11 datasets found
  1. a

    LA County Home Owners' Loan Corporation (HOLC) Redlining

    • equity-lacounty.hub.arcgis.com
    • hub.arcgis.com
    Updated Sep 20, 2021
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    County of Los Angeles (2021). LA County Home Owners' Loan Corporation (HOLC) Redlining [Dataset]. https://equity-lacounty.hub.arcgis.com/datasets/la-county-home-owners-loan-corporation-holc-redlining
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    Dataset updated
    Sep 20, 2021
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    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.This layer is an extract of the ArcGIS Online nationwide layer, clipped to Los Angeles County.For more information about this dataset, please contact egis@isd.lacounty.gov

  2. Landsat Explorer App

    • data.amerigeoss.org
    esri rest, html
    Updated Jun 1, 2020
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    Esri (2020). Landsat Explorer App [Dataset]. https://data.amerigeoss.org/de/dataset/landsat-explorer-app2
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    esri rest, htmlAvailable download formats
    Dataset updated
    Jun 1, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Description

    This web application highlights some of the capabilities for accessing Landsat imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Landsat images on a daily basis.

    Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.

    Quick access to the following band combinations and indices is provided:

    • Agriculture : Highlights agriculture in bright green; Bands 6, 5, 2
    • Natural Color : Sharpened with 15m panchromatic band; Bands 4, 3, 2 +8
    • Color Infrared : Healthy vegetation is bright red; Bands 5, 4 ,3
    • SWIR (Short Wave Infrared) : Highlights rock formations; Bands 7, 6, 4
    • Geology : Highlights geologic features; Bands 7, 6, 2
    • Bathymetric : Highlights underwater features; Bands 4, 3, 1
    • Panchromatic : Panchromatic images at 15m; Band 8
    • Vegetation Index : Normalized Difference Vegetation Index(NDVI); (Band 5 - Band 4)/(Band 5 + Band 4)
    • Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 5 - Band 6)/(Band 5 + Band 6)
    • SAVI : Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 5 - Band 4)/(Band 5 + Band 4 + 0.5))
    • Water Index : Offset + Scale*(Band 3 - Band 6)/(Band 3 + Band 6)
    • Burn Index : Offset + Scale*(Band 5 - Band 7)/(Band 5 + Band 7)
    • Urban Index : Offset + Scale*(Band 5 - Band 6)/(Band 5 + Band 6)
    Optionally, you can also choose the "Custom Bands" or "Custom Index" option to create your own band combinations

    The Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Stories tool will direct you to pre-selected interesting locations.

    The application is written using Web AppBuilder for ArcGIS accessing imagery layers using ArcGIS API for JavaScript.

    The following Imagery Layers are being accessed :
    • Multispectral Landsat - Provides access to 30m 8-band multispectral imagery and a range of functions that provide different band combinations and indices.
    • Pansharpened Landsat - Provides access to 15m 4-band (Red, Green, Blue and NIR) panchromatic-sharpened imagery.
    • Panchromatic Landsat - Provides access to 15m panchromatic imagery.

    These imagery layers can be accessed through the public group Landsat Community on ArcGIS Online.

  3. l

    TerritorialAuthorities

    • visionzero.geohub.lacity.org
    Updated Oct 3, 2023
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    em10227_AUT_GIS (2023). TerritorialAuthorities [Dataset]. https://visionzero.geohub.lacity.org/content/43f698c9898c45fd99e0ad3f50da9394
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    Dataset updated
    Oct 3, 2023
    Dataset authored and provided by
    em10227_AUT_GIS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Topicality: 01-01-2020Projection: New Zealand Transverse Mercator (NZTM)This layer is based on the dataset TA2020_V1_00_Clipped retrieved from Stat NZ. It contains the major released version of the annually released territorial authority boundaries as at 1 January 2020, clipped to the coastline.This clipped version contains 67 territorial authorities and has been created for map creation/cartographic purposes and does not fully represent the official full extent boundaries.The official dataset can be found on https://datafinder.stats.govt.nz.This layer contains the Territorial Authority name and code and the total area and the total land area in square kilometers. The layer is further generalised by Eagle Technology for improved performance on the web, therefore it doesn't fully represent the official boundaries.This layer is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers services that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or comments about the content, please let us now at livingatlas@eagle.co.nzFor the purposes of this tutorial, the source data has been modified by Esri in the following ways:The dataset name was changed.Names of territorial authorities were edited to replace spaces and hyphens with underscores. For example, "Far North District" was changed to "Far_North_District." Fields were aliased and formatted.

  4. a

    St Catharines Schools (March 2019)

    • edu.hub.arcgis.com
    Updated Mar 12, 2019
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    Education and Research (2019). St Catharines Schools (March 2019) [Dataset]. https://edu.hub.arcgis.com/datasets/st-catharines-schools-march-2019
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    Dataset updated
    Mar 12, 2019
    Dataset authored and provided by
    Education and Research
    Area covered
    Description

    NOTE: For an updated version of this dataset, please see https://arcg.is/05ybSm.---Derived from the CSV of school locations in Niagara Region available at https://niagaraopendata.ca/dataset/schools (downloaded March 11th, 2019). Using the Overlay Layers tool in ArcGIS Online ("Intersect" method), this layer was clipped to the St. Catharines urban boundary available at https://niagaraopendata.ca/dataset/st-catharines-urban-boundary (downloaded April 8th, 2017) to create a layer of schools in St. Catharines.

  5. a

    gSSURGO User Guide ArcMap version 2.4

    • ngda-portfolio-community-geoplatform.hub.arcgis.com
    Updated Jun 24, 2025
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    GeoPlatform ArcGIS Online (2025). gSSURGO User Guide ArcMap version 2.4 [Dataset]. https://ngda-portfolio-community-geoplatform.hub.arcgis.com/datasets/gssurgo-user-guide-arcmap-version-2-4-
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    GeoPlatform ArcGIS Online
    Description

    Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

  6. a

    2023 Census population change by age group and RC (clipped)

    • maps-by-statsnz.hub.arcgis.com
    Updated May 29, 2024
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    Statistics New Zealand (2024). 2023 Census population change by age group and RC (clipped) [Dataset]. https://maps-by-statsnz.hub.arcgis.com/datasets/StatsNZ::2023-census-population-change-by-age-group-and-rc?layer=0
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    Dataset updated
    May 29, 2024
    Dataset authored and provided by
    Statistics New Zealandhttp://www.stats.govt.nz/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    The life-cycle age groups are:

    • under 15 years
    • 15 to 29 years
    • 30 to 64 years
    • 65 years and over.

    Map shows the percentage change in the census usually resident population count for life-cycle age groups between the 2018 and 2023 Censuses.

    Download lookup file from Stats NZ ArcGIS Online or Stats NZ geographic data service.

    Footnotes

    Geographical boundaries
    Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

    Subnational census usually resident population
    The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

    Caution using time series
    Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

    About the 2023 Census dataset
    For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

    Data quality
    The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

    Quality rating of a variable
    The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

    Age concept quality rating
    Age is rated as very high quality.
    Age – 2023 Census: Information by concept has more information, for example, definitions and data quality.

    Using data for good
    Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga".

    Confidentiality
    The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

  7. a

    Heat Severity - USA 2023

    • community-climatesolutions.hub.arcgis.com
    • hub.arcgis.com
    Updated Apr 24, 2024
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    The Trust for Public Land (2024). Heat Severity - USA 2023 [Dataset]. https://community-climatesolutions.hub.arcgis.com/datasets/db5bdb0f0c8c4b85b8270ec67448a0b6
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    Dataset updated
    Apr 24, 2024
    Dataset authored and provided by
    The Trust for Public Land
    Area covered
    Description

    Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

  8. a

    St. Catharines Schools (October 2020)

    • edu.hub.arcgis.com
    Updated Oct 6, 2020
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    Education and Research (2020). St. Catharines Schools (October 2020) [Dataset]. https://edu.hub.arcgis.com/maps/edu::st-catharines-schools-october-2020
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    Dataset updated
    Oct 6, 2020
    Dataset authored and provided by
    Education and Research
    Area covered
    Description

    NOTE: For an updated version of this dataset, please see https://arcg.is/05ybSm.---Derived from the CSV of school locations in Niagara Region available at https://niagaraopendata.ca/dataset/schools (downloaded October 5th, 2020; last updated December 20th, 2019). Using the Overlay Layers tool in ArcGIS Online ("Intersect" method), this layer was clipped to the St. Catharines urban boundary available at https://niagaraopendata.ca/dataset/st-catharines-urban-boundary (downloaded April 8th, 2017) to create a layer of schools in St. Catharines.

  9. a

    Santa Clara County Digital Terrain Model

    • opendata-mrosd.hub.arcgis.com
    • hub.arcgis.com
    Updated Jun 22, 2021
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    Midpeninsula Regional Open Space District (2021). Santa Clara County Digital Terrain Model [Dataset]. https://opendata-mrosd.hub.arcgis.com/maps/44a391b570a14d4687591fa2e89ebb11
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    Dataset updated
    Jun 22, 2021
    Dataset authored and provided by
    Midpeninsula Regional Open Space District
    Area covered
    Santa Clara County
    Description

    Methods: This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution raster was produced from a ground classified 2020 Quality Level 1 lidar point cloud. This DTM is hyroflattened, meaning that water bodies are represented as flat surfaces. Hydroflattening improves the aesthetics of the DEM and is consistent with USGS’s 3-DEP specifications.

    This DTM was derived by Sanborn and Tukman Geospatial using the following process:

    QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hydroflattened raster DEM (geotiff) posted on ArcGIS Online (Tukman Geospatial)

    The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet.

    Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area.

    An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.

    Uses and Limitations: The DTM provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis. The DTM will be most accurate in open terrain and less accurate in areas of very dense vegetation.

    Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

  10. 2010-2014 ACS Health Insurance by Age by Race Variables - Boundaries

    • gis-for-racialequity.hub.arcgis.com
    • hub.arcgis.com
    Updated Dec 1, 2020
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    Esri (2020). 2010-2014 ACS Health Insurance by Age by Race Variables - Boundaries [Dataset]. https://gis-for-racialequity.hub.arcgis.com/maps/1de77825c6af4da1aab7b51ed8cb9b64
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    Dataset updated
    Dec 1, 2020
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black). Later vintages of this layer have a different age group for children that includes age 18. This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  11. Population by Age and Sex 2018-2022 - STATES

    • hub.arcgis.com
    • mce-data-uscensus.hub.arcgis.com
    Updated Feb 2, 2024
    + more versions
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    US Census Bureau (2024). Population by Age and Sex 2018-2022 - STATES [Dataset]. https://hub.arcgis.com/maps/6ac8da545d254c529b3a83685fbdd179
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    Dataset updated
    Feb 2, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    US Census Bureau
    Area covered
    Description

    This layer shows Population by Age and Sex. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the Total population ages 65 and over. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B01002, DP05Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

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County of Los Angeles (2021). LA County Home Owners' Loan Corporation (HOLC) Redlining [Dataset]. https://equity-lacounty.hub.arcgis.com/datasets/la-county-home-owners-loan-corporation-holc-redlining

LA County Home Owners' Loan Corporation (HOLC) Redlining

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Dataset updated
Sep 20, 2021
Dataset authored and provided by
County of Los Angeles
Area covered
Description

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.This layer is an extract of the ArcGIS Online nationwide layer, clipped to Los Angeles County.For more information about this dataset, please contact egis@isd.lacounty.gov

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