Field Definition:GEOID - "Census tract identifier; a concatenation of 2020 Census state FIPS code, county FIPS code, and census tract code"NAMELSAD - Census translated legal/statistical area description and the census tract nameALAND - Census Area LandAWATER - Census Area waterINTPTLAT - Census Internal Point (Latitude)INTPTLON - Census Internal Point (Longitude)NAME20 - "2020 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"POPULATION - Total PopulationP18PLUS - Population 18 years and olderHHPOP - Household PopulationGQ - Group Quarters PopulationHOUSING - Total Housing unitsOCCUNITS - Occupied Housing Units (Households)VACUNITS - Vacant Housing UnitsVACRATE -Vacancy RateHISPANIC - Hispanic or Latino NH_WHT - Not Hispanic or Latino, White alone NH_BLK - Not Hispanic or Latino, Black or African American alone NH_IND - Not Hispanic or Latino, American Indian and Alaska Native aloneNH_ASN - Not Hispanic or Latino, Asian aloneNH_HWN - Not Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone NH_OTH - Not Hispanic or Latino, Some Other Race alone NH_TWO - Not Hispanic or Latino, Population of two or more races

Dataset Summary About this data: This layer presents the USA 2020 Census tracts within the City of Rochester boundary. The geography is sourced from US Census Bureau 2020 TIGER FGDB (National Sub-State) and cut by the City of Rochester boundary. Data Dictionary: STATE_ABBR: The two-letter abbreviation for a state (such as NY). STATE_FIPS: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state. New York State is 36. COUNTY_FIP: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county. Monroe County is 055. STCO_FIPS: The five-digit Federal Information Processing Standards (FIPS) code assigned to iedntify a unique county, typically as a concatenation of the State FIPS code and the County FIPS code. TRACT_FIPS: The six-digit number assigned to each census tract in a US county. FIPS: A unique geographic identifier, typically as a concatenation of State FIPS code, County FIPS code, and Census tract code. POPULATION: The population of a census tract. POP_SQMI: The population per square mile of a census tract. SQMI: The size of a census tract in square miles. Division: The name of the City of Rochester data division that the census tract falls in to. Source: This data comes from the Census Bureau.

Abstract (en): This data collection contains FIPS codes for state, county, county subdivision, and place, along with the 1990 Census tract number for each side of the street for the urban cores of 550 counties in the United States. Street names, including prefix and/or suffix direction (north, southeast, etc.) and street type (avenue, lane, etc.) are provided, as well as the address range for that portion of the street located within a particular Census tract and the corresponding Census tract number. The FIPS county subdivision and place codes can be used to determine the correct Census tract number when streets with identical names and ranges exist in different parts of the same county. Contiguous block segments that have consecutive address ranges along a street and that have the same geographic codes (state, county, Census tract, county subdivision, and place) have been collapsed together and are represented by a single record with a single address range. 2006-01-12 All files were removed from dataset 551 and flagged as study-level files, so that they will accompany all downloads. (1) Due to the number of files in this collection, parts have been eliminated here. For a complete list of individual part names designated by state and county, consult the ICPSR Website. (2) There are two types of records in this collection, distinguished by the first character of each record. A "0" indicates a street name/address range record that can be used to find the Census tract number and other geographic codes from a street name and address number. A "2" indicates a geographic code/name record that can be used to find the name of the state, county, county subdivision, and/or place from the FIPS code. The "0" records contain 18 variables and the "2" records contain 10 variables.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

