This layer reflects the district boundaries adopted by the Seattle Redistricting Commission in November 2022.Voters approved Charter Amendment 19 in the November 5, 2013 General And Special Election. The 2015 election was the first election conducted by district. In addition to the seven councilmembers from the districts there are two at-large positions. The voter-approved changes to the City Charter require that the redrawing process happen every ten years.For more information, please see Office of City Clerk site.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Trails dataset.

Neighborhood Map Atlas neighborhoods are derived from the Seattle City Clerk's Office Geographic Indexing Atlas. These are the smallest neighborhood areas and have been supplemented with alternate names from other sources in 2020. They roll up to the district areas. The sub-neighborhood field contains the most common name and the alternate name field is a comma delimited list of all the alternate names.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Play Area dataset.

Table from the American Community Survey (ACS) 5-year series on education enrollment and attainment related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B14007/B14002 School Enrollment, B15003 Educational Attainment. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

description: Seattle Parks and Recreation GIS Map Layer Shapefile - Play Area Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata. OR Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/33; abstract: Seattle Parks and Recreation GIS Map Layer Shapefile - Play Area Shapefile - This Seattle Parks and Recreation ARCGIS park feature map layer was exported from SPU ARCGIS and converted to a shapefile then manually uploaded to data.seattle.gov via Socrata. OR Web Services - Live "read only" data connection ESRI web services URL: http://gisrevprxy.seattle.gov/arcgis/rest/services/DPR_EXT/ParksExternalWebsite/MapServer/33

Neighborhood map atlas district areas are derived from the Seattle City Clerk's Office Geographic Indexing Atlas. These are the largest neighborhood areas and have been supplemented with alternate names from other sources in 2020. They are subdivided further into the neighborhood map atlas sub-areas called neighborhoods. The sub-neighborhoods field contains a comma delimited list of all the sub-areas and their alternate names.The original atlas is designed for subject indexing of legislation, photographs, and other documents and is an unofficial delineation of neighborhood boundaries used by the City Clerks Office. Sources for this atlas and the neighborhood names used in it include a 1980 neighborhood map produced by the Department of Community Development, Seattle Public Library indexes, a 1984-1986 Neighborhood Profiles feature series in the Seattle Post-Intelligencer, numerous parks, land use and transportation planning studies, and records in the Seattle Municipal Archives. Many of the neighborhood names are traditional names whose meaning has changed over the years, and others derive from subdivision names or elementary school attendance areas.Disclaimer: The Seattle City Clerk's Office Geographic Indexing Atlas is designed for subject indexing of legislation, photographs, and other records in the City Clerk's Office and Seattle Municipal Archives according to geographic area. Neighborhoods are named and delineated in this collection of maps in order to provide consistency in the way geographic names are used in describing records of the Archives and City Clerk, thus allowing precise retrieval of records. The neighborhood names and boundaries are not intended to represent any "official" City of Seattle neighborhood map. The Office of the City Clerk makes no claims as to the completeness, accuracy, or content of any data contained in the Geographic Indexing Atlas; nor does it make any representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the representations furnished herein. The maps are subject to change for administrative purposes of the Office of the City Clerk. Information contained in the site, if used for any purpose other than as an indexing and search aid for the databases of the Office of the City Clerk, is being used at one's own risk.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:Existing tree canopy percentPossible tree canopy - vegetation percentRelative percent changeAbsolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.

Contains data from CARTO.CTYLIMIT.Updated as needed.

Analysis of ‘A Council Districts Profile ACS 5-year 2013-2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f041fcfa-c22a-41ef-887c-08661c181447 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Data from: American Community Survey, 5-year Series 2013-2017

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Council Districts Profile ACS 5-year 2006-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/eb898acb-5fb0-4a1a-a501-ca4e618f4044 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Data from: American Community Survey, 5-year Series 2006-2010

--- Original source retains full ownership of the source dataset ---

Table from the American Community Survey (ACS) 5-year series on transportation related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B08303 Travel Time to Work, B25044 Tenure by Vehicles Available, B08301 Means of Transportation to Work. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B08303, B25044, B08301Data downloaded from: Census Bureau's Explore Census Data The 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. Da

Locations of Seattle Parks and Recreation (SPR) responsibilities within the city limits. SPR location responsibilities may include: ownership, leases, maintenance, temporary transfer of jurisdiction, life estate, crew quarters, headquarters, storage facilities, tidelands, joint use agreements. This is the detailed transactions level park GIS layer. It is the most current Parks GIS layer. Park uses can be sorted using the "Park Use" field. If you just want the Park outlines (boundaries) then use DPR.ParksBND.

This part of USGS Data Series 935 (Cochrane, 2014) presents substrate, geomorphic, and biotope data in the Offshore of Seattle, California, map area, a part of the Southern Salish Sea Habitat Map Series. Given the variable bathymetric resolution, the complex geologic history of the region, and the lack of acoustic backscatter data, automated and semi-automated classification schemes of classifying seafloor substrate and geoform were deemed to have very low accuracy. Instead, classification of these properties was performed manually following the Coastal and Marine Ecological Classification Standard (CMECS; Madden and others, 2009) using observations from underwater video footage. The best overall predictors of biotic assemblage were used to generate the CMECS biotopes. However, the nature of the biological data gathered makes it difficult to define clear biotopes. It was difficult to see or identify many organisms in the underwater video, and with an average of only 3-4 taxa identified per sampling unit, it is hard to characterize biotic assemblages. Some biological clusters of taxa were identified statistically for multiple map areas, and within each area, some of these groupings were found at consistent depths and/or with predictable substrates. The maps are not fine-grained enough to capture the physical variation seen within one-minute video units. Depth zones in the biotope map are based on Dethier (1992).

This part of USGS Data Series 935 (Cochrane, 2014) presents bathymetry and topography data for the Offshore of Seattle, California, map area, a part of the Southern Salish Sea Habitat Map Series. The data for this map area are a combination of topography extracted from a pre-existing Digital Elevation Model (DEM) merged with bathymetry data that were collected by the National Oceanic and Atmospheric Administration (NOAA) using multibeam sonar systems. The merged data are available for download in a single zip file (BathyTopo_OffshoreSeattle.zip).

This data layer describes the boundaries of the director districts within the Seattle Public School District.

Note: This map is not an official zoning map. For precise zoning information, please call or visit the Seattle Municipal Tower, Seattle Department of Construction and InspectionsCity of Seattle's land use zoning historic district and special review district overlays.

Analysis of ‘Council Districts Profile ACS 5-year 2006-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9e79a8db-d8f9-4fe0-a95c-58508b01efa4 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Data from: American Community Survey, 5-year Series 2006-2010

--- Original source retains full ownership of the source dataset ---