17 datasets found
  1. Percent of Population with Limited Ability to Speak English

    • data.amerigeoss.org
    • coronavirus-resources.esri.com
    • +2more
    esri rest, html
    Updated Jul 24, 2019
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    ESRI (2019). Percent of Population with Limited Ability to Speak English [Dataset]. https://data.amerigeoss.org/dataset/percent-of-population-with-limited-ability-to-speak-english
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    esri rest, htmlAvailable download formats
    Dataset updated
    Jul 24, 2019
    Dataset provided by
    Esrihttp://esri.com/
    Description
    This map shows the percent of population with a limited ability to speak English by census tract. Search to your community and investigate the top language needs in nearby census tracts.


    *DATA AS OF 2011-2015*
    Data Source: U.S. Census Bureau's American Community Survey 5-year estimates, 2011-2015, Table B16001.

    Complete list of all languages available in this data set (29):
    Spanish or Spanish Creole; French (including Patois, Cajun); French Creole; Italian; Portuguese; German; Yiddish; Greek; Russian; Polish; Serbo-Croatian; Armenian; Persian; Gujarati; Hindi; Urdu; Chinese; Japanese; Korean; Mon-Khmer, Cambodian; Hmong; Thai; Laotian; Vietnamese; Tagalog; Navajo; Hungarian; Arabic; Hebrew. Those who have limited English ability and speak other languages are included in the percentage depicted in the map, but other languages will not appear in the ranked list or in the table.

    Accompanying feature layer and viewing app are also available.
  2. ACS English Ability and Linguistic Isolation Variables - Boundaries

    • covid-hub.gio.georgia.gov
    • hub.arcgis.com
    • +4more
    Updated Nov 14, 2019
    + more versions
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    Esri (2019). ACS English Ability and Linguistic Isolation Variables - Boundaries [Dataset]. https://covid-hub.gio.georgia.gov/maps/0c4d1027de6b4d6eb896d95f1240e1aa
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    Dataset updated
    Nov 14, 2019
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows English ability and linguistic isolation by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Linguistically isolated households are households in which no one 14 and over speak English only or speaks a language other than English at home and speaks English very well. This layer is symbolized to show the percent of adult (18+) population who have limited English ability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B16003, B16004 (Not all lines of ACS table B16004 are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  3. V

    Virginia Population by Language Spoken at Home by Ability to Speak English...

    • data.virginia.gov
    csv
    Updated Jan 3, 2025
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    Office of INTERMODAL Planning and Investment (2025). Virginia Population by Language Spoken at Home by Ability to Speak English by Census Block Group (ACS 5-Year) [Dataset]. https://data.virginia.gov/dataset/virginia-population-by-language-spoken-at-home-by-ability-to-speak-english-by-census-block-group
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    csv(28410756)Available download formats
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    Office of INTERMODAL Planning and Investment
    License

    Open Data Commons Attribution License (ODC-By) v1.0https://www.opendatacommons.org/licenses/by/1.0/
    License information was derived automatically

    Area covered
    Virginia
    Description

    2013-2023 Virginia Population by Age by Language Spoken at Home by Ability to Speak English for the Population 5 years and over by Census Block Group. Contains estimates and margins of error.

    U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B16004 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

    The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

    Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

    Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

    Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

  4. Language Spoken at Home 2018-2022 - STATES

    • mce-data-uscensus.hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +1more
    Updated Feb 5, 2024
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    US Census Bureau (2024). Language Spoken at Home 2018-2022 - STATES [Dataset]. https://mce-data-uscensus.hub.arcgis.com/maps/d89bebf3729d4540856fb3176c9d32f8
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    Dataset updated
    Feb 5, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    US Census Bureau
    Area covered
    Description

    This layer shows Language Spoken at Home. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with Limited English Speaking Status. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B16004, DP02, S1601, S1602Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

