Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

An Agreement is a legal contract, where developers agree to install and convey water and/or sewer infrastructure to support their development. Agreement projects are created by WASD New Business Section in eBuilder and automatically digitized in GIS. Attribute data is imported from eBuilder.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

A letter of availability is a customer request for a proposed development. Letters of Availability are created by WASD New Business Section in eBuilder and automatically digitized in GIS. Attribute data is imported from eBuilder.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

Building footprints from the 2011 LiDAR project. Includes outlines of buildings with an area of 40 square feet or greater. Automated classification of buildings performed using TerraScan. Manual cleanup of building classification was then carried out within point cloud data using TerraScan or LP360. Building footprints were digitized automatically using the LP360 building extraction feature. Footprints cleaned up manually using ArcGIS.This dataset is static and has not been edited since its original delivery.

A verification form is a certification of water and/or sewer infrastructure availability and capacity that will support a project. Verification Forms are created by WASD New Business Section in eBuilder and automatically digitized in GIS. Attribute data is imported from eBuilder.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

(Link to Metadata) RF 5000 NAD83 orthophoto edge lines (4000 x 4000 meter grid cells) were generated automatically from the known corner locations (generated by Gary Smith). Corner tics were added by VCGI. Nodes-intersections at the corner of each ortho tile (polygon) was converted into a point data layer. The Arc/Info NEAR command was then used to transfer tic IDs from the ORTHO data layer to this data layer. These points were then converted to tics and appended to this coverage. These corners apply to only to NAD83 orthophotos (digital and hardcopy). The corners of digital orthophotos DO NOT precisely match the older corners/boundaries in the BoundaryTile_ORTHO27 data layer. Corners and boundaries have been shifted on the X-axis (easting) by approximately 135 meters to the west, and 8 meters in the Y-axis (northing) . Please refer to BoundaryTile_ORTHO27 data layer documentation for additional information. The corner tic IDs and IDTIC attributes contain the same numbering scheme used in the BoundaryTile_ORTHO27 coverage (even though corners and boundaries are not the same). These tics can be used to digitize NAD83 orthophotos . The BoundaryTile_ORTHO27 data layer should be used when digitizing from NAD27 orthophotos. The ORTHO data layer should NOT be used for digitizing.

An Ordinance Letter is a compliance letter associated to a wholesale water and sewer customer development. Ordinance Letters are created by WASD New Business Section in eBuilder and automatically digitized in GIS. Attribute data is imported from eBuilder.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

This Landslide Compilation Scarps feature class represents landslide scarps throughout Utah, is the result of multiple landslide compilations (that include landslide scarps) and digitizing efforts by the Utah Geological Survey (UGS), and is not new landslide-specific mapping. Harty (1992, 1993) produced a statewide landslide scarp compilation on 46 30’ x 60’ quadrangle maps at 1:100,000 scale. Landslide scarps were compiled from all known pre-1989 published and unpublished references available at the time (Harty, 1992, 1993). The Utah Automated Geographic Reference Center (AGRC) digitized the 30’ x 60’ Harty (1992, 1993) quadrangle maps to create a statewide landslide scarp feature class. Elliott and Harty (2010) updated the AGRC feature class by adding additional landslides and scarps from 1989 to mid-2007 geologic maps and internal UGS landslide investigations. Elliott and Harty (2010), also added additional fields to the feature class. As a compilation, this data represents existing mapping from multiple sources, at a variety of scales and accuracies, and not new detailed comprehensive mapping of landslides. Last updated March, 2016.

This dataset contains files created, digitized, or georeferenced by Chris DeRolph for mapping the pre-urban renewal community within the boundaries of the Riverfront-Willow St. and Mountain View urban renewal projects in Knoxville TN. Detailed occupant information for properties within boundaries of these two urban renewal projects was extracted from the 1953 Knoxville City Directory. The year 1953 was chosen as a representative snapshot of the Black community before urban renewal projects were implemented. The first urban renewal project to be approved was the Riverfront-Willow Street project, which was approved in 1954 according to the University of Richmond Renewing Inequality project titled ‘Family Displacements through Urban Renewal, 1950-1966’ (link below in the 'Other shapefiles' section). For ArcGIS Online users, the shapefile and tiff layers are available in AGOL and can be found by clicking the ellipsis next to the layer name and selecting 'Show item details' for the layers in this webmap https://knoxatlas.maps.arcgis.com/apps/webappviewer/index.html?id=43a66c3cfcde4f5f8e7ab13af9bbcebecityDirectory1953 is a folder that contains:JPG images of 1953 City Directory for street segments within the urban renewal project boundaries; images collected at the McClung Historical CollectionTXT files of extracted text from each image that was used to join occupant information from directory to GIS address datashp is a folder that contains the following shapefiles:Residential:Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and property ownersBlack_rented_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and non-owners of the propertyNon_Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as property owners that were not listed as BlackNon_Black_rented_residential_1953.shp: residential entries in the 1953 City Directory not listed as Black or property ownersResidential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposeslastName: occupant's last namelabelShort: combines the Number and lastName fields for map labeling purposesNon-residential:Black_nonResidential_1953.shp: non-residential entries in the 1953 City Directory listed as Black-occupiedNonBlack_nonResidential_1953.shp: non-residential entries in the 1953 City Directory not listed as Black-occupiedNon-residential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationNAICS6: 2022 North American Industry Classification System (NAICS) six-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS6title: NAICS6 title/short descriptionNAICS3: 2022 North American Industry Classification System (NAICS) three-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS3title: NAICS3 title/short descriptionflag: flags whether the occupant is part of the public sector or an NGO; a flag of '0' indicates the occupant is assumed to be a privately-owned businessrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposesOther shapefiles:razedArea_1972.shp: approximate area that appears to have been razed during urban renewal based on visual overlay of usgsImage_grayscale_1956.tif and usgsImage_colorinfrared_1972.tif; digitized by Chris DeRolphroadNetwork_preUrbanRenewal.shp: road network present in urban renewal area before razing occurred; removed attribute indicates whether road was removed or remains today; historically removed roads were digitized by Chris DeRolph; remaining roads sourced from TDOT GIS roads dataTheBottom.shp: the approximate extent of the razed neighborhood known as The Bottom; digitized by Chris DeRolphUrbanRenewalProjects.shp: boundaries of the East Knoxville urban renewal projects, as mapped by the University of Richmond's Digital Scholarship Lab https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram&city=knoxvilleTN&loc=15/35.9700/-83.9080tiff is a folder that contains the following images:streetMap_1952.tif: relevant section of 1952 map 'Knoxville Tennessee and Surrounding Area'; copyright by J.U.G. Rich and East Tenn Auto Club; drawn by R.G. Austin; full map accessed at McClung Historical Collection, 601 S Gay St, Knoxville, TN 37902; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphnewsSentinelRdMap_1958.tif: urban renewal area map from 1958 Knox News Sentinel article; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphusgsImage_grayscale_1956.tif: May 18, 1956 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/ARA550590030582/usgsImage_colorinfrared_1972.tif: April 18, 1972 color infrared USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR6197002600096/usgsImage_grayscale_1976.tif: November 8, 1976 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR1VDUT00390010/

The Multi-Temporal Landslide Inventory for the Far-Western region of Nepal datasets comprises 26350 different landslide events digitize in form of polygons from Google Earth satellite imagery interpretation. In Google earth has been used for interpretation 93 different sources for 79 different time slices between 2002 and 2018. The maximum scale of interpretation used is 1:1000, meanwhile the scale of digitalization was constant between 1:800 and 1:2000, resulting in a final visualization scale of 1:1000. All landslides in the inventory have been classified between deep-seated and shallow types (attribute field "Depth") by visual interpretation which have been later corroborated with calculations of the elevation differences within the surface of rupture area of the landslides

The dataset comprises 4 different shapefiles:

• "LandslideInventory_FarWesternNepal_Pol.shp": Shapefile with 26350 Polygon features that bound completely the “zone of depletion” and partially the “zone of accumulation” of each identified landslide. Including completely the surface of rupture and more or less partially the depositional zone of the landslides. Landslide
• "LandslideInventory_FarWesternNepal_Points.shp": Shapefile with 25639 Point features that approximately correspond with the center of the surface of rupture area, the point location within each landslide has ben extracted automatically with GIS tools using ALOS PALSAR (12.5 m) DEM.
• "LandslideInventory_FarWesternNepal_Points_Dated1992_2018.shp": Shapefile with 8778 Point features for landslides in the inventory that have been dated within the period 1992-2018 (attribute field "Year". The dating of the landslides has been perform automatically by an own new toolbox in ArcGIS that compare annual Landsat (4-5, 7 and 8), to find sudden vegetation changes within the areas of the digitized landsldies. The tool has an accuracy of 83% to detect annual dates of activation or reactivations of the inventoried landslides.
• "LandslideInventory_FarWesternNepal_AOI.shp": Shapefile with the Polygon boundary of the landslide inventory Area of Interpretation.

All shapefiles are in a UTM projected coordinate system UTM44N (WGS84).

This research was funded by the UK Natural Environment Research Council (NERC) and Department for International Development (DFID) as project NE/P000452/1 (LandslideEVO) under the Science for Humanitarian Emergencies and Resilience (SHEAR) program.

