The food access profiles aim to democratize food resources and food access metrics in the mountain state through a combination of data that is representative of different food system stakeholders. Datasets that were used in these profiles are from the Department of Health and Human Resources, West Virginia Department of Education, U.S. Census Bureau, U.S. Bureau of Labor Statistics, and research conducted by the WVU Food Justice Lab. Department of Health and Human ResourcesData was collected by WVDHHR and provided by request.West Virginia Department of EducationData was collected by WVDE and provided by request.This profile was created in 2016.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

A web map containing the locations of the various historical structures located at Early Heritage Village. This service is updated on an as-needed basis. By using this service you agree to the terms outlined in the disclaimer available at https://maps.sccmo.org/disclaimer.

This map service includes trailhead locations within the Daniel Boone National Forest (DBNF) in the Commonwealth of Kentucky. The data was provided by the US Forest Service.

Civil townships are a unit of local government, typically subordinate to the County. In Indiana, each township is served by an elected township trustee.Civil townships are primarily represented as Minor Civil Divisions (MCDs) in the source data by the US Census Bureau. Per the US Census, MCDs are the primary governmental or administrative divisions of a county in many states. This data mostly contains MCD boundaries, with some corrected township boundaries (see explanation below). On 1/3/2024, IGIO staff updated the boundaries and/or attributes of 6 townships: Center (Delaware Co), Mount Pleasant (Delaware Co), Eagle (Boone Co), Perry (Boone Co), Union (Boone Co), and Worth (Boone Co). The Delaware County townships were attributed as either Muncie City or Yorktown Town and had associated incorporated area boundaries overlapping the civil township boundary. Boone County had townships attributed as Zionsville City and Whitestown Town and had associated incorporated are boundaries overlapping the real civil township boundary. Civil township boundaries from the 2023 Data Harvest were used when possible to correct geometry, and the neighboring census township boundaries to maintain topology when needed. Eagle and Union townships do not have a populated GEOID since the corrected boundary does not correspond to a census MCD geography to avoid incorrect joins to census tabular data by end users.

The NAD is a spatial database containing address data, point location coordinates, jurisdictions, record level metadata and other supporting data for addressable locations including structures, some sub-units within those structures and landmarks as included in the aggregated datasets from providers included therein. Its coverage includes twenty five whole and partial coverage states, including Arizona, Arkansas, Colorado, Connecticut, Delaware, Indiana, Iowa, Maine, Maryland, Massachusetts, Kansas, Montana, New Jersey, New Mexico, New York, North Carolina, Ohio, Oregon, Rhode Island, Tennessee, Texas, Utah, Vermont, Virginia and Wisconsin, and the District of Columbia, as well as variable numbers of counties in several states: Anchorage Municipality, Haines and Matanuska-Susitna Boroughs, Yakutat City and Borough, and Dillingham City Alaska; Merced County, California; East Baton Rouge and Terrebonne Parishes, Louisiana; Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Le Sueur, Ramsey, Scott, Sherburne and Washington Counties, Minnesota; Boone, Christian, Cole, Greene, Jasper, St. Charles, St. Louis, Stone and Taney Counties, and Independence City, Missouri; Campbell, Crook and Teton Counties, Wyoming; Sioux Falls City and Rosebud Sioux Reservation, South Dakota.

