{c2959c77-1c79-4e2f-ba99-e70fc1f73e19} eng; USA utf8 dataset EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact 2025-02-13 ISO 19115 Geographic Information - Metadata ISO 19115:2003/Cor.1:2006(E) EnviroAtlas - Los Angeles, CA - Tree Cover Along Walkable Roads by Block Group 2019-08-19 publication U.S. EPA Office of Research and Development (ORD) - National Exposure Research Laboratory (NERL) Research Triangle Park NC publisher U.S. Environmental Protection Agency (US EPA) originator This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. In this community, tree cover is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health affects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). The purpose of this dataset is to identify areas that could benefit from additional tree buffers, and the populations living in those areas. The overall goal of EnviroAtlas is to employ and develop the best available science to map indicators of ecosystem services production, demand, and drivers for the nation. completed EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact asNeeded Ecosystem Environment theme EPA GIS Keyword Thesaurus Air Census Block Groups Communities Ecosystem Services EnviroAtlas Environment Environmental Atlas Health Human Well-being Land Cover Transportation Trees theme EnviroAtlas California Los Angeles, CA place None https://edg.epa.gov/EPA_Data_License.html otherRestrictions Use Constraints: None. Please check sources, scale, accuracy, currency and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of the EPA would be appreciated. otherRestrictions Access Constraints: None. PUBLIC https://project-open-data.github.io/schema/#accessLevel Standard Technical Controls eng; USA environment -118.9272 -117.65058 33.24023 34.83098 2010-01-01 2011-01-01 2014-01-01 2016-01-01 true Entity and Attribute Information Entity and Attribute Overview: For more information about EnviroAtlas data, go to https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets. This dataset describes the population potentially affected by the presence or absence of near-road tree cover. The processing steps were completed using Python scripts. A comprehensive list of metric creation steps is included in the Processing Steps. This dataset is based on the land cover data derived for each EnviroAtlas community, the best available local road layer that included either surface width or number of lanes as an attribute, and the 2010 U.S. Census of the Population. The land cover data were created from one-meter aerial photography through remote-sensing methods. A moving-window analysis first determined the percent of tree cover within a given distance from every point in the community. Then the roads layer was used to extract only those windows that captured percent tree cover along road edges. The 25% threshold estimates a 10m buffer depth, which was selected to represent three adjacent rows of trees. On average across all possible orientations of the roadway within the moving window, a 10m strip of tree cover that is parallel to the road is equivalent to 25% of the area of the moving window. The population estimate was calculated by creating a 300m buffer on each side of the roads and extracting the modeled population values for all areas adjacent to buffered roadways from EPA's "dasymetric" population product, mapped to 30m resolution, from 2010 Census block-level data. Residents estimated to live within 26 meters of the edge of a buffered road were excluded from the population total because there is uncertainty with the methods used as to where the tree cover is located within the moving window. Therefore, it cannot be determined if people living within the window itself are buffered from traffic pollution. Each 1m pixel was attributed with the total number of tree-cover pixels in the surrounding window (circular moving window with at radius of 14.5m for a 26m buffer with a 3m road overlap). By retaining only the pixels along a set distance from road edges, the analysis captured tree cover beyond the road shoulder and cleared right-of-way, if present, and avoided tree cover in the median, if present, and on the opposite side of the road. The fact sheets for the Near Road Environments metric contain a diagram further explaining the moving window analysis. SBuff/SB = Sufficient Buffer IBuff/IB = Insufficient Buffer. When calculating the EnviroAtlas Dasymetric Population Map, people are not allocated to certain land cover types or areas with steep slopes. All percentages were calculated using the total dasymetric population per block group rather than the census population for accuracy's sake. Because of this, the modeled dasymetric population for 105 Los Angeles block groups are missing a total of 610.0 people as compared to the Census data due to steep slopes and 7.0 people as compared to the Census data due to the model's showing no livable area. All percentages were calculated using the total dasymetric population per block group rather than the census population for accuracy's sake. Entity and Attribute Detail Citation: https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas distributor https://enviroatlas.epa.gov/download//LACA_metrics_May2021_CSV_Shapes.zip download Download The URL providing direct access to the downloadable dataset. download https://enviroatlas.epa.gov/download//LACA_metrics_May2021_FGDB.zip ESRI:ArcGIS API The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint). webMapService https://enviroatlas.epa.gov/arcgis/rest/services/Communities/Community_BGmetrics/MapServer information Documentation URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc. information https://www.epa.gov/enviroatlas/enviroatlas-data information Home Page Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API information dataset Horizontal Positional Accuracy Report Data were collected using methods that have unknown accuracy (EPA National Geospatial Data Policy [NGDP] Accuracy Tier 10). For more information, please see EPA's NGDP at https://epa.gov/geospatial/policies.html Features represented have not been tested for completeness Features represented have not been tested for completeness Tests for integrity have not been performed. This lineage was generated by the FGDC CSDGM to 19139 transformation Begin with 2011 NavTeq Streets Layer and 1-Meter Land Cover Classification for the EnviroAtlas community created by the US EPA EnviroAtlas Team. 2019-04-10T00:00:00 Project NavTeq Streets layer into UTM. 2019-04-10T00:00:00 Clip NavTeq Streets Layer to 1-km Buffer of the EnviroAtlas community boundary. 