracts are relatively stable over time, allowing for consistent longitudinal analysis. ZCTAs, however, can change frequently as the USPS adjusts postal routes and boundaries. These changes can lead to inconsistencies and challenges in tracking demographic trends and making accurate comparisons over time.5. Spatial Analysis: Census tracts are designed to maintain a level of spatial proximity, adjacency, or connectedness of these data containers while providing consistency and continuity over time - making them useful for spatial analysis. Mapping. ZCTAs, on the other hand, may not exhibit the same level of spatial coherence due to their primary purpose being mail delivery efficiency rather than geographic representation.State Agencies - Contact RIDOH GIS - Learn More About Mapping Data Available at the Census Tract LevelRIDOH GIS releases this database with the caveats noted above and that the researcher can accurately align the ZCTAs with the corresponding census tracts. Careful consideration should be given to the comparability and compatibility of the data collected at different geographic levels to ensure valid and meaningful statistical conclusions. Data Dictionary: 2010 Decennial CensusOBJECT ID - the count of each census tract entity.GEOID (10) STATE,COUNTY,TRACT - Numeric US CENSUS Tract Description (2010) HEZ (10) - Health Equity Zone (2020)LOCATION (10) - Plain Language Census Tract Descriptor (2010)COUNTY (10) NAME - County Name (2010)STATE (10) NAME - State Name (2010)ZCTA (23) - Zip Code Tabulation Area - Numeric US CENSUS ZCTA Description (2023)ZCTA/TRACT CONTEXT - Number of ZCTAs (Singular/Multiple) that reside within a US CENSUS TractST (10) - Numeric US CENSUS Tract Description (2010) CO (10) - Numeric US CENSUS Tract Description (2010)ST (10) CO (10) - Numeric US CENSUS Tract Description (2010)TRACT (10) - Numeric US CENSUS Tract Description (2010)GEOID (10) - Numeric US CENSUS Tract Description (2010)TRIBAL TRACT (10) - Numeric US CENSUS Tract Description (2010)Additional Mapping DataThe user is provided authoritative Federal Information Processing Standards (FIPS) such as numeric descriptions of state, county and tract identification, in addition to shape and length measurements of each census tract for data joining purposes.STATE (10) - Federal Information Processing Standards (FIPS)COUNTY (10) - Federal Information Processing Standards (FIPS)STATE (10), COUNTY (10) - Federal Information Processing Standards (FIPS)TRACT (10) - Federal Information Processing Standards (FIPS)TRIBAL TRACT (10) - Federal Information Processing Standards (FIPS)ST ABBRV (10) - State AbbreviationShape_Length - Total length of the polygon's (census tract) perimeter, in the units used by the feature class' coordinate system.Shape_Area - Total area of the polygon's (census tract) in the units used by the feature class' coordinate system.Data Source: Series Information for 2020 Census 5-Digit ZIP Code Tabulation Area (ZCTA5) National TIGER/Line Shapefiles, Current Open Geospatial Consortium (OGC) Application Programming Interface (API) Census ZIP Code Tabulation Areas - OGC Features copy this link to embed it in OGC Compliant viewers. For more information, please visit: ZIP Code Tabulation Areas (ZCTAs)To Report Data Discrepancies Contact the Rhode Island Department of Health (RIDOH) GIS (mapping) OfficePlease Be Certain To --Provide a Brief Description of What the Discrepancy IsInclude Your, Name, Organization, Telephone NumberAttach the Complete .xlsx with the Discrepancy Highlighted

CrimeStat III is a spatial statistics program for the analysis of crime incident locations, developed by Ned Levine and Associates under the direction of Ned Levine, PhD, that was funded by grants from the National Institute of Justice (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037). The program is Windows-based and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. CrimeStat is being used by many police departments around the country as well as by criminal justice and other researchers. The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant formats using either spherical or projected coordinates. It calculates various spatial statistics and writes graphical objects to ArcGIS, MapInfo, Surfer for Windows, and other GIS packages. CrimeStat is organized into five sections: Data Setup Primary file - this is a file of incident or point locations with X and Y coordinates. The coordinate system can be either spherical (lat/lon) or projected. Intensity and weight values are allowed. Each incident can have an associated time value. Secondary file - this is an associated file of incident or point locations with X and Y coordinates. The coordinate system has to be the same as the primary file. Intensity and weight values are allowed. The secondary file is used for comparison with the primary file in the risk-adjusted nearest neighbor clustering routine and the duel kernel interpolation. Reference file - this is a grid file that overlays the study area. Normally, it is a regular grid though irregular ones can be imported. CrimeStat can generate the grid if given the X and Y coordinates for the lower-left and upper-right corners. Measurement parameters - This page identifies the type of distance measurement (direct, indirect or network) to be used and specifies parameters for the area of the study region and the length of the street network. CrimeStat III has the ability to utilize a network for linking points. Each segment can be weighted by travel time, travel speed, travel cost or simple distance. This allows the interaction between points to be estimated more realistically. Spatial Description Spatial distribution - statistics for describing the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, the convex hull, or directional mean. Spatial autocorrelation - statistics for describing the amount of spatial autocorrelation between zones, including general spatial autocorrelation indices - Moran's I , Geary's C, and the Getis-Ord General G, and correlograms that calculate spatial autocorrelation for different distance separations - the Moran, Geary, Getis-Ord correlograms. Several of these routines can simulate confidence intervals with a Monte Carlo simulation. Distance analysis I - statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis, and Ripley's K statistic. There is also a routine that assigns the primary points to the secondary points, either on the basis of nearest neighbor or point-in-polygon, and then sums the results by the secondary point values. Distance analysis II - calculates matrices representing the distance between points for the primary file, for the distance between the primary and secondary points, and for the distance between either the primary or secondary file and the grid. 'Hot spot' analysis I - routines for conducting 'hot spot' analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, and risk-adjusted nearest neighbor hierarchical clustering. The hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls. 'Hot spot' analysis II - more routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC), K-means clustering, Anselin's local Moran, and the Getis-Ord local G statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation. Spatial Modeling Interpolation I - a single-variable kernel density estimation routine for producin

The DSL SWI Soils dataset represents two selected subsets of the USDA NRCS gNATSGO dataset for Oregon. The “SWI Predominantly Hydric Soil Map Units” layer represents soil map units that are comprised of greater than 50 percent hydric soil components. The Agate-Winlo Soil Map Units layer is associated with vernal pools in Jackson County. These two subsets indicate areas where unmapped wetlands may be present for the purpose of planning, scoping projects, and coordination with DSL.The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS-SPSD composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.Consult the gNATSGO home page for more information: https://www.nrcs.usda.gov/resources/data-and-reports/gridded-national-soil-survey-geographic-database-gnatsgo and the web soil survey: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.NRCS description of SSURGO Database:The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings.The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses.SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database.https://www.nrcs.usda.gov/resources/data-and-reports/soil-survey-geographic-database-ssurgoNRCS description of STATSGO2 Database:The Digital General Soil Map of the United States or STATSGO2 is a broad-based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto Rico, and the Virgin Islands and 1:1,000,000 in Alaska. The level of mapping is designed for broad planning and management uses covering state, regional, and multi-state areas. The U.S. General Soil Map is comprised of general soil association units and is maintained and distributed as a spatial and tabular dataset.The U.S. General Soil Map was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled and related to Land Remote Sensing Satellite (LANDSAT) images. Soils of similar areas were studied, and the probable classification and extent of the soils were determined.Map unit composition was determined by transecting or sampling areas on the more detailed maps and then statistically expanding the data to characterize the whole map unit.The dataset consists of georeferenced, vector and tabular data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The dataset is distributed in state, territorial, and national extents. The spatial units are linked to attributes in the tabular data, which give the proportionate extent of the component soils and their properties.The tabular data contains estimates of physical and chemical soil properties, soil interpretations, and static and dynamic metadata. Most of the tabular data exists in the database as a range of values for soil properties. The values depict the range for the geographic extent of the map unit. For most properties, the data include high, low, and representative values.Spatial data are available in ESRI® shapefile format. Spatial reference is decimal degrees, World Geodetic System 1984 (WGS84). Tabular data are available as ASCII text files (.txt). Fields are pipe delimited, and text is double-quote delimited. A Microsoft® Access® template database is available for use with the tabular data.https://www.nrcs.usda.gov/resources/data-and-reports/description-of-statsgo2-databaseCitation: Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at. http://websoilsurvey.nrcs.usda.gov/

Private Forest Wind Damage Assessment Spatial Database - May 2025. Published by Department of Agriculture, Food and the Marine. Available under the license Licence Not Specified (notspecified).Following Storm Darragh and Storm Éowyn during the winter of 2024/2025, and noting that many forests have been windblown around the country, Minister for Agriculture, Food and the Marine, Martin Heydon, and Minister of State for Forestry, Horticulture and Farm Safety, Michael Healy-Rae, invited key stakeholders to join department officials on a taskforce to ensure that storm-damaged forests were managed safely and appropriately. A Forestry Windblow Taskforce was set up to quantify forest damage and to identify approaches to facilitate the mobilisation of wind damaged timber.

Part of the Taskforce’s work involved initiating a detailed mapping assessment using high-resolution satellite imagery to provide information at a local or forest stand level scale. The detailed assessment of windblow damage was undertaken using high resolution satellite imagery from SkySat, and supplemented with pre and post storm Sentinel-2 and PlanetScope satellite data. In addition, drone imagery was also acquired for a number of specific locations.

The mapping exercise relied on the tasking of SkySat imagery during cloud-free weather conditions to acquire the necessary imagery data. The mapping was conducted largely between early February and the beginning of April 2025. The mapping effort focused on a target area of interest where the damage was deemed most likely to have occurred. These target areas were forests stands that were predominantly coniferous species and at least 15 years of age. These age and species criteria were used to filter both Coillte’s sub-compartment database and DAFM’s private forest dataset to confine the wind damage mapping exercise to the most relevant forests.

The windblow mapping exercise utilised a range of available EO datasets of varying spatial and temporal resolution which included: SkySat: 75% (c. 0.50 m resolution), Sentinel-2/PlanetScope: 20% (c. 10 m resolution/c. 3 m resolution), and drone imagery: 5% ( c. 0.2 m resolution).