description: This dataset contains point features representing the approximate location of tax parcels contained in County Assessor tax rolls. Individual county data was integrated into this statewide publication by the Arkansas Geographic Information Office (AGIO). The Computer Aided Mass Appraisal (CAMA) systems maintained in each county are used to populate the database attributes for each centroid feature. The entity attribute structure conforms to the Arkansas Cadastral Mapping Standard. The digital cadastral data is provided as a publication version that only represents a snapshot of the production data at the time it was received from the county. Published updates may be made to counties throughout the year. These will occur after new data is digitized or updates to existing data are finished. Production versions of the data exist in the various counties where daily and weekly updates occur. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This column reflects the date when AGIO received the data from the county. Only parcels with an associated Computer Assisted Mass Appraisal (CAMA) record are provided. This means a CAMA record may exist, but no point geometry or vice-versa. Cadastral data is dynamic by its nature; therefore it is impossible for any county to ever be considered complete. The data is NOT topologically enforced. As a statewide integrator, AGIO publishes the data but does not make judgment calls about where points or polygon lines are meant to be located. Therefore each county data set is published without topology rules being enforced. GIS Technicians use best practices such as polygon closure and vertex snapping, however, topology is not built for each county. Users should be aware, by Arkansas Law (15-21-504 2 B) digital cadastral data does not represent legal property boundary descriptions, nor is it suitable for boundary determination of the individual parcels included in the cadastre. Users requiring a boundary determination should consult an Arkansas Registered Land Surveyor (http://www.arkansas.gov/pels/search/search.php) on boundary questions. The digital cadastral data is intended to be a graphical representation of the tax parcel only. Just because a county is listed does NOT imply the data represents county wide coverage. AGIO worked with each county to determine a level of production that warranted the data was ready to be published. For example, in some counties only the north part of the county was covered or in other cases only rural parcels are covered and yet in others only urban parcels. The approach is to begin incremental publishing as production blocks are ready, even though a county may not have county wide coverage. Each case represents a significant amount of data that will be useful immediately. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This date reflects when the data was received from the county. Digital cadastral data users should be aware the County Assessor Mapping Program adopted a phased approach for developing cadastral data. Phase One includes the production of a parcel centroid for each parcel that bears the attributes prescribed by the state cadastral mapping standard. Phase Two includes the production of parcel polygon geometry and bears the standard attributes. The Arkansas standard closely mirrors the federal Cadastral Core Data Standard established by the Federal Geographic Data Committee, Subcommittee for Cadastral Data. Counties within this file include: Arkansas, Ashley, Baxter, Boone, Carroll, Chicot, Clark, Clay, Columbia, Conway, Craighead, Crawford, Cross, Desha, Faulkner, Franklin, Hot Spring, Howard, Izard, Jackson, Jefferson, Lafayette, Lincoln, Little River, Logan, Lonoke, Madison, Mississippi, Montgomery, Nevada, Newton, Perry, Pike, Poinsett, Polk, Pope, Pulaski, Randolph, Saline, Sebastian, Stone, Van Buren, Washington and White.; abstract: This dataset contains point features representing the approximate location of tax parcels contained in County Assessor tax rolls. Individual county data was integrated into this statewide publication by the Arkansas Geographic Information Office (AGIO). The Computer Aided Mass Appraisal (CAMA) systems maintained in each county are used to populate the database attributes for each centroid feature. The entity attribute structure conforms to the Arkansas Cadastral Mapping Standard. The digital cadastral data is provided as a publication version that only represents a snapshot of the production data at the time it was received from the county. Published updates may be made to counties throughout the year. These will occur after new data is digitized or updates to existing data are finished. Production versions of the data exist in the various counties where daily and weekly updates occur. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This column reflects the date when AGIO received the data from the county. Only parcels with an associated Computer Assisted Mass Appraisal (CAMA) record are provided. This means a CAMA record may exist, but no point geometry or vice-versa. Cadastral data is dynamic by its nature; therefore it is impossible for any county to ever be considered complete. The data is NOT topologically enforced. As a statewide integrator, AGIO publishes the data but does not make judgment calls about where points or polygon lines are meant to be located. Therefore each county data set is published without topology rules being enforced. GIS Technicians use best practices such as polygon closure and vertex snapping, however, topology is not built for each county. Users should be aware, by Arkansas Law (15-21-504 2 B) digital cadastral data does not represent legal property boundary descriptions, nor is it suitable for boundary determination of the individual parcels included in the cadastre. Users requiring a boundary determination should consult an Arkansas Registered Land Surveyor (http://www.arkansas.gov/pels/search/search.php) on boundary questions. The digital cadastral data is intended to be a graphical representation of the tax parcel only. Just because a county is listed does NOT imply the data represents county wide coverage. AGIO worked with each county to determine a level of production that warranted the data was ready to be published. For example, in some counties only the north part of the county was covered or in other cases only rural parcels are covered and yet in others only urban parcels. The approach is to begin incremental publishing as production blocks are ready, even though a county may not have county wide coverage. Each case represents a significant amount of data that will be useful immediately. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This date reflects when the data was received from the county. Digital cadastral data users should be aware the County Assessor Mapping Program adopted a phased approach for developing cadastral data. Phase One includes the production of a parcel centroid for each parcel that bears the attributes prescribed by the state cadastral mapping standard. Phase Two includes the production of parcel polygon geometry and bears the standard attributes. The Arkansas standard closely mirrors the federal Cadastral Core Data Standard established by the Federal Geographic Data Committee, Subcommittee for Cadastral Data. Counties within this file include: Arkansas, Ashley, Baxter, Boone, Carroll, Chicot, Clark, Clay, Columbia, Conway, Craighead, Crawford, Cross, Desha, Faulkner, Franklin, Hot Spring, Howard, Izard, Jackson, Jefferson, Lafayette, Lincoln, Little River, Logan, Lonoke, Madison, Mississippi, Montgomery, Nevada, Newton, Perry, Pike, Poinsett, Polk, Pope, Pulaski, Randolph, Saline, Sebastian, Stone, Van Buren, Washington and White.

