The land area of Bylas, AZ was 4 in 2018. The land area of Airport, CA was 1 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 Bylas, AZ or Airport, CA
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Maricopa County Census Tracts
citydata.mesaaz.gov | Last Updated 2024-08-29T23:01:23.000ZGeospatial attributes of census tracts in Maricopa County, version 2022. Sourced from US Census https://catalog.data.gov/dataset/tiger-line-shapefile-2022-state-arizona-az-census-tract and filtered for County = 013
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HCD Racially Concentrated Areas of Affluence ACS 2019
data.bayareametro.gov | Last Updated 2023-06-07T00:29:07.000ZRacially Concentrated Areas of Affluence (RCAA's) The concept of Racially Concentrated Areas of Affluence (RCAAs) was originally developed by scholars at the University of Minnesota to illustrate the flip side of the Racially and Ethnically Concentrated Areas of Poverty (R/ECAPs) metric used by the California Department of Housing and Community Development (HCD) in the 2015 Affirmatively Furthering Fair Housing (AFFH) rule to more fully tell the story of segregation in the United States. As stated in HCD’s AFFH Guidance Memo, when analyzing patterns and trends of segregation and proposing policy approaches in the Housing Element, localities should not only focus on communities of color. Segregation is a continuum, with polarity between race, poverty, and affluence, which can be a direct product of the same policies and practices. To better evaluate these conditions, both sides of the continuum should be considered and compare patterns within the community and across the region. This more holistic approach will better unveil deeply rooted policies and practices and improve identification and prioritization of contributing factors to inform more meaningful actions. HCD has created a new version of the RCAA metric to better reflect California’s relative diversity and regional conditions, and to aid local jurisdictions in their analysis of racially concentrated areas of poverty and affluence pursuant to AB 686 and AB 1304. HCD’s RCAA metric is provided as a resource to be paired with local data and knowledge – jurisdictions are encouraged but not required to use the RCAA layer provided by HCD in their housing element analyses. To develop the RCAA layer, staff first calculated a Location Quotient (LQ) for each California census tract using data from the 2015-2019 American Community Survey data. This LQ represents the percentage of total white population (White Alone, Not Hispanic or Latino) for each census tract compared to the average percentage of total white population for all census tracts in a given Council of Governments' (COG) region. For example, a census tract with a LQ of 1.5 has a percentage of total white population that is 1.5 times higher than the average percentage of total white population in the given COG region. To determine the RCAAs, census tracts with a LQ of more than 1.25 and a median income 1.5 times higher than the COG Area Median Income (AMI) (or 1.5x the State AMI, whichever is lower) were assigned a numeric score of 1 (Is a RCAA). Census tracts that did not meet this criterion were assigned a score of 0 (Not a RCAA). COG AMI was determined by averaging the 2019 ACS established AMI's for each county within the given COG region. 2019 ACS AMI limits can be found here: https://www.census.gov/quickfacts/fact/table/US/PST045219 [census.gov]. State AMI was based on the ACS 2019 California state AMI ($75,235), which can be found here: https://www.census.gov/quickfacts/fact/table/CA/INC110219 [census.gov]. Census tracts with a total population of less than 75 people, in which the census tract was also largely contained within a non-urbanized area such as a park, open space, or airport, were not identified as RCAAs. Data Source: American Community Survey (ACS), 2015-2019 References: Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press. Damiano, T., Hicks, J., & Goetz, E. (2017). Racially Concentrated Areas of Affluence: A Preliminary Investigation. To learn more about R/ECAPs visit: https://www.huduser.gov/portal/periodicals/cityscpe/vol21num1/ch4.pdf [huduser.gov] Original data created by HCD, PlaceWorks 2021
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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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California Protected Area Database (HESS)
data.bayareametro.gov | Last Updated 2023-06-09T00:41:33.000ZCalifornia Protected Area Database (CPAD) for development of the Parcel Inventory dataset for the Housing Element Site Selection (HESS) Pre-Screening Tool. This feature set contains a subset of the statewide source. Its geographic extent covers the San Francisco Bay Region. CPAD inventories open space lands that have been protected for open space uses through fee ownerships. CPAD is not a database of all public lands – for example, it does not include public buildings, water treatment sites, or other non-open space public land. CPAD is suitable for a wide range of planning, assessment, analysis, and display purposes. CPAD should not be used as the basis for official regulatory, legal, or other such governmental actions without more detailed review of current official land records in the area of focus. This feature set contains the CPAD Super Unit features of the database. Super Units are aggregations of Units (which themselves are aggregations of Holdings) to create use-focused polygons for each site name (e.g. Las Trampas Regional Wilderness). Super Units are useful for recreation applications and for cartographic representation. Note: ● Super Units aggregate units based on the managing agency. ● Super Units maintain distinct units for different types of public access. ● Super Units cross county boundaries. ● Super Units have fewer attributes and are primarily used for cartography/display purposes, and to support recreational access applications. The lands in CPAD are defined by their owning and managing agencies at the Holdings and Units levels. At the Super Units level (a version of the release meant primarily for recreation applications, and for general cartography), CPAD lands are defined simply by name, managing agency, and public access. Access to CPAD GIS data is primarily through the State of California’s Atlas open data portal – a download link and more information about CPAD, including a PDF manual about the data, is at https://www.calands.org/cpad/. CPAD is released in shapefile format. The state site also hosts map services with CPAD data displayed by Access Type, Agency Classification, and Agency Level.