  5. R2 & NE: State Level 2006-2010 ACS Language Summary

    • datadiscoverystudio.org
    • data.amerigeoss.org
    tgrshp (compressed)
    Updated May 17, 2012
    + more versions
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division (2012). R2 & NE: State Level 2006-2010 ACS Language Summary [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/89f1ae1b3c0140c7ab8de40e19e9f2b1/html
    Explore at:
    tgrshp (compressed)Available download formats
    Dataset updated
    May 17, 2012
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    U.S. Department of Commerce, U.S. Census Bureau, Geography Division
    Area covered
    Description

    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. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

    This table contains data on language ability and linguistic isolation from the American Community Survey 2006-2010 database for states. Linguistic isolation is defined as no one 14 and over speaks English only or speaks English 'very well '. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in 'American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010) '. Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

    The name for table 'ACS10LANSTMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

  6. d

    Language spoken - ACS 2015-2019 - Tempe Tracts

    • catalog.data.gov
    • open.tempe.gov
    • +5more
    Updated Sep 20, 2024
    + more versions
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    City of Tempe (2024). Language spoken - ACS 2015-2019 - Tempe Tracts [Dataset]. https://catalog.data.gov/dataset/language-spoken-acs-2015-2019-tempe-tracts-28081
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    Dataset updated
    Sep 20, 2024
    Dataset provided by
    City of Tempe
    Area covered
    Tempe
    Description

    Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter. -----------------------------------------This layer shows language group of language spoken at home by age. This layer is Census data from Esri's Living Atlas and is clipped to only show Tempe census tracts. This layer is symbolized to show the percentage of the population age 5+ who speak Spanish at home. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Data is from US Census American Community Survey (ACS) 5-year estimates. Vintage: 2015-2019 ACS Table(s): B16007 (Not all lines of these ACS tables are available in this feature layer.) Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: December 10, 2020 National Figures: data.census.gov Additional Census data notes and data processing notes are available at the Esri Living Atlas Layer: https://tempegov.maps.arcgis.com/home/item.html?id=527ea2b5ba814c8ca1c34a2945e1b751

  7. a

    Language Spoken at Home - States 2015-2019

    • covid19-uscensus.hub.arcgis.com
    Updated Mar 20, 2021
    + more versions
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    US Census Bureau (2021). Language Spoken at Home - States 2015-2019 [Dataset]. https://covid19-uscensus.hub.arcgis.com/datasets/40abc5de06e84925984151d43c6349ff
    Explore at:
    Dataset updated
    Mar 20, 2021
    Dataset authored and provided by
    US Census Bureau
    Area covered
    Description

    This layer shows Language Spoken at Home. This is shown by state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.
    This layer is symbolized to show the percentage of households with Limited English Speaking Status. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B16004, DP02, S1601, S1602Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 10, 2021National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS): About the SurveyGeography & ACSTechnical Documentation News & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes.
    All of these are rendered in this dataset as null (blank) values.

  8. E

    TED Translanguage English Database

    • catalog.elra.info
    • live.european-language-grid.eu
    Updated Jun 26, 2017
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    ELRA (European Language Resources Association) and its operational body ELDA (Evaluations and Language resources Distribution Agency) (2017). TED Translanguage English Database [Dataset]. https://catalog.elra.info/en-us/repository/browse/ELRA-S0031/
    Explore at:
    Dataset updated
    Jun 26, 2017
    Dataset provided by
    ELRA (European Language Resources Association) and its operational body ELDA (Evaluations and Language resources Distribution Agency)
    ELRA (European Language Resources Association)
    License

    https://catalog.elra.info/static/from_media/metashare/licences/ELRA_VAR.pdfhttps://catalog.elra.info/static/from_media/metashare/licences/ELRA_VAR.pdf

    https://catalog.elra.info/static/from_media/metashare/licences/ELRA_END_USER.pdfhttps://catalog.elra.info/static/from_media/metashare/licences/ELRA_END_USER.pdf

    Description

    LDC reference: https://catalog.ldc.upenn.edu/LDC2002S04The Translanguage English Database (TED) is a corpus of recordings made of oral presentations at Eurospeech'93 in Berlin. The corpus name derives from the high percentage of oral presentations given in English by non-native speakers of English. Two hundred twenty-four (224) oral presentations at the conference were successfully recorded, providing a total of about 75 hours of speech material. These recordings provide a large number of presenters, speaking multiple variants of English, over a relatively large amount of time (15 minutes for each presentation + 5 minutes of discussion), on a specific topic. This release of TED (6 CDROMs) includes 188 speeches, without the ensuing discussion periods. This database was produced with the support of ELSNET. Associated text materials consist of ASCII versions of over 400 proceedings papers and oral preparations that were supplied by the authors, as well as, 250 speaker questionnaires.