blockgroupvulnerability OPPORTUNITY The US Centers for Disease Control (CDC) publishes a set of percentiles that compare US geographies by vulnerability across household, socioeconomic, racial/ethnic and housing themes. These Social Vulnerability Indexes (SVI) were originally intended to to help public health officials and emergency response planners identify communities that will need support around an event. They are generally valuable for any public interest that wants to relate themselves to needy communities by geography. The SVI publication and its basis variables are provided at the Census tract level of geographic detail. The Census' American Community Survey is available down the to the block group level, however. Recasting the SVI methods at this lower level of geography allows it to be tied to thousands of other demographic variables available. Because the SVI relies on ACS variables only available at the tract level, a projection model needs to applied to approximate its results using blockgroup level ACS variables. The blockgroupvulnerability dataset casts a prediction for the CDCs logic for a new contribution to the Open Environments blockgroup series available on Harvard's dataverse platform. DATA The CDC's annual SVI publication starts with 23 simple derivations using 50 ACS Census variables. Next the SVI process ranks census geographies to calculate a rank for each, where Percentile Rank = (Rank-1) / (N-1). The SVI themes are then calculated at the tract level as a percentile rank of a sum of the percentile ranks of the first level ACS derived variables. Finally, the overall ranking is taken as the sum of the theme percentile rankings. The SVI data publication is keyed by geography (7 cols) where ultimately the Census Tract FIPS code is 2 State + 3 County + 4 Tract + 2 Tract Decimals eg, 56043000301 is 56 Wyoming, 043 Washakie County, Tract 3.01 republishes Census demographics called 'adjunct variables' including area, population, households and housing units from the ACS daytime population taken from LandScan 2020 estimates derives 23 SVI variables from 50 ACS 5 Year variables with each having an estimate (E_), estimate precentage (EP_), margin of error (M_), margin percentage (MP_) and flag variable (F_) for those greater than 90% or less than 10% provides the final 4 themes and a composite SVI percentile annually vars = ['ST', 'STATE', 'ST_ABBR', 'STCNTY', 'COUNTY', 'FIPS', 'LOCATION'] +\ ['SNGPNT','LIMENG','DISABL','AGE65','AGE17','NOVEH','MUNIT','MOBILE','GROUPQ','CROWD','UNINSUR','UNEMP','POV150','NOHSDP','HBURD','TWOMORE','OTHERRACE','NHPI','MINRTY','HISP','ASIAN','AIAN','AFAM','NOINT'] +\ ['TOTAL','THEME1','THEME2','THEME3','THEME4'] + \ ['AREA_SQMI', 'TOTPOP', 'DAYPOP', 'HU', 'HH'] knowns = vars + \ # Estimates, the result of calc against ACS vars [('E_'+v) for v in vars] + \ # Flag 0,1 whether this geog is in 90 percentile rank (its vulnerable) [('F_'+v) for v in vars] +\ # Margine of error for ACS calcs [('M_'+v) for v in vars] + \ # Margine of error for ACS calcs, as percentage [('MP_'+v) for v in vars] +\ # Estimates of ACS calcs, as percentage [('EP_'+v) for v in vars] + \ # Estimated percentile ranks [('EPL_'+v) for v in vars] + \ # Sum across var percentile ranks [('SPL_'+v) for v in vars]+ \ # Percentile rank of the sum of percentile ranks [('RPL_'+v) for v in vars] [c for c in svitract.columns if c not in knowns] The SVI themes range over [0,1] but the CDC uses -999 as an NA value; this is set for ~800 or 1% of tracts which have no total poulation. The themes are numbered: Socioeconomic Status – RPL_THEME1 Household Characteristics – RPL_THEME2 Racial & Ethnic Minority Status – RPL_THEME3 Housing Type & Transportation – RPL_THEME4 The themes with their variables and ACS sources are as follows: Unlike Census data, the CDC ranks Puerto Rico and Tribal tracts separately from the US otherwise. Theme SVI Variable ACS Table ACS Variables Socioeconomic E_UNINSUR S2701 S2701_C04_001E Socioeconomic E_UNEMP DP03 DP03_0005E Socioeconomic E_POV150 S1701 S1701_C01_040E Socioeconomic E_NOHSDP B06009 B06009_002E Socioeconomic E_HBURD S2503 S2503_C01_028E + S2503_C01_032E + S2503_C01_036E + S2503_C01_040E Household E_SNGPNT B11012 B11012_010E + B11012_015E Household E_LIMENG B16005 B16005_007E + B16005_008E + B16005_012E + B16005_013E + B16005_017E + B16005_018E + B16005_022E + B16005_023E + B16005_029E + B16005_030E + B16005_034E + B16005_035E + B16005_039E + B16005_040E + B16005_044E + B16005_045E Household E_DISABL DP02 DP02_0072E Household E_AGE65 S0101 S0101_C01_030E Household E_AGE17 B09001 B09001_001E Racial & Ethnic E_TWOMORE DP05 DP05_0083E Racial & Ethnic E_OTHERRACE DP05 DP05_0082E Racial & Ethnic E_NHPI DP05 DP05_0081E Racial & Ethnic E_MINRTY DP05 DP05_0071E + DP05_0078E + DP05_0079E + DP05_0080E + DP05_0081E + DP05_0082E + ... Visit https://dataone.org/datasets/sha256%3A3edd5defce2f25c7501953ca3e77c4f15a8c71251352373a328794f961755c1c for complete metadata about this dataset.

US Northeast Census Tracts contains the US Census tract geometries used as the unit of analysis for network metrics. The file "northeast_tracts.shp" includes a merged dataset with the borders of all census tracts in Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. All other files in this repository are the original state-by-state sources used to create the final merged dataset. Census Tracts The 2020 census tract file is based on the 2020 Census. The following fields are included: USPS: United States Postal Service state abbreviation. GEOID: Geographic identifier — fully concatenated geographic code (State FIPS, County FIPS, Census Tract number). GEOIDFQ: Fully qualified geographic identifier — used to join with data.census.gov data tables. ALAND: Land area (square meters) — created for statistical purposes only. AWATER: Water area (square meters) — created for statistical purposes only. ALAND_SQMI: Land area (square miles) — created for statistical purposes only. AWATER_SQMI: Water area (square miles) — created for statistical purposes only. INTPTLAT: Latitude (decimal degrees). The first character is blank or “–” denoting North or South latitude respectively. INTPTLONG: Longitude (decimal degrees). The first character is blank or “–” denoting East or West longitude respectively. The .shp file in this repository includes its required companion files for correct GIS operation: .shx (spatial index), .dbf (attribute table), .prj (projection information), and .cpg (character encoding).

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.

GEOID - Census geographic record identifier, a concatenation of 2020 Census state FIPS code, county FIPS code, census tract code, census block group code, and census block code"NAME20 - 2020 Census block nameSTATE – State FIPS codeCOUNTY - County FIPS codePLACE - Place FIPS codeTRACT - Census Tract codeBLKGRP - Block Group codeBLOCK - Block codePOPULATION - Total PopulationHOUSING - Total Housing unitsOCCUPIED_H - Occupied Housing Units (Households)VACANT_H - Vacant 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

PRISM data converted into FIPS, ZIP Code, and census tract summaries in the USA Introduction: Parameter-elevation Regressions on Independent Slopes Model (PRISM) by PRISM Climate group Oregon State temperature, precipitation 4km daily weather variable grids that I have converted to daily county FIPS, ZIP Code, and census tract summaries for use in several papers. Available for download (see Data below) in RDS (compact) format. CSV available on request. In Python it is easy to load RDS files and much more compact files than CSVs too. Note that ZIP Code throughout is actually ZIP Code Tabulation Area (ZCTA), which was developed to overcome the difficulties in precisely defining the land area covered by each ZIP Code. Defining the extent of an area is necessary in order to tabulate census data for that area.

Census tracts are designated as urban, rural center, or rural through SB 1000 analysis. These designations are being used for the REV 2.0 and Community Charging in Urban Areas GFOs.

Very often in my work I am requested to calculate disease rates for small areas for time periods that fall between censuses or span multiple censuses, such as 1995-2013. In the past I have used population estimates from private vendors, but these have had two important limitations: one, they are proprietary and cannot be shared, and two, they often contain significant omissions and errors. I decided instead to calculate my own populations using publicly available data and established interpolation methods.

To generate the data here, I began with the census tract populations by age (5-year age groups) and sex published in the 1990, 2000, and 2010 federal censuses (citations to exact tables to be added). These were converted to 2010 census definitions using the Longitudinal Tract Data Base (LTDB), available here: http://www.s4.brown.edu/us2010/Researcher/Bridging.htm. The LTDB provides precise conversions between different censuses. For example, 45.4% of the population of 1990 Bronx census tract 50 is assigned to 2010 tract 50.01, while 54.6% is assigned to tract 50.02. Census tracts with zero population in all three decades, consisting of water and certain parks and cemeteries in New York City, were omitted. The resulting file has data for 4,893 tracts.