(Link to Metadata) EmergencyE911_RDS was originally derived from RDSnn (now called TransRoad_RDS). "Zero-length ranges" in the ROADS layer pertain to grand-fathered towns that have not yet provided the Enhanced 9-1-1 Board road segment range information. RDSnn was originally developed using a combination of paper and RC Kodak RF 5000 orthophotos (visual image interpretation and manual digitizing of centerlines). Road attributes (RTNO and CLASS) were taken from the official VT Agency of Transportation (VTrans) highway maps. New roads not appearing on the photos were digitized with locations approximated from the VTrans highway maps. State Forest maps were used to determine both location and attributes of state forest roads. Some data updates have used RF 2500 or RF 1250 orthophotos and GPS, or other means for adding new roads and improving road locations. The Enhanced E911 program added new roads from GPS and orthos between 1996-1998. Also added road name and address geocoding. VCGI PROCESSING (Tiling and Added items); E911 provides the EmergencyE911_RDS data to VCGI in a statewide format. It lacks FIPS6 coding, making it difficult to extract data on the basis of town/county boundaries. As a result, VCGI has added FIPS6 to the attribute table. This field was originally populated by extracting MCODE value from RDNAME and relating to TBPOLY.PAT to bring over matching MCODE values. FIPS6 problems along the interstates and "Gores & Grants" in the Northeast Kingdom, were corrected. All features with an MCODE equal to 200 or 579 were assigned a FIPS6 equal to 0. The center point of these arcs were then intersected with BoundaryTown_TBHASH to assign a FIPS6 value. This information was then transfered back into the RDS.AAT file via a relate. A relate was established between the ROADNAMES.DBF file (road name lookup table) and the RDS.AAT file. The RDFLNAME attribute was populated by transfering the NAME value in the ROADNAMES.DBF table. The RDFLNAME item was then parsed into SUF.DIR, STREET.NAME, STREET.TYPE, and PRE.DIR, making addressing matching functions a little easier. See the "VT Road Centerline Data FAQ" for more information about TransRoad_RDS and EmergencyE911_RDS. https://vcgi.vermont.gov/techres/?page=./white_papers/default_content.cfmField Descriptions:OBJECTID: Internal feature number, automatically generated by Esri software.SEGMENTID: Unique segment ID.ARCID: Arc identifier, unique statewide. The ARCID is a unique identifier for every ARC in the EmergencyE911_RDS data layer.PD: Prefix Direction, previously name PRE.DIR.PT: Prefix Type.SN: Street Name. Previously named STREET.ST: Street Type.SD: Suffix Direction, i.e., W for West, E for East, etc.GEONAMEID: Unique ID for each road name.PRIMARYNAME: Primary name.ALIAS1: Alternate road name 1.ALIAS2: Alternate road name 2.ALIAS3: Alternate road name 3.ALIAS4: Alternate road name 4.ALIAS5: Alternate road name 5.COMMENTS: Free text field for miscellaneous comments.ONEWAY: One-way street. Uses the Oneway domain*.NO_MSAG:MCODE: Municipal code.LESN: Left side of road Emergency Service Number.RESN: Right side of road Emergency Service Number.LTWN: Left side of road town.RTWN: Right side of road town.LLO_A: Low address for left side of road.RLO_A: Low address for right side of road.LHI_A: High address for left side of road.RHI_A: High address for right side of road.LZIP: Left side of road zip code.RZIP: Right side of road zip code.LLO_TRLO_TLHI_TRHI_TRTNAME: Route name.RTNUMBER: Route number.HWYSIGN: Highway sign.RPCCLASSAOTCLASS: Agency of Transportation class. Uses AOTClass domain**.ARCMILES: ESRI ArcGIS miles.AOTMILES: Agency of Transportation miles.AOTMILES_CALC:UPDACT:SCENICHWY: Scenic highway.SCENICBYWAY: Scenic byway.FORMER_RTNAME: Former route name.PROVISIONALYEAR: Provisional year.ANCIENTROADYEAR: Ancient road year.TRUCKROUTE: Truck route.CERTYEAR:MAPYEAR:UPDATEDATE: Update date.GPSUPDATE: Uses GPSUpdate domain***.GlobalID: GlobalID.STATE: State.GAP: Gap.GAPMILES: Gap miles.GAPSTREETID: Gap street ID.FIPS8:FAID_S:RTNUMBER_N:LCOUNTY:RCOUNTY:PRIMARYNAME1:SOURCEOFDATA: Source of data.COUNTRY: Country.PARITYLEFT:PARITYRIGHT:LFIPS:RFIPS:LSTATE:RSTATE:LESZ:RESZ:SPEED_SOURCE: Speed source.SPEEDLIMIT: Speed limit.MILES: Miles.MINUTES: Minutes.Shape: Feature geometry.Shape_Length: Length of feature in internal units. Automatically computed by Esri software.*Oneway Domain:N: NoY: Yes - Direction of arcX: Yes - Opposite direction of arc**AOTClass Domain:1: Town Highway Class 1 - undivided2: Town Highway Class 2 - undivided3: Town Highway Class 3 - undivided4: Town Highway Class 4 - undivided5: State Forest Highway6: National Forest Highway7: Legal Trail. Legal Trail Mileage Approved by Selectboard after the enactment of Act 178 (July 1, 2006). Due to the introduction of Act 178, the Mapping Unit needed to differentiate between officially accepted and designated legal trail versus trails that had traditionally been shown on the maps. Towns have until 2015 to map all Class 1-4 and Legal Trails, based on new changes in VSA Title 19.8: Private Road - No Show. Private road, but not for display on local maps. Some municipalities may prefer not to show certain private roads on their maps, but the roads may need to be maintained in the data for emergency response or other purposes.9: Private road, for display on local maps10: Driveway (put in driveway)11: Town Highway Class 1 - North Bound12: Town Highway Class 1 - South Bound13: Town Highway Class 1 - East Bound14: Town Highway Class 1 - West Bound15: Town Highway Class 1 - On/Off Ramp16: Town Highway Class 1 - Emergency U-Turn20: County Highway21: Town Highway Class 2 - North Bound22: Town Highway Class 2 - South Bound23: Town Highway Class 2 - East Bound24: Town Highway Class 2 - West Bound25: Town Highway Class 2 - On/Off Ramp30: State Highway31: State Highway - North Bound32: State Highway - South Bound33: State Highway - East Bound34: State Highway - West Bound35: State Highway - On/Off Ramp40: US Highway41: US Highway - North Bound42: US Highway - South Bound43: US Highway - East Bound44: US Highway - West Bound45: US Highway - On/Off Ramp46: US Highway - Emergency U-Turn47: US Highway - Rest Area50: Interstate Highway51: Interstate Highway - North Bound52: Interstate Highway - South Bound53: Interstate Highway - East Bound54: Interstate Highway - West Bound55: Interstate Highway - On/Off Ramp56: Interstate Highway - Emergency U-Turn57: Interstate Highway - Rest Area59: Interstate Highway - Other65: Ferry70: Unconfirmed Legal Trail71: Unidentified Corridor80: Proposed Highway Unknown Class81: Proposed Town Highway Class 182: Proposed Town Highway Class 283: Proposed Town Highway Class 384: Proposed State Highway85: Proposed US Highway86: Proposed Interstate Highway87: Proposed Interstate Highway - Ramp88: Proposed Non-Interstate Highway - Ramp89: Proposed Private Road91: New - Class Unknown92: Military - no public access93: Public - Class Unknown95: Class Under Review96: Discontinued Road97: Discontinued Now Private98: Not a Road99: Unknown***GPSUpdate Domain:Y: Yes - Needs GPS UpdateN: No - Does not need GPS UpdateG: GPS Update CompleteV: GPS Update Complete - New RoadX: Unresolved Segment

Authority In the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas. The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies. Previous Methods The land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information. After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets. In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS). For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking. In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes. Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data. Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side. The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location. Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map. Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles. The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI). Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process. Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Present Methodology Using the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized. Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed. Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies. In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas. Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county. During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state. Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands.

Update May 2015 - This dataset has been rename from "Brattstrand Bluff penguin GIS dataset" to "Islands NE of Brattstrand Bluff penguin GIS dataset" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The vegetation map for Fort Davis NHS was developed using a combined strategy of automated digital image classification and direct analog image interpretation of aerial photography and satellite imagery. Initially, the aerial photography and satellite imagery were processed and entered into a geographic information system (GIS) along with ancillary spatial layers. A working map legend of ecologically based vegetation map units was developed using the vegetation classification described in Chapter 2 as the foundation. The intent was to develop map units that targeted the plant-association level wherever possible within the constraints of image quality, information content, and resolution. With the provisional legend and ground-control points provided by the field-plot data (the same data used to develop the vegetation classification), a combination of hands-on manual digitizing on a screen (heads-up screen digitizing) of polygons based on image interpretation and supervised image classifications was conducted. The outcome was a vegetation map composed of a suite of map units defined by plant associations and represented by sets of mapped polygons with similar spectral and site characteristics

This shapefile contains information about the shallow-water (<40 meters) geology and biology of the seafloor in Coral Bay, St. John in the U.S. Virgin Islands (USVI). It was created by manually delineating and classifying habitats visible in a 0.3x0.3 meter aerial photograph mosaic, and by using edge detection algorithms and boosted regression trees to automatically delineate and classify habitat features visible in 0.3x0.3 meter LiDAR surfaces. Habitat features less than 100 square meters were not delineated from the orthomosaic, and were removed from habitat polygons derived from the LiDAR surfaces using ET Geowizards ArcGIS extension. Manually delineated habitat polygons were digitized at a scale of 1:1,000. Habitat polygon boundaries derived from the LiDAR surfaces were smoothed in ArcGIS to more closely match the 1:1,000 scale used for manual digitizing. Georeferenced underwater video & photos were used to train the analyst and algorithm to classify the major and detailed geomorphological structure, percent hard bottom, major and detailed biological cover and live coral cover for each polygon. The thematic accuracy of the map was assessed qualitatively by local experts and quantitatively using randomly sampled locations stratified by detailed geomorpholoigcal structure type. Thematic accuracies for major and detailed geomorphological structure, percent hardbottom, major and detailed biological cover, live coral cover and dominant coral type were: 93.0%, 75.1%, 86.2%, 86.5%, 74.5%, 83.3% and 88.2%, respectively. These thematic accuracies are similar to the thematic accuracies reported for other NOAA benthic habitat mapping efforts around Buck Island in St. Croix (>81.4%), in St. John (>80%), in the Main Eight Hawaiian Islands (>84.0%) and in the Republic of Palau (>80.0%).

Manitoba Infrastructure --> Highway Engineering--> Highway Planning and Design (HPD) manages the Highway Inventory System (HIS). This system tracks road structure and construction history of Manitoba's Road Network. Published annually, this layer aids provincial engineers in planning and managing construction projects, both present and future. The department’s linear referencing system (LRS) uses control sections as a linear referencing method (LRM). These control section are further divided into subsections. It is on these subsections that road structure and construction history are tracked in the highway inventory system database. This is the first year that HPD is making its annual HIS export available to the public. Please note that this feature layer primarily contains only the roads Manitoba Infrastructure is responsible for, i.e. Provincial Trunk Highways, Provincial Roads and Access Roads. It does not include municipal mile or grid roads. However, you may also see Earth Roads (under construction) or Other roads (such as those passing through national parks and are federal responsibility).A newer version of this data is available: Manitoba Highway Inventory 2018. Field List - FIELD NAME (Field Alias (i.e. display name)) in parenthesis) OBJECTID (OBJECTID) - Sequential, unique whole numbers are automatically generated. SHAPE (SHAPE) - A field to hold geometry information. ID (ID) - Oracle's unique identifier CS_ID (Control Section ID) - Non unique identifier linking segment to its respective control section. For example, a control section may have numerous geometry segments and each will have the same CS_ID. CS_KEY (Control Section Key) - An information rich key containing Region (two digits), Road Number (three digits), Section number (three digits), the Road Type (one character), and direction of travel (one character). SS_ID (Subsection ID) - Control Sections are further divided into Subsections. This is the subsection ID field. REGION_NO (MI Region) - The Manitoba Infrastructure region the segment is in Regions 1-5. ROAD_NO (Road Number) - The road number. Note co-routes will have the lesser highway number. SECTION_NO (Section Number) - The section number from the CS_KEY. ROAD_TYPE (Road Type) - H = Highway, E = Earth Road, and O= 'Other" road (such as through a national park because the road is not a responsibility of MI.) ROAD_DIRECTION (Road Direction) - A = ahead direction on a divided highway (i.e. following the digitizing direction), B = Back direction on a divided highway (i.e. against the digitizing direction, and U= an undivided highway. START_KM (Start Kilometre (KM)) - Start KM, is the start of the sub section within a control Section. This is part of the linear referencing method used for dynamic segmentation. END_KM (End Kilometre (KM)) - End KM is the end of the sub section within a Control Section. This is part of the linear referencing method used for dynamic segmentation. LENGTH_KM (Length (KM)) - End Kilometre minus the Start Kilometre. SECTION_DESC (Subsection Description) - A text description of where the subsection starts and ends. ROAD_TYPE_DESC (Road Type Description) - An unabbreviated version of Road Type above. ROAD_DIRECTION_DESC (Road Direction Description) - An unabbreviated version of Road Direction above. FUNCTIONAL_CLASS (Functional Class) - Functional Class: Expressway, Primary Arterial, Secondary Arterial and Collector DIVIDED_STATUS (Divided Status) - Highway is divided or undivided. LANES (Number of Lanes) - The number of lanes the road has. MEDIAN_TYPE (Median Type) - Type of median: Raised Median, Depressed Median, Flush Median, Barrier and No Median. MEDIAN_WIDTH (Median Width (m)) - The width of the median, in metres. ROW_WIDTH (Row Width (m)) - The total width of the right-of-way in metres. SUBGRADE_WIDTH (Subgrade Width (m)) - The width of the earth embankment in metres. REGRADE_YEAR (Re-grade Year) - The year the road was last regraded. REGRADE_DESCRIPTION (Re-grade Description) - Description of what re-grading was done. OUTSIDE_SHLDR_TYPE (Outside Shoulder Type) - Type of outside shoulder: AST, Curbed, Fully Paved, Gravel, No Shoulders and N/A. OUTSIDE_SHLDR_WIDTH (Outside Shoulder Width (m)) - The width of the outside shoulder in metres. OUTSIDE_PAVED_WIDTH (Outside Paved Width (m)) - The width of the paved surface of the outside shoulder in metres. INSIDE_SHLDR_TYPE (Inside Shoulder Type) - Type of inside shoulder: Curbed, Fully Paved, Gravel, No Shoulders and N/A. INSIDE_SHLDR_WIDTH (Inside Shoulder Width (m)) - The width of the inside shoulder in metres. INSIDE_PAVED_WIDTH (Inside Paved Width (m)) - The width of the paved surface of the inside shoulder in metres. SURFACE_TYPE (Surface Type) - The type of surfacing on the road: A Base, AST, Bituminous (Bpm), Bituminous B, Bituminous C, C Base, Concrete, Granular, Reclaimed Asphalt Pavement and Road Mix SURFACE_WIDTH (Surface Width (m)) - The surface width is the width of the driving lanes. SURFACE_DEPTH (Surface Depth (mm)) - Thickness of the surface layer (mm). SURFACE_YEAR (Surface Year) - The year the surface was constructed. BASE_1 (Base Layer 1) - 1st subsurface layer. 1 = present, 0 = not present BASE_1_TYPE (Base Layer 1 Type) - Material type of 1st subsurface layer of the road structure. BASE_1_DEPTH (Base Layer 1 Depth (mm)) - Thickness of 1st subsurface layer of road structure in mm. BASE_1_YEAR (Base Layer 1 Year) - Year materials placed or modified for 1st subsurface layer. BASE_2 (Base Layer 2) - 2nd subsurface layer. 2 = present, 0 = not present BASE_2_TYPE (Base Layer 2 Type) - Material type of 2nd subsurface layer of road structure. BASE_2_DEPTH (Base Layer 2 Depth (mm)) - Thickness of 2nd subsurface layer of road structure in mm. BASE_2_YEAR (Base Layer 2 Year) - Year materials placed or modified for 2nd subsurface layer. BASE_3 (Base Layer 3) - 3rd subsurface layer. 3 = present, 0 = not present BASE_3_TYPE (Base Layer 3 Type) - Material type of 3rd subsurface layer of road structure. BASE_3_DEPTH (Base Layer 3 Depth (mm)) - Thickness of 3rd subsurface layer of road structure in mm. BASE_3_YEAR (Base Layer 3 Year) - Year materials placed or modified for 3rd subsurface layer. BASE_4 (Base Layer 4) - 4th subsurface layer. 4 = present, 0 = not present BASE_4_TYPE (Base Layer 4 Type) - Material type of 4th subsurface layer of road structure. BASE_4_DEPTH (Base Layer 4 Depth (mm)) - Thickness of 4th subsurface layer of road structure in mm. BASE_4_YEAR (Base Layer 4 Year) - Year materials placed or modified for 4th subsurface layer. BASE_5 (Base Layer 5) - 5th subsurface layer. 5 = present, 0 = not present BASE_5_TYPE (Base Layer 5 Type) - Material type of 5th subsurface layer of road structure. BASE_5_DEPTH (Base Layer 5 Depth (mm)) - Thickness of 5th subsurface layer of road structure in mm. BASE_5_YEAR (Base Layer 5 Year) - Year materials placed or modified for 5th subsurface layer. BASE_6 (Base Layer 6) - 6th subsurface layer. 6 = present, 0 = not present BASE_6_TYPE (Base Layer 6 Type) - Material type of 6th subsurface layer of road structure. BASE_6_DEPTH (Base Layer 6 Depth (mm)) - Thickness of 6th subsurface layer of road structure in mm. BASE_6_YEAR (Base Layer 6 Year) - Year materials placed or modified for 6th subsurface layer. BASE_7 (Base Layer 7) - 7th subsurface layer. 7 = present, 0 = not present BASE_7_TYPE (Base Layer 7 Type) - Material type of 7th subsurface layer of road structure. BASE_7_DEPTH (Base Layer 7 Depth (mm)) - Thickness of 7th subsurface layer of road structure in mm. BASE_7_YEAR (Base Layer 7 Year) - Year materials placed or modified for 7th subsurface layer.TERRAIN_TYPE (Terrain Type) - General terrain conditions: Flat, Rolling and Rugged. CONSTRUCTION_STATUS (Construction Status) - Construction completed or carried over. UPDATED_YYYYMMDD (Updated Date) - Date the record was updated. SHAPE_Length (Length) - Segment length, automatically generated by ArcGIS.

