This is a map of historical ("presettlement") vegetation for the state of Oregon, created with Arcview 3.2 by merging digital data from different sources: H. J. Andrews, General Land Office (GLO), Soil Survey Geographic (SSURGO), BLM, Oregon Gap analysis. The H. J. Andrews map forms the background into which more detailed coverages were incorporated. The data consist of an ArcView shapefile. Scale varies throughout the coverage, depending on the source of the data; the overall scale is 1:100,000.

This shapefile was derived from a predecessor coverage named "IN_LANDSURVEY." IN_LANDSURVEY is a digital representation of land survey features as represented on the USGS 1:24,000 digital raster graphic (DRG) series. ESRI ArcEdit 8.1 and ESRI ArcView 3.2 were used to extract the following shapefiles from IN_LANDSURVEY: LANDSURVEY_COUNTY_LINE_IN, LANDSURVEY_COUNTY_POLY_IN, LANDSURVEY_SECTIONS_LINE_IN, LANDSURVEY_SECTIONS_POLY_IN, LANDSURVEY_STATE_LINE_IN, LANDSURVEY_STATE_POLY_IN, LANDSURVEY_TOWNSHIPS_LINE_IN, and LANDSURVEY_TOWNSHIPS_POLY_IN.NOTE -- This shapefile is identical to a version named "INDIANA_STATEBDY_24K_IGS_L". This metadata file is a copy of the metadata for "INDIANA_STATEBDY_24K_IGS_L" that has had minor edits. The shapefile and metadata were renamed to conform to a file-naming convention for a project funded outside the IGS.

LANDSURVEY_COUNTY_POLY_IN is a 1:24,000-scale polygon shapefile, originally with a projection of Universal Transverse Mercator (UTM) North American Datum (NAD) 1983 zone 16. After republishing this shapefile by the IGIO, this data is with a projection of WGS84.This shapefile was derived from a predecessor coverage named "IN_LANDSURVEY." IN_LANDSURVEY is a digital representation of land survey features as represented on the USGS 1:24,000 digital raster graphic (DRG) series. ESRI ArcEdit 8.1 and ESRI ArcView 3.2 were used to extract the following shapefiles from IN_LANDSURVEY: LANDSURVEY_COUNTY_LINE_IN, LANDSURVEY_COUNTY_POLY_IN, LANDSURVEY_SECTIONS_LINE_IN, LANDSURVEY_SECTIONS_POLY_IN, LANDSURVEY_STATE_LINE_IN, LANDSURVEY_STATE_POLY_IN, LANDSURVEY_TOWNSHIPS_LINE_IN, and LANDSURVEY_TOWNSHIPS_POLY_IN.

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. Following the vegetation data analysis, the formation-level vegetation map was further edited and refined to develop an association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data and additional field observations. Each polygon was assigned one of eight vegetation association types based on plot data, field observations, aerial photography signatures, and topographic maps. An aerial photograph interpretation key for the vegetation associations was created. However, several associations could not be distinguished reliably by aerial photography signatures alone. Plot data, field observations, and topographic maps were relied upon in these circumstances to inform the polygon delineation and association name assignments. After the vegetation association map was completed, the thematic accuracy of this map was assessed.

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. Following the vegetation data analysis, the vegetation cover-type map was edited and refined to develop a preliminary association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. Each polygon was assigned the name of a preliminary vegetation association based on the five information sources listed above. A mirror stereoscope type F-71 and a Bausch and Lomb zoom stereoscope were used to interpret the aerial photography signatures. The field-collected “true” or “reference” GPS coordinates for the remaining 41 points were compared to the coordinates obtained from the mosaic viewed in ArcMap.

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.

Following the vegetation data analysis, the formation-level vegetation map was further edited and refined to develop an association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data and additional field observations. Each polygon was assigned one of seven vegetation associations based on plot data, field observations, aerial photography signatures, and topographic maps. An aerial photograph interpretation key for the vegetation associations and Anderson level II categories (modified) is located in Appendix A. After the vegetation association map was completed, the thematic accuracy of this map was assessed.