Each age-sex group was summed to the county total, and compared with the county total as published by the National Cancer Institute’s SEER program. The SEER counts make adjustments to the counts by race and ethnicity, adjust the counts to reflect totals as of July rather than April, and other small enhancements, all of which are documented on their web page, http://seer.cancer.gov/popdata/. The census tract counts were then proportionally adjusted to match the SEER totals. For example, if the census tracts in a particular county added to 127 males aged 5-9, and the SEER total for this county was 131, then the count in each tract was multiplied by 131/127. This resulted in fractional populations, which were retained. Any user not desirous of fractional populations can simply round the values given here.

Next, geometric interpolation between census years was used to estimate tract-level counts for all of the non-census years, using the Das Gupta method that has been used extensively by the Census Bureau and described here: https://www.census.gov/popest/methodology/intercensal_nat_meth.pdf. For census tracts that are growing in population, this method results in more of the growth occurring later in the period. For census tracts that are shrinking, it results in more of the shrinkage occurring earlier in the period. For the relatively small numbers seen in individual census tracts by age and sex, the results are not very different than those that would have been obtained from linear interpolation. (For the years after 2010, this step was skipped because the 2020 census obviously does not yet exist). These interpolated counts were then proportionally adjusted to match the SEER totals by year and county, using the same procedure as above.

Data dictionary

The data file is a comma-separated file containing the following variables:

Year

Geoid10 – 11 digit code consisting of state FIPS code (36 for New York), county FIPS code (001-123 for New York), and census tract (6 digits, with leading and trailing zeroes as needed). These are the identical values used in many Census tables.

M0 – male population aged 0

M1 – male population aged 1-4

M2 – male population aged 5-9

…

M17 – male population aged 80-84

M18 – male population aged 85+

F0 – female population aged 0

…

F18 – female population aged 85+

Future work

Future versions of these data may add some or all of the following:

• Additional states

• Counts by race and ethnicity

• Incorporation of a method to capture abrupt changes in census tract populations, such as when a new retirement community is constructed. The idea is to use American Community Survey population estimates to identify such instances.

• Incorporation of post-censal corrections. Here, I have used the official tables published after each census. They do not incorporate the various small corrections that were made as a result of appeals and identification of errors. These corrections are mainly given in narrative form rather than in tables, and so incorporating them may be somewhat involved.

  • Francis BoscoeUniversity at Albany

Department of Epidemiology and Biostatistics

Send questions, comments to fboscoe@albany.edu

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

This polygon layer contains the boundaries of the 33 Census tracts that make up the City of Cambridge for the 2020 Census. 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

AWATER20 type: Doublewidth: 8precision: 38 2020 water area

INTPTLAT20 type: Stringwidth: 11precision: 0 2020 latitude of the internal point

INTPTLON20 type: Stringwidth: 12precision: 0 2020 longitude of the internal point

This data was compiled by the Washington Military Department on June 27, 2019. The Limited English Proficiency (LEP) information is summarized at the county level as well as at the census track/ county subdivision layer level. County level data was derived from the 2016 Office of Financial Management (OFM) study which provided an estimate of population with limited English proficiency at the state and county levels. The census tract data are derived from the 2015 census update and indicates language spoken at home and ability to speak English for those over five years old. All data displayed indicate a population of at least 1,000 or 5% of the population.LEPCountyv2 = Limited English Proficiency County version 2 – from OFM census dataLEPCSDv2 = Limited English Proficiency County Subdivision version 2 – data drawn from US Census 2010

LEPtractsv2 = Limited English Proficiency Census Tracts version 2 – data drawn from US Census 2010Attribute DescriptionCounty - County nameLanguage - Limited English Proficiency Language(s) spoken for the corresponding polygon - each Language is followed by a number to indicate a sequence number for each data fieldSym - Symbology field used to symbolize the polygons - holds the total count of LEP languages spoken for that polygonAFFGEOID - American Fact Finder Geospatial ID used to link tabular data to the polygons - consists of the -- Census block identifier; a concatenation of 2010 Census state FIPS code, 2010 Census county FIPS code, 2010 Census tract code, and 2010 Census block numberName - County Subdivision name from American Fact Finder (AFF) dataLanguage - Limited English Proficiency Language(s) spoken for the corresponding polygon - each Language is followed by a number to indicate a sequence number for each data fieldSym - Symbology field used to symbolize the polygons - holds the total count of LEP languages spoken for that polygonNAMELSAD10 2010 Census translated legal/statistical area description and the block group numberAFFGEOID - American Fact Finder Geospatial ID used to link tabular data to the polygons - consists of the -- Census block identifier; a concatenation of 2010 Census state FIPS code, 2010 Census county FIPS code, 2010 Census tract code, and 2010 Census block numberDisplay Label - Geographic name for each polygon from AFFSym - Symbology field used to symbolize the polygons - holds the total count of LEP languages spoken for that polygonLanguage - Limited English Proficiency Language(s) spoken for the corresponding polygon - each Language is followed by a number to indicate a sequence number for each data field