The national estimate of private wind damage area (11,414 ha) as included in the private forest wind damage spatial database is within approximately +/- 500 hectares of the actual windblown private forest area. This uncertainty is due in part to the fact that for some parts of the country, SkySat satellite imagery has not yet been acquired. It is expected that there will be an ongoing refinement of the private forest windblow area estimate when new SkySat or other Earth Observation data becomes available over the coming months.

As part of this mapping exercise, “older” windblown areas, i.e. windblown forest areas that are more than 4 years old, were also identified and mapped. It is estimated these damage forest areas represent between 750 and 1,000 hectares of the total national area estimate of private wind damaged forest.

The area of wind damage in broadleaf stands may be greater than identified in the private forest wind damage database given the focus in the mapping exercise on coniferous species that were at least 15 years of age. This is also due in part to the fact that the identification of windblow in broadleaf stands is more challenging, particularly if the damage impacts individual trees.

The output from the mapping assessment is an ESRI Shapefile polygon database of wind damaged, privately owned forest areas greater than or equal to 0.1 hectares. The Shapefile is provided in the Irish Transverse Mercator geographic coordinate system. The main attribute included the spatial database table is area in hectares for each wind damaged forest area delineated.

These data are provisional in that they are a record of DAFM data holding in relation to private wind damaged forest at this time (May 2025). They are not published as legal definitions of the current actuality with regard to their geographic extent. They may contain errors and omissions and it should also be noted that the data cannot be taken as being absolutely current. Therefore they should be treated as indicative of the actual geographic situation. The Department of Agriculture, Food and the Marine will accept no liability for any loss or damage suffered by those using this data for any purpose whatsoever....

The ckanext-ksgeo extension for CKAN appears to be designed to enhance CKAN's geospatial capabilities; however, the provided README offers minimal information on its specific features or functionality. It seems to provide a framework for extending CKAN with geospatial features, potentially related to managing and visualizing geospatial datasets. Due to the lack of detail in the README, the below description is based on reasonable assumptions about what a 'ksgeo' extension might do. Key Features (Inferred): Geospatial Dataset Management: Potentially allows users to manage datasets with geospatial components (e.g., shapefiles, GeoJSON) within CKAN. Geographic Search: Possibly enables searching for datasets based on geographic location or spatial criteria. Geospatial Visualization: Might integrate with mapping libraries to provide map-based previews and visualizations of geospatial data. Metadata Enrichment: Perhaps supports adding geospatial metadata (e.g., coordinate reference systems, bounding boxes) to datasets. Technical Integration: Based on the installation instructions, the ckanext-ksgeo extension integrates with CKAN by being added to the ckan.plugins setting in the CKAN configuration file. Activation of the plugin would enhance CKAN's functionality with the added features from the extension. This plugin has general steps like activating a Python virtual environment, installing the package, configuring CKAN's INI file settings, then restarting CKAN. Benefits & Impact (Assumed): If the presumed features are accurate, implementing ckanext-ksgeo would enhance CKAN's ability to handle and present geospatial data effectively. This would allow users to discover and interact with geographic data more easily, improving data utilization and decision-making based on spatial insights. These advantages help foster better management and interaction with geospatial datasets through CKAN.

For the full FGDC metadata record, please click here. These data have been created to represent General/Suggested Oil Spill Protective Booming Strategies designed to protect areas that are environmentally and economically sensitive to oil and hazardous material spills (Oil Spill Sensitive Areas). These data were originally created and assembled by the NOAA Scientific Support Coordinator for US Coast Guard District Seven in circa 1992-1993 in cooperation with local Area Committees in accordance with regulations set forth by the National Response Plan of the Oil Pollution Act of 1990. They were provided to FWC-FWRI (Florida Fish and Wildlife Conservation Commission - Fish and Wildlife Research Institute, (at that time known as the Florida Marine Research Institute) in the fall of 2003 as paper maps and PDF maps for each of the US Coast Guard's Marine Safety Office (MSO) Areas of Responsibility (Captain of the Port Zones for Miami (at that time consisting of both what are now known as Sector Miami and Sector Key West), Tampa (now Sector Saint Petersburg), Jacksonville, Savannah, Charleston, and San Juan (Puerto Rico/US Virgin Islands)). In 1999-2000, FWC-FWRI began the process of digitizing the boom strategies depicted on these paper and PDF maps into arc (line) shapefiles, beginning with the maps from MSO Tampa, followed by MSO Miami, then MSO Jacksonville. In the Winter & Spring of 2003-2004 FWC-FWRI mapjoined these data to expand and improve upon the database so it could be used as a core business data layer for the Marine Resources Geographic Information System (MRGIS) library. Using various spatial coding functions, such as "calculate length" and "build geometry", additional attribute information has been added to the spatial database to generate length in feet and meters for summary and reporting purposes. An example of where this can be useful is when performing a spatial selection a summary of the total length of boom can be easily generated. These data are maintained as a part of the MRGIS Library and used with automated map production software to create new printed Geographic Response Plan maps for spill contingency planning and response purposes. Through the years of 2008-2009, FWC-FWRI partnered with the US Coast Guard and Florida Department of Environmental Protection - Bureau of Emergency Response to conduct a series of workshops to review and update these detailed Geographic Response Plan (GRP) data and maps for revised Digital Area Contingency Plans. The GRP revision workshop attendees were from or determined by the specific Area Committee of each Sector. The process of data entry and maintenance is ongoing at FWRI as of July 2011. Data will be entered and undergo quality assurance/quality control processes before new maps are re-produced for distribution and inclusion into Digital Area Contingency Plans and other GIS and/or map products. A versioned geodatabase has been created in SQL/SDE to track changes and manage data entry as well as digital QA/QC processes, such as consistency checks. A map service has also been created that is available to all the public and stakeholder community to view the latest version of this geodata. The map service displays data directly from the Enterprise versioned database.

The spatial data is used to produce response maps and in a GIS (The Florida Marine Spill Analysis System and Digital Area Contingency Plans) to provide timely, accurate, and valuable information to oil spill responders. Maps are produced (as PDF) with the sensitive area sites and protective boom strategies depicted on them. The maps are then "hyperlinked" in PDF to the sensitive area detail data sheets that contain the attribute data for the site in a data report form. The report form contains information on key stakeholders for the area, wildlife resources to be protected, nearby staging areas, recommended protection strategies (a verbal description of the booming strategy depicted on the map), the latitude/longitude of the site, and other response related information needed by first responders. The Booming Strategies have been developed by professional oil spill responders who have participated in the Geographic Response Plan Revision Workshops described. Please see process steps for more information about the history of the GRP revision workshops. NOTE: Booming Strategies were not done at the Sector Mobile (USCG District 7) GRP Workshop. These have been compiled from approved booming strategies related to the Deepwater Horizon oil spill response and are NOT YET approved as "Official Area Contingency Plan" booming strategies.

The ability of fair investments in local general higher education to drive sustainable regional economic growth is explored. Based on spatial theory, the exploratory spatial data analysis method is used to examine the spatial characteristics of local general higher education expenditures in China’s 30 provinces from 2000 to 2021. The spatial Durbin model is employed to analyze the impact of education expenditures on regional economic growth. The results reveal that education expenditures had positive spatial autocorrelation. Education expenditures promoted regional economic growth, and the long-term effect was greater than the short-term effect. These expenditures also had a positive spillover effect, showing that strategic spatial interactions between provinces positively influence growth. The positive spillover effects nationwide and in the eastern region were significantly greater than the direct effect, whereas the spillover effects in both the middle and western regions were negative.

Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models’ predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies. Methods Presence absence (PA) data PA data for each species were obtained from Coillte based on surveys performed in a fraction of the 6,000 properties they manage (Table 1) by asking property managers (who visit the forests they manage on a regular basis) whether deer were present and, if so, what species. Properties range in size from less than one to around 2,900 ha, and to assign the PA value to a specific location, we calculated the centroid of each property using the function st_centroid() from the package sf in R (Pebesma 2018). The survey was mainly performed in 2010 and 2013, in addition to further data collected between 2014 and 2016. Some properties were surveyed only once in the period 2010–2016, but for those that were surveyed more than once, the value for that location was considered “absence” if deer had never been detected in the property in any of the surveys, and “presence” in all other cases. In addition to these surveys, Coillte commissioned density surveys based on faecal pellet sampling in a subset of their properties between the years 2007 and 2020. Any non-zero densities in these data were considered “presences”, and all zeros were considered “absences”. These data were also summarised across years when a property had been repeatedly sampled and counted as presence if deer had been detected in any of the sampling years. PA data for NI were obtained from a survey carried out by the British Deer Society in 2016. The survey divided the British territory into 100 km2 grid cells, and deer presence or absence was assessed based on public contributions, which were then reviewed and collated by BDS experts. Since 100 km2 grid cells are quite large, we did not, as with the Coillte properties, calculate the centroid of each cell and assign the PA value of the cell to it. Instead, we randomly simulated positions within each cell and assigned the presence or absence value of the cell to each of them. We performed a sensitivity analysis to calculate an optimal number of positions that would capture the environmental variability within each cell, which was set to 5 random positions per grid cell. After processing, we obtained a total of 920 PA data across NI. 2.2.2 Presence-only (PO) data PO data were collected from various sources, mainly (but not only) from citizen science initiatives. The National Biodiversity Data Centre (NBDC) is an Irish initiative that collates biodiversity data coming from different sources, from published studies to citizen contributions. From this repository, we obtained all contributions on the three species, a total of 1,430 records. To this, we added the 164 records of deer in Ireland downloaded from the iNaturalist site, another citizen-contributed database that collects the same type of data. From the resulting dataset, we (1) removed all observations with a spatial resolution lower than 1 km2; (2) did a visual inspection of the data and comments and removed all observations that were obviously incorrect (i.e. at sea or that the comment specified it was a different species); (3) filtered out all the fallow deer reported in Dublin’s enclosed city park (Phoenix Park) since the population there was introduced and is artificially maintained and disconnected from the rest of populations in Ireland; and (4) filtered duplicate observations by retaining only one observation per user, location, and day. The Centre for Environmental Data and Recording (CEDaR) is a data repository for Northern Ireland (NI) that operates in the same way as the NBDC. They provided 872 records of deer in NI, coming from different survey, scientific, and citizen science initiatives, from which we removed all records provided with a spatial resolution lower than 1 km2. The location and species of 469 deer culled between 2019 and 2021 in NI were obtained from the British Agri-Food and Biosciences Institute. For the observations that did not have specific coordinates, we derived them from the location name or postcode if provided. As part of a nationally funded initiative to improve deer monitoring in Ireland (SMARTDEER), we developed a bespoke online tool to facilitate the reporting of deer observations by the general public and all relevant stakeholders e.g. hunters, farmers, or foresters. Observations were reported in 2021 and 2022 by clicking on a map to indicate a 1 km2 area where deer have been observed. For each user and session, we calculated the area of the surface covered in squares, simulated a number of positions proportional to the size of the polygon, and distributed them within it to generate a number of exact positions equivalent to the area where the user had indicated an observation. In total, the SMARTDEER tool allowed us to collect 4,078 presences across Ireland and NI. 2.3.2 Covariate selection Raster environmental covariates used in the models were obtained from the Copernicus Land Monitoring Service (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency EEA), whereas the vector layers (roads, paths) were obtained from the Open Street Map service (OpenStreetMap contributors, 2017. Planet dump [Data file from January 2022]. https://planet.openstreetmap.org). Vector layers were transformed into distance layers (distance to roads, distance to paths) using the distance() function from the package raster, and into density layers (density of roads, paths) using the rasterize() function of the same package (Hijmans 2021). All raster layers were resampled to the lowest resolution available in the used covariates, resulting in a 1 km2 resolution. A full description of the process of covariate selection (including screening for collinearity) can be found in the supplementary material. The covariates eventually used in the model were elevation (m), slope (degrees), tree cover (%), small woody feature density (%), distances to forest edge (m, positive distances indicate a location outside a forest, negative distances indicate a location within a forest), and human footprint index (Venter et al. 2016, 2018). All covariates were scaled by subtracting the mean and dividing by the standard deviation before entering the model (function scale() from the raster package).

Landscape ecology is an important subtopic of ecology. As a field, it is inherently interdisciplinary and provides opportunities to teach not only content, but transferable ecological and geographical skills. Given the broad spectrum of topics in general ecology courses, landscape ecology is often relegated to a single lecture or not covered at all, meaning that there is a need for concise, effective lesson content for teachers. Here, we present an adaptable, active lesson on landscape ecology that can be implemented to help address this gap and support student engagement and the application of content knowledge to local systems. Students are presented with an active lecture that introduces key concepts of landscape ecology, then presented with opportunities to apply and reinforce these concepts. First, students work in groups to solve applied challenges about a real environment. These group challenges support greater understanding, content retention, and engage students in collaborative creative problem-solving with their classmates. Second, students apply the concepts of the lesson to a natural space that they are familiar with to increase personal connections to the content and again reinforce the topics. This lesson has been implemented a total of three times in two courses across two universities with distinct student populations. Student feedback has been largely favorable, with students reporting increased understanding of the subject and enjoyment of the lesson, especially the group problem-solving challenges. We provide avenues for instructors to localize the content to help students connect to the topics using examples with which they are familiar.

Primary Image: Group Problem-Solving in Practice: Resources (a map of potential purchase parcels, and a table with the price and a description of each of those parcels) given to students in a group problem-solving challenge that asks students to prioritize funds and maximize the positive impacts on the landscape by purchasing land for a nature preserve.

Species' threat assessments produce generalized threat impact scores, often by considering regional-scale representations of threats. Cities, on the other hand, produce municipal-scale, high resolution data that are proxies for threats; furthermore, cities in mega-diverse regions are home to a high number of threatened species. Prioritization of conservation action is biased for where more information is known (about the ecosystem), and where a positive outcome can be anticipated. Eight Cape peninsula amphibian species have a threatened conservation status. They are isolated on highlands or are restricted to remnant and suburban habitats, dependent on both urban and protected terrestrial and freshwater habitats found in the City of Cape Town and Table Mountain National Park.

In Chapter Two, I used spatial data (shapefiles) to represent threats in a Geographic Information System to spatially define threats to eight amphibian species (five lowland, three upland). I used two approaches: weighted and un-weighted by a threat impact-score, to produce five indices of local threats. The Micro Frog (Microbatrachella capensis) is assessed as the most threatened peninsula frog species by three of the five indices considered. The results show that for lowland species, the threat-class of greatest extent is 'Residential and commercial development'. The three lowland species most exposed to this threat are M. capensis (100% exposed to potential development), Breviceps gibbosus (55.6% of its 8.5 km2 putative peninsula distribution), and Sclerophrys pantherina (38.4% of its 199.7 km2 distribution). The Compounded and the General Threat Index correlate to the (global) Redlist Index (P < 0.05); but no correlation to the regional Red Listing, indicating congruency of threats and threat status.

