The 2019 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks in the United States. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Census Tracts from 2020. The TIGER/Line shapefiles 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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2020 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2010 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area.

Context

Cartographic Boundary Shapefiles - Census Tracts

The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping.

Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files.

Content

File Naming Convention: cb_2017_ss_tract_500k.zip, where ss is the 2 digit state FIPS code.

ss= 25 for Massachusetts

Acknowledgements

https://www.census.gov/geo/maps-data/data/cbf/cbf_tracts.html

Inspiration

Combine this data with other Census DataSets for insightful results

Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file.

All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25d

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.Field Definition:STATEFP10 - 2010 Census State FIPS codesCOUNTYFP10 - 2010 Census County FIPS CodesTRACTCE10 - 2010 Census Census TractGEOID10 - "Census tract identifier; a concatenation of 2010 Census state FIPS code, county FIPS code, and census tract code"NAME10 - "2010 Census census tract name, this is the census tract code converted to an integer or integer plus two-digit decimal if the last two characters of the code are not both zeros"NAMELSAD10 - 2010 Census translated legal/statistical area description and the census tract nameMTFCC10 - 2010 Census MAF/TIGER featture class codeFUNCSTAT10 - 2010 Census Functional Statitical CodeALAND10 - 2010 Census Area LandAWATER10 - 2010 Census Area waterINTPTLAT10 - 2010 Census Internal Point (Latitude)INTPTLON10 - 2010 Census Internal Point (Longtitude)POPULATION - Total PopulationHOUSING_UNITS - Total Housing units

This polygon layer contains the boundaries of the 33 Census tracts that make up the City of Cambridge for the 2020 Census. Where appropriate, the boundary lines provided by the U.S. Census were adjusted by City staff to match those lines to such features as the City boundary, street centerlines, and parcel lines.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription STATEFP20 type: Stringwidth: 2precision: 0 2020 Census state FIPS code

COUNTYFP20 type: Stringwidth: 3precision: 0 2020 Census county FIPS code

TRACTCE20 type: Stringwidth: 6precision: 0 2020 Census tract code

GEOID20 type: Stringwidth: 12precision: 0 Census tract identifier; a concatenation of Current state FIPS code, county FIPS code, and census tract code

NAME20 type: Stringwidth: 7precision: 0 2020 Census tract name, this is the census tract code converted to an integer or integer with 2-decimals if the last two characters of the code are not both zeros.

NAMELSAD20 type: Stringwidth: 13precision: 0 2020 translated legal/statistical area description and the census tract name

MTFCC20 type: Stringwidth: 5precision: 0 MAF/TIGER Feature Class Code

FUNCSTAT20 type: Stringwidth: 1precision: 0 2020 functional status

ALAND20 type: Doublewidth: 8precision: 38 2020 land area (unadjusted - matches raw Census TIGER data)

AWATER20 type: Doublewidth: 8precision: 38 2020 water area (unadjusted - matches raw Census TIGER data)

INTPTLAT20 type: Stringwidth: 11precision: 0 2020 latitude of the internal point (unadjusted - matches raw Census TIGER data)

INTPTLON20 type: Stringwidth: 12precision: 0 2020 longitude of the internal point (unadjusted - matches raw Census TIGER data)

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.Field Definiiton:STATEFP10 - 2010 Census State FIPS codesCOUNTYFP10 - 2010 Census County FIPS CodesTRACTCE10 - 2010 Census Census Tract codeBLOCKCE10 - 2010 Census Census block codeGEOID10 - "Census block group identifier; a concatenation of 2010 Census state FIPS code, county FIPS code, and census tract code, and the block group number"NAME10 - 2010 Census translated legal/statistical area description and the block group numberMTFCC10 - 2010 Census MAF/TIGER featture class codeUR10 - 2010 Census Urban/RuralUACE10 - 2010 Census Urban AreaFUNCSTAT10 - 2010 Census Functional Statitical CodeALAND10 - 2010 Census Area LandAWATER10 - 2010 Census Area waterINTPTLAT10 - 2010 Census Internal Point (Latitude)INTPTLON10 - 2010 Census Internal Point (Longtitude)POPULATION - Total PopulationHOUSING_UNITS - Total Housing units

