In 2013, Seattle voters passed a measure amending the city's charter to establish City Council districts. In 2015, voters will elect seven out of the nine City Council members by district. The remaining two positions will be elected "at-large" (city-wide) in positions 8 and 9.For more information about the Council Districts and the schedule for implementing the elections see the Office of the City Clerk.Click on a neighborhood to see basic demographics, reports and maps from the 2010 Decennial Census and the 2006-2010 American Community Survey 5-Year Series.* This map is for informal purposes only. The data on which this map is based has not been audited and is subject to verification, revision or correction. Use of or reliance on this information for any purpose is at your own risk. The City of Seattle makes no representation regarding this information and disclaims any responsibility for any and all claims or actions arising out of the use of this information.

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.

Data from: American Community Survey, 5-year Series 2009-2013There are seven council districts in the City of Seattle, positions 1-7, and two positions elected "at-large" (city-wide) in positions 8 and 9.For more information about the Council Districts see the Office of the City Clerk.Click on a neighborhood to see basic demographics, charts and reports.* This map is for informal purposes only. The data on which this map is based has not been audited and is subject to verification, revision or correction. Use of or reliance on this information for any purpose is at your own risk. The City of Seattle makes no representation regarding this information and disclaims any responsibility for any and all claims or actions arising out of the use of this information.

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 Laboratory

This 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 percent
• Possible tree canopy - vegetation percent
• Relative percent change
• Absolute percent change

For more information, please see the 2021 Tree Canopy Assessment.

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

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.

Table from the American Community Survey (ACS) 5-year series on race and ethnicity related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B03002 Hispanic or Latino Origin by Race, B02008-B02013 Race Alone or in Combination with One or More. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Table from the American Community Survey (ACS) 5-year series on languages spoken and English ability related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B16004 Age by Language Spoken at Home by Ability to Speak English, C16002 Household Language by Household Limited English-Speaking Status. 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): B16004, C16002Data 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. 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

Table from the American Community Survey (ACS) 5-year series on household types and population related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B11003 Family Type by Presence and Age of Own Children under 18 Years, B11005 Households by Presence of People Under 18 Years by Household Type, B11007 Households by Presence of People 65 Years and Over by Household Type, B11001 Household Type (Including Living Alone), B11002 Household Type by Relatives and Nonrelatives for Population in Households, B25003 Tenure, B25008 Total Population in Occupied Housing Units by Tenure, B09019 Household Type (Including Living Alone) by Relationship. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Table from the American Community Survey (ACS) 5-year series on income and earning related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B19025 Aggregate Household Income, B19013 Median Household Income, B19001 Household Income, B19113 Median Family Household Income, B19101 Family Household Income, B19202 Median Nonfamily Household Income, B19201 Nonfamily Household Income, B19301 Per Capita Income/B19313 Aggregate Income/B01001 Sex by Age, C24010 Sex by Occupation of the Civilian Employed Population 16 years and Over, B20017 Median Earnings by Sex by Work Experience for the Population 16 years and over with Earnings, B20001 Sex by Earnings for the Population 16 years and over with Earnings. 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): B19013, B19001, B19113, B19101, B19202, B19201, B19301, B19313, B01001, C24010, B20017, B20001, B19025Data 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. 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 US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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.

Table from the American Community Survey (ACS) 5-year series on housing characteristics related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B25002 Occupancy Status, B24042 Tenure by Number of Bedrooms, B25024 Units in Structure, B25014 Tenure by Occupants per Room, B25040 House Heating Fuel, B25049 Tenure by Plumbing Facilities, B25053 Tenure by Kitchen Facilities, B25043 Tenure by Telephone Service Available by Age of Householder, B28003 Presence of a Computer and Type of Internet in Household. 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): B25002, B24042, B25024, B25014, B25040, B25049, B25053, B25043, B28003Data 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. 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 US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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.

Table from the American Community Survey (ACS) 5-year series on disabilities and health insurance related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes C21007 Age by Veteran Status by Poverty Status in the Past 12 Months by Disability Status, B27010 Types of Health Insurance Coverage by Age, B22010 Receipt of Food Stamps/SNAP by Disability Status for Households. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Table from the American Community Survey (ACS) 5-year series on poverty and employment status related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B23025 Employment Status for the Population 16 years and over, B23024 Poverty Status by Disability Status by Employment Status for the Population 20 to 64 years, B17010 Poverty Status of Families by Family Type by Presence of Related Children under 18 years, C17002 Ratio of Income to Poverty Level in the Past 12 Months. 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): B23025, B23024, B17010, C17002Data 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. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.<d