Road segments representing centerlines of all roadways or carriageways in a local government. Typically, this information is compiled from orthoimagery or other aerial photography sources. This representation of the road centerlines support address geocoding and mapping. It also serves as a source for public works and other agencies that are responsible for the active management of the road network. (From ESRI Local Government Model "RoadCenterline" Feature)**This dataset was significantly revised in August of 2014 to correct for street segments that were not properly split at intersections. There may be issues with using data based off of the original centerline file. ** The column Speed Limit was updated in November 2014 by the Transportation Intern and is believed to be accurate** The column One Way was updated in November of 2014 by core GIS and is believed to be accurate.[MAXIMOID] A unique id field used in a work order management software called Maximo by IBM. Maximo uses GIS CL data to assign locations to work orders using this field. This field is maintained by the Transportation GIS specialists and is auto incremented when new streets are digitized. For example, if the latest digitized street segment MAXIMOID = 999, the next digitized line will receive MAXIMOID = 1000, and so on. STREET NAMING IS BROKEN INTO THREE FIELDS FOR GEOCODING:PREFIX This field is attributed if a street name has a prefix such as W, N, E, or S.NAME Domain with all street names. The name of the street without prefix or suffix.ROAD_TYPE (Text,4) Describes the type of road aka suffix, if applicable. CAPCOG Addressing Guidelines Sec 504 U. states, “Every road shall have corresponding standard street suffix…” standard street suffix abbreviations comply with USPS Pub 28 Appendix C Street Abbreviations. Examples include, but are not limited to, Rd, Dr, St, Trl, Ln, Gln, Lp, CT. LEFT_LOW The minimum numeric address on the left side of the CL segment. Left side of CL is defined as the left side of the line segment in the From-To direction. For example, if a line has addresses starting at 101 and ending at 201 on its left side, this column will be attributed 101.LEFT_HIGH The largest numeric address on the left side of the CL segment. Left side of CL is defined as the left side of the line segment in the From-To direction. For example, if a line has addresses starting at 101 and ending at 201 on its left side, this column will be attributed 201.LOW The minimum numeric address on the RIGHT side of the CL segment. Right side of CL is defined as the right side of the line segment in the From-To direction. For example, if a line has addresses starting at 100 and ending at 200 on its right side, this column will be attributed 100.HIGHThe maximum numeric address on the RIGHT side of the CL segment. Right side of CL is defined as the right side of the line segment in the From-To direction. For example, if a line has addresses starting at 100 and ending at 200 on its right side, this column will be attributed 200.ALIAS Alternative names for roads if known. This field is useful for geocode re-matching. CLASSThe functional classification of the centerline. For example, Minor (Minor Arterial), Major (Major Arterial). THIS FIELD IS NOT CONSISTENTLY FILLED OUT, NEEDS AN AUDIT. FULLSTREET The full name of the street concatenating the [PREFIX], [NAME], and [SUFFIX] fields. For example, "W San Antonio St."ROWWIDTH Width of right-of-way along the CL segment. Data entry from Plat by Planning GIS Or from Engineering PICPs/ CIPs.NUMLANES Number of striped vehicular driving lanes, including turn lanes if present along majority of segment. Does not inlcude bicycle lanes. LANEMILES Describes the total length of lanes for that segment in miles. It is manually field calculated as follows (( [ShapeLength] / 5280) * [NUMLANES]) and maintained by Transportation GIS.SPEEDLIMIT Speed limit of CL segment if known. If not, assume 30 mph for local and minor arterial streets. If speed limit changes are enacted by city council they will be recorded in the Traffic Register dataset, and this field will be updating accordingly. Initial data entry made by CIP/Planning GIS and maintained by Transportation GIS.[YRBUILT] replaced by [DateBuilt] See below. Will be deleted. 4/21/2017LASTYRRECON (Text,10) Is the last four-digit year a major reconstruction occurred. Most streets have not been reconstructed since orignal construction, and will have values. The Transportation GIS Specialist will update this field. OWNER Describes the governing body or private entity that owns/maintains the CL. It is possible that some streets are owned by other entities but maintained by CoSM. Possible attributes include, CoSM, Hays Owned/City Maintained, TxDOT Owned/City Maintained, TxDOT, one of four counties (Hays, Caldwell, Guadalupe, and Comal), TxState, and Private.ST_FROM Centerline segments are split at their intersections with other CL segments. This field names the nearest cross-street in the From- direction. Should be edited when new CL segments that cause splits are added. ST_TO Centerline segments are split at their intersections with other CL segments. This field names the nearest cross-street in the To- direction. Should be edited when new CL segments that cause splits are added. PAV_WID Pavement width of street in feet from back-of-curb to back-of-curb. This data is entered from as-built by CIP GIS. In January 2017 Transportation Dept. field staff surveyed all streets and measured width from face-of-curb to face-of-curb where curb was present, and edge of pavement to edge of pavement where it was not. This data was used to field calculate pavement width where we had values. A value of 1 foot was added to the field calculation if curb and gutter or stand up curb were present (the face-of-curb to back-of-curb is 6 in, multiple that by 2 to find 1 foot). If no curb was present, the value enter in by the field staff was directly copied over. If values were already present, and entered from asbuilt, they were left alone. ONEWAY Field describes direction of travel along CL in relation to digitized direction. If a street allows bi-directional travel it is attributed "B", a street that is one-way in the From_To direction is attributed "F", a street that is one-way in the To_From direction is attributed "T", and a street that does not allow travel in any direction is attibuted "N". ROADLEVEL Field will be aliased to [MINUTES] and be used to calculate travel time along CL segments in minutes using shape length and [SPEEDLIMIT]. Field calculate using the following expression: [MINUTES] = ( ([SHAPE_LENGTH] / 5280) / ( [SPEEDLIMIT] / 60 ))ROWSTATUS Values include "Open" or "Closed". Describes whether a right-of-way is open or closed. If a street is constructed within ROW it is "Open". If a street has not yet been constructed, and there is ROW, it is "Cosed". UPDATE: This feature class only has CL geometries for "Open" rights-of-way. This field should be deleted or re-purposed. ASBUILT field used to hyper link as-built documents detailing construction of the CL. Field was added in Dec. 2016. DateBuilt Date field used to record month and year a road was constructed from Asbuilt. Data was collected previously without month information. Data without a known month is entered as "1/1/YYYY". When month and year are known enter as "M/1/YYYY". Month and Year from asbuilt. Added by Engineering/CIP. ACCEPTED Date field used to record the month, day, and year that a roadway was officially accepted by the City of San Marcos. Engineering signs off on acceptance letters and stores these documents. This field was added in May of 2018. Due to a lack of data, the date built field was copied into this field for older roadways. Going forward, all new roadways will have this date. . This field will typically be populated well after a road has been drawn into GIS. Entered by Engineering/CIP. ****In an effort to make summarizing the data more efficient in Operations Dashboard, a generic date of "1/1/1900" was assigned to all COSM owned or maintained roads that had NULL values. These were roads that either have not been accepted yet, or roads that were expcepted a long time ago and their accepted date is not known. WARRANTY_EXP Date field used to record the expiration date of a newly accepted roadway. Typically this is one year from acceptance date, but can be greater. This field was added in May of 2018, so only roadways that have been excepted since and older roadways with valid warranty dates within this time frame have been populated.