This polygon layer contains the boundaries of the 1044 Census blocks that make up the City of Cambridge for the 2020 Census. Where appropriate, the boundary lines provided by the U.S. Census were adjusted by City staff to match those lines to such features as the City boundary, street centerlines, and parcel lines.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription STATEFP20 type: Stringwidth: 2precision: 0 2020 Census state FIPS code

COUNTYFP20 type: Stringwidth: 3precision: 0 2020 Census county FIPS code

TRACTCE20 type: Stringwidth: 6precision: 0 2020 Census tract code

BLOCKCE20 type: Stringwidth: 4precision: 0 2020 Census tabulation block number

GEOID20 type: Stringwidth: 15precision: 0 Census block identifier; a concatenation of 2020 Census state FIPS code, 2020 Census county FIPS code, 2020 Census tract code, and 2020 Census block number

NAME20 type: Stringwidth: 10precision: 0 2020 Census tabulation block name; a concatenation of ‘Block’ and the tabulation block number

MTFCC20 type: Stringwidth: 5precision: 0 MAF/TIGER Feature Class Code

UR20 type: Stringwidth: 1precision: 0 Reserved for 2020 Census urban/rural indicator (2020 Urban Areas are not yet defined)

UACE20 type: Stringwidth: 5precision: 0 Reserved for 2020 Census urban area code (2020 Urban Areas are not yet defined)

UATYPE20 type: Stringwidth: 1precision: 0 Reserved for 2020 Census urban area type (2020 Urban Areas are not yet defined)

FUNCSTAT20 type: Stringwidth: 1precision: 0 2020 Census functional status

ALAND20 type: Doublewidth: 8precision: 38 2020 Census land area (unadjusted - matches raw Census TIGER data)

AWATER20 type: Doublewidth: 8precision: 38 2020 Census water area (unadjusted - matches raw Census TIGER data)

INTPTLAT20 type: Stringwidth: 11precision: 0 2020 Census latitude of the internal point (unadjusted - matches raw Census TIGER data)

INTPTLON20 type: Stringwidth: 12precision: 0 2020 Census longitude of the internal point (unadjusted - matches raw Census TIGER data)

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

This data set contains estimates of the percentage smoke detector utilization at the Census Tract level for the United States. Development of this data set is described in NIST TN 2020 (see references below). The zip file contains the data in shapefile format. Each record is a single census tract (using the 2013 Tiger files for census tracts) with associated data. Fields contained in the data set are: geoid: Geographic ID of the census tract. Format is '14000USXXYYYZZZZZZ', where XX is the FIPS code for the state, YYY is the FIPS code for the county, and ZZZZZZ is the census tract number. This field serves as a unique ID for the dataset. state: FIPS code for the state. county: FIPS code for the county. tract: Tract number. smsa: Standard Metropolitan Statistical Area as used in the American Housing Survey. PUMA: Public Use Microdata Area ID. region: Census region. dtctrs: Estimated fraction of households in the census tract with smoke detectors installed.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

2023 Tract-level Indicators of Potential Disadvantage for the DVRPC Region Title VI of the Civil Rights Act states that "no person in the United States, shall, on the grounds of race, color, or national origin be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving federal financial assistance.” Under Title VI of the Civil Rights Act, Metropolitan Planning Organizations (MPOs) are directed to create a method for ensuring that Title VI compliance issues are investigated and evaluated in transportation decision-making. There is additional guidance from the FHWA’s Title VI and Additional Nondiscrimination requirements (2017), and FTA’s Title VI requirements and guidelines (2012). The Indicators of Potential Disadvantage (IPD) analysis is used throughout DVRPC to demonstrate compliance with Title VI of the Civil Rights Act.

This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field)

Field	Alias	Description	Source
year	IPD analysis year		DVRPC
geoid20	11-digit tract GEOID	Census tract identifier	ACS 5-year
statefp	2-digit state GEOID	FIPS Code for State	ACS 5-year
countyfp	3-digit county GEOID	FIPS Code for County	ACS 5-year
tractce	Tract number	Tract Number	ACS 5-year
name	Tract number	Census tract identifier with decimal places	ACS 5-year
namelsad	Tract name	Census tract name with decimal places	ACS 5-year
d_class	Disabled percentile class	Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average	calculated
d_est	Disabled count estimate	Estimated count of disabled population	ACS 5-year
d_est_moe	Disabled count margin of error	Margin of error for estimated count of disabled population	ACS 5-year
d_pct	Disabled percent estimate	Estimated percentage of disabled population	ACS 5-year
d_pct_moe	Disabled percent margin of error	Margin of error for percentage of disabled population	ACS 5-year
d_pctile	Disabled percentile	Tract's regional percentile for percentage disabled	calculated
d_score	Disabled percentile score	Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4	calculated
em_class	Ethnic minority percentile class	Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average	calculated
em_est	Ethnic minority count estimate	Estimated count of Hispanic/Latino population	ACS 5-year
em_est_moe	Ethnic minority count margin of error	Margin of error for estimated count of Hispanic/Latino population	ACS 5-year
em_pct	Ethnic minority percent estimate	Estimated percentage of Hispanic/Latino population	calculated
em_pct_moe	Ethnic minority percent margin of error	Margin of error for percentage of Hispanic/Latino population	calculated
em_pctile	Ethnic minority percentile	Tract's regional percentile for percentage Hispanic/Latino	calculated
em_score	Ethnic minority percentile score	Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4	calculated
f_class	Female percentile class	Classification of tract's female percentage as: well below average, below average, average, above average, or well above average	calculated
f_est	Female count estimate	Estimated count of female population	ACS 5-year
f_est_moe	Female count margin of error	Margin of error for estimated count of female population	ACS 5-year
f_pct	Female percent estimate	Estimated percentage of female population	ACS 5-year
f_pct_moe	Female percent margin of error	Margin of error for percentage of female population	ACS 5-year
f_pctile	Female percentile	Tract's regional percentile for percentage female	calculated
f_score	Female percentile score	Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4	calculated
fb_class	Foreign-born percentile class	Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average	calculated
fb_est	Foreign-born count estimate	Estimated count of foreign born population	ACS 5-year
fb_est_moe	Foreign-born count margin of error	Margin of error for estimated count of foreign born population	ACS 5-year
fb_pct	Foreign-born percent estimate	Estimated percentage of foreign born population	calculated
fb_pct_moe	Foreign-born percent margin of error	Margin of error for percentage of foreign born population	calculated
fb_pctile	Foreign-born percentile	Tract's regional percentile for percentage foreign born	calculated
fb_score	Foreign-born percentile score	Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4	calculated
le_class	Limited English proficiency percentile class	Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average	calculated
le_est	Limited English proficiency count estimate	Estimated count of limited english proficiency population	ACS 5-year
le_est_moe	Limited English proficiency count margin of error	Margin of error for estimated count of limited english proficiency population	ACS 5-year
le_pct	Limited English proficiency percent estimate	Estimated percentage of limited english proficiency population	ACS 5-year
le_pct_moe	Limited English proficiency percent margin of error	Margin of error for percentage of limited english proficiency population	ACS 5-year
le_pctile	Limited English proficiency percentile	Tract's regional percentile for percentage limited english proficiency	calculated
le_score	Limited English proficiency percentile score	Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4	calculated
li_class	Low-income percentile class	Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average	calculated
li_est	Low-income count estimate	Estimated count of low income (below 200% of poverty level) population	ACS 5-year
li_est_moe	Low-income count margin of error	Margin of error for estimated count of low income population	ACS 5-year
li_pct	Low-income percent estimate	Estimated percentage of low income (below 200% of poverty level) population	calculated
li_pct_moe	Low-income percent margin of error	Margin of error for percentage of low income population	calculated
li_pctile	Low-income percentile	Tract's regional percentile for percentage low income	calculated
li_score	Low-income percentile score	Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4	calculated
oa_class	Older adult percentile class	Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average	calculated
oa_est	Older adult count estimate	Estimated count of older adult population (65 years or older)	ACS 5-year
oa_est_moe	Older adult count margin of error	Margin of error for estimated count of older adult population	ACS 5-year
oa_pct	Older adult percent estimate	Estimated percentage of older adult population (65 years or older)	ACS 5-year
oa_pct_moe	Older adult percent margin of error	Margin of error for percentage of older adult population	ACS 5-year
oa_pctile	Older adult percentile	Tract's regional percentile for percentage older adult	calculated
oa_score	Older adult percentile score	Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4	calculated
rm_class	Racial minority percentile class	Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average	calculated
rm_est	Racial minority count estimate	Estimated count of non-white population	ACS 5-year
rm_est_moe	Racial minority count margin of error	Margin of error for estimated count of non-white population	ACS 5-year
rm_pct	Racial minority percent estimate	Estimated percentage of non-white population	calculated
rm_pct_moe	Racial minority percent margin of error	Margin of error for

