The land area of Napa, CA was 18 in 2018. The land area of Medford, OR was 26 in 2018.
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 -
Geographic and Area Datasets Involving Napa, CA or Medford, OR
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Population Projections for Napa County
data.countyofnapa.org | Last Updated 2024-02-21T23:24:18.000ZData Source: CA Department of Finance, Demographic Research Unit Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021. This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org. Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data. Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California. Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population. Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted. Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete. Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf. Where was the data collected? Data is collected throughout California. This subset of data includes Napa County. When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021. These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060. Why was the data collected? The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5). Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Proj
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Vital Signs: Migration - Bay Area
data.bayareametro.gov | Last Updated 2019-10-25T20:40:04.000ZVITAL SIGNS INDICATOR Migration (EQ4) FULL MEASURE NAME Migration flows LAST UPDATED December 2018 DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables. DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration. Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23) One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.
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ART Bay Area Inundation Scenario - 36" Sea Level Rise
data.bayareametro.gov | Last Updated 2023-06-09T00:07:18.000ZInundation feature set representing areas vulnerable to a 36 inch rise in sea level for the San Francisco Bay Region. This is a derivative feature set, assembled by the Metropolitan Transportation Commission (MTC), created by merging county-specific, land-only inundation feature sets. The source, county-level feature sets were produced for Adapting to Rising Tides (ART), a program led by the San Francisco Bay Conservation and Development Commission (BCDC), in September 2017. The sea level rise (SLR) scenario used to produce this data represents 36 inches (three feet) of water level above the current mean higher high water (MHHW) tidal datum. This is considered the most likely level of sea level rise expected by 2100; or an existing 50-year extreme tide. The polygons contain the extent and depth of land-only inundation (in feet) flooding of the bayside shoreline. Depth of flooding were created by subtracting a land surface Digital Elevation Model (DEM) from the water surface DEM representing the SLR scenario (MHHW + SLR). Extent of flooding were created by employing a two rule assessment to determine if an area is inundated. It must be below the assigned water surface DEM elevation value, and it must be connected to an adjacent area that was either flooded or open water. This method applies an "eight-side rule" for connectedness, where the area is considered "connected" if any of its cardinal or diagonal directions is connected to a flooded area or open water. Hydraulic connectivity assessment removes areas from the inundation zone if they are protected by levees or other topographic features that prevent inland inundation. This assessment also removed areas that are low lying but inland and not directly connected to an adjacent inundated area. The 36 inch SLR scenario can be used to approximate all extreme tide/sea level rise combinations that produce a water level in the range of MHHW + 33 inches to MHHW + 39 inches, including: - 36 inches of SLR; - 1-year extreme tide event coupled with 24 inches of SLR; - 2-year extreme tide event coupled with 18 inches of SLR; - 5-year extreme tide event coupled with 12 inches of SLR; - 25-year extreme tide event coupled with 6 inches of SLR, and - 50-year extreme tide event under existing conditions (no SLR). Publication Date: June 2019 Creation Date: March 2019 Status: Progress: Complete Maintenance and Update Frequency: None planned Contact Information: Contact Organization: Metropolitan Transportation Commission Contact Person: Data & Visualization Contact Address: Address Type: mailing and physical Address: 375 Beale Street, Suite 800 City: San Francisco State or Province: California Postal Code: 94105 Country: United States of America Contact Voice Telephone: (415) 778-6700 Contact Electronic Mail Address: dataviz@bayareametro.gov Hours: 9:00 AM - 5:00 PM (PST) Monday through Friday
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ART Bay Area Inundation Scenario - 77" Sea Level Rise
data.bayareametro.gov | Last Updated 2023-06-09T00:15:10.000ZInundation feature set representing areas vulnerable to a 77 inch rise in sea level for the San Francisco Bay Region. This is a derivative feature set, assembled by the Metropolitan Transportation Commission (MTC), created by merging county-specific, land-only inundation feature sets. The source, county-level feature sets were produced for Adapting to Rising Tides (ART), a program led by the San Francisco Bay Conservation and Development Commission (BCDC), in September 2017. The sea level rise (SLR) scenario used to produce this data represents 77 inches (a little less than six and one-half feet) of water level above the current mean higher high water (MHHW) tidal datum. This is also considered equivalent to 36 inches of SLR plus a 100-year extreme tide. The polygons contain the extent and depth of land-only inundation (in feet) flooding of the bayside shoreline. Depth of flooding were created by subtracting a land surface Digital Elevation Model (DEM) from the water surface DEM representing the SLR scenario (MHHW + SLR). Extent of flooding were created by employing a two rule assessment to determine if an area is inundated. It must be below the assigned water surface DEM elevation value, and it must be connected to an adjacent area that was either flooded or open water. This method applies an "eight-side rule" for connectedness, where the area is considered "connected" if any of its cardinal or diagonal directions is connected to a flooded area or open water. Hydraulic connectivity assessment removes areas from the inundation zone if they are protected by levees or other topographic features that prevent inland inundation. This assessment also removed areas that are low lying but inland and not directly connected to an adjacent inundated area. The 77 inch SLR scenario can be used to approximate all extreme tide/sea level rise combinations that produce a water level in the range of MHHW + 74 inches to MHHW + 80 inches, including: - 77 inches of SLR; - 1-year extreme tide event coupled with 66 inches of SLR; - 2-year extreme tide event coupled with 60 inches of SLR; - 5-year extreme tide event coupled with 54 inches of SLR; - 10-year extreme tide event coupled with 52 inches of SLR; - 25-year extreme tide event coupled with 48 inches of SLR; - 50-year extreme tide event coupled with 42 inches of SLR, and - 100-year extreme tide event coupled with 36 inches of SLR. **In 2019, The San Francisco Bay Conservation and Development Commission released additional data to add East Contra Costa and Solano areas to the existing, 2017 data that focused on San Francisco Bay. This update did not include all the sea level scenarios produced in 2017. The 77-inch scenario was one of the ones for which data for East Contra Costa and Solano was not produced.** Source Data Produced: September 2017 MTC Publication Date: June 2019 Status: Progress: Complete Maintenance and Update Frequency: None planned Contact Information: Contact Organization: Metropolitan Transportation Commission Contact Person: Data & Visualization Contact Address: Address Type: mailing and physical Address: 375 Beale Street, Suite 800 City: San Francisco State or Province: California Postal Code: 94105 Country: United States of America Contact Voice Telephone: (415) 778-6700 Contact Electronic Mail Address: dataviz@bayareametro.gov Hours: 9:00 AM - 5:00 PM (PST) Monday through Friday
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Wildfire - Fire Risk and Fire Responsibility Areas (HESS)
data.bayareametro.gov | Last Updated 2023-06-09T19:12:13.000ZWildfire - Fire Risk and Fire Responsibility Areas (CAL FIRE) for development of the Parcel Inventory dataset for the Housing Element Site Selection (HESS) Pre-Screening Tool. ** This data set represents Moderate, High, and Very High Fire Hazard Severity Zones in State Responsibility Areas (SRA) and Very High Fire Hazard Severity Zones in Local Responsibility Areas (LRA) for the San Francisco Bay Region and some of its surrounding counties. The data was assembled by the Metropolitan Transportation Commission from multiple shapefiles provided by the California Department of Forestry and Fire Protection. The SRA data was extracted from a statewide shapefile and the LRA data is a combination of county shapefiles. All source data was downloaded from the Office of the State Fire Marshal's Fire Hazard Severity Zones Maps page (https://osfm.fire.ca.gov/divisions/community-wildfire-preparedness-and-mitigation/wildland-hazards-building-codes/fire-hazard-severity-zones-maps/). ** State Responsibility Areas PRC 4201 - 4204 and Govt. Code 51175-89 direct CAL FIRE to map areas of significant fire hazards based on fuels, terrain, weather, and other relevant factors. These zones, referred to as Fire Hazard Severity Zones (FHSZ), define the application of various mitigation strategies to reduce risk associated with wildland fires. CAL FIRE is remapping FHSZ for SRA and Very High Fire Hazard Severity Zones (VHFHSZ) recommendations in LRA to provide updated map zones, based on new data, science, and technology. Local Responsibility Areas Government Code 51175-89 directs the CAL FIRE to identify areas of very high fire hazard severity zones within LRA. Mapping of the areas, referred to as VHFHSZ, is based on data and models of, potential fuels over a 30-50 year time horizon and their associated expected fire behavior, and expected burn probabilities to quantify the likelihood and nature of vegetation fire exposure (including firebrands) to buildings. Details on the project and specific modeling methodology can be found at https://frap.cdf.ca.gov/projects/hazard/methods.html. Local Responsibility Area VHFHSZ maps were initially developed in the mid-1990s and are now being updated based on improved science, mapping techniques, and data. Local government had 120 days to designate, by ordinance, very high fire hazard severity zones within their jurisdiction after receiving the CAL FIRE recommendations. Local governments were able to add additional VHFHSZs. There was no requirement for local government to report their final action to CAL FIRE when the recommended zones are adopted. Consequently, users are directed to the appropriate local entity (county, city, fire department, or Fire Protection District) to determine the status of the local fire hazard severity zone ordinance. In late 2005, to be effective in 2008, the California Building Commission adopted California Building Code Chapter 7A requiring new buildings in VHFHSZs to use ignition resistant construction methods and materials. These new codes include provisions to improve the ignition resistance of buildings, especially from firebrands. The updated very high fire hazard severity zones will be used by building officials for new building permits in LRA. The updated zones will also be used to identify property whose owners must comply with natural hazards disclosure requirements at time of property sale and 100 foot defensible space clearance. It is likely that the fire hazard severity zones will be used for updates to the safety element of general plans.
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CTCAC/HCD Resource Opportunity Areas 2022
data.bayareametro.gov | Last Updated 2023-06-08T23:15:47.000ZIn 2017, the California Tax Credit Allocation Committee (CTCAC) and the Department of Housing and Community Development (HCD) created the California Fair Housing Task Force (Task Force). The Task Force was asked to assist CTCAC and HCD in creating evidence-based approaches to increasing access to opportunity for families with children living in housing subsidized by the Low-Income Housing Tax Credit (LIHTC) program. This feature set contains Resource Opportunity Areas (ROAs) that are the results of the Task Force's analysis for the two regions used for the San Francisco Bay Region; one is for the cities and towns (urban) and the other is for the rural areas. The reason for treating urban and rural areas as separate reasons is that using absolute thresholds for place-based opportunity could introduce comparisons between very different areas of the total region that make little sense from a policy perspective — in effect, holding a farming community to the same standard as a dense, urbanized neighborhood. ROA analysis for urban areas is based on census tract data. Since tracts in rural areas of are approximately 37 times larger in land area than tracts in non-rural areas, tract-level data in rural areas may mask over variation in opportunity and resources within these tracts. Assessing opportunity at the census block group level in rural areas reduces this difference by 90 percent (each rural tract contains three block groups), and thus allows for finer-grained analysis. In addition, more consistent standards can be useful for identifying areas of concern from a fair housing perspective — such as high-poverty and racially segregated areas. Assessing these factors based on intraregional comparison could mischaracterize areas in more affluent areas with relatively even and equitable development opportunity patterns as high-poverty, and could generate misleading results in areas with higher shares of objectively poor neighborhoods by holding them to a lower, intraregional standard. To avoid either outcome, the Task Force used a hybrid approach for the CTCAC/HCD ROA analysis — accounting for regional differences in assessing opportunity for most places, while applying more rigid standards for high-poverty, racially segregated areas in all regions. In particular: Filtering for High-Poverty, Racially Segregated Areas The CTCAC/HCD ROA filters areas that meet consistent standards for both poverty (30% of the population below the federal poverty line) and racial segregation (over-representation of people of color relative to the county) into a “High Segregation & Poverty” category. The share of each region that falls into the High Segregation & Poverty category varies from region to region. Calculating Index Scores for Non-Filtered Areas The CTCAC/HCD ROAs process calculates regionally derived opportunity index scores for non-filtered tracts and rural block groups using twenty-one indicators (see Data Quality section of metadata for more information). These index scores make it possible to sort each non-filtered tract or rural block group into opportunity categories according to their rank within the urban or rural areas. To allow CTCAC and HCD to incentivize equitable development patterns in each region to the same degree, the CTCAC/HCD analysis 20 percent of tracts or rural block groups in each urban or rural area, respectively, with the highest relative index scores to the "Highest Resource” designation and the next 20 percent to the “High Resource” designation. The region's urban area thus ends up with 40 percent of its total tracts with reliable data as Highest or High Resource (or 40 percent of block groups in the rural area). The remaining non-filtered tracts or rural block groups are then evenly divided into “Low Resource” and “Moderate Resource” categories. Excluding Tracts or Block Groups The analysis also excludes certain census areas from being categorized. To improve the accuracy of the mapping, tracts and rural bl
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Land Use_data
opendata.utah.gov | Last Updated 2024-04-10T19:40:16.000ZThis 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.