The Critically Endangered Table Mountain Ghost Frog (Heleophryne rosei) is torrent adapted, and found only on the Table Mountain massif. CapeNature monitors tadpoles, and SANParks monitors (selected) stream parameters. In Chapter Three, I analyse water-habitat monitoring data (controlled for altitude) to show where threats of habitat alteration, drought, or temperature extremes may affect the H. rosei metapopulation. Permanence of water-flow and water temperature are shown to be very highly significant predictors of tadpole presence (p = 0.0005, r = 0.78). The lower the water temperature, the more likely tadpoles are present. Streams with a mean summer temperature greater than 17.2°C (n=3) at 400 to 300 meters above sea level were found to have no tadpoles at this altitude. Permanence of water flow is significant, as tadpoles need more than one year to reach metamorphosis. Summer water temperatures over an average of 17.2°C should be a red-flag for management authorities responsible for bulk-water supply, threat mitigation efforts, and biodiversity conservation.

Spatial indices of threat are useful to illustrate the relative exposure to threats at a local (city) scale. Threats to different lowland amphibians are similar (e.g. residential and commercial development), which varies from the mutual threats to different upland amphibians. Fundamental to stream species' conservation is water supply and demand management, while upland terrestrial species are most affected by veld age and invasive alien flora. Some threats are common for both areas (e.g. invasive alien species).

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

The Sheeprocks (UT) was revised to resync with the UT habitat change as reflected in the Oct 2017 habitat data, creating the most up-to-date version of this dataset. Data submitted by Wyoming in February 2018 and by Montana and Oregon in May 2016 were used to update earlier versions of this feature class. The biologically significant unit (BSU) is a geographical/spatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual states/EISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areas/core habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLM's GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographical/spatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority and/or Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority. Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also. California and Nevada's BSUs were developed by Nevada Department of Wildlife's Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevada's Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding). PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat. PGH is defined as Greater sage-grouse Occupied Range outside of PPH. Datasets used to create PPH and PGH: Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06/09/2012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacant/unknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. Vacant or Unknown Habitat: Suitable habitat for sage-grouse that is separated (not contiguous) from occupied habitats that either: 1) Has not been adequately inventoried, or 2) Has not had documentation of grouse presence in the past 10 years Potentially Suitable Habitat: Unoccupied habitats that could be suitable for occupation of sage-grouse if practical restoration were applied. Soils or other historic information (photos, maps, reports, etc.) indicate sagebrush communities occupied these areas. As examples, these sites could include areas overtaken by pinyon-juniper invasions or converted rangelandsUpdate October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat and management zones, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Oregon submitted updated BSU boundaries in May 2016 and again in October 2016, which were incorporated into this latest version. In Oregon, the Core Area maps and data were developed as one component of the Conservation Strategy for sage-grouse. Specifically, these data provide a tool in planning and identifying appropriate mitigation in the event of human development in sage-grouse habitats. These maps will assist in making

This GIS dataset offers a link to the California portion of the Nonindigenous Aquatic Species (NAS) information resource for the United States Geological Survey. The NAS program has been established as a central repository for accurate and spatially referenced biogeographic accounts of nonindigenous aquatic species. The program provides scientic reports, online/realtime queries, spatial data sets, regional contact lists, and general information. The goal of the information system is to provide timely, reliable data about the presence and distribution of nonindigenous aquatic species. The NAS database contains locality information for more than 1100 species of vertebrates, invertebrates, and vascular plants. The NAS program provides a continual national repository of distribution information for nonindigenous aquatic species that is used to gain an understanding of aquatic introductions, identify geographic gaps, and access the status of introduced aquatic species nationwide. Data are obtained from many sources including literature, museums, databases, monitoring programs, state and federal agencies, professional communications, online reporting forms, and Aquatic Nuisance Species (ANS) hotline reports. The NAS program defines a nonindigenous aquatic species as a member(s) of a species that enters a body of water of aquatic ecosystem outside of its historic or native range. This includes not only species that arrived from outside of North America but also species native to North America that have been introduced to drainages outside their ranges within the country. Please visit http://nas.er.usgs.gov for more information and to see all of the products and data available through the NAS program.

This GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes.No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is to be used solely for illustrative, reference and statistical purposes and may be used for government to government Tribal consultation. Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate Tribe and then the BIA to obtain interpretations of the reservation boundary.The land areas and their representations are compilations defined by the official land title records of the Bureau of Indian Affairs (BIA) which include treaties, statutes, Acts of Congress, agreements, executive orders, proclamations, deeds and other land title documents. The trust, restricted, and mixed ownership land area shown here, are suitable only for general spatial reference and do not represent the federal government’s position on the jurisdictional status of Indian country. Ownership and jurisdictional status is subject to change and must be verified with plat books, patents, and deeds in the appropriate federal and state offices.Included in this dataset are the exterior extent of off reservation trust, restricted fee tracts and mixed tracts of land including Public Domain allotments, Dependent Indian Communities, Homesteads and government administered lands and those set aside for schools and dormitories. There are also land areas where there is more than one tribe having an interest in or authority over a tract of land but this information is not specified in the AIAN-LAR dataset. The dataset includes both surface and subsurface tracts of land (tribal and individually held) “off reservation” tracts and not simply off reservation “allotments” as land has in many cases been subsequently acquired in trust.These data are public information and may be used by various organizations, agencies, units of government (i.e., Federal, state, county, and city), and other entities according to the restrictions on appropriate use. It is strongly recommended that these data be acquired directly from the BIA and not indirectly through some other source, which may have altered or integrated the data for another purpose for which they may not have been intended. Integrating land areas into another dataset and attempting to resolve boundary differences between other entities may produce inaccurate results. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. Users are cautioned that digital enlargement of these data to scales greater than those at which they were originally mapped can cause misinterpretation.The BIA AIAN-LAR dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. These data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support.These data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2021.html#list-tab-790442341The BIA Indian Lands dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. This data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support. Please feel free to contact us at 1-877-293-9494 geospatial@bia.gov

