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The land area of Portland, OR was 133 in 2014. The land area of Seattle, WA was 84 in 2014.

Land Area

Water Area

Land area is a measurement providing the size, in square miles, of the land portions of geographic entities for which the Census Bureau tabulates and disseminates data. Area is calculated from the specific boundary recorded for each entity in the Census Bureau's geographic database. Land area is based on current information in the TIGER® data base, calculated for use with Census 2010.

Water Area figures include inland, coastal, Great Lakes, and territorial sea water. Inland water consists of any lake, reservoir, pond, or similar body of water that is recorded in the Census Bureau's geographic database. It also includes any river, creek, canal, stream, or similar feature that is recorded in that database as a two- dimensional feature (rather than as a single line). The portions of the oceans and related large embayments (such as Chesapeake Bay and Puget Sound), the Gulf of Mexico, and the Caribbean Sea that belong to the United States and its territories are classified as coastal and territorial waters; the Great Lakes are treated as a separate water entity. Rivers and bays that empty into these bodies of water are treated as inland water from the point beyond which they are narrower than 1 nautical mile across. Identification of land and inland, coastal, territorial, and Great Lakes waters is for data presentation purposes only and does not necessarily reflect their legal definitions.

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Geographic and Area Datasets Involving Seattle, WA or Portland, OR

  • API

    WAOFM - Census - Population and Housing, 2000 and 2010

    data.wa.gov | Last Updated 2021-09-01T17:20:31.000Z

    Population and housing information extracted from decennial census Public Law 94-171 redistricting summary files for Washington state for years 2000 and 2010.

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    WAOFM - Congressional Districts - Table 1: Census 2010 Population and Housing

    data.wa.gov | Last Updated 2021-09-01T17:19:01.000Z

    Census 2010 population and housing for 2012 congressional districts based on Washington State Redistricting Commission plan C-JOINTSUB_2-1 as amended by Engrossed House Concurrent Resolution 4409.

  • API

    WAOFM - Census - Population Density by County by Decade, 1900 to 2020

    data.wa.gov | Last Updated 2023-07-06T16:48:57.000Z

    Washington state population density by county by decade 1900 to 2020.

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    Annual Water Quality Index Score

    internal.open.piercecountywa.gov | Last Updated 2022-07-13T15:41:09.000Z

    2015 data source: https://data.wa.gov/Natural-Resources-Environment/Annual-2015-Water-Quality-Index-Data/u9d5-kb9m/data data source: https://data.wa.gov/Natural-Resources-Environment/Water-Quality-Index-Scores-1994-2013-from-The-WA-S/k5fe-2e4s/data

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    WAOFM - Legislative Districts - Table 1: Census 2010 Population and Housing

    data.wa.gov | Last Updated 2021-09-01T17:19:16.000Z

    Census 2010 population and housing for legislative districts based on Washington State Redistricting Commission plan L-JOINTSUB_3-2 as amended by Engrossed House Concurrent Resolution 4409.

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    Historical personal income tax return statistics by county and tax year

    data.oregon.gov | Last Updated 2024-08-22T15:07:40.000Z

    A historical tabulation of selected tax return statistics by counties and other geographic areas reported every year by the Research Section at the Department of Revenue. Source data comes from Tables A through D in the "Returns by county, other states, and city" spreadsheet that accompanies every annual Personal Income Tax publication. Data are reported for all 36 Oregon counties as well as five areas outside Oregon, based on the mailing address on the return when it was filed. Clark County, Washington, is reported separately from the remainder of Washington because so many Clark County residents work in Portland. Idaho and California also have individual tables. Returns from all other states (and outside of the US) are grouped together as "Other". For full-year resident returns, Oregon AGI is the same as federal AGI. For part-year resident and nonresident returns, Oregon AGI is determined from Oregon sourced income and adjustments. Note that some rows have blank cells indicating that data has been omitted, often for disclosure reasons. See "data limitations" below.

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    WAOFM - April 1 - Population Density by County, 2000 to Present

    data.wa.gov | Last Updated 2024-07-11T21:24:42.000Z

    Intercensal and postcensal estimates of population density by county 2000 to present.

  • API

    County to CBSA Mapping for Large Metros

    data.bayareametro.gov | Last Updated 2022-08-26T07:12:04.000Z

    Data contains counties in the following list of CBSAS (per OMB Mar 2020 definition): Bay Area CBSAs: San Francisco-Oakland-Berkeley, CA San Jose-Sunnyvale-Santa Clara, CA Napa, CA Santa Rosa-Petaluma, CA Other CBSAs: Los Angeles-Long Beach-Anaheim, CA Washington-Arlington-Alexandria, DC-VA-MD-WV Denver-Aurora-Lakewood, CO Detroit-Warren-Dearborn, MI Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Boston-Cambridge-Newton, MA-NH New York-Newark-Jersey City, NY-NJ-PA Phoenix-Mesa-Chandler, AZ Houston-The Woodlands-Sugar Land, TX Seattle-Tacoma-Bellevue, WA Atlanta-Sandy Springs-Alpharetta, GA Chicago-Naperville-Elgin, IL-IN-WI Austin-Round Rock-Georgetown, TX Dallas-Fort Worth-Arlington, TX Miami-Fort Lauderdale-Pompano Beach, FL

  • API

    Land Use_data

    opendata.utah.gov | Last Updated 2024-04-10T19:40:16.000Z

    This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.