1990 Census blocks for the Wichita / Sedgwick County area, clipped to the county line. Features were extracted based upon County FIPS code 173 which correlates to Sedgwick County Kansas.US Census Defines Census Block as the following: A census block is the smallest geographic unit used by the United States Census Bureau for tabulation of 100-percent data (data collected from all houses, rather than a sample of houses).Census blocks are grouped into block groups, which are grouped into census tracts. There are on average about 39 blocks per block group. Blocks typically have a four-digit number; the first number indicates which block group the block is in. For example, census block 3019 would be in block group 3.[2]Blocks are typically bounded by roads and highways, town/city/county/state boundaries, creeks and rivers, etc. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be delimited by other features such as political boundaries, rivers and other natural features, as well as parks and similar facilities, etc. The population of a census block varies greatly. As of the 2010 census, there were 4,871,270 blocks with a reported population of zero,[3] while a block that is entirely occupied by an apartment complex might have several hundred inhabitants.Census blocks covering the entire country were introduced with the 1990 census. Before that, back to the 1940 census, only selected areas were divided into blocks.

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field)

Field	Alias	Description	Source
year	IPD analysis year		DVRPC
geoid20	11-digit tract GEOID	Census tract identifier	ACS 5-year
statefp	2-digit state GEOID	FIPS Code for State	ACS 5-year
countyfp	3-digit county GEOID	FIPS Code for County	ACS 5-year
tractce	Tract number	Tract Number	ACS 5-year
name	Tract number	Census tract identifier with decimal places	ACS 5-year
namelsad	Tract name	Census tract name with decimal places	ACS 5-year
d_class	Disabled percentile class	Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average	calculated
d_est	Disabled count estimate	Estimated count of disabled population	ACS 5-year
d_est_moe	Disabled count margin of error	Margin of error for estimated count of disabled population	ACS 5-year
d_pct	Disabled percent estimate	Estimated percentage of disabled population	ACS 5-year
d_pct_moe	Disabled percent margin of error	Margin of error for percentage of disabled population	ACS 5-year
d_pctile	Disabled percentile	Tract's regional percentile for percentage disabled	calculated
d_score	Disabled percentile score	Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4	calculated
em_class	Ethnic minority percentile class	Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average	calculated
em_est	Ethnic minority count estimate	Estimated count of Hispanic/Latino population	ACS 5-year
em_est_moe	Ethnic minority count margin of error	Margin of error for estimated count of Hispanic/Latino population	ACS 5-year
em_pct	Ethnic minority percent estimate	Estimated percentage of Hispanic/Latino population	calculated
em_pct_moe	Ethnic minority percent margin of error	Margin of error for percentage of Hispanic/Latino population	calculated
em_pctile	Ethnic minority percentile	Tract's regional percentile for percentage Hispanic/Latino	calculated
em_score	Ethnic minority percentile score	Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4	calculated
f_class	Female percentile class	Classification of tract's female percentage as: well below average, below average, average, above average, or well above average	calculated
f_est	Female count estimate	Estimated count of female population	ACS 5-year
f_est_moe	Female count margin of error	Margin of error for estimated count of female population	ACS 5-year
f_pct	Female percent estimate	Estimated percentage of female population	ACS 5-year
f_pct_moe	Female percent margin of error	Margin of error for percentage of female population	ACS 5-year
f_pctile	Female percentile	Tract's regional percentile for percentage female	calculated
f_score	Female percentile score	Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4	calculated