In Oklahoma, historic depictions of the land areas representations, as described in 1867-1870, were developed and called Tribal Statistical Areas (TSA) in the AIAN-LAR. These areas are similar to the Bureau of Census Oklahoma Tribal Statistical Areas (OTSA) which are areas used for the collection, tabulation and presentation of decennial census data for the 36 Federally- recognized American Indian tribes located in the state. No legal inference can or should be made from the TSA information in the GIS dataset. Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate tribe and then the BIA to obtain interpretations of the reservation boundary. This GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes. No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is to be used solely for illustrative, reference and statistical purposes and may be used for government to government Tribal consultation. Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate Tribe and then the BIA to obtain interpretations of the reservation boundary. The land areas and their representations are compilations defined by the official land title records of the Bureau of Indian Affairs (BIA) which include treaties, statutes, Acts of Congress, agreements, executive orders, proclamations, deeds and other land title documents. The trust, restricted, and mixed ownership land area shown here, are suitable only for general spatial reference and do not represent the federal government’s position on the jurisdictional status of Indian country. Ownership and jurisdictional status is subject to change and must be verified with plat books, patents, and deeds in the appropriate federal and state offices. Included in this dataset are the exterior extent of off reservation trust, restricted fee tracts and mixed tracts of land including Public Domain allotments, Dependent Indian Communities, Homesteads and government administered lands and those set aside for schools and dormitories. There are also land areas where there is more than one tribe having an interest in or authority over a tract of land but this information is not specified in the AIAN-LAR dataset. The dataset includes both surface and subsurface tracts of land (tribal and individually held) “off reservation” tracts and not simply off reservation “allotments” as land has in many cases been subsequently acquired in trust. These data are public information and may be used by various organizations, agencies, units of government (i.e., Federal, state, county, and city), and other entities according to the restrictions on appropriate use. It is strongly recommended that these data be acquired directly from the BIA and not indirectly through some other source, which may have altered or integrated the data for another purpose for which they may not have been intended. Integrating land areas into another dataset and attempting to resolve boundary differences between other entities may produce inaccurate results. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. Users are cautioned that digital enlargement of these data to scales greater than those at which they were originally mapped can cause misinterpretation. The BIA AIAN-LAR dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. These data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support.

The purpose of the American Indian and Alaska Native Land Area Representation (AIAN-LAR) Geographic Information System (GIS) dataset is to depict the external extent of federal Indian reservations and the external extent of associated land held in “trust” by the United States, “restricted fee” or “mixed ownership” status for federally recognized tribes and individual Indians. This dataset includes other land area types such as Public Domain Allotments, Dependent Indian Communities and Homesteads. This GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes. No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is to be used solely for illustrative, reference and statistical purposes and may be used for government to government Tribal consultation. Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate Tribe and then the BIA to obtain interpretations of the reservation boundary. The land areas and their representations are compilations defined by the official land title records of the Bureau of Indian Affairs (BIA) which include treaties, statutes, Acts of Congress, agreements, executive orders, proclamations, deeds and other land title documents. The trust, restricted, and mixed ownership land area shown here, are suitable only for general spatial reference and do not represent the federal government’s position on the jurisdictional status of Indian country. Ownership and jurisdictional status is subject to change and must be verified with plat books, patents, and deeds in the appropriate federal and state offices. Included in this dataset are the exterior extent of off reservation trust, restricted fee tracts and mixed tracts of land including Public Domain allotments, Dependent Indian Communities, Homesteads and government administered lands and those set aside for schools and dormitories. There are also land areas where there is more than one tribe having an interest in or authority over a tract of land but this information is not specified in the AIAN-LAR dataset. The dataset includes both surface and subsurface tracts of land (tribal and individually held) “off reservation” tracts and not simply off reservation “allotments” as land has in many cases been subsequently acquired in trust. These data are public information and may be used by various organizations, agencies, units of government (i.e., Federal, state, county, and city), and other entities according to the restrictions on appropriate use. It is strongly recommended that these data be acquired directly from the BIA and not indirectly through some other source, which may have altered or integrated the data for another purpose for which they may not have been intended. Integrating land areas into another dataset and attempting to resolve boundary differences between other entities may produce inaccurate results. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. Users are cautioned that digital enlargement of these data to scales greater than those at which they were originally mapped can cause misinterpretation. The BIA AIAN-LAR dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. These data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support.

While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show that, for suitably formatted data, we can actually fit these models using generalized additive model software, via a simple line of code, demonstrated on R by the popular mgcv package. We are able to do this because a common and computationally efficient way to fit a log-Gaussian Cox process model is to use a basis function expansion to approximate the Gaussian random field, as is provided by a generic bivariate smoother over geographic space. We further show that if basis functions are parameterized appropriately then we can estimate parameters in the spatial covariance function for the latent random field using a generalized additive model. We use simulation to show that this approach leads to model fits of comparable quality to state-of-the-art software, often more quickly. But we see the main advance from this work as lowering the technology barrier to spatial statistics for applied researchers, many of whom are already familiar with generalized additive model software.

The 1885 UK parliamentary constituencies for Ireland were re-created in 2017 as part of a conference paper delivered at the Southern Irish Loyalism in Context conference at Maynooth University. The intial map only included the territory of the Irish Free State and was created by Martin Charlton and Jack Kavanagh. The remaining six counties of Ulster were completed by Eoin McLaughlin in 2018-19, the combined result is a GIS map of all the parliamentary constituecies across the island of Ireland for the period 1885-1918. The map is available in both ESRI Shapefile format and as a GeoPackage (GPKG). The methodology for creating the constituencies is outlined in detail below.

Methodology

A map showing the outlines of the 1855 – 1918 Constituency boundaries can be found on page 401 of Parliamentary Elections in Ireland, 1801-1922 (Dublin, 1978) by Brian Walker. This forms the basis for the creation of a set of digital boundaries which can then be used in a GIS. The general workflow involves allocating an 1885 Constituency identifier to each of the 309 Electoral Divisions present in the boundaries made available for the 2011 Census of Population data release by CSO. The ED boundaries are available in ‘shapefile’ format (a de facto standard for spatial data transfer). Once a Constituency identifier has been given to each ED, the GIS operation known as ‘dissolve’ is used to remove the boundaries between EDs in the same Constituency. To begin with Walker’s map was scanned at 1200 dots per inch in JPEG form. A scanned map cannot be linked to other spatial data without undergoing a process known as georeferencing. The CSO boundaries are available with spatial coordinates in the Irish National Grid system. The goal of georeferencing is to produce a rectified version of the map together with a world file. Rectification refers to the process of recomputing the pixel positions in the scanned map so that they are oriented with the ING coordinate system; the world file contains the extent in both the east-west and north-south directions of each pixel (in metres) and the coordinates of the most north-westerly pixel in the rectified image.

Georeferencing involves the identification of Ground Control Points – these are locations on the scanned map for which the spatial coordinates in ING are known. The Georeferencing option in ArcGIS 10.4 makes this a reasonably pain free task. For this map 36 GCPs were required for a local spline transformation. The Redistribution of Seats Act 1885 provides the legal basis for the constituencies to be used for future elections in England, Wales, Scotland and Ireland. Part III of the Seventh Schedule of the Act defines the Constituencies in terms of Baronies, Parishes (and part Parishes) and Townlands for Ireland. Part III of the Sixth Schedule provides definitions for the Boroughs of Belfast and Dublin.

The CSO boundary collection also includes a shapefile of Barony boundaries. This makes it possible code a barony in two ways: (i) allocated completely to a Division or (ii) split between two Divisions. For the first type, the code is just the division name, and for the second the code includes both (or more) division names. Allocation of these names to the data in the ED shapefile is accomplished by a spatial join operation. Recoding the areas in the split Baronies is done interactively using the GIS software’s editing option. EDs or groups of EDs can be selected on the screen, and the correct Division code updated in the attribute table. There are a handful of cases where an ED is split between divisions, so a simple ‘majority’ rule was used for the allocation. As the maps are to be used at mainly for displaying data at the national level, a misallocation is unlikely to be noticed. The final set of boundaries was created using the dissolve operation mentioned earlier. There were a dozen ED that had initially escaped being allocated a code, but these were quickly updated. Similarly, a few of the EDs in the split divisions had been overlooked; again updating was painless. This meant that the dissolve had to be run a few more times before all the errors have been corrected.

For the Northern Ireland districts, a slightly different methodology was deployed which involved linking parishes and townlands along side baronies, using open data sources from the OSM Townlands.ie project and OpenData NI.

This map shows the distribution of the iceberg data extracted from ERS SAR images.

Icebergs are identified in Synthetic Aperture Radar [SAR] images by image analysis using the texture and intensity of the microwave backscatter observations. The images are segmented using an edge detecting algorithm, and segments identified as iceberg or background, which may be sea ice, open water, or a mixture of both. Dimensions of the icebergs are derived by spatial analysis of the corresponding image segments. Location of the iceberg is derived from its position within the image and the navigation data that gives the location and orientation of the image.

More than 20,000 individual observations have been extracted from SAR images acquired by the European Space Agency's ERS-1 and 2 satellites and the Canadian Space Agency's Radarsat satellite. Because images can overlap, some proportion of the observations represent multiple observations of the same set of icebergs.

Most observations relate to the sector between longitudes 70E and 135E. The data set includes observations from several other discrete areas around the Antarctic coast. In general observations are within 200 km of the coast but in limited areas extend to about 500 km from the coast.

This metadata record has been derived from work performed under the auspices of ASAC project 2187 (ASAC_2187).

The map in the pdf file shows the extent of the coverage of individual SAR scenes used in the analysis and the abundance and size characteristics (by a limited colour palette) of the identified icebergs.