2019-04-10T00:00:00 Extract roads from NavTeq Streets where Func_Class = 1-4 to a new layer. 2019-04-10T00:00:00 Add Field to the new streets layer: LANES (double) and calculate where LANES = TO_LANES + FROM_LANES. 2019-04-10T00:00:00 For any records where LANES = 0, use Esri Aerial basemap to fill in correct lane value. 2019-04-10T00:00:00 Reclassify the Land Cover into Binary Forest. (Trees & Forest - 40, Woody Wetlands - 91 = 1; All Else = 0) 2019-08-13T00:00:00 Run Focal Statistics on the Forest Binary Raster with a circular cell neighborhood with a radius of 14.5m in map units 2019-08-13T00:00:00 Add Field to streets layer: HALFWIDTH (double) and calculate where HALFWIDTH = LANES * 3.6576 / 2. 2019-08-13T00:00:00 Buffer streets using the value in HALFWIDTH with options FULL, FLAT, ALL. 2019-08-13T00:00:00 Rebuffer the buffered streets by 11.5 meters with options FULL, FLAT, ALL. 2019-08-13T00:00:00 Convert the resulting polygons into polylines - referred to as analysis lines. 2019-08-13T00:00:00 Delete analysis lines that are unnecessary for analysis, for example, lines in between two lanes of a divided highway and lines on the interior of a freeway ramp. 2019-08-13T00:00:00 Extract the Focal Statistics Raster using the analysis lines. 2019-08-13T00:00:00 Reclassify the extracted raster into above and below 25% tree cover: 0-154 = 1; 155-613 = 2. 2019-08-13T00:00:00 Convert the reclassified raster into a polygon WITHOUT simplifying. 2019-08-13T00:00:00 Intersect the analysis line with the polygons and the community block groups, splitting the analysis line into pieces of greater than and less than 25% tree cover within each block group. 2019-08-13T00:00:00 Add a new field to the analysis line: Length_KM (double) and calculate the geometry of the lines using length in kilometers. 2019-08-13T00:00:00 Summarize the analysis line layer by block group and greater than vs less than 25% tree cover where the summary statistics is the sum of Length_KM. 2019-08-13T00:00:00 Buffer the analysis line twice: by 288.5m with LEFT, FLAT, ALL and by 11.5m with RIGHT, FLAT, ALL. Merge the two buffers together to create the population analysis zone. 2019-08-13T00:00:00 Buffer the analysis line twice again: by 14.5m with LEFT, FLAT, ALL and by 11.5m with RIGHT, FLAT, ALL. Select the analysis line pieces with grid_code = 1 and buffer by 288.5m with LEFT, FLAT, ALL. Merge the three buffers together to identify areas of less than 25% tree cover. 2019-08-14T00:00:00 Erase the areas of less than 25% tree cover from the population analysis area to identify areas buffered by greater than 25% tree cover. 2019-08-14T00:00:00 Clip the area buffered by less than 25% tree cover to the population analysis zone for consistency's sake. 2019-08-14T00:00:00 Project both the less than and greater than areas into Albers. 2019-08-14T00:00:00 Add a field to each polygon layer: Value (short) and calculate where Value=1. 2019-08-14T00:00:00 Convert each polygon layer into a raster. 2019-08-14T00:00:00 Extract by Mask the EnviroAtlas Dasymetric (2011/October 2015) within each of the rasterized zones. 2019-08-14T00:00:00 Calculate Zonal Statistics as a Table for the two extracted dasymetric rasters with the zones being the 2010 block groups within the EnviroAtlas community boundary. 2019-08-14T00:00:00 Calculate Zonal Statistics as a Table for the EnviroAtlas Dasymetrics (2011/October 2015) with the zones being the 2010 block groups within the EnviroAtlas community boundary. Add resulting population sums to the community block groups as attribute Dasy_Pop 2019-08-12T00:00:00 Create a new table based on the EnviroAtlas community block groups table retaining the BGRP and Dasy_Pop fields 2019-08-14T00:00:00 Add fields to the new table for IBuff_Pop (long), SBuff_Pop (long), Buff_Pop (long), IBuff_Pct (float), SBuff_Pct (float), Buff_Pct (float), Lane_KMN (double), Lane_KMY (double), Lane_KMAll (double), Lane_PctSB (float), Lane_PctIB (float). 2019-08-14T00:00:00 Join the zonal statistics and length statistics tables with the new table and calculate IBuff_Pop, SBuff_Pop, Lane_KMN, Lane_KMY. Remove Joins. 2019-08-14T00:00:00 Calculate remaining fields: Buff_Pop = IBuff_Pop + SBuff_Pop; IBuff_Pct = IBuff_Pop/Dasy_Pop*100; SBuff_Pct = SBuff_Pop/Dasy_Pop*100; Lane_KMAll = Lane_KMN + Lane_KMY; Lane_PctSB = Lane_KMY/Lane_KMAll*100; Lane_PctIB = Lane_KMN/Lane_KMAll*100. 2019-08-14T00:00:00 Calculate Fields where Dasy_Pop = 0: IBuff_Pop, SBuff_Pop, Buff_Pop, IBuff_Pct, SBuff_Pct, Buff_Pct = -99999 2019-08-14T00:00:00 Calculate Field for BGs within 50m of the edge of the land cover, all fields = -99998. 2019-08-19T00:00:00 Export the fields to be displayed in the EnviroAtlas to a final gdb table: IBuff_Pop, SBuff_Pop, Buff_Pop, Buff_Pct, Lane_PctSB, Lane_PctIB. 2019-08-19T00:00:00 unknown 2029-02-13 This metadata was automatically generated from the FGDC Content Standard for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2024-02-09T11:11:00 version of the FGDC CSDGM to ISO 19139 transform. EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact