The land area of Nome Census Area, AK was 22,962 in 2018. The land area of Benewah County, ID was 777 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 Benewah County, ID or Nome Census Area, AK
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National Incorporated Places and Counties
mydata.iowa.gov | Last Updated 2023-08-30T17:36:07.000ZThis dataset contains a listing of incorporated places (cities and towns) and counties within the United States including the GNIS code, FIPS code, name, entity type and primary point (location) for the entity. The types of entities listed in this dataset are based on codes provided by the U.S. Census Bureau, and include the following: 1. C1 - An active incorporated place that does not serve as a county subdivision equivalent; 2. C2 - An active incorporated place legally coextensive with a county subdivision but treated as independent of any county subdivision; 3. C3 - A consolidated city; 4. C4 - An active incorporated place with an alternate official common name; 5. C5 - An active incorporated place that is independent of any county subdivision and serves as a county subdivision equivalent; 6. C6 - An active incorporated place that partially is independent of any county subdivision and serves as a county subdivision equivalent or partially coextensive with a county subdivision but treated as independent of any county subdivision; 7. C7 - An incorporated place that is independent of any county; 8. C8 - The balance of a consolidated city excluding the separately incorporated place(s) within that consolidated government; 9. C9 - An inactive or nonfunctioning incorporated place; 10. H1 - An active county or statistically equivalent entity; 11. H4 - A legally defined inactive or nonfunctioning county or statistically equivalent entity; 12. H5 - A census areas in Alaska, a statistical county equivalent entity; and 13. H6 - A county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.
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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.
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CommunityVulnerability_BCDC_2020
data.bayareametro.gov | Last Updated 2022-11-29T13:48:16.000ZThe San Francisco Bay Conservation and Development Commission Adapting to Rising Tides Program developed a dataset to better understand community vulnerability to current and future flooding due to sea level rise and storm surges. This data has been used in the Adapting To Rising Tides Bay Area Sea Level Rise Vulnerability and Assessment project as well as helping inform the implementation of the BCDC Environmental Justice and Social Equity Bay Plan amendment. Data and resources can be accessed at https://www.bcdc.ca.gov/data/community.html. For information about data development and access please review the Community Vulnerability User Guide and BCDC’s Github Repository. For additional descriptions of GIS methods used in ART Bay Area, please see the ART Bay Area Report Appendix: GIS Data and Methods. For more information, please contact GIS@bcdc.ca.gov.The community vulnerability dataset contains four categories of information:1. Social Vulnerability Indicators: Certain socioeconomic characteristics may reduce ability to prepare for, respond to, or recover from a hazard event. Census block groups with high concentrations (relative to the nine county Bay Area) of these characteristics are flagged as socially vulnerable, with each block group assigned a rank of highest, high, moderate, and low. Data is currently from American Community Survey (ACS) 2018 5-year estimates but is anticipated to be updated as new ACS 5-year estimates become available.2. Contamination Vulnerability Indicators: The presence of contaminated lands and water raises health and environmental justice concerns, which worsen with flooding and sea level rise. A rank of highest, high, moderate, and lower for the severity of contamination in each block group was calculated using data compiled by CalEPA Office of Environmental Health Hazard Assessment (OEHHA) for use in CalEnviroScreen 3.0.3. Residential Exposure to Sea Level Rise: Calculated by joining Metropolitan Transportation Commission 2010 residential parcel data with 2017 ART Bay Area Sea Level Rise and Shoreline Analysis data, FEMA 100 and 500 year flood zone data, and San Francisco 100-year precipitation data to generate the number of residential units exposed at each water level summed by block group. This methodology assumes that once a parcel is exposed to any amount of flooding, the entire number of residential units within that parcel are considered impacted.4. Complementary Community Vulnerability Screening Tools: Many screening approaches exist to characterize disadvantaged or vulnerable communities. Often in the Bay Area, different designations of disadvantaged/vulnerable communities are located in the same area. It is recommended to use the ART approach in combination with other complementary tools and designations. The following are included in this shapefile as fields for cross-referencing: CalEnviroScreen 3.0 total score, Metropolitan Transportation Commission Community of Concern designation, UC Berkeley Displacement and Gentrification Typologies.