Data from: American Community Survey, 5-year Series 2006-2010Community Reporting Area boundaries with American Community Survey data and attachments of census reports. Community Reporting Areas (CRAs) were established in 2004 as a standard, consistent, citywide geography for reporting purposes. There are 53 CRAs composed of from one to six census tracts.Neighborhood aggregations of American Community Survey tract-based data derived from the U.S. Census Bureau's demographic profiles (DP02-DP05). The geo service includes over 50 attributes of the most frequently requested data.Also includes custom reports in pdf format as attachments to each neighborhood.Please see the item page for the source map service for more information.When downloading the data, please select "GDB Download" under "Additional Resources" to preserve long field names and attachments. The associated file geodatabase contains a separate feature class for three levels of neighborhood geography - council districts, community reporting areas, and urban village demographic areas that includes these 50+ attributes.

Table from the American Community Survey (ACS) 5-year series on housing tenure and cost related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B25003 Tenure of Occupied Housing Units, B25070 Gross Rent as a Percentage of Household Income in the Past 12 Months, B25063 Gross Rent, B25091 Mortgage Status by Selected Monthly Owner Costs as a Percentage of Household Income in the Past 12 Months, B25087 Mortgage Stauts and Selected Monthly Owner Costs, B25064 Median Gross Rent, B25088 Median Selected Monthly Owner Costs by Mortgage Status. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

This data layer includes key performance metrics collected by the City and partners tracking the progress towards the goals of the Internet for All Seattle Initiative. Internet for All Seattle Dashboards. The data points reflect activities in five categories: 1) Affordable Connectivity Program, 2) Internet Connectivity, 3) Devices, 4) Digital Skills & Technical Support, and 5) Outreach & Assistance. The majority of the Internet for All Seattle Action Plan items and data fall under these five areas. Source data for Internet for All maps and dashboards.Updated quarterly. Last update: March 4, 2024. ATTRIBUTE NAME DEFINITION ADDITIONAL INFORMATION

Resource Organization or program providing metrics for this dashboard. Access for All Program - City of Seattle program to connect eligible organizations and locations in Seattle with free high speed internet service in partnership with Comcast, Astound Broadband, and Lumen. City of Seattle Facilities - City owned buildings, including Community Centers, City Hall, Seattle Center and others. Internet Essentials Program - Low-cost internet program provided by Comcast offering $9.95/month + tax for eligible households. Internet First Program - Low-cost internet program provided by Astound offering $50 Mbps Internet* to qualifying low-income households. Other Partners - Other organizations partnering with the City of Seattle. Seattle Housing Authority - An independent public corporation in the city of Seattle responsible for public housing for low-income, elderly, and disabled residents. Seattle IT Digital Equity - City of Seattle, Seattle Information Technology Department Digital Equity Program. Seattle IT Digital Navigator - Seattle IT grant program providing funding to community-based organizations to provide digital navigation services. Seattle IT Technology Matching Fund - City of Seattle grant program providing funding to community-based organizations to increase internet access and adoption. Seattle Public Library - The public library system serving the city of Seattle Seattle Public Schools - The public school district serving the city of Seattle. Simply Internet Program - Low-cost internet program provided by Astound offering for $9.95/month + tax for eligible households.

Location_Name Additional info about physical location.

Organization Nonprofit or community group funded by the City.

Project_Title Title of a project funded by the City.

Budget Budget value associated with a resource.

Date Date metrics were reported.

Award_Year Year a grant was awarded to a grantee.

Street_Address Address of physical location.

City City of physical location.

State State of physical location.

ZIP ZIP of physical location.

Council_District Council District resource is located in.

Longitude Longitude of physical location.

Latitude Latitude of physical location.

ISP An organization that provides services for accessing, using, or participating in the Internet.

Citywide_Y_N Is resource provided throughout City.

Devices_Distributed The number of devices that were provided to residents.

Devices_Distributed_Y_N Is there a value in Devices_Distributed field (used to create dashboards).

Devices_Loaned The number of devices that were loaned to residents for temporary use.

Devices_Loaned_Y_N Is there a value in Devices_Loaned field (used to create dashboards).

DSTS_TotalServed The number of residents served by digital skills training and technical support programs. DSTS refers to Digital Skills and Training Support

DSTS_TotalServed_Y_N Is there a value in DSTS_TotalServed field (used to create dashboards).

DSTS_Hours The number of hours of digital skills training and technical support provided.

DSTS_Hours_Y_N Is there a value in DSTS_Hours field (used to create dashboards).

IC_Hotspots_Sponsored Number of residents provided with hotspots or sponsored internet service. IC refers to Internet Connectivity

IC_Hotspots_Sponsored_Y_N Is there a value in IC_Hotspots_Sponsored field (used to create dashboards).

IC_PubWiFiConnections Number of Wi-Fi connections provided at public Wi-Fi sites.

IC_PubWiFiConnections_Y_N Is there a value in IC_PubWiFiConnections field (used to create dashboards).

IC_PubWiFiSites Number of sites providing public Wi-Fi.

IC_PubWiFiSites_Y_N Is there a value in IC_PubWiFiSites field (used to create dashboards).

IC_LowCostServices The number of residents enrolled in Low-cost internet programs offered by Comcast and Astound.

IC_LowCostServices_Y_N Is there a value in IC_LowCostServices field (used to create dashboards).

IC_Organizations Sites providing internet connectivity through their organization. Federal Subsidy Program Emergency Broadband Program (EBB) was a federal program to help low-income households afford broadband services and internet-connected devices during the pandemic. The program officially ended in early 2022 and was replaced by the Affordable Connectivity Program. The Affordable Connectivity Program (ACP) is a federal program to help low-income households afford broadband services and internet-connected devices during the pandemic. The Program provides a discount of up to $30 per month for broadband services for eligible consumers.

IC_Organizations_Y_N Is there a value in IC_Organizations field (used to create dashboards).

IC_FedSubsEBBACP Number of total households that participated in the EBB or ACP programs.

IC_FedSubsEBBACP_Y_N Is there a value in IC_FedSubsEBBACP field (used to create dashboards).

OA_InternetServReqs The number of requests from the public for information about internet service. These requests come to the City and are fulfilled by Seattle IT Digital Equity staff. OA refers to Outreach and Assistance

OA_InternetServReqs_Y_N Is there a value in OA_InternetServReqs field (used to create dashboards).

OA_LowInternetInfo The number of requests from the public for information about low-income internet service. These requests come to the City and are fulfilled by Seattle IT Digital Equity staff.

OA_LowInternetInfo_Y_N Is there a value in OA_LowInternetInfo field (used to create dashboards).

OA_LowInternetevent Number of residents provided with information about free or low-cost internet at outreach events. This outreach is conducted by Seattle IT Digital Equity staff.

OA_LowInternetevent_Y_N Is there a value in OA_LowInternetevent field (used to create dashboards)

This polygon feature contains geographic and attribute information for the purpose of depicting Comprehensive Plan Areas within the City of SeaTac, Washington. The data was compiled from existing comprehensive plan information and King County parcel data.This data layer is a reflection of the comprehensive plan that indicates how the City wants to grow and function in the future. The source document contains broad statements of community goals and policies, as well as specific steps for achieving them. The City of SeaTac Comprehensive Plan serves as a "blueprint" for how SeaTac can achieve its vision for itself over the next 20 to 26 years. The Comprehensive Plan will provide the legal basis for future zoning and other implementation measures.City of SeaTac Comprehensive Plan as prepared and adopted by the SeaTac City Council.Last amended in June 23, 2015 (Ord. 15-1009).The change to Angle Lake District Area Boundary was adopted on July 9th, 2015 (Ord. 15-1010).The Washington Growth management Act (GMA) mandates that cities in high growth areas, like Puget Sound region, prepare and adopt comprehensive plans that are consistent with the GMA. The content was last modified in Dec 2013. Boundaries were updated based on parcel data from 02/13/2015, to reflect changes in ROWs and Parcel line, etc. Comprehensive plan boundaries have been adjusted to line up with King County Assessor parcel lines that were improved in 2006 and 2007. Slivers and gaps will appear if this comprehensive plan layer is overlaid with historical parcel, zoning, or comprehensive plan layers. The geometry of this data derives from KC parcel data which is updated quarterly. Then it was intersected with the existing Comprehensive Plan data to transfer the attribute.Incorporated in February 1990, the City of SeaTac is located in the Pacific Northwest, approximately midway between the cities of Seattle and Tacoma in the State of Washington. SeaTac is a vibrant community, economically strong, environmentally sensitive, and people-oriented. The City boundaries surround the Seattle-Tacoma International Airport, (approximately 3 square miles in area) which is owned and operated by the Port of Seattle. For additional information regarding the City of SeaTac, its people, or services, please visit https://www.seatacwa.gov. For additional information regarding City GIS data or maps, please visit https://www.seatacwa.gov/our-city/maps-and-gis.