Edited: This dataset contains a 500 foot buffered subset of features for "Hard Banks" in the Northwest Gulf of Mexico. Features in this dataset were used to satisfy Texas Resource Management Codes Sensitive Areas definitions requirements. Original: This layer depicts shelf sediment textures, hard banks, and gravel deposits on the continental shelf of the U. S. Gulf of Mexico as a map and is summarized from 16 different sources (U. S. Department of the Interior 1983, Visual No. 3). The visual was prepared from existing sources to accompany an Environmental Impact Statement. The visual displays general classification of bottom sediments (using the Shepard pyramid) throughout the Gulf of Mexico. The U. S. Department of the Interior does not guarantee the accuracy of the map to the extent of responsibility of liability for reliance thereon. This visual is not to be used for navigation purposes, nor is it a legal document for federal leasing purposes. Polygons delineating sediment types in the Gulf of Mexico were digitized with ArcView 3.2a software and a CalComp Drawing Board III. The layer was modified from the map and stored in a non-projected format with coordinates in decimal degrees.Field Definition:For field definitions contact the National Oceanic and Atmospheric Administration

"Pre-European Vegetation Map of Boorowa Shire and surrounds.;\r Vegetation map based on classified vegetation survey data, and modelling layers, derived from a 25 metre Digital Elevation Model, and a composite geology map derived from Department of Minerals geology data. Data derived from the following sources: Digital elevation model in integer format, 25 m grid cells, produced 1997, Land Information Centre; Catchment variables derived from DEM, using Arcview 3.2; Geology data from 1:250 K Geology Map, Department of Mineral Resources of NSW; Derived Elevation, Slope Steepness, Drainage from DEM; Combined Geology and sub-catchments within Boorowa Shire; Derivation of individual grid layers for each map unit; Compilation of individual map units, using merge request function in Arcview 3.2; Derivation of vegetation mask, using Landsat ETM band 5 to create a native forest/woodland cover map; Intersection of pre-european vegetation map with M305 native woody vegetation map to produce extant layer.;\r ;\r Method used was based on expert modelling of vegetation types, based on consultant EcoGIS's (Nic Gellie) knowledge of distribution of similar vegetation types in relation to lithology and broad landscape variables. To reduce possible error in expert models, modelling zones based on a combination of lithology classes and sub-catchments were produced from expert examination of the spread and patterns of each vegetation group. The modelling zones helped to reduce the number of vegetation groups to be modelled down to 2-3 groups;\r Careful inspection of sites within each vegetation group helped to determine the broad environmental niche of each vegetation group. A table of possible relationships between vegetation groups and environmental variables was drawn up to help with the modelling process. It was clear that the patterns of vegetation in the study area were more influenced by geochemistry of the lithology classes and topographic position in the landscape, rather than the conventional aspect and moisture relationships found in coastal higher rainfall environments. This conclusion helped to determine the development of terrain variables that could separate vegetation groups that occurred predominantly on ridges and hillslopes from those vegetation groups that occurred in valley bottoms. A neighbourhood variable, using stream pattern derived from the watershed models within Arcview, helped to distinguish hillslopes from valley bottoms.;\r ;\r The modelling process enabled a complete audit of all vegetation types mapped in the study area and allowed a transparent and flexible process of mapping to be documented. In the event that detailed inspection of the results of the model or field validation resulted in possible changes to the map, individual modelling zones could be remodelled with the new knowledge, or new site data. This approach also prevented grid layers from spreading to areas where the vegetation groups would logically not occur in. When all modelling zones had been modelled, the resultant grid layers were then compiled into a single Arcview view. The data layers were then sorted into an agreed order of precedence that enabled each grid layer to be displayed on the final vegetation map. Reclassification and merge request functions within Arcview Spatial Analyst then produced a pre-European vegetation map. The final pre-European vegetation map was then masked with an extant vegetation cover to produce an extant vegetation map.";\r ;\r VIS_ID 1626;\r ;\r ANZLIC: ANZNS0208000216

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.

Following the vegetation data analysis, the formation-level vegetation map was further edited and refined to develop an association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data and additional field observations. Each polygon was attributed with the name of a vegetation association based on plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. Several polygons were labeled as mosaics of two associations because both types were present in the polygons and clear boundaries between the two associations could not be delineated. The category of Cleared Land was added as an Anderson level II category (modified) for polygons that had recently undergone woodlot removal as part of the battlefield rehabilitation. After the vegetation association map was completed, the thematic accuracy of this map was assessed.

