Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Low Income Population Concentration * Tier: 2 * Data Vintage: 2020 * Unit Of Measure: Categorical \- Class Code 0: _Zero or nearly zero_. The variable is absent (observed value = 0) or is very low; the local proportion of the subject population variable is 10% or less than the same proportion in the Southern California region population in total \- Class Code 1: _Low_. The subject population concentration is low; the local proportion of the subject population variable is between roughly 10% and 50% of the corresponding proportion in the Southern California region population in total \- Class Code 2: _Somewhat low_. The subject population concentration is somewhat low; the local proportion of the subject population variable is between roughly 50% and 85% of the corresponding proportion in the Southern California region population in total \- Class Code 3: _Proportionate._ The subject population concentration is roughly proportionate to the corresponding proportion in the Southern California region population in total - from about 85% to 115% of the regional proportion \- Class Code 4: _Somewhat high._ The subject population concentration is somewhat high; the local proportion of the subject population variable is between roughly 115% and 150% of the corresponding proportion in the Southern California region population in total \- Class Code 5: _High._ The subject population concentration is high; the local proportion of the subject population variable is between roughly 150% and 200% of the corresponding proportion in the Southern California region population in total \- Class Code 6: _Very high._ The subject population concentration is very high; the local proportion of the subject population variable roughly 2 to 3 times that of the corresponding proportion in the Southern California region population in total \- Class Code 7: _Extremely high._ The subject population concentration is very extremely high; the local proportion of the subject population variable is at least 3 times that of the corresponding proportion in the Southern California region population in total (the upper limit is determined by natural breaks if exceptional outliers are present, but is typically over 6 times (600%) \- Class Code 99: _Unclassifiable._ The 90% confidence interval for the estimate is wide enough to cause the values to span four or more classes. In these cases, it is impossible to say with any reasonable certainty whether the concentration is "low" or "high." * Metric Definition and Relevance: Relative concentration of the estimated number of people in the Southern California region that live in a household defined as “low income.” There are multiple ways to define low income. These data apply the most common standard: low income population consists of all members of households that collectively have income less than twice the federal poverty threshold that applies to their household type. Household type refers to the household’s resident composition: the number of independent adults plus dependents that can be of any age, from children to elderly. For example, a household with four people – one working adult parent and three dependent children – has a different poverty threshold than a household comprised of four unrelated independent adults. Due to high estimate uncertainty for many block group estimates of the number of people living in low income households, some records cannot be reliably assigned a class and class code comparable to those assigned to race/ethnicity data from the decennial Census. “Relative concentration” is a measure that compares the proportion of population within each Census block group data unit to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region. See the “Data Units” description below for how these relative concentrations are broken into categories in this “low income” metric. * Creation Method: Data are reported in Census block groups. Standard block groups are clusters of Census blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Block groups delineated for the 2020 Census generally contain 600 to 3,000 people. Census blocks are statistical areas bounded on all sides by visible features (e.g., streets, roads, streams, and railroad tracks), and by non-visible boundaries (e.g., city, town, township, county limits, and short line-of-sight extensions of streets and roads). Census blocks in suburban and rural areas may be large, irregular, and bounded by a variety of features (e.g., roads, streams, and/or transmission line rights-of-way). In remote areas, census blocks may encompass hundreds of square miles. Census blocks cover all territory in the United States, Puerto Rico, and the Island areas. Blocks do not cross the boundaries of any entity for which the Census Bureau tabulates data. See note 1. Data describing concentrations of population characteristics that are potentially related to environmental justice issues were provided to CWI through a collaboration with the USDA Forest Service, Geospatial Technology and Applications Center. The concentration methodology was created by GTAC for social science analysis applications within the Forest Service; it is based on research published in 2018 and 2020 (See Note 2). Data were compiled and prepared for incorporating in the regional resource kits by Mark Adams, Geographer, USFS-GTAC. For more information, contact: [mark.adams1@usda.gov](mailto:mark.adams1@usda.gov). Notes: The pixels attributed with a categorical data unit describing the relative concentration of LOW_INCOME population are derived from a vector polygon feature that has been modified as follows: Census block groups from the Census Bureau’s TIGER/Line geodatabase features for 2021 are selected based on their spatial intersection with the Southern California RRK boundary. The resulting 13,312 block group features are modified by first erasing from the feature the area of all constituent Census blocks which have neither housing nor population recorded in the PL-94171 Redistricting dataset for 2020. In a second step, areas of federal and state public lands on which housing by definition is not located are erased from the interim feature. The result is a block group feature that depicts to the maximum practicable extent the areas within the block group where people that are represented by the Census Bureau’s Census count could actually be residing. It is this modified block group feature that has been rasterized to match the 30m pixel grid that all biophysical datasets are reported in. References for the concentration levels analysis: \- Adams, Mark D. O. and S. Charnley. 2020. The Environmental Justice Implications of Managing Hazardous Fuels on Federal Forest Lands, Annals of the American Association of Geographers, 110:6, 1907-1935, DOI: 10.1080/24694452.2020.1727307 \- Adams, Mark D. O. and S. Charnley. 2018. Environmental justice and U.S. Forest Service hazardous fuels reduction: A spatial method for impact assessment of federal resource management actions. https://doi.org/10.1016/j.apgeog.2017.12.014 * Credits: U.S. Department of Commerce, Census Bureau, 2020 American Community Survey 5-Year Survey Estimates. Data estimating household income as a percent of the applicable federal poverty threshold are reported in Table C17002 of the 2016-2020 ACS 5-year data. Estimates of population living in low income households were obtained via the Data.Census.Gov web portal and joined to the Census Bureau’s TIGER/line feature classes for block groups (see reporting units below). Table C17002 provides estimates and error margins for total population living in households with income, and population by ratio of income to applicable poverty: 50% of poverty, 50-99%, etc. Additional calculations are performed to generate an estimate for all people in households with income less than 200% of applicable poverty. FMI: