View and Download PDF Maps from Map Library School Districts and Buffer Zones (11 X 17)School Districts and Buffer Zones (8.5 X 11)Individual School MapBaker School District and Buffer Zones (22 X 34)Baker School District and Buffer Zones (8.5X 11)Devotion School District and Buffer Zones (22 X 34)Devotion School District and Buffer Zones (8.5 X 11)Driscoll School District and Buffer Zones (22 X 34)Driscoll School District and Buffer Zones (8.5 X 11)Health School District and Buffer Zones (22 X 34)Health School District and Buffer Zones (8.5 X 11)Lawrence School District and Buffer Zones (22 X 34)Lawrence School District and Buffer Zones (8.5 X 11)Lincoln School District and Buffer Zones (22 X 34)Lincoln School District and Buffer Zones (8.5 X 11)Pierce School District and Buffer Zones (22 X 34)Pierce School District and Buffer Zones (8.5 X 11)Runkle School District and Buffer Zones (22 X 34)Runkle School District and Buffer Zones (8.5 X 11)SCHOOL DISTRICTS: This data layer is created by Brookline GIS based upon the street centerline layer developed by Boston Edison and the hard copy school district map provided by the school department.SCHOOL BUFFER ZONES: This data layer is created by Brookline GIS based upon the parcel boundaries and the address list from the school department. Updated on 06/14/2001, 08/27/2002 and 06/16/2004 according to changes made by the School Committee

This GIS layer is the product of interpreted multibeam acoustic data charaterising the distribution pattern of seafloor habitats at forty sampling sites within the Flinders Commonwealth Marine Reserve. The three classes that were mapped include hard, mixed and soft substrate. Mappin the Flinders CMR is a prerequisite to understanding the relationships between inshore (shelf) and offshore (slope) habitats and therefore representing a key element in developing effective management for the depth strata across the entire CMR. Habitat characterisation provides the underlying spatial framework for developing models of habitat dynamics, trophic interactions and spatial distribution of marine biodiversity.

social system, socio-economic resources, justice, BES, Environmental disamentities, Environmental Justice, Zoning Board of Appeals Summary For use in the environmental injustices study of Baltimore relating to patterns of environmental disamenties in relation to low income/minority communities. Description This feature class layer is a point dataset of authorizing ordinances from the Baltimore City Council and Mayor from 1930 until 1999 concerning identified environmental disamentities. The data was gathered from records from the City Council since 1930 relating to decisions concerning land-uses considered to be environmental disamentities and is to be used to examine environmental injustices involving low income/minority communities in Baltimore. To examine if environmental injustices exist in Baltimore, this point layer will be overlayed with race/income data to determine if patterns of inequity exist. Points were placed manually using the associated addresses from the Ordinance_master dataset and using ISTAR 2004 data in conjunction with Baltimore parcel data. The Ordinance_ID number associated with each point relates to its appeal number from the City Council. Multiple points on the data layer have the same Ordinance_ID. This point layer can be joined with the Ordinance_master data layer based on the field "Ordinance_ID" and using the relationship "Ordinance_point_relationship". Credits UVM Spatial Analysis Lab Use limitations None. There are no restrictions on the use of this dataset. The authors of this dataset make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data. Extent West -76.707701 East -76.526991 North 39.371885 South 39.200794 This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

The Inventory of New Jersey’s The Inventory of New Jersey’s Estuarine Shellfish Resources is conducted on a rotating basis throughout the major Atlantic coastal estuaries of New Jersey. The primary purpose of the work is to estimate the standing stock of hard clams (Mercenaria mercenaria) and describe their relative distribution. Additionally, the survey describes the relative distribution of other commercially important bivalve species and vascular submerged aquatic vegetation (“SAV”), also known as seagrasses. Hard Clam: The substrate is sampled with a hydraulic hard clam dredge designed to retain clams sized 30mm and larger. All live clams collected are counted and measured to the nearest millimeter. The density of clams at each station is reported in clams per square foot. The resulting geospatial data represents the relative distribution of hard clams at either “none” (no clams collected), “low” (0.01 to 2), “moderate” (>0.20 - 2), or “high” (>0.50 clams/ft2) densities. Where no category designation is given, the area is considered a “no data” area relative to this survey. This means that the survey did not sample within this area for reasons including shallow water, obstructions, or the presence of shellfish aquaculture leases. The area may or may not be marked formally as such. However, a “no data” area may contain shellfish resources unknown to the Marine Resources Administration (MRA) or the MRA may have data for the area from other investigations. It does not automatically mean that the area is devoid of shellfish resources. This data represents a one point in time documentation of relative abundance of hard clams, and hard clams may be found presently in areas not previously sampled or at stations where they were not historically collected. Complete reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be found on the NJ Fish and Wildlife’s website. The NJ Coastal Zone Management rules at N.J.A.C. 7:7 define shellfish densities of 0.2 clams per square foot or greater as productive shellfish habitat. The Leasing of Atlantic Coast Bottom for Aquaculture regulations discourages establishing leases in productive shellfish habitat (NJAC 7:25-24.6(d)). Note that this layer does not include delineation of shellfish leases or aquaculture development zones. Those data are provided separately. Data from 1980s were digitized based primarily on the georeferenced images of the 1980s’ map series, in combination with usage of the 1986 NJDEP Landuse/Landcover geospatial dataset to more accurately depict shoreline boundaries. Digitizing was completed using freehand and/or copying/pasting/editing waterbody features from the 1986 NJDEP Landuse/Landcover geospatial dataset. Digitizing was completed at a scale between 1:4,000 to 1:12,000. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Data from 2000s to present were created based upon survey station tabular data which was then mapped as a point feature class. Several GIS tools were then used to generate polygon features surrounding the stations to represent hard clam distribution (see Process Steps for more detail). Associated Species: When other commercially or recreationally important bivalve species are retained in the sample, they are documented, along with common invertebrate species. Data from the 1980s documents the presence of all other commercially and recreationally important bivalve species that are regulated by the State of New Jersey as well as common (but not all) shellfish predators that were retrained in the dredge while targeting hard clams. Presence indicates the area is productive for the species. The regulated bivalve species are soft clams (Mya arenaria), bay scallops (Argopecten irradians), surf clam (Spisula solidissima), Eastern oyster (Crassostrea virginica), and blue mussel (Mytilus edulis). This data is a point in time observation of production areas and regulated bivalve species may be found presently in areas not previously sampled or at stations where they were not historically collected. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. It is important to note that this data is not a comprehensive evaluation of Eastern oyster populations in the Mullica River, Great Egg Harbor River, or Delaware Bay, which are surveyed separately and specifically for that species. Similarly, although surf clams are occasionally found in estuarine environments, the species primarily dwells in the Atlantic Ocean and separate comprehensive population surveys of state and federal waters are available. For additional species collected (for example sponges, non-commercial shellfish, etc.) please contact the Bureau of Shellfisheries. Historical reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be requested by contacting the Marine Resources Administration. The features were digitized based primarily on the georeferenced images of the 1980s’ map series, in combination with usage of the 1986 NJDEP Land use/Landcover geospatial dataset in order to more accurately depict shoreline boundaries. Digitizing was completed using freehand and/or copying/pasting/editing waterbody features from the 1986 NJDEP Landuse/Landcover geospatial dataset. Digitizing was completed at a scale between 1:4,000 to 1:12,000. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Data from 2000 to present also documents the presence of all other commercially and recreationally important bivalve species that are regulated by the State of New Jersey as well as common invertebrates, including common bivalve predators. Presence indicates that area is productive for the species listed. The regulated bivalve species are soft clams (Mya arenaria), bay scallops (Argopecten irradians), surf clam (Spisula solidissima), Eastern oyster (Crassostrea virginica), and blue mussel (Mytilus edulis). This data is a one point in time observation of production areas and regulated bivalve species may be found presently in areas not previously sampled or at stations where they were not historically collected. It is important to note that this data is not a comprehensive evaluation of Eastern oyster populations in the Mullica River, Great Egg Harbor River, or Delaware Bay, which are surveyed separately and specifically for that species. Similarly, although surf clams are occasionally found in estuarine environments, the species primarily dwells in the Atlantic Ocean and separate comprehensive population surveys of state and federal waters are available. Further, data on channeled whelk (Busycotypus canaliculatus), knobbed whelk (Busycon carica), Atlantic horseshoe crab (Limulus polyphemus) and blue crab (Callinectes sapidus) are not intended for use in fishery management plans at this time. For additional species collected (for example sponges, non-commercial shellfish, etc.) please contact the Marine Resources Administration. This feature class was created based upon survey station tabular data which was then mapped as a point feature class. Several GIS tools were then used to generate polygon features surrounding the stations to represent each species’ distribution (see Process Steps for more detail). Submerged Aquatic Vegetation: When submerged aquatic vegetation (SAV; seagrass) is retained in the sample, or observed visually from the research vessel, the presence of the vegetation and species is noted. Only presence of the vegetation is provided, without inference regarding coverage, shoot density, or any other characteristic. Only regulated species (per N.J.A.C. 7:7-9.6) of vascular vegetation is presented here. This is primarily eelgrass (Zostera marina) and widgeon grass (Ruppia maritima. However, other regulated species are found in New Jersey. Data from 1980s is a “snapshot in time” of relative distribution of SAV, and SAV may be found presently in areas not previously sampled or at stations where they were not historically collected. Species composition may change over time. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Where hard copy charts were not previously created (Shrewsbury, Manasquan, and Metedeconk Rivers), a 1,000ft buffer was placed around the survey station where SAV was found. Historical reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be requested by contacting the Marine Resources Administration. The SAV data from the 1980s can confirm the history of SAV in a given area, corroborating other survey years. However, further investigation is necessary if it is the only dataset available for a project. In such cases, please contact the Marine Resources Administration (MRA) as they may have information on the area that was collected during different surveys or is not yet published. Data from 2000s to present is also a “one point in time” documentation of relative distribution of SAV, and SAV may be found presently in areas not previously sampled or at stations where they were not historically collected. Species composition may change over time. Where SAV was found, a 1,000ft

description: The Ouray National Wildlife Refuge (ONWR) was established in 1960 as an inviolate sanctuary for migratory birds and any other management purpose. In 2000, the Refuge published a Comprehensive Conservation Plan in accordance with the 1997 National Wildlife Refuge Improvement Act. The plan shifted the Refuge s emphasis toward ecosystem-based management of all resident and migratory species. Refuge and Regional staff asked that a detailed and accurate vegetation map be developed for planning and for managing the Refuge effectively. The Bureau of Reclamation s Remote Sensing and Geographic Information Group (RSGIS) was contracted by US Fish and Wildlife Service to map vegetation and land-use classes at ONWR using remote sensing and GIS technologies originally developed for the National Park Service s Vegetation Mapping Program. The diverse vegetation and complicated land-use history of Ouray National Wildlife Refuge presented a unique challenge to mapping vegetation at the plant association level of the US National Vegetation Classification. To meet this challenge, the project consisted of two linked phases: (1) vegetation classification and (2) digital vegetation map production. To classify the vegetation, we sampled representative plots located throughout the 14,025-acre (5676 ha) project area. Analysis of the plot data using ordination and clustering techniques yielded 58 distinct plant associations. To produce the digital map, we used a combination of new color-infrared aerial photography and fieldwork to interpret the complex patterns of vegetation and land-use at ONWR. Eighty-one map units were developed and the vegetation units matched to the corresponding plant associations. The interpreted map data were converted to a GIS database using ArcInfo. Draft maps created from the vegetation classification were field-tested and revised before an independent ecologist conducted an assessment of the map s accuracy. The accuracy assessment revealed an overall database accuracy of 75.2%. Products developed for the Ouray National Wildlife Refuge Vegetation Mapping Project include the final report, vegetation key, map accuracy assessment results and contingency table, and photo interpretation key; spatial database coverages of the vegetation map, vegetation plots, accuracy assessment sites, and flight line index; digital photos (scanned from 35mm slides) of each vegetation type; graphics of all spatial database coverages; Federal Geographic Data Committee-compliant metadata for all spatial database coverages and field data. 12 In addition, the Refuge and USFWS copies of this report contain original aerial photographs of the project area; digital data files and hard copy data sheets of the observation points, vegetation field plots, and accuracy assessment sites; original slides of each vegetation type.; abstract: The Ouray National Wildlife Refuge (ONWR) was established in 1960 as an inviolate sanctuary for migratory birds and any other management purpose. In 2000, the Refuge published a Comprehensive Conservation Plan in accordance with the 1997 National Wildlife Refuge Improvement Act. The plan shifted the Refuge s emphasis toward ecosystem-based management of all resident and migratory species. Refuge and Regional staff asked that a detailed and accurate vegetation map be developed for planning and for managing the Refuge effectively. The Bureau of Reclamation s Remote Sensing and Geographic Information Group (RSGIS) was contracted by US Fish and Wildlife Service to map vegetation and land-use classes at ONWR using remote sensing and GIS technologies originally developed for the National Park Service s Vegetation Mapping Program. The diverse vegetation and complicated land-use history of Ouray National Wildlife Refuge presented a unique challenge to mapping vegetation at the plant association level of the US National Vegetation Classification. To meet this challenge, the project consisted of two linked phases: (1) vegetation classification and (2) digital vegetation map production. To classify the vegetation, we sampled representative plots located throughout the 14,025-acre (5676 ha) project area. Analysis of the plot data using ordination and clustering techniques yielded 58 distinct plant associations. To produce the digital map, we used a combination of new color-infrared aerial photography and fieldwork to interpret the complex patterns of vegetation and land-use at ONWR. Eighty-one map units were developed and the vegetation units matched to the corresponding plant associations. The interpreted map data were converted to a GIS database using ArcInfo. Draft maps created from the vegetation classification were field-tested and revised before an independent ecologist conducted an assessment of the map s accuracy. The accuracy assessment revealed an overall database accuracy of 75.2%. Products developed for the Ouray National Wildlife Refuge Vegetation Mapping Project include the final report, vegetation key, map accuracy assessment results and contingency table, and photo interpretation key; spatial database coverages of the vegetation map, vegetation plots, accuracy assessment sites, and flight line index; digital photos (scanned from 35mm slides) of each vegetation type; graphics of all spatial database coverages; Federal Geographic Data Committee-compliant metadata for all spatial database coverages and field data. 12 In addition, the Refuge and USFWS copies of this report contain original aerial photographs of the project area; digital data files and hard copy data sheets of the observation points, vegetation field plots, and accuracy assessment sites; original slides of each vegetation type.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Dataset contains framework layers compiled for representation on state reference map, scale 1:1.5 million. Line and polygon features only. Road, rail, waterbody and watercourse themes included. State coastline not included. ## Purpose Can be used as a framework layer for whole of state mapping or for a generalised framework for regional mapping. Not suitable for analysis. ## Dataset History Information was compiled and digitised in generalised form from 1:250 000 scale hard copy maps. The individual CAD files were combined into seamless form and converted to Lambert Conformal Conic projection, standard parallels 29 degrees and 35 degrees S, central meridian 135 degrees E. Subsequently the information was converted to GIS format and re-projected to the state standard LCC projection. ## Dataset Citation SA Department of Environment, Water and Natural Resources (2015) Topography - State Refence Map layers - PED. Bioregional Assessment Source Dataset. Viewed 12 October 2016, http://data.bioregionalassessments.gov.au/dataset/431f9e07-7436-4820-9bfd-c90a0a6cc293.

This feature class is a dataset based on liquor licenses as reported by Baltimore City Liquor License Board listing circa October 2004. This dataset includes the establishments that sell liquor in Baltimore, Maryland. Each establishment was geocoded by its street address. Those unable to be placed with a point by geocoding were given "U" for unmatched under the field "Status". Each establishment also has an associated liquor license particular to what type of alcohol is sold and the type of establishment. These liquor license types are defined by the Baltimore City Liquor License Board on their website, http://www.ci.baltimore.md.us/government/liquor. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset was sourced from the Queensland Department of Natural Resources and Mines in 2012. Information provided by the Department describes the dataset as follows:

This data was originally provided on DVD and contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application.

The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale.

In the ESRI version, a layer file is provided which presents the units in the colours and patterns used on the printed hard copy map. For Map Info users, a simplified colour palette is provided without patterns. However a georeferenced image of the hard copy map is included and can be displayed as a background in both Arc Map and Map Info.

The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics).

For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info.

Dataset History

Details on the source data can be found in the xml file associated with data layer.

Data in this release

*ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology

*ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework

*ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology

*Georeferenced Queensland geology map, gravity and magnetic images

*Queensland geology map, structural framework and schematic diagram PDF files

*Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94

Accessing the data

Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites").

Metadata

Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders.

Disclaimer

The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites.

Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination.

The State of Queensland disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages and costs you might incur as a result of accessing the websites or using the products available on the websites in any way, and for any reason.

The State of Queensland has included the websites in this document as an information source only. The State of Queensland does not promote or endorse the websites or the programs contained on them in any way.

WARNING: The Queensland Government and the Department of Natural Resources and Mines accept no liability for and give no undertakings, guarantees or warranties concerning the accuracy, completeness or fitness for the purposes of the information provided. The consumer must take all responsible steps to protect the data from unauthorised use, reproduction, distribution or publication by other parties.

Please view the 'readme.html' and 'licence.html' file for further, more complete information

Dataset Citation

Geological Survey of Queensland (2012) Queensland geology and structural framework - GIS data July 2012. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/69da6301-04c1-4993-93c1-4673f3e22762.

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

Last data update: March 17, 2023The land surfaces that do not allow water to easily penetrate or be absorbed. A polygon feature class that presents the percentage of the impervious surfaces within the total areas of each zone of the wellhead protection area (WHPA) and intake protection zone (IPZ), where road salt is applied and could be a potential drinking water threat.The impervious surface is a part of the sub-data set in the land cover feature class. It includes the hard surfaces of roads and parking lots, but excludes the other hard surfaces, like sidewalks and buildings, etc.

There are three data sources for the feature class. [1] It can be derived from the land cover feature class, where it is available within WHPAs and IPZs. [2] GIS work to buffer the road network based on the road classification and screening digitizing the parking lots from ortho images. [3] Provided from the outside organizations.

Difficult Development AreasThis U.S. Department of Housing and Urban Development feature layer depicts Difficult Development Areas in the United States. Per HUD, "Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data." All DDA's in Metropolitan Statistical Areas (MSA) and Primary Metropolitan Statistical Areas (PMSA) may not contain more than 20% of the aggregate population of all MSA's/PMSA's, and all designated areas not in metropolitan areas may not contain more than 20% of the aggregate population of the non-metropolitan counties.Baltimore/Columbia/Towson Small Area DDAData currency: Current Federal ServiceData modification: NoneFor more information: Housing and Urban Development; Qualified Census Tracts and Difficult Development AreasFor feedback, please contact: ArcGIScomNationalMaps@esri.comDepartment of Housing and Urban DevelopmentPer HUD, "The Department of Housing and Urban Development administers programs that provide housing and community development assistance. The Department also works to ensure fair and equal housing opportunity for all."

Recreation services has a critical in the role of preserving natural resources that have real economic benefits for communities by connecting children to nature, and providing education and programming that helps communities engage in conservation practices. Recreation Services provides a variety of programs and services to assist in living a healthier lifestyle and combat our countries challenges of poor nutrition, hunger, obesity, and physical inactivity. Recreation Services provides universal access to public parks and recreation programs. Our team works hard to ensure that the members of the communities we serve have access to the resources and programs available.

High Quality Successional and Native Forests of Tallahassee and Leon County, Florida. This feature class was created as part of the Environmentally Sensitive Areas (ESA) Mapping Project.The Native and High Quality Successional Forests were mapped in GIS as part of a larger Environmentally Sensitive Areas (ESA) mapping project, which was a collaborative effort between the city and county growth management departments, the planning department and TLCGIS. This project was driven out of a need for better data that was standardized county-wide to support environmental resource planning, protection and regulation, etc. It was also needed to support public works and emergency management. Additionally, the Tallahassee-Leon County Comprehensive Plan Conservation Element Objectives and Policies required compilation and maintenance of maps of conservation and preservation features, effective 1990--the same year that the GIS Interlocal office was created. Prior to this in 1988, the original ESA data layers were mapped by environmental professionals and stormwater engineers working in what was then called the Leon County Department of Public Works. The data was drawn onto USGS quad maps (base) using mylar overlays (scale 24,000)--drainage basins were also mapped. It was supported by a Department of Community Affairs grant, and there was assistance from the FSU Geography Department. The Native and HQS Forests were identified using DOT Vegetation Inventory Maps, USFS Soils and Vegetation maps, some aerial photo review, and ground truthing.In 1996 a proposal was brought to the GIS Executive Committee by the departments as an ESA Remap Project that would map the layers (also Greenways) at larger scales of 1,200 and 2,400 using the new GIS base map. The project was funded by the TLCGIS. The layers were mapped by OPS environmental/biology students and graduates with oversight by the departments.Native and HQS Forests originally mapped on the quad maps were then heads up digitized into the GIS using base map data and two sets of digital aerial photos from '94 & '96. Hard copy aerials from '37 and '76 were also used for comparison. In the southeastern US mixed hardwood/beech-magnolia forests have a fairly distinct spectral signature on the false color infrared imagery which staff learned and were trained to recognize, with ground truthing where possible (property owners were called for access). The upland pine/longleaf and pine oak hickory signatures were not as easy to identify. The goal of the ESA Remap was to identify forests that had been continually forested since at least '37 (less so for longleaf forests where bulk of the diversity is in the groundcover). The soil and contour layers, with the aerials were used to find additional areas that hadn't been previously mapped. So, essentially it was a process of identifying a specific signature and also the environmental conditions (soils, slope, aspect, topography) that would have supported or allowed a forest type to have been protected throughout history. For example, beech-magnolia forests are often found on steep slopes that were historically difficult to access for logging or around wetland/floodplain areas that would have potentially limited logging due to saturated soils. Florida Natural Areas Inventory Element Occurrence data and Florida Fish and Wildlife Conservation Commission Closing the Gaps data were also used.

The land use/land cover was created using USDA’s 1-meter National Agriculture Imagery Program (NAIP) 2017 aerial imagery, and leaf-off aerial orthoimagery captured in March 2018 (6-inch resolution everywhere except the mountainous regions to the west, which came in 1-foot resolution).Where available and accurate, high resolution planimetric datasets were incorporated, reflecting ground conditions in 2016: building footprints, driveways and sidewalks, edge of pavement (including most roads), and parking lots. Class Definitions:1. StructuresHuman-constructed objects made of impervious materials that are greater than approximately 2 meters in height. Houses, malls, and electrical towers are examples of structures. MMU = 9 square meters.2. Impervious SurfacesHuman-constructed surfaces through which water cannot penetrate, and that are below approximately 2 meters in height. This includes asphalt, concrete, gravel, pavement, treated lumber (e.g. docks and decks), and dirt roads/hard-packed dirt lots, etc. MMU = 9 square meters, minimum 2 meters wide for linear features.3. WaterAll areas of open water, generally with less than 25% cover of vegetation/land cover. This includes water-filled backyard pools, ponds, lakes, rivers, natural tidal pools in wetland areas, and boats that are not attached to docks. MMU = 9 square meters.4. Prairie/Grassland/Natural Ground CoverLarge open semi-arid areas composed of perennial grasses, herbaceous vegetation, and shrubs. These lands are often used for ranching and grazing but are not managed beyond these activities. This class also includes unmanaged natural ground cover that is less than a meter tall, such as wetland areas. MMU = 9 square meters.5. Tree CanopyDeciduous and evergreen woody vegetation of either natural succession or human planting that is over approximately 5 meters in height. Stand-alone individuals, discrete clumps, and interlocking individuals are included. MMU = 9 square meters. Includes individual large shrubs. 6. Turf/Irrigated LandsTurf grass and areas of land that are actively managed and watered that do not fall in the cropland class. Examples of turf: lawns, cemeteries, golf courses, sports fields. MMU = 9 square meters.7. Barren landAreas void of vegetation consisting of natural earthen material regardless of how it has been cleared. This includes beaches, mud flats, bedrock, xeriscaped lawns, and bare ground in construction sites (hard-packed paths/roads in construction sites would be better suited for the impervious class). MMU = 25 square meters.8. CroplandLarge fields, generally found in non-urban areas used for the production of various annual crops. These lands can be in active or inactive use, but must show visual signs of recent usage from the 2017 NAIP imagery, such as tilled fields or tire tracks. The USDA Farm Service Agency 2008 Common Land Unit (CLU) boundaries were used to guide the visual interpretation of the landscape. Irrigated fields that were not in relative proximity to the CLU boundaries and were well within the Census Urban Area boundary were not included in the cropland class.

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf of Mexico Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 ofthe final reportprovides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on theNOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on theMarine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster.Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among

Point feature class includes cumulative scores and individual characteristic scores with class descriptions for symbology representing shoreline characteristics along the Maine coast that help determine suitability of that section of shoreline for potential living shoreline applications. This data in no way is meant to supersede site specific data, and is meant for guidance planning purposes only. The feature includes the following fields:FETCH_SCORE (Fetch Score) - determined by calculating the annualized fetch based on 10 years of wind data (2006-2016) from NDBC Buoy 44007 and the shoreline feature class. Data was input into the USGS fetch tool in order to determine the miles of potential fetch applicable to each shoreline segment. Scored as follows: Very Low (<=0.5 miles) = 8 pointsLow (0.5-1.0 miles) = 6 pointsModerate (1.0-3.0 miles) = 2 pointsHigh (3.0-5.0 miles) = 1 pointVery High (>5.0 miles) = 0 pointsBATHY_SCORE (Bathymetry Score) - determined by calculating the nearshore bathymetry using NOAA Portland 1/3 ArcSec DEM. If bathymetry within 100 feet of the MHW line was 1 meter or shallower, it was considered appropriate for living shorelines. Scored as follows:Shallow (<=1m within 30 m) = 6 pointsDeep (>1m within 30 m) = 0 pointsLAND_SCORE (Landward Shoreline Type) - determined using the landward shoreline type (landward of the MHW) from Environmental Vulnerability Index (EVI) mapping data by Woolpert for NOAA. Scored as follows:Wetlands, Swamps, Marshes = 6 pointsBeaches, Scarps, Banks = 5 pointsSheltered hard shorelines, rip-rap = 3 pointsExposed hard shorelines, rip-rap = 1 pointSEA_SCORE (Seaward Shoreline Type) - determined using the seaward shoreline type (seaward of the MHW) from Environmental Vulnerability Index (EVI) mapping data by Woolpert for NOAA, and as needed, MGS Coastal Marine Geological Environments (CMGE) maps. Scored as follows:Marshes and flats = 6 pointsBeaches, dunes, sand flats = 5 pointsLow-Moderate channels = 3 pointsHigh energy channels = 1 pointLedge/man-made land = 0 pointsRELIEF_SCORE (Relief Score) - determined by calculating the overall relief from the MHW to the elevation 50 feet inland. Note that this characteristic may lend itself to the stability of the shore, but may not be a key factor necessarily for whether or not a living shoreline may be suitable Scored as follows:0-5 feet = 6 points5-10 feet = 5 points10-20 feet = 3 points>20 feet = 1 pointSLOPE_SCORE (Slope Score) - determined by dividing the RELIEF_SCO by 50 feet to determine the slope of the shoreline (rise/run). Note that this characteristic may lend itself to the stability of the shore, but may not be a key factor necessarily for whether or not a living shoreline may be suitable Scored as follows:0-3% = 6 points4-9% = 5 points10-15% = 4 points15-30% = 2 points>30% = 1 pointASPECT_SCORE (Aspect Score) - determined using GIS to calculate the dominant apsect at each shoreline segment. This helps determine how well planted material may grow. Scored as follows:SE, SW = 6 pointsS, E, W = 4 pointsNE, NW = 2 pointsN = 0 pointsTOTAL_SCORE (Total Score) = determined by adding all of the factors. Scored as follows:0-15 (Probably Not Suitable)16-22 (LIkely Not Suitable)23-28 (Possibly Suitable)29-35 (Moderately Suitable)36-44 (Highly Suitable)38-44 (Highly Suitable)Additional Characteristics - determined by whether or not a special habitat type or structures are mapped within 100 feet of the shoreline (presence or absence). These factors are not included in the total living shoreline score, just provided as additional information. Factors include:TWWH_PA (Tidal Wading Bird and Waterfowl Habitat) - Present = 1, Absent = 0EEL_PA (Eelgrass Beds) - Present = 1, Absent = 0SHELL_PA (Shellfish) - Present = 1, Absent = 0 STRUCT_PA (Structures such as roads or buildings) - Present = 1, Absent = 0

The Montgomery Parks trails are used for active and passive recreation and exploring. Much of our trail system is also used for commuting and connecting to the greater street network and trail network of the County. With over 200 miles of natural surface trail, over 70 miles of paved, and paths in nearly every one of our 421 parks, its necessary that each trail segment is recorded as it changes park unit, user type, owner type, surface type, and other aspects.Natural Surface Trails – Natural surface trails are typically narrow (2-4 feet wide) dirt trails. Types of uses associated with these trails are hiking, horseback riding, and all-terrain biking. Unless noted otherwise on the trail map, natural surface trails are “shared by all”.Hard Surface Trails – Hard surface trails may include asphalt paths but they may also be any firm and stable surface capable of supporting casual walkers and cyclists.Park Trails in Montgomery County, MD with emphasis on the M-NCPPC Montgomery Parks network. There is also information on trails provided by other entities. Only officially sanctioned trails are displayed on public interface, however internal users can view trail units in their varying stages of construction or unsanctioned status.The data is used for analysis as it pertains to trail and amenity planning, regional network planning, route and program planning. The data is also used on our public facing materials such as maps and webpages.Contact the Parks GIS Team for more information via email: MCParksGIS@montgomeryparks.org.Data LinksAGOL Feature Service: https://mcplanning.maps.arcgis.com/home/item.html?id=4a5674832aea4ffd9b86812531b12170Services:https://montgomeryplans.org/server/rest/services/Parks/ParkUnits_Py/MapServerhttps://montgomeryplans.org/server/rest/services/Parks/ParkUnits_Py/FeatureServer