This study presents an ArcGIS geoprocessing protocol for quickly processing large amounts of data from publicly available government sources to consider both water quality standards (WQS) and nonpoint pollution source (NPS) control, on a watershed-by-watershed basis to administratively predict locations where nonpoint source pollutants may contribute to the impairment of downstream waters and locations where nonpoint source pollutants are not expected to contribute to the impairment of downstream waters. This dissertation also presents an ArcGIS geoprocessing protocol to calculate the hydrological response time of a watershed and to predict the potential for soil erosion and nonpoint source pollutant movement on a landscape scale. The standardized methodologies employed by the protocol allow for its use in various geographic regions. The methodology has been performed on sites in Linn County and Boone County, Missouri, and produces results consistent with those expected from other widely accepted methods. These protocols were developed studying the movement of atrazine. but may be used for various nonpoint source pollutants that are water soluble, have an affinity to soil binding, and associated with a particular land use. All data and code are available in Mendeley Data (doi: 10.17632/wdjzftxyfd.1).

This dataset provides detailed unit descriptions for the Bedrock Units in the digital representation of the paper map "Bedrock Geologic Map of Maine, Osberg, Hussey and Boone, 1985" and could be used for various purposes related to statewide geological studies and planning.

This dynamic map service displays the boundary of the Daniel Boone National forest (DBNF) as a polygon.

The Technology Transfer (T2) Program is a nationwide effort financed jointly by the Federal Highway Administration and individual state departments of transportation. Its purpose is to transfer the latest state-of-the-art technology in the areas of roads and bridges by translating the technology into terms understood by local and state highway or transportation personnel.Filter by county using the codes below and the query: example Peoria (INV_CO = '072')

001 ADAMS

019 DE KALB

037 HENRY

055 MC DONOUGH

073 PERRY

091 UNION

002 ALEXANDER

020 DE WITT

038 IROQOUIS

056 MC HENRY

074 PIATT

092 VERMILION

003 BOND

021 DOUGLAS

039 JACKSON

057 MC LEAN

075 PIKE

093 WABASH

004 BOONE

022 DU PAGE

040 JASPER

058 MACON

076 POPE

094 WARREN

005 BROWN

023 EDGAR

041 JEFFERSON

059 MACOUPIN

077 PULASKI

095 WASHINGTON

006 BUREAU

024 EDWARDS

042 JERSEY

060 MADISON

078 PUTNAM

096 WAYNE

007 CALHOUN

025 EFFINGHAM

043 JO DAVIESS

061 MARION

079 RANDOLPH

097 WHITE

008 CARROLL

026 FAYETTE

044 JOHNSON

062 MARSHALL

080 RICHLAND

098 WHITESIDE

009 CASS

027 FORD

045 KANE

063 MASON

081 ROCK ISLAND

099 WILL

010CHAMPAIGN

028 FRANKLIN

046KANKAKEE

064 MASSAC

082 ST. CLAIR

100 WILLIAMSON

011 CHRISTIAN

029 FULTON

047 KENDALL

065 MENARD

083 SALINE

101 WINNEBAGO

012 CLARK

030 GALLATIN

048 KNOX

066 MERCER

084 SANGAMON

102 WOODFORD

013 CLAY

031 GREENE

049 LAKE

067 MONROE

085 SCHUYLER

014 CLINTON

032 GRUNDY

050 LA SALLE

068 MONTGOMERY

086 SCOTT

015 COLES

033 HAMILTON

051 LAWRENCE

069 MORGAN

087 SHELBY

016 COOK

034 HANCOCK

052 LEE

070 MOULTRIE

088 STARK

017 CRAWFORD

035 HARDIN

053 LIVINGSTON

071 OGLE

089 STEPHENSON

018 CUMBERLAND

036 HENDERSON

054 LOGAN

072 PEORIA

090 TAZEWELL

Bedrock maps bedrock geology units and major faults for Maine at 1:500,000 scale. The dataset was developed by the Maine Geological Survey (MGS) from the "Bedrock Geologic Map of Maine, Osberg, Hussey, and Boone, 1985". The data for this dataset were scanned off 1:500,000 scale mylars by the United States Geological Survey (USGS) in 1987. The original bedrock unit codes were added by the J.W. Sewall Co. in 1990 for the Maine Low-Level Radioactive Waste Authority. In 1994, staff at MGS identified and added codes for major bedrock faults. Bedrock UNIT codes assigned to this dataset are available in comma delimited text, and .dbf format, on the Maine GIS Data Catalog.

This dataset contains the simplified unit data for the currently published Simplified Bedrock Geologic Map of Maine modified from Osberg, P. H., Hussey, A. M., II, and Boone, G. M., Bedrock Geologic Map of Maine, 1985, Maine Geological Survey.

This dataset contains the generalized regional metamorphic zones data for the currently published Simplified Bedrock Geologic Map of Maine modified from Osberg, P. H., Hussey, A. M., II, and Boone, G. M., Bedrock Geologic Map of Maine, 1985, Maine Geological Survey.

PRIORITY CATEGORIESThe Southeast Blueprint covers 50% of the SECAS geography, as described below.Priorities for a Connected Network of Lands & WatersHighest priority: Areas where conservation action would make the biggest impact, based on a suite of natural and cultural resource indicators. This class covers roughly 10% of the Southeast Blueprint geography.High priority: Areas where conservation action would make a big impact, based on a suite of natural and cultural resource indicators. This class covers roughly 15% of the Southeast Blueprint geography.Medium priority: Areas where conservation action would make an above-average impact, based on a suite of natural and cultural resource indicators. This class covers roughly 20% of the Southeast Blueprint geography.Priority connections: Connections between priority areas that cover the shortest distance possible while routing through as much Blueprint priority as possible. This class covers roughly 5% of the Southeast Blueprint geography.COMBINING ZONATION RESULTS WITH CORRIDORS TO CREATE THE SOUTHEAST BLUEPRINTInput Data2024 Southeast Blueprint combined Zonation results(for continental)Southeast Blueprint 2024 hubs and corridors (for continental)2024 Southeast Blueprint subregions2023 combined Zonation results (for Caribbean)Southeast Blueprint 2023 hubs and corridors(for Caribbean)Mapping StepsCreating the Continental BlueprintStart with the mosaiced, rebalanced, integer Zonation scores for all continental subregions. In this layer, each pixel in the continental Southeast Blueprint geography has a continuous value ranging from 0 to 100 according to its rank by Zonation prioritization, rebalanced by linear rescale.Pixels with values >89 are in the highest tier of indicator value. Select all pixels with values >89 and classify them as “highest priority for a connected network of lands and waters”.Pixels with values >74 that aren’t already classified as highest priority are in the second-highest tier of indicator value. Select all pixels >74 and ≤89 and classify them as “high priority for a connected network of lands and waters”.Pixels with values >55 that aren’t already classified as highest or high priority are in the third-highest tier of indicator values. Select all pixels >55 and ≤74 and classify them as “medium priority for a connected network of lands and waters”. This makes up the first portion of the medium priority class.Add to the medium priority class any inland hubs used in the connectivity analysis that that were not already classified as highest, high, or medium priority in the steps above. This ensures that the large patches of protected lands used as hubs in the connectivity analysis can score no lower than medium priority in the Blueprint. This adds an additional 0.7% of total area to the medium priority class.Use the inland continental corridors to fill in the priority connections class. Classify as “priority connections” any pixel identified as a corridor in the inland corridor analysis that is not already assigned to the highest, high or medium priority categories in the steps above. This contributes an additional 5% to the total Blueprint area, ensuring the final Blueprint ultimately covers 50% of the Southeast Blueprint landscape.Creating the Caribbean BlueprintNote: Since we only updated the continental portion of the Blueprint in Southeast Blueprint 2024, to create the Caribbean portion of Southeast Blueprint 2024, we simply clipped the 2023 Southeast Blueprint to the Caribbean subregion. However, we provide the previous year's input data and mapping steps for clarity. As a result, the mapping steps for the Caribbean portion will continue to refer to version 2023.Start with the mosaiced, rebalanced Zonation scores for the Caribbean subregion. In this layer, each pixel in the Caribbean Blueprint geography has a continuous value ranging from 0 to 100 according to its rank by Zonation prioritization, rebalanced by linear rescale.Pixels with values >89 are in the highest tier of indicator value. Select all pixels with values >89 and classify them as “highest priority for a connected network of lands and waters”.Pixels with values >74 that aren’t already classified as highest priority are in the second-highest tier of indicator value. Select all pixels >74 and ≤89 and classify them as “high priority for a connected network of lands and waters”.Pixels with values >54 that aren’t already classified as highest or high priority are in the third-highest tier of indicator value. Select all pixels >54 and ≤74 and classify them as “medium priority for a connected network of lands and waters”. This makes up the first portion of the medium priority class.Add to the medium priority class any hubs used in the Caribbean connectivity analysis that that were not already classified as highest, high, or medium priority in the steps above. This ensures that the large patches of protected lands used as hubs in the connectivity analysis can score no lower than medium priority in the Blueprint. This adds an additional 1% of total area to the medium priority class.Use the Caribbean corridors to fill in the priority connections class. Classify as “priority connections” any pixel identified as a corridor in the corridor analysis that is not already assigned to the highest, high or medium priority categories in the steps above. This contributes an additional 5% to the total Blueprint area, ensuring the final Blueprint ultimately covers 50% of the Southeast Blueprint landscape.Combining the Continental & Caribbean Components into Southeast Blueprint 2024As a final step, combine the continental and Caribbean results into a single raster representing final Southeast Blueprint 2024. Do this using the ArcPy Cell Statistics “MAX” function.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.KNOWN ISSUESContinentalTerrestrial - UplandsSome managed private grasslands are underprioritized. Examples include Prairie Wildlife grasslands west of Vinton, MS; an area southeast of Guadalupe Mountains National Park in TX; East Foundation lands in South TX; Dixon Water Foundation lands in West and North TX; remnant prairie north of Brookston, TX; grasslands of special significance southeast of Starkville, MS and northwest of Egypt, MS; a grassland restoration area northeast of Starkville, MS; and sections of Southwest MO. Improvements to the fire frequency and grassland indicators could fix this in the future.Some managed public grasslands are underprioritized. Examples include Perryville Battlefield State Historic Site in KY; Taylor Fork Ecological Area near Richmond, KY; multiple prairies in MO (McGee Family Conservation Area, Jerry Smith Park, and Stilwell Prairie). These will likely be fixed in the next version of the Blueprint.Some important riverscour grasslands downstream of major dams are underprioritized (e.g., part of the Rockcastle River in Daniel Boone National Forest in KY). Improvements to the reservoir mask, which currently removes these areas from the prioritization, could fix this in the future.Parts of some important ecological corridors are underprioritized. Examples include parts of the corridor between Ocmulgee Mounds National Historic Park and Bond Swamp National Wildlife Refuge in GA; the South Fork of the Forked Deer River in TN; parts of the corridor between Fort Campbell, Land Between the Lakes, and Clarks River National Wildlife Refuge in KY and TN; some of the areas from Alligator River National Wildlife Refuge to Pocosin Lakes National Wildlife Refuge in NC; multiple corridors coming out of Okefenokee Swamp in GA; and the Osceola to Ocala corridor in FL. Improvements to prioritization methods and indicators will likely fix these in the future.Some patches of open pine with good local conditions are underprioritized. Examples include parts of Yellow River Marsh Preserve State Park in FL; important gopher tortoise habitat in an area just east of Mauk, GA; a pitcher plant flat south of Rowlands, MS; the Farmer's Home tract managed by Mississippi Sandhill Crane National Wildlife Refuge; wet pine savanna mitigation bank areas east of Pearl River Wildlife Management Area, MS; longleaf pine in Sehoy Plantation in AL, longleaf south of Pine Hill, NC; savanna northwest of Mississippi Sandhill Crane National Wildlife Refuge in MS; and a shortleaf pine site north of Marshes Siding in Daniel Boone National Forest in KY. Ongoing updates to the grasslands and savannas and fire frequency indicators could continue to improve this issue in future updates.Some parts of small, low-elevation islands are underprioritized. The exact boundaries of these highly dynamic islands can be hard to predict. The boundaries used in the islands indicator and areas used for critical habitat of key island species don’t always align perfectly—especially in the most dynamic parts of the island. A potential improvement to address this is under investigation. Examples include Tybee Bar in GA, Crab Bank Seabird Sanctuary in SC, Lanark Reef in FL, and the Chandeleur Islands off of LA.Some recently developed areas are overprioritized (e.g., a solar field near Wedgefield, FL; a limestone barren west of Lime, TN; and the Moncure Megasite in NC). Updated landcover and indicator updates based on newer landcover should fix this issue.Some new conservation areas where restoration has only started recently are underprioritized. Examples include Wolfe Creek Forest in FL, roadside and savanna restoration sites in Daniel Boone National Forest in KY, and the Wolf River corridor in MS. Updated landcover and indicator updates based on newer landcover should fix this issue.Some important urban natural areas are underprioritized. Examples include Kapok Park in Clearwater, FL; the West Atlanta Watershed Alliance education hub in Atlanta, GA; Lost Corner Preserve in

This dataset contains the generalized Northern Appalachain geology data for the currently published Simplified Bedrock Geologic Map of Maine modified from Osberg, P. H., Hussey, A. M., II, and Boone, G. M., Bedrock Geologic Map of Maine, 1985, Maine Geological Survey.