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

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

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 layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to represent the United States Census Bureau's 2000 Decennial Census data at the block geography.Attributes:FIPSSTCO = The Federal Information Processing Series (FIPS) state and county codes. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.TRACT2000 = Census Tract Codes and Numbers. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.BLOCK2000= Census Block Numbers are numbered uniquely with a four-digit census block number from 0000 to 9999 within census tract, which nest within state and county. The first digit of the census block number identifies the block group. Block numbers beginning with a zero (in Block Group 0) are only associated with water-only areas.STFID = A concatenation of FIPSSTCO, TRACT2000, and BLOCK2000, which creates the entire FIPS code for this geography.WFD = Workforce Development Area (WFD) is a seven-county area created by agreement of county chief-elected officials, administered by the Atlanta Regional Commission and funded for training and employment activities under the federal Workforce Investment Act (WIA). For more information on ARC’s Workforce Development programs and services please consult www.atlantaregional.com/workforce/workforce.html.RDC_AAA = ARC Area Agency on Aging is a 10-county area funded by the Department of Human Resources and designated by the Older Americans Act to plan for the needs of the rapidly expanding group of older citizens in the Atlanta region. It is part of a statewide network of 12 AAAs and a national network of more than 670 AAAs. For more information on aging services please consult www.agewiseconnection.com.MNGWPD = The Metro North Georgia Water Planning District provides water resource plans, policies and coordination for metropolitan Atlanta. The District has developed regional plans for stormwater management, wastewater treatment and water supply and water conservation. The 15-county Water Planning District includes the ten counties in the ARC plus five additional counties (Bartow, Coweta, Forsyth, Hall, & Paulding). For more information please consult www.northgeorgiawater.org. MPO = The Metropolitan Planning Organization (MPO) is a 19-county area federally-designated for regional transportation planning to meet air quality standards and for programming projects to implement the adopted Regional Transportation Plan (RTP). The MPO planning area boundary includes the 10-county state-designated Regional Commission and nine additional counties (all of Coweta, Forsyth, & Paulding and parts of Barrow, Dawson, Newton, Pike, Spalding and Walton). This boundary takes into consideration both the current urbanized area as well as areas forecast to become urbanized in the next 20 years.MSA = the 29-County “Atlanta-Sandy Springs-Roswell, GA” Metropolitan Statistical Area (MSA) and the 39-county “Atlanta--Athens-Clarke County--Sandy Springs, GA” Combined Statistical Area (CSA), which includes the 29 counties of the Atlanta MSA along with the Athens-Clarke County and Gainesville MSAs and the micropolitan statistical areas of Calhoun, Cedartown, Jefferson, LaGrange and Thomaston, GA. The U.S. Office of Management and Budget (OMB) defines CSAs, MSAs and the smaller micropolitan statistical areas nationwide according to published standards applied to U.S. Census Bureau data. These various statistical areas describe substantial core areas of population together with adjacent communities having a high degree of economic and social integration, often illustrated in high rates of commuting from the adjacent areas to job locations in the core. For more information, please consult http://www.census.gov/population/metro/data/metrodef.htmlF1HR_NA = The Federal 1-Hour Air Quality Non-Attainment Area is a fine particulate matter standard (PM2.5). The non-attainment area under this standard includes the 15-county eight-hour ozone nonattainment area plus Barrow, Carroll, Hall, Spalding, Walton, and small parts of Heard and Putnam counties.F8HR_NA: The Federal 8-Hour Air Quality Non-Attainment Area for the 2008 eight-hour ozone standard is 15 counties.ACRES = The number of acres contained within the Block.SQ_MILES = The number of square miles contained within the Block.Source: United States Census Bureau, Atlanta Regional CommissionDate: 2000For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).