fb_class	Foreign-born percentile class	Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average	calculated
fb_est	Foreign-born count estimate	Estimated count of foreign born population	ACS 5-year
fb_est_moe	Foreign-born count margin of error	Margin of error for estimated count of foreign born population	ACS 5-year
fb_pct	Foreign-born percent estimate	Estimated percentage of foreign born population	calculated
fb_pct_moe	Foreign-born percent margin of error	Margin of error for percentage of foreign born population	calculated
fb_pctile	Foreign-born percentile	Tract's regional percentile for percentage foreign born	calculated
fb_score	Foreign-born percentile score	Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4	calculated
le_class	Limited English proficiency percentile class	Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average	calculated
le_est	Limited English proficiency count estimate	Estimated count of limited english proficiency population	ACS 5-year
le_est_moe	Limited English proficiency count margin of error	Margin of error for estimated count of limited english proficiency population	ACS 5-year
le_pct	Limited English proficiency percent estimate	Estimated percentage of limited english proficiency population	ACS 5-year
le_pct_moe	Limited English proficiency percent margin of error	Margin of error for percentage of limited english proficiency population	ACS 5-year
le_pctile	Limited English proficiency percentile	Tract's regional percentile for percentage limited english proficiency	calculated
le_score	Limited English proficiency percentile score	Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4	calculated
li_class	Low-income percentile class	Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average	calculated
li_est	Low-income count estimate	Estimated count of low income (below 200% of poverty level) population	ACS 5-year
li_est_moe	Low-income count margin of error	Margin of error for estimated count of low income population	ACS 5-year
li_pct	Low-income percent estimate	Estimated percentage of low income (below 200% of poverty level) population	calculated
li_pct_moe	Low-income percent margin of error	Margin of error for percentage of low income population	calculated
li_pctile	Low-income percentile	Tract's regional percentile for percentage low income	calculated
li_score	Low-income percentile score	Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4	calculated
oa_class	Older adult percentile class	Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average	calculated
oa_est	Older adult count estimate	Estimated count of older adult population (65 years or older)	ACS 5-year
oa_est_moe	Older adult count margin of error	Margin of error for estimated count of older adult population	ACS 5-year
oa_pct	Older adult percent estimate	Estimated percentage of older adult population (65 years or older)	ACS 5-year
oa_pct_moe	Older adult percent margin of error	Margin of error for percentage of older adult population	ACS 5-year
oa_pctile	Older adult percentile	Tract's regional percentile for percentage older adult	calculated
oa_score	Older adult percentile score	Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4	calculated
rm_class	Racial minority percentile class	Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average	calculated
rm_est	Racial minority count estimate	Estimated count of non-white population	ACS 5-year
rm_est_moe	Racial minority count margin of error	Margin of error for estimated count of non-white population	ACS 5-year
rm_pct	Racial minority percent estimate	Estimated percentage of non-white population	calculated
rm_pct_moe	Racial minority percent margin of error	Margin of error for percentage of non-white population	calculated
rm_pctile	Racial minority percentile	Tract's regional percentile for percentage non-white	calculated
rm_score	Racial minority percentile score	Corresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4	calculated
tot_pp	Total population estimate	Estimated total population of tract (universe [or

This script is used to analyze tree canopy and its change from 2006 to 2011 in Washington, D.C. with in American Community Survey (ACS) boundaries. The script will automatically read a small *.csv file (52kb) into memory and analyze in R. To download the file directly use the first link below. Rows correspond to block groups, data types using the R nomenclature shown in parentheses, the fields (columns) are: [1] "OBJECTID" - created by ArcGIS, unique (integer) [2] "AREAKEY" - the US Census Bureau FIPS code, the unique identifier for joining to other ACS/Census data (factor) [3] "EHHMEDINC" - Median Household Income in $'s (integer) [4] "Shape_Leng" - The length of the perimeter of the block group in meters (num) [5] "Shape_Area" - The area of the block group polygon in square meters (num) [6] "PctCanArea" - The percent of the block group that is covered by the sum of tree canopy datasets three categories 1) no change, 2) loss, and 3) gain. No change indicates that the tree canopy has not changed substantially from 2006 to 2011. Loss indicates that tree canopy was removed from 2006 to 2011. Gain indicates that new tree canopy was established between 2006 and 2011. The canopy data are described using the Letters from the SAL link provided below (num) [7] "PctNo_Chan" - The proportion of "PctCanArea" that is from the no change class (num) [8] "PctLoss" - The proportion of "PctCanArea" that is from the loss class (num) [9] "PctGain"- The proportion of "PctCanArea" that is from the gain class (num) [10] "IncomeQuan" - The median household income from "EHHMEDINC" categorized into quintiles (factor)

Original census file name: tl_2020_

As part of Phase 4 of the 2020 Census redistricting data program, the State of Maine submitted Block Equivalency Files (BEFs) for Congressional Districts (CDs) and State Legislative Districts (SLDs) to the United States Census Bureau. The Congressional District BEF is a table in CSV format that lists all Census blocks in Maine along with their Congressional District number, Census tract, County FIPS, and State FIPS codes.The U.S. Census Bureau uses the Block Equivalency Files to create geographic products such as maps and shapefiles for visualization and analysis. For more information about the 2020 Census, please visit: https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html. For more information about 2022 redistricting in Maine and maps of current voting districts, please visit: https://www.maine.gov/sos/cec/elec/apport/index.html.

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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

Broadband internet speed map showing maximum available broadband internet speed per US Census block (2010). Data does not include satellite internet providers and terrestrial fixed wireless. Only the providers with the highest maximum advertised downstream speed are displayed. Providers with a lower maximum advertised downstream speed are omitted. Geolocation of 2020 FCC Fixed Broadband Deployment data is based upon the 2010 census blocks created by the US Census Bureau.Data Fields:Max Advertised Downstream Speed (mbps) (megabit per second)Max Advertised Upstream Speed (mbps) (megabit per second)Provider NameHolding Company Name (as filed on FCC Form 477)Technology Code (2-digit code indicating the Technology of Transmission used to offer broadband service); 10 - Asymmetrical xDSL (copper wireline), 11 - ADSL2 (copper wireline), 12 - VDSL (copper wireline), 20 - Symmetrical xDSL (copper wireline), 30 - Other Copper Wireline, 40 - Cable Modem, 41 - Cable Modem DOCSIS 1, 1.1, and 2.0 (DOCSIS: Data Over Cable Service Interface Specification), 42 - Cable Modem DOCSIS 3.0 (DOCSIS: Data Over Cable Service Interface Specification), 43 - Cable Modem DOCSIS 3.1 (DOCSIS: Data Over Cable Service Interface Specification), 50 - Optical Carrier/Fiber to the End User (FTTx), 0 - All OtherBLOCKCE10 (Census Block FIPS Code)STATEFP10 (State FIPS Code)COUNTYFP10 (County FIPS Code)TRACTFP10 (Tract FIPS Code)GEOID10 (Census Block Geographic Identification Number)StateData Sources:External Link: FCC Fixed Broadband Deployment Data: December 2020External Link: US Census Bureau TIGER/Line Shapefiles, 2010 CensusExternal Link: US Census Bureau TIGER/Line Shapefiles, 2020 Census_For questions, problems, or more information, contact gis@atcog.org Ark-Tex Council of Governments Homepage: https://atcog.org/Open Data Portal Homepage: https://open-data-portal-atcog.hub.arcgis.com/_

This is SCAG's 2019 Annual Land Use (ALU v. 2019.1) at the parcel-level, updated as of February 2021. This dataset has been modified to include additional attributes in order to feed SCAG's Housing Element Parcel Tool (HELPR), version 2.0. The dataset will be further reviewed and updated as additional information is released. Please refer to the tables below for data dictionary and SCAG’s land use classification. Field Name Data TypeField DescriptionPID19Text2019 SCAG’s parcel unique IDAPN19Text2019 Assessor’s parcel numberCOUNTYTextCounty name (based on 2016 county boundary)COUNTY_IDDoubleCounty FIPS code (based on 2016 county boundary)CITYTextCity name (based on 2016 city boundary)CITY_IDDoubleCity FIPS code (based on 2016 city boundary)MULTIPARTShort IntegerMultipart feature (the number of multiple polygons; '1' = singlepart feature)STACKLong IntegerDuplicate geometry (the number of duplicate polygons; '0' = no duplicate polygons)ACRESDoubleParcel area (in acreage)GEOID20Text2020 Census Block Group GEOIDSLOPEShort IntegerSlope information1APN_DUPLong IntegerDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIODoubleRatio of improvement assessed value to land assessed valueLU19Text2019 existing land useLU19_SRCTextSource of 2019 existing land use2SCAGUID16Text2016 SCAG’s parcel unique IDAPNText2016 Assessor’s parcel numberCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeCITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeLU16Text2016 existing land useYEARLong IntegerDataset yearPUB_OWNShort IntegerPublic-owned land index ('1' = owned by public agency)PUB_NAMETextName of public agencyPUB_TYPETextType of public agency3BF_SQFTDoubleBuilding footprint area (in square feet)4BSF_NAMETextName of brownfield/superfund site5BSF_TYPETextType of brownfield/superfund site5FIREShort IntegerParcel intersects CalFire Very High Hazard Local Responsibility Areas or State Responsibility Areas (November 2020 version) (CalFIRE)SEARISE36Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)1 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)SEARISE72Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)2 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)FLOODShort IntegerParcel intersects with a FEMA 100 Year Flood Plain data from the Digital Flood Insurance Rate Map (DFIRM), obtained from Federal Emergency Management Agency (FEMA) in August 10, 2017EQUAKEShort IntegerParcel intersects with an Alquist-Priolo Earthquake Fault Zone (California Geological Survey; 2018) LIQUAFAShort IntegerParcel intersects with a Liquefaction Susceptibility Zone (California Geological Survey; 2016)LANDSLIDEShort IntegerParcel intersects with a Landslide Hazard Zone (California Geological Survey; 2016)CPADShort IntegerParcel intersects with a protected area from the California Protected Areas Database(CPAD) – www.calands.org (accessed April 2021)RIPARIANShort IntegerParcel centroid falls within Active River Areas(2010)or parcel intersects with a Wetland Area in the National Wetland Inventory(Version 2)WILDLIFEShort IntegerParcel intersects with wildlife habitat (US Fish & Wildlife ServiceCritical Habitat, Southern California Missing Linkages, Natural Lands & Habitat Corridors from Connect SoCal, CEHC Essential Connectivity Areas,Critical Coastal Habitats)CNDDBShort IntegerThe California Natural Diversity Database (CNDDB)includes the status and locations of rare plants and animals in California. Parcels that overlap locations of rare plants and animals in California from the California Natural Diversity Database (CNDDB)have a greater likelihood of encountering special status plants and animals on the property, potentially leading to further legal requirements to allow development (California Department of Fish and Wildlife). Data accessed in October 2020. HCPRAShort IntegerParcel intersects Natural Community & Habitat Conservation Plans Reserve Designs from the Western Riverside MHSCP, Coachella Valley MHSCP, and the Orange County Central Coastal NCCP/HCP, as accessed in October 2020WETLANDShort IntegerParcel intersects a wetland or deepwater habitat as defined by the US Fish & Wildlife Service National Wetlands Inventory, Version 2. UAZShort IntegerParcel centroid lies within a Caltrans Adjusted Urbanized AreasUNBUILT_SFDoubleDifference between parcel area and building footprint area expressed in square feet.6GRCRY_1MIShort IntegerThe number of grocery stores within a 1-mile drive7HEALTH_1MIShort IntegerThe number of healthcare facilities within a 1-mile drive7OPENSP_1MIShort