This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework and has been clipped to the Oregon boundary and reprojected to Oregon Lambert (2992). The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

US Postal Service Zone Improvement Plan (ZIP) Codes are used throughout the United States to improve mail delivery. There are 479 unique 5-digit ZIP Codes in Oregon all starting with 97. All ZIP Codes are assigned and managed exclusively by the US Postal Service. There are three main categories of ZIP Codes - 1) Standard, 2) PO Box Only, 3) Unique for large commercial and government customers.Each ZIP Code is assigned a preferred city name by the US Postal Service. NOTE - these city names may not correspond with the city limits or other jurisdiction boundaries of incorporated cities. There are other acceptable city names listed that may be used for mailing addresses for some ZIP Codes. There are also other city names to avoid using for mailing addresses. To verify the preferred, acceptable, or city names to avoid enter the ZIP Code in this tool from the US Postal Service - https://tools.usps.com/zip-code-lookup.htm?citybyzipcodeThis is not a product of the US Postal Service. It was compiled by checking all numbers from 97000 - 97999 with the ZIP Code Lookup tool.Most Standard and some PO Box Only ZIP Codes may also be listed as Census ZIP Code Tabulation Areas (ZTCA). NOTE - The ZTCA is only an approximation of a ZIP Code area based on the predominate ZIP Code of all housing units in each Census block. ZIP Codes actually follow lines of travel along letter carrier routes and are not polygons as shown by the ZTCA.

The State Library of Oregon collects annual service measures, financial data, and other statistics from all legally-established public libraries in the state, as per Oregon Revised Statue 357.520 (Annual report). The data reporting period matches the state fiscal year (July 1 through June 30). This dataset includes all Oregon Public Library Statistical Report data from each year starting in FY2009-2010, and is updated annually. Reporting periods are identified as the year the report was submitted (i.e., FY2009-2010 data is identified as 2010 in the Year column).

To access the tax lot layer you will need to contact the county Assessor's office. ORMAP is a statewide digital cadastral base map that is publicly accessible, continually maintained, supports the Oregon property tax system, supports a multi-purpose land information system, strives to comply with appropriate state and national standards, and will continue to be improved over time.

Graph and download economic data for Initial Claims in Oregon (ORICLAIMS) from 1986-02-15 to 2025-06-14 about initial claims, OR, and USA.

This data layer is an element of the Oregon GIS Framework. The Oregon Wetlands Database (2019) is a geodatabase consisting of three feature classes: 1) Local Wetland Inventory (LWI) Wetlands, 2) National Wetland Inventory (NWI) Wetlands, and 3) More Oregon Wetlands (MOW). Each feature class contains polygons representing wetland boundaries. The three feature classes originated from different sources with distinct purposes, methodologies and time frames for generating geospatial wetlands data, and there is overlap among them; however, all are relevant to the conservation and management of wetlands across the state of Oregon. See the metadata of the individual feature classes for more background information and details per dataset.

In 2023, about **** million people lived in Oregon. This was a slight decrease from the previous year, when about **** million people lived in the state. In 1960, the resident population of Oregon stood at about **** million people.

Graph and download economic data for Unemployment Rate in Oregon (ORUR) from Jan 1976 to May 2025 about OR, unemployment, rate, and USA.

Graph and download economic data for Median Household Income in Oregon (MEHOINUSORA646N) from 1984 to 2023 about OR, households, median, income, and USA.

Historical Dataset of Oregon is provided by PublicSchoolReview and contain statistics on metrics:Trends in the Average Number of Students Per Public School,Trends in the Average Number of Teachers Per Public School,Student-Teacher Ratio Trends (1987-2023),Percentage of Public School Students of American Indian Trends,Asian Student Percentage Trends,Hispanic Student Percentage Trends,Black Student Percentage Trends,White Student Percentage Trends,Native Hawaiian or Pacific Islander Student Percentage Trends,Two or More Races Student Percentage Trends,Diversity Score Trends,Free Lunch Eligibility Trends,Reduced-Price Lunch Eligibility Trends,Median Total Revenues Trends,Median Total Expenditures Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends

About this dataset

Context

The dataset tabulates the Oregon population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Oregon across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of Oregon was 4.27 million, a 0.44% increase year-by-year from 2023. Previously, in 2023, Oregon population was 4.25 million, an increase of 0.15% compared to a population of 4.25 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Oregon increased by 841,632. In this period, the peak population was 4.27 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Oregon is shown in this column.
• Year on Year Change: This column displays the change in Oregon population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Oregon Population by Year. You can refer the same here

The purpose of this dataset is to show the building shape and building locations within the state of Oregon. The building footprints contain attributes to document source information and for ease of updates. https://ftp.gis.oregon.gov/framework/Preparedness/SBFO_v1.zip

This feature class GIS dataset contains building footprints depicting building shape and location in the state of Oregon. All contributing datasets were compiled into the stateside dataset. Static datasets or infrequently maintained datasets were reviewed for quality. New building footprint data were reviewed and digitized from the Oregon Statewide Imagery Program 2017 and 2018.

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census, but some CDPs were added or updated through the 2023 BAS as well.

DOGAMI has been supervising and coordinating the collection of large swaths of high resolution, high accuracy lidar data in Oregon and adjacent states since 2006. Following a successful 2500 mi2 consortium effort in the Portland urban area, the Oregon legislature designated DOGAMI as the lead agency for lidar acquisition in Oregon. DOGAMI used a nationwide selection process that resulted in a state price agreement (OPA 8865) with Watershed Sciences Inc. of Corvallis, Oregon. The price agreement specifies data collection (8 pulse/m2, Zerror 2, has taken final delivery of 16,000 mi2 of data. Funding for these projects has come from consortia organized by DOGAMI that include several dozen Federal, State and local government agencies, non-profits and public utilities. The data quality for all projects that DOGAMI has completed under OPA 8865 has been consistently excellent, substantially exceeding the minimum specifications. All DOGAMI lidar data is in the public domain, please reference DOGAMI as the data source. All DOGAMI lidar program data are systematically evaluated for: Completeness and useability by loading all files; swath to swath consistency by using TerraMatch to compare elevations of millions of coincident points from adjacent swaths, all values to date < 5cm; absolute vertical accuracy by comparing delivered DEMs to an large independent set of RTK GPS control points collected by DOGAMI, all values to date < 7cm RMSE; grid artifacts by visual examination of hillshade and slopeshade images of all bare earth and highest hit DEMs.

The Geographic Names Information System (GNIS) is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types.

The original source for these data is Geonames, the ORGNIS dataset contains only features located in Oregon. Minimal processing of data downloaded from the GNIS repository was performed by the Oregon Geospatial Enterprise Office, these changes are documented in the last process step in the metadata.

Purpose

The Geographic Names Information System contains information about physical and cultural geographic features of all types in the United States, associated areas, and Antarctica, current and historical, but not including roads and highways. The database holds the Federally recognized name of each feature and defines the feature location by state, county, USGS topographic map, and geographic coordinates. Other attributes include names or spellings other than the official name, feature designations, feature classification, historical and descriptive information, and for some categories the geometric boundaries. The database assigns a unique, permanent feature identifier, the Feature ID, as a standard Federal key for accessing, integrating, or reconciling feature data from multiple data sets. The GNIS collects data from a broad program of partnerships with Federal, State, and local government agencies and other authorized contributors. The GNIS provides data to all levels of government and to the public, as well as to numerous applications through a web query site, web map and feature services, file download services, and customized files upon request.

This layer is sourced from gis.odot.state.or.us.

Oregon State Highways

© Oregon Department of Transportation

This dataset tracks annual median total expenditures from 1990 to 2021 for Oregon

This dataset tracks annual median total revenues from 1990 to 2021 for Oregon

In 2023, the GDP of Oregon totaled around 261.95 billion U.S. dollars. The finance, insurance, real estate, rental, and leasing industry added the most real value to the gross domestic product (GDP) of the state, amounting to around 48.92 billion U.S. dollars. In the same year, the construction industry contributed around 10.91 billion U.S. dollars worth of value to the state's GDP.