These polygons are the boundaries of the Skillman Good Neighborhoods, as of March 2014

1930's Neighborhood Redlining Grade (ESRI Living Atlas, 2022). The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.For more detailed information use this link.

This is a polygon data set of the Neighborhood Watch Group boundaries within City of Boise limits. A Neighborhood Watch Group is defined as a neighborhood surveillance program or group in which residents keep watch over one another's houses, patrol the streets, etc., in an attempt to prevent crime. When available Neighborhood Watch Group boundaries are derived from information provided from the Neighborhood Watch Group chairpersons. Where data was not provided, boundaries are estimated using best judgment from the Boise Police Department Neighborhood Watch Group Coordinator.

This data set consists of 1:1,000,000-scale areas where shallow ground water is consumed by evapotranspiration (ET) in the Great Basin. The source of this data set is sheet 2 of a 1988 U.S. Geological Survey hydrologic investigations atlas map (Harrill and others, 1988.) Reference Cited Harrill, J.R., Gates, J.S., and Thomas, J.M., 1988, Major ground-water flow systems in the Great Basin region of Nevada, Utah, and adjacent states: U.S. Geological Survey Hydrologic Investigations Atlas HA-694-C, scale 1:1,000,000, 2 sheets.

The Durham Neighborhood Compass is a quantitative indicators project with qualitative values, integrating data from local government, the Census Bureau and other state and federal agencies. Measurements are identified through local strategic planning, resident input, research and best practices for neighborhood indicators. The project objective is to provide data that allows all local stakeholders to track quality of life and provision of services throughout Durham.

A data dictionary for the headings is available in the export tab.

Updated as changes are made.

Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).

Population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2023 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries as of July 1, 2023. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/)released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Fields:CT20: 2020 Census tractFIP22: 2023 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2023) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP23CSA: 2020 census tract with 2023 city FIPs for incorporated cities and unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP23_AGE_0_4: 2023 population 0 to 4 years oldPOP23_AGE_5_9: 2023 population 5 to 9 years old POP23_AGE_10_14: 2023 population 10 to 14 years old POP23_AGE_15_17: 2022 population 15 to 17 years old POP23_AGE_18_19: 2023 population 18 to 19 years old POP23_AGE_20_44: 2023 population 20 to 24 years old POP23_AGE_25_29: 2023 population 25 to 29 years old POP23_AGE_30_34: 2023 population 30 to 34 years old POP23_AGE_35_44: 2023 population 35 to 44 years old POP23_AGE_45_54: 2023 population 45 to 54 years old POP23_AGE_55_64: 2023 population 55 to 64 years old POP23_AGE_65_74: 2023 population 65 to 74 years old POP23_AGE_75_84: 2023 population 75 to 84 years old POP23_AGE_85_100: 2023 population 85 years and older POP23_WHITE: 2023 Non-Hispanic White POP23_BLACK: 2023 Non-Hispanic African AmericanPOP23_AIAN: 2023 Non-Hispanic American Indian or Alaska NativePOP23_ASIAN: 2023 Non-Hispanic Asian POP23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific IslanderPOP23_HISPANIC: 2023 HispanicPOP23_MALE: 2023 Male POP23_FEMALE: 2023 Female POV23_WHITE: 2023 Non-Hispanic White below 100% Federal Poverty Level POV23_BLACK: 2023 Non-Hispanic African American below 100% Federal Poverty Level POV23_AIAN: 2023 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV23_ASIAN: 2023 Non-Hispanic Asian below 100% Federal Poverty Level POV23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV23_HISPANIC: 2023 Hispanic below 100% Federal Poverty Level POV23_TOTAL: 2023 Total population below 100% Federal Poverty Level POP23_TOTAL: 2023 Total PopulationAREA_SQMil: Area in square mile.POP23_DENSITY: 2023 Population per square mile.POV23_PERCENT: 2023 Poverty rate/percentage.How this data created?Population by age groups, ethnic groups and gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Notes:1. Population and poverty data estimated as of July 1, 2023. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundaries are as of July 1, 2023.

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.The Ocean Base map currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas.The World Ocean Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the World Ocean Base with the Reference service drawn on top. Article in the Fall 2011 ArcUser about this basemap: "A Foundation for Ocean GIS".The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, and Esri for topographic content. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details on the users who contributed bathymetric data for this map via the Community Maps Program, view the list of Contributors for the Ocean Basemap. The basemap was designed and developed by Esri. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO's website: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/.The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system.Tip: Here are some famous oceanic locations as they appear this map. Each URL launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

The Durham Neighborhood Compass is a quantitative indicators project with qualitative values, integrating data from local government, the Census Bureau and other state and federal agencies. Measurements are identified through local strategic planning, resident input, research and best practices for neighborhood indicators. The project objective is to provide data that allows all local stakeholders to track quality of life and provision of services throughout Durham.

A data dictionary for the headings is available in the export tab.

Updated as changes are made.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Mayor Greg Fischer formed the Louisville Metro Office of Sustainability in 2012 with a mission of promoting environmental conservation, the health, wellness and prosperity of our citizens, and embedding sustainability into the culture of the Louisville community. Creating a culture of sustainability will be achieved through broad-based education and awareness efforts as well as implementation of projects and initiatives to influence behavior change.Data Dictionary: NEIGHBORHOOD - The neighborhood in Louisville.TOTAL NEW GREEN ROOFS - The number of green roofs installed. Each green roof is assumed to be 10,000 square feet.TOTAL GRASS PLANTED - The amount of bare dirt land planted with grass or other greenery, measured in hectares.TOTAL TREES PLANTED - The number of new trees planted.TOTAL COOL PAVING - Cool paving is pavement material engineered to exhibit a higher reflectivity than conventional pavement. Cool paving can be porous, made of a light colored material, or both. Cool paving is measured in hectares.TOTAL NEW COOL ROOFS - The number of cool roofs installed. Each cool roof is assumed to be 10,000 square feet. A cool roof can be steep-sloped or low-sloped or flat. A cool roof is define as a roof with a top-level material certified by ENERGY STAR or rated by the Cool Roof Rating Council as "cool." More information is available at https://louisvilleky.gov/government/sustainability/incentives#1Contact: sustainability@louisvilleky.gov

description: Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).; abstract: Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).

This dataset was created in support of a U.S. Geological Survey (USGS) study focusing on groundwater resources in the Great Basin carbonate and alluvial aquifer system (GBCAAS). The GBCAAS is a complex aquifer system comprised of both unconsolidated and bedrock formations covering an area of approximately 110,000 square miles. The aquifer system is situated in the eastern portion of the Great Basin Province of the western United States. The eastern Great Basin is experiencing rapid population growth and has some of the highest per capita water use in the Nation. These factors, combined with its arid setting, have levied intensive demand upon current groundwater resources and, thus, predictions of future shortages. Because of the large regional extent of the aquifer system, rapid growth in the region, and the reliance upon groundwater for urban populations, agriculture, and native habitats, the GBCAAS was selected by the USGS Water Resources program as part of the National Water Census Initiative to evaluate the nation's groundwater availability. This dataset contains hydrographic area (HA) boundaries and polygons for the GBCAAS study area. The study area consists of 165 HAs based on Great Basin HAs defined by the USGS in 1988 (Harrill and others, 1988; Buto, 2009). The study area is characterized by north-south trending alluvial basins separated by intervening mountain ranges. HA boundaries generally coincide with the topographic highs separating these basins but may also contain arbitrary divisions that have no topographic control. HAs generally consist of thick layers of unconsolidated geologic deposits in the basins and consolidated bedrock in the mountain ranges. The basins are underlain by bedrock at varying depths. Much of the bedrock in the study area consists of permeable carbonate and volcanic rock strata, both of which allow some degree of hydraulic connection between hydrographic areas. The hydrographic area boundaries in this dataset have been assigned a code identifying each boundary as a potential barrier, conduit, or neutral zone to groundwater flow between basins. References cited: Buto, S.G., 2009, Digital representation of 1:1,000,000-scale Hydrographic Areas of the Great Basin: U.S. Geological Survey Digital Data Report 457, 5 p., https://pubs.usgs.gov/ds/457/ Harrill, J.R., Gates, J.S., and Thomas, J.M., 1988, Major ground-water flow systems in the Great Basin region of Nevada, Utah, and adjacent states: U.S. Geological Survey Hydrologic Investigations Atlas HA-694-C, 2 sheets, scale 1:1,000,000. http://pubs.er.usgs.gov/usgspubs/ha/ha694C

This map serves as the baseline for the green infrastructure apps that visualize areas that are relatively undisturbed by development or agriculture.

The habitat cores shown were derived using a model built by the Green Infrastructure Center Inc. and adapted by Esri.

The Intact Habitat Near Me app uses this web map as its basis.

description: Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).; abstract: Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).

Most indicators throughout Vital Signs are created by acquiring and analyzing data collected from governmental agencies for some public administration purpose, such as 311 calls or housing inspections. However, data from the United States Bureau of the Census remains the best source for demographic and socioeconomic indicators for neighborhoods. The Census Bureau collects a wide variety of information through administration of both the decennial Census and the annual American Community Survey (ACS).