IntegerQuantity of open space (roughly corresponding to city blocks’ worth) within a 1-mile drive7TCAC_2021TextThe opportunity level based on the 2021 CA HCD/TCAC opportunity scores.HQTA45Short IntegerField takes a value of 1 if parcel centroid lies within a 2045 High-Quality Transit Area (HQTA)JOB_CTRShort IntegerField takes a value of 1 if parcel centroid lies within a job centerNMAShort IntegerField takes a value of 1 if parcel centroid lies within a neighborhood mobility area. ABS_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within an absolute constraint area. See the Sustainable Communities Strategy Technical Reportfor details.VAR_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within a variable constraint area. See the Sustainable Communities Strategy Technical Reportfor details.EJAShort IntegerField takes a value of 1 if parcel centroid lies within an Environmental Justice Area. See the Environmental Justice Technical Reportfor details.SB535Short IntegerField takes a value of 1 if parcel centroid lies within an SB535 Disadvantaged Community area. See the Environmental Justice Technical Reportfor details.COCShort IntegerField takes a value of 1 if parcel centroid lies within a Community of Concern See the Environmental Justice Technical Reportfor details.STATEShort IntegerThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)8. SBShort IntegerIndex of ADU eligibility according to the setback reduction policy scenario (from 4 to 2 feet) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SMShort IntegerIndex of ADU eligibility according to the small ADU policy scenario (from 800 to 600 square feet ADU) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)PKShort IntegerIndex of ADU eligibility according to parking space exemption (200 square feet) policy scenario (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SMShort IntegerIndex of ADU eligibility according to both the setback reduction and small ADU policy scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_PKShort IntegerIndex of ADU eligibility according to both the setback reduction and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SM_PKShort IntegerIndex of ADU eligibility according to both the small ADU policy and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SM_PKShort IntegerIndex of ADU eligibility according to the setback reduction, small ADU, and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. Source of 2019 existing land use: SCAG_REF- SCAG's regional geospatial datasets;ASSESSOR- Assessor's 2019 tax roll records; CPAD- California Protected Areas Database (version 2020a; accessed in September 2020); CSCD- California School Campus Database (version 2018; accessed in September 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (accessed in September 2020); MIRTA- U.S. Department of Defense's Military Installations, Ranges, and Training Areas GIS data (accessed in September 2020)3. Type of public agency includes federal, state, county, city, special district, school district, college/university, military.4. Based on 2019 building footprint data obtained from BuildingFootprintUSA (except that 2014 building footprint data was used for Imperial County). Please note that 2019 building footprint data does not cover the entire SCAG region (overlapped with 83% of parcels in the SCAG Region).5. Includes brownfield/superfund site whose address information are matched by SCAG rooftop address locator. Brownfield data was obtained from EPA's Assessment, Cleanup and Redevelopment Exchange System (ACRES) database, Cleanups in my community (CIMC), DTSC brownfield Memorandum of Agreement (MOA). Superfund site data was obtained from EPA's Superfund Enterprise Management System (SEMS) database.6. Parcels with a zero value for building footprint area are marked as NULL to indicate this field is not reliable.7. These values are intended as a rudimentary indicator of accessibility developed by SCAG using 2016 InfoUSA business establishment data and 2017 California Protected Areas data. See documentation for details.8. A detailed study conducted by Cal Poly Pomona (CPP) and available hereconducted an extensive review of state and local requirements and development trends for ADUs in the SCAG region and developed a baseline set of assumptions for estimating how many of a jurisdiction’s parcels could accommodate a detached ADU. Please note that these estimates (1) do not include attached or other types of ADUs such as garage conversions or Junior ADUs, and (2)