This Landslide Compilation Polygons feature class represents landslide deposits throughout Utah, is the result of multiple landslide compilations and digitizing efforts by the Utah Geological Survey (UGS), and is not new landslide-specific mapping. Harty (1992, 1993) produced a statewide landslide compilation on 46 30’ x 60’ quadrangle maps at 1:100,000 scale. Landslides were compiled from all known pre-1989 published and unpublished references available at the time (Harty, 1992, 1993). The Utah Automated Geographic Reference Center (AGRC) digitized the 30’ x 60’ Harty (1992, 1993) quadrangle maps to create a digital statewide landslide deposit feature class. Elliott and Harty (2010) updated the AGRC feature class by adding additional landslides from 1989 to mid-2007 geologic maps and internal UGS landslide investigations. Elliott and Harty (2010) also added additional fields to the feature class. As a compilation, this data represents existing mapping from multiple sources, at a variety of scales and accuracies, and not new detailed comprehensive mapping of landslides. Last updated March 2016.

Download .zipA soil mapping unit designates a specific type of soil which has unique characteristics including texture, slope, and erosion class.

The soil lines were raster scanned from inked mylars at 1 : 15,840 scale. Automated digitizing procedures were performed as found in the MAPLE SYRUP Manual (Soil Survey Orthorectification and Line Extraction). Raster to vector software was used for soil lines and CAD Software was used to clean-up. Labels were placed into polygons by visual alignment using CAD. Bodies of Water were alligned to an ortho-photo image except for areas less than two acres in size or areas that have been surface mined for coal since the Soil Survey was published. Areas less than two acres in size were shown as a point special feature labeled "WAT" in a separate coverage. Most errors found on the published soil maps were corrected by a soil scientist who referred to copies of the original soil survey field sheets. A few errors were field checked by soil scientists and corrected. Quality Assurance/ Quality control was conducted by the Ohio Department of Natural Resources. All soil line placements and labels were checked and verified. In addition label placement locations for soil polygons were moved to the centroid of polygons where possible or to other locations to prevent the overlap of labels from adjoining polygons and special features. ARC/INFO software was used to edgematch quarter quadrangles of soil data which were then merged into a county-wide layer. This coverage is presently being reviewed by the USDA, Natural Resources Conservation Service for compliance with SSURGO standards. This review may require that some changes be made to the data.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov Data Update Frequency: As Needed

Download .zipA soil mapping unit designates a specific type of soil which has unique characteristics including texture, slope, and erosion class.

The soil lines were raster scanned from inked mylars at 1 : 15,840 scale. Automated digitizing procedures were performed as found in the MAPLE SYRUP Manual (Soil Survey Orthorectification and Line Extraction). Raster to vector software was used for soil lines and CAD Software was used to clean-up. Labels were placed into polygons by visual alignment using CAD. Bodies of Water were alligned to an ortho-photo image except for areas less than two acres in size or areas that have been surface mined for coal since the Soil Survey was published. Areas less than two acres in size were shown as a point special feature labeled "WAT" in a separate coverage. Most errors found on the published soil maps were corrected by a soil scientist who referred to copies of the original soil survey field sheets. A few errors were field checked by soil scientists and corrected. Quality Assurance/ Quality control was conducted by the Ohio Department of Natural Resources. All soil line placements and labels were checked and verified. In addition label placement locations for soil polygons were moved to the centroid of polygons where possible or to other locations to prevent the overlap of labels from adjoining polygons and special features. ARC/INFO software was used to edgematch quarter quadrangles of soil data which were then merged into a county-wide layer. This coverage is presently being reviewed by the USDA, Natural Resources Conservation Service for compliance with SSURGO standards. This review may require that some changes be made to the data.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov Data Update Frequency: As Needed