"Extant Native Vegetation of Boorowa Shire and surrounds.;\r Vegetation map based on classified vegetation survey data, and modelling layers, derived from a 25 metre Digital Elevation Model, and a composite geology map derived from Department of Minerals geology data. Data derived from the following sources: Digital elevation model in integer format, 25 m grid cells, produced 1997, Land Information Centre; Catchment variables derived from DEM, using Arcview 3.2; Geology data from 1:250 K Geology Map, Department of Mineral Resources of NSW; Derived Elevation, Slope Steepness, Drainage from DEM; Combined Geology and sub-catchments within Boorowa Shire; Derivation of individual grid layers for each map unit; Compilation of individual map units, using merge request function in Arcview 3.2; Derivation of vegetation mask, using Landsat ETM band 5 to create a native forest/woodland cover map; Intersection of pre-european vegetation map with M305 native woody vegetation map to produce extant layer.;\r ;\r Method used was based on expert modelling of vegetation types, based on consultant EcoGIS's (Nic Gellie) knowledge of distribution of similar vegetation types in relation to lithology and broad landscape variables. To reduce possible error in expert models, modelling zones based on a combination of lithology classes and sub-catchments were produced from expert examination of the spread and patterns of each vegetation group. The modelling zones helped to reduce the number of vegetation groups to be modelled down to 2-3 groups;\r Careful inspection of sites within each vegetation group helped to determine the broad environmental niche of each vegetation group. A table of possible relationships between vegetation groups and environmental variables was drawn up to help with the modelling process. It was clear that the patterns of vegetation in the study area were more influenced by geochemistry of the lithology classes and topographic position in the landscape, rather than the conventional aspect and moisture relationships found in coastal higher rainfall environments. This conclusion helped to determine the development of terrain variables that could separate vegetation groups that occurred predominantly on ridges and hillslopes from those vegetation groups that occurred in valley bottoms. A neighbourhood variable, using stream pattern derived from the watershed models within Arcview, helped to distinguish hillslopes from valley bottoms.;\r ;\r The modelling process enabled a complete audit of all vegetation types mapped in the study area and allowed a transparent and flexible process of mapping to be documented. In the event that detailed inspection of the results of the model or field validation resulted in possible changes to the map, individual modelling zones could be remodelled with the new knowledge, or new site data. This approach also prevented grid layers from spreading to areas where the vegetation groups would logically not occur in. When all modelling zones had been modelled, the resultant grid layers were then compiled into a single Arcview view. The data layers were then sorted into an agreed order of precedence that enabled each grid layer to be displayed on the final vegetation map. Reclassification and merge request functions within Arcview Spatial Analyst then produced a pre-European vegetation map. The final pre-European vegetation map was then masked with an extant vegetation cover to produce an extant vegetation map.";\r ;\r VIS_ID 1624;\r ;\r ANZLIC: ANZNS0208000217

Description

Functional archaeological sites (sites properly dated and interpreted), indications of occupation (sites poorly dated and/or misinterpreted) and isolated discoveries known from the documentation of the Heritage Office of the Seine-Saint-Denis department, and validated by the Regional Archaeology Service. Several sites can be grouped into a global site. The contours were produced under ArcView 3.2 on the Perdif. Since initial location documents relate to variable scales, the contour may have variable meanings specified in the location accuracy field of the winning table.

Sources

Department of Seine-Saint-Denis — Departmental Geographic Information System (GIS)

** Origin of the data — Producer**

Directorate for Culture, Heritage, Sport and Recreation

Column list

— codeite: Site code — ID: Geometry (Lambert93) — source: Digitisation source — country code: Patriarch Code — codemerimee: Mérimé code — municipality: main municipality — commune2: secondary municipality — address: address — inventinforenq: Referrer — anneedecouvenq: Year of discovery — etatdecouverte: State of discovery — levelinterpretation: Level of interpretation — precision grip: Accuracy of the right-of-way — denomination: Name — precisiondeno: Accuracy of the right-of-way — destsuccactu: destsuccactu — name: name — description: description

Distribution format

These files are offered in the form of:

Geojson files (UTF-8 encoding and WGS84 projection) Csv files (ANSI encoding)

Contact

If you have any questions, or report an improvement, you can contact us at data93@cg93.fr

Links

— See the sheet on the Seine-Saint-Denis Open Data Portal — See on the map portal of Seine-Saint-Denis

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 classification and mapping processes were conducted essentially in tandem. Mappers and ecologists conferred to review the list of potential associations, as well as the appropriate scale for mapping. Photos were viewed in stereo and preliminary polygon boundaries were delineated with a .30-mm rapidograph pen on polypropylene sleeves placed over the aerial photos. Preliminary polygons were classified and labeled with their appropriate USNVC association using the aerial photograph interpretation key and USNVC descriptions, and by conferring with NatureServe ecologists. The initial line work was also used to determine a sampling scheme for plot and observation data collection, and the USNVC association list resulting from the field work was used to aid polygon classification. Once delineations were groundtruthed and rectified, USNVC association-level polygon line work was transferred to GIS shapefiles via onscreen digitizing in ArcView v.3.2a (ESRI 1992–2000). USNVC association and Anderson Level II (modified) land use names and codes were added to the attribute table of the vegetation shapefile. A separate wetland map for the park was created from the vegetation map polygons belonging to the Saturated Cold-deciduous Forest and Saturated Temperate Perennial Forb Vegetation formations.

This archive consists of two-dimensional marine seismic reflection profile data collected in Timbalier Bay and in the Gulf of Mexico offshore East Timbalier Island, Louisiana. These data were acquired June 30 - July 9 (01SCC01) and August 1 - 18 (01SCC02), 2001, aboard the R/V G.K. Gilbert and a University of New Orleans 21-foot Proline. Included here are data in a variety of formats including binary, American Standard Code for Information Interchange (ASCII), Hyper Text Markup Language (HTML), Portable Document Format (PDF), Rich Text Format (RTF), Graphics Interchange Format (GIF) and Joint Photographic Experts Group (JPEG) images, and shapefiles. Binary data are in Society of Exploration Geophysicists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with a web browser. The Geographic Information Systems (GIS) map documents provided were created with Environmental Systems Research Institute (ESRI) GIS software ArcView 3.2 and 8.1. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g401la/html/g-4-01-la.meta.html and http://walrus.wr.usgs.gov/infobank/g/g501la/html/g-5-01-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

"Pre-European Vegetation Map of Boorowa Shire and surrounds.; Vegetation map based on classified vegetation survey data, and modelling layers, derived from a 25 metre Digital Elevation Model, and a composite geology map derived from Department of Minerals geology data. Data derived from the following sources: Digital elevation model in integer format, 25 m grid cells, produced 1997, Land Information Centre; Catchment variables derived from DEM, using Arcview 3.2; Geology data from 1:250 K Geology Map, Department of Mineral Resources of NSW; Derived Elevation, Slope Steepness, Drainage from DEM; Combined Geology and sub-catchments within Boorowa Shire; Derivation of individual grid layers for each map unit; Compilation of individual map units, using merge request function in Arcview 3.2; Derivation of vegetation mask, using Landsat ETM band 5 to create a native forest/woodland cover map; Intersection of pre-european vegetation map with M305 native woody vegetation map to produce extant layer.; ; Method used was based on expert modelling of vegetation types, based on consultant EcoGIS's (Nic Gellie) knowledge of distribution of similar vegetation types in relation to lithology and broad landscape variables. To reduce possible error in expert models, modelling zones based on a combination of lithology classes and sub-catchments were produced from expert examination of the spread and patterns of each vegetation group. The modelling zones helped to reduce the number of vegetation groups to be modelled down to 2-3 groups; Careful inspection of sites within each vegetation group helped to determine the broad environmental niche of each vegetation group. A table of possible relationships between vegetation groups and environmental variables was drawn up to help with the modelling process. It was clear that the patterns of vegetation in the study area were more influenced by geochemistry of the lithology classes and topographic position in the landscape, rather than the conventional aspect and moisture relationships found in coastal higher rainfall environments. This conclusion helped to determine the development of terrain variables that could separate vegetation groups that occurred predominantly on ridges and hillslopes from those vegetation groups that occurred in valley bottoms. A neighbourhood variable, using stream pattern derived from the watershed models within Arcview, helped to distinguish hillslopes from valley bottoms.; ; The modelling process enabled a complete audit of all vegetation types mapped in the study area and allowed a transparent and flexible process of mapping to be documented. In the event that detailed inspection of the results of the model or field validation resulted in possible changes to the map, individual modelling zones could be remodelled with the new knowledge, or new site data. This approach also prevented grid layers from spreading to areas where the vegetation groups would logically not occur in. When all modelling zones had been modelled, the resultant grid layers were then compiled into a single Arcview view. The data layers were then sorted into an agreed order of precedence that enabled each grid layer to be displayed on the final vegetation map. Reclassification and merge request functions within Arcview Spatial Analyst then produced a pre-European vegetation map. The final pre-European vegetation map was then masked with an extant vegetation cover to produce an extant vegetation map."; ; VIS_ID 1626; ; ANZLIC: ANZNS0208000216

This dataset contains the irrigated lands mapped for the Wind/Bighorn River Basin Plan. Irrigated lands were divided into the following categories: Irrigated Lands, Sub-irrigated Lands, and Water Awards. All previously unmapped areas were assigned to either Irrigated Lands or Sub-Irrigated Lands based on the visual interpretation of Digital Ortho Quarter Quadrangles (DOQQs) and 1999 Landsat color-infrared satellite imagery. Identified lands were then transposed onto USGS 1:24,000-scale, 7.5 Minute Quadrangle maps, attributed with water rights, digitized and placed into an ArcView 3.2a shapefile. Verification of irrigated versus sub-irrigated lands occurred during the assignment of water right attributes. Previously mapped lands - obtained from the Wyoming State Engineer's Office - were then incorporated into the irrigated lands shapefile and assigned to the appropriate category based on existing attributes.NOTE- This dataset does not include Water Awards.

The unsewered communities file was originally conceived as a representation of communities without a municipal sewer system or on-site septic systems. The selection of communities was based on criteria established by a committee of DNR staff (Brent Parker, Field Office 5, Todd Bishop, GIS Section and Scott Vanderhart, Director's Office). The committee decided to look for groups of ten houses or more clumped within a 10 acre area which did not fall into an incorporated area. The areas that were detected were characterized into 1 of 2 classes: Officially Named, Unincorporated Towns (from the USGS Geographic Names Information System (GNIS) shapefile) or Unamed but Developed Rural Subdivisions. To attribute the data, the unincorporated GNIS point file was joined with 2000 census data (Population, Median House Income, Housing Units, and Occupancy). For the rural subdivisions, polygons were digitized around these areas in ArcView 3.2a using 2002 color-infrared aerial photography and USGS digital topographic maps (DRGs). A point file was then generated using the polygons' centroids and attributed with the number of visible housing units observed within the corresponding polygon boundaries. The resulting two point files were then merged and the sites were assessed to determine if they had access to a wastewater treatment plant. The polygon and point files were both used as an initial source of locations for potentially unsewered communities. Digital incorporated area files from 1990 census data were used to exclude communities that fell within the boundaries of known incorporated areas. The product of this analysis was then submitted for review to county sanitarians and rural water entities. After receiving their responses, some communities were removed from, and others added to, the initial file. The reviewers also added comments in regard to specific communities.

"Extant Native Vegetation of Boorowa Shire and surrounds.; Vegetation map based on classified vegetation survey data, and modelling layers, derived from a 25 metre Digital Elevation Model, and a composite geology map derived from Department of Minerals geology data. Data derived from the following sources: Digital elevation model in integer format, 25 m grid cells, produced 1997, Land Information Centre; Catchment variables derived from DEM, using Arcview 3.2; Geology data from 1:250 K Geology Map, Department of Mineral Resources of NSW; Derived Elevation, Slope Steepness, Drainage from DEM; Combined Geology and sub-catchments within Boorowa Shire; Derivation of individual grid layers for each map unit; Compilation of individual map units, using merge request function in Arcview 3.2; Derivation of vegetation mask, using Landsat ETM band 5 to create a native forest/woodland cover map; Intersection of pre-european vegetation map with M305 native woody vegetation map to produce extant layer.; ; Method used was based on expert modelling of vegetation types, based on consultant EcoGIS's (Nic Gellie) knowledge of distribution of similar vegetation types in relation to lithology and broad landscape variables. To reduce possible error in expert models, modelling zones based on a combination of lithology classes and sub-catchments were produced from expert examination of the spread and patterns of each vegetation group. The modelling zones helped to reduce the number of vegetation groups to be modelled down to 2-3 groups; Careful inspection of sites within each vegetation group helped to determine the broad environmental niche of each vegetation group. A table of possible relationships between vegetation groups and environmental variables was drawn up to help with the modelling process. It was clear that the patterns of vegetation in the study area were more influenced by geochemistry of the lithology classes and topographic position in the landscape, rather than the conventional aspect and moisture relationships found in coastal higher rainfall environments. This conclusion helped to determine the development of terrain variables that could separate vegetation groups that occurred predominantly on ridges and hillslopes from those vegetation groups that occurred in valley bottoms. A neighbourhood variable, using stream pattern derived from the watershed models within Arcview, helped to distinguish hillslopes from valley bottoms.; ; The modelling process enabled a complete audit of all vegetation types mapped in the study area and allowed a transparent and flexible process of mapping to be documented. In the event that detailed inspection of the results of the model or field validation resulted in possible changes to the map, individual modelling zones could be remodelled with the new knowledge, or new site data. This approach also prevented grid layers from spreading to areas where the vegetation groups would logically not occur in. When all modelling zones had been modelled, the resultant grid layers were then compiled into a single Arcview view. The data layers were then sorted into an agreed order of precedence that enabled each grid layer to be displayed on the final vegetation map. Reclassification and merge request functions within Arcview Spatial Analyst then produced a pre-European vegetation map. The final pre-European vegetation map was then masked with an extant vegetation cover to produce an extant vegetation map."; ; VIS_ID 1624; ; ANZLIC: ANZNS0208000217

SW_COAL_ENTRY, the predecessor of COAL_MINE_ENTRIES_IN, is a point- based ESRI ArcView shapefile that shows the locations of underground coal mine entrances in the coal region of Indiana. SW_COAL_ENTRY includes entrance locations of underground mines that operated in Indiana since the mid-1800s. COAL_MINE_ENTRIES_IN is attributed to allow the mine entrances to be differentiated based on entrance type (hoist shaft, other shafts, slope, unknown), depth, mine number, source information (map number), and azimuth (a numeric value between 0 and 360 used in ArcView GIS version 3.2, with the symbol used by COAL_MINE_ENTRIES_IN.avl, to rotate the slope entrance symbols to the approximate orientation of the actual mine entrances).

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. Following the vegetation data analysis, the formation-level vegetation map was further edited and refined to develop an association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data and additional field observations. Each polygon was attributed with the name of a vegetation association based on plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. An aerial photograph interpretation key for the vegetation associations and Anderson level II categories (modified) is located in Appendix A. After the vegetation association map was completed, the thematic accuracy of this map was assessed

This dataset represents parcels not mapped or sourced in Vector Property Map. Please refer to the common ownership lots layer in https://opendata.dc.gov for the most current data on ownership. Property Owner Points. This dataset contains points that represent the approximate location of real property lots within the District of Columbia. Each property point is generated based on a corresponding record maintained within the Office of Tax and Revenue (OTR) Real Property Tax Administration's (RPTA) real property database. Each point contains the full attribution of database fields derived from ITS public release extract. The initial data conversion effort was begun in 1997 as a means to provide RPTA with a digital mapping system which could be maintained to reflect ongoing changes to property lots and ownership. The initial step was to scan RPTA tax square maps from aperture cards at an effective paper resolution of 400 DPI. The resulting images were then georeferenced to DC's 0.2-meter resolution 1995 digital orthophotos. During the georeferencing process, the images were not warped; they were simply scaled and rotated to best fit the orthophotos. The DC tax assessor provided a database of active tax accounts which were placed interactively by an operator using the georeferenced square image and the orthophoto. Centroids were placed on the primary structure visible in the orthophoto within the raster property polygon. The placement was performed within ArcView 3.2 using a customized data production application. Accounts which could not be placed in the first pass were then reviewed by another operator to attempt to find their correct location. The placed points were QC'd through a spatial overlay with the square index to assure a match between the square field value within the property database and the actual square polygon into which the point was placed. Spot checking was then performed to confirm that the centroids fell within the correct raster lot. The centroids were delivered to OTR as a single citywide AutoCAD DWG file. Attribute features with square, suffix, and lot numbers (SSLs) were included as an AutoCAD block.