  9. A

    Climate Ready Boston Social Vulnerability

    • data.boston.gov
    • hub.arcgis.com
    csv, geojson, html +2
    Updated Sep 21, 2017
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    Boston Maps (2017). Climate Ready Boston Social Vulnerability [Dataset]. https://data.boston.gov/dataset/climate-ready-boston-social-vulnerability
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    zip, csv, geojson, kml, htmlAvailable download formats
    Dataset updated
    Sep 21, 2017
    Dataset provided by
    BostonMaps
    Authors
    Boston Maps
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Boston
    Description
    Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses.

    Source:

    The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.

    Population Definitions:

    Older Adults:
    Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.
    Attribute label: OlderAdult

    Children:
    Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.
    Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.
    Attribute label: TotChild

    People of Color:
    People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups as
    well. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.
    Attribute label: POC2

    Limited English Proficiency:
    Without adequate English skills, residents can miss crucial information on how to prepare
    for hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more socially
    isolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.
    Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.
    Attribute label: LEP

    Low to no Income:
    A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.
    Attribute label: Low_to_No

    People with Disabilities:
    People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.
    Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty.
    Attribute label: TotDis

    Medical Illness:
    Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.
    Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.
    Attribute label: MedIllnes

    Other attribute definitions:
    GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census Tract
    AREA_SQFT: Tract area (in square feet)
    AREA_ACRES: Tract area (in acres)
    POP100_RE: Tract population count
    HU100_RE: Tract housing unit count
    Name: Boston Neighborhood
  10. Symbolic Institutional Traps: Language Regimes, Legal Legacy, and...

    • zenodo.org
    bin
    Updated Apr 26, 2025
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    Scott Brown; Scott Brown (2025). Symbolic Institutional Traps: Language Regimes, Legal Legacy, and Organizational Constraint in Postcolonial Economies [Dataset]. http://doi.org/10.5281/zenodo.15285179
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    binAvailable download formats
    Dataset updated
    Apr 26, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Scott Brown; Scott Brown
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    README: Symbolic Institutional Traps and the Liability of Foreignness

    Scott M. Brown (University of Puerto Rico)
    Email: scott.brown@upr.edu
    Data DOI: 10.5281/zenodo.15050209

    Overview

    This project empirically tests how language regimes embedded in legal and administrative systems create institutional traps that constrain multinational enterprise (MNE) operations and economic integration.
    The study combines national and subnational data across four key datasets to measure how symbolic misalignment (such as monolingualism in non-commercial languages) affects regulatory quality, business formation, and workforce access.

    📂 Datasets

    You must upload the following four files into your Google Colab session before running the code:

    Uploaded FileDescription
    /content/2020_Rankings.xlsxWorld Bank Ease of Doing Business (EODB) — Global regulatory efficiency indicators (2020 Edition)
    /content/DBNA 2022 Rank and Scores.xlsxDoing Business North America (DBNA 2022) — City-level institutional performance across 83 U.S. cities
    /content/Spanish_Speakers_All_States.xlsxU.S. Census American Community Survey (ACS) — State-level Spanish-speaking and English proficiency data
    /content/wgidataset.xlsxWorld Governance Indicators (WGI) — Governance quality measures (Regulatory Quality, Government Effectiveness, etc.)

    📋 How to Run the Study

    1. Open Google Colab.

    2. Upload the four Excel files listed above.

    3. Copy and paste the Python code provided below into a Colab notebook cell.

    4. Run the code to automatically load the datasets, clean the data, and estimate key regression models.

    🚀 Required Python Code

    python
    # --- 0. Imports ---
    import pandas as pd
    import statsmodels.api as sm
    import statsmodels.formula.api as smf


    # --- 1. Load Clean Datasets ---
    dbna = pd.read_excel('/content/DBNA 2022 Rank and Scores.xlsx')
    acs = pd.read_excel('/content/Spanish_Speakers_All_States.xlsx')
    wgi = pd.read_excel('/content/wgidataset.xlsx') # Optional: Governance analysis

    # --- 2. Standardize Column Names ---
    dbna.columns = dbna.columns.str.strip().str.replace(' ', '_')
    acs.columns = acs.columns.str.strip().str.replace(' ', '_')
    wgi.columns = wgi.columns.str.strip().str.replace(' ', '_')

    # --- 3. Merge Datasets ---
    # Merge DBNA and ACS on 'State'
    merged_dbna = dbna.merge(acs, on='State', how='left')

    # --- 4. Regressions: Language vs Institutional Outcomes ---

    # H1: Language (% Spanish) and Starting a Business Score
    model1 = smf.ols('Starting_a_Business_Score ~ Percent_Spanish_Speakers', data=merged_dbna).fit()
    print(" Regression: Starting a Business Score ~ Percent Spanish Speakers")
    print(model1.summary())

    # H3: Language (% Spanish) and Land and Space Use Score
    model2 = smf.ols('Land_and_Space_Use_Score ~ Percent_Spanish_Speakers', data=merged_dbna).fit()
    print(" Regression: Land and Space Use Score ~ Percent Spanish Speakers")
    print(model2.summary())

    # H3: Language (% Spanish) and Getting Electricity Score
    model3 = smf.ols('Getting_Electricity_Score ~ Percent_Spanish_Speakers', data=merged_dbna).fit()
    print(" Regression: Getting Electricity Score ~ Percent Spanish Speakers")
    print(model3.summary())

    # H4: Language (% Spanish) and Employing Workers Score
    model4 = smf.ols('Employing_Workers_Score ~ Percent_Spanish_Speakers', data=merged_dbna).fit()
    print(" Regression: Employing Workers Score ~ Percent Spanish Speakers")
    print(model4.summary())

    # --- 5. (Optional) Governance Analysis: Percent Spanish vs. WGI Regulatory Quality ---
    # If WGI includes 'State' or 'Country' to merge, otherwise skip
    # Example assuming WGI has 'Country' to match 'State'

    #wgi_merged = wgi.merge(acs, left_on='Country', right_on='State', how='left')
    #model5 = smf.ols('Regulatory_Quality ~ Percent_Spanish_Speakers', data=wgi_merged).fit()
    #print(" Regression: Regulatory Quality ~ Percent Spanish Speakers")
    #print(model5.summary())

    # --- 6. End ---
    print(" All regressions completed.")

    🧠 Key Concepts

    • Symbolic Institutional Traps: Language regimes act as hidden barriers, complicating regulatory navigation and labor market integration.

    • Symbolic Misalignment: Misfit between administrative languages and global commercial norms raises onboarding costs for MNEs.

    • Institutional Friction: Language encapsulation isolates economies and reduces foreign direct investment (FDI) attractiveness.

    📜 Data Documentation

    Each dataset has been:

    • Cleaned for consistent formatting.

    • Harmonized for cross-dataset integration.

    • Standardized to facilitate reproducible econometric analysis.

    • Full codebooks and metadata are available in the appendix of the research paper.

    ⚡ Notes

    • The EF EPI (English Proficiency) dataset was not uploaded here. If available, further regressions on symbolic distance can be run.

    • If any columns do not match exactly (e.g., different spellings), modify the variable names slightly based on print(dbna.columns).

    📈 Planned Outputs

    The code generates:

    • Regression outputs on how Spanish-speaking prevalence correlates with:

      • Starting a business

      • Ease of Doing Business

      • Regulatory quality

    • Subnational institutional performance differences (Puerto Rico vs. U.S. states).

    🌍 License and Reuse

    • Open Data: CC BY 4.0 License

    • Citation Requested:
      Brown, S.M. (2025). Symbolic Institutional Traps and the Liability of Foreignness: Language Regimes as Hidden Barriers to Multinational Entry. University of Puerto Rico. DOI: 10.5281/zenodo.15050209

  11. a

    LIMITED ENGLISH SPEAKING HOUSEHOLDS (S1602)

    • data-seattlecitygis.opendata.arcgis.com
    • data.seattle.gov
    • +1more
    Updated Aug 22, 2023
    + more versions
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    City of Seattle ArcGIS Online (2023). LIMITED ENGLISH SPEAKING HOUSEHOLDS (S1602) [Dataset]. https://data-seattlecitygis.opendata.arcgis.com/datasets/limited-english-speaking-households-s1602
    Explore at:
    Dataset updated
    Aug 22, 2023
    Dataset authored and provided by
    City of Seattle ArcGIS Online
    Description

    Table from the American Community Survey (ACS) S1602 limited English speaking households (households where no one age 14 and over speaks English "very well"). These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): S1602Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  12. ACS Language Spoken at Home Variables - Boundaries

    • hub.arcgis.com
    • mapdirect-fdep.opendata.arcgis.com
    • +1more
    Updated Oct 20, 2018
    + more versions
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    Esri (2018). ACS Language Spoken at Home Variables - Boundaries [Dataset]. https://hub.arcgis.com/maps/527ea2b5ba814c8ca1c34a2945e1b751
    Explore at:
    Dataset updated
    Oct 20, 2018
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows language group of language spoken at home by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population age 5+ who speak Spanish at home. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B16007Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  13. a

    LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER IN LIMITED...

    • data-seattlecitygis.opendata.arcgis.com
    • data.seattle.gov
    • +1more
    Updated Sep 3, 2023
    + more versions
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    City of Seattle ArcGIS Online (2023). LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER IN LIMITED ENGLISH SPEAKING HOUSEHOLDS (B16003) [Dataset]. https://data-seattlecitygis.opendata.arcgis.com/datasets/language-spoken-at-home-for-the-population-5-years-and-over-in-limited-english-speaking-households-b16003/about
    Explore at:
    Dataset updated
    Sep 3, 2023
    Dataset authored and provided by
    City of Seattle ArcGIS Online
    Description

    Table from the American Community Survey (ACS) B16003 of age by language spoken at home for the population 5 years and over in limited English-speaking households. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): B16003Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

  14. Language Spoken at Home 2018-2022 - COUNTIES

    • mce-data-uscensus.hub.arcgis.com
    • covid19-uscensus.hub.arcgis.com
    • +2more
    Updated Feb 5, 2024
    + more versions
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    US Census Bureau (2024). Language Spoken at Home 2018-2022 - COUNTIES [Dataset]. https://mce-data-uscensus.hub.arcgis.com/maps/5413d242f5ad4855a9b2e8fbe431cbd9
    Explore at:
    Dataset updated
    Feb 5, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    US Census Bureau
    Area covered
    Description

    This layer shows Language Spoken at Home. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with Limited English Speaking Status. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B16004, DP02, S1601, S1602Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

  15. a

    Language Spoken at Home 2017-2021 - COUNTIES

    • covid19-uscensus.hub.arcgis.com
    • mce-data-uscensus.hub.arcgis.com
    Updated Mar 24, 2023
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    US Census Bureau (2023). Language Spoken at Home 2017-2021 - COUNTIES [Dataset]. https://covid19-uscensus.hub.arcgis.com/datasets/language-spoken-at-home-2017-2021-counties
    Explore at:
    Dataset updated
    Mar 24, 2023
    Dataset authored and provided by
    US Census Bureau
    Area covered
    Description

    This layer shows Language Spoken at Home. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with Limited English Speaking Status. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B16004, DP02, S1601, S1602Data downloaded from: CensusBureau's API for American Community Survey Date of API call: February 16, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

  16. a

    Language Spoken at Home 2017-2021 - STATES

    • hub.arcgis.com
    • covid19-uscensus.hub.arcgis.com
    • +1more
    Updated Mar 24, 2023
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    US Census Bureau (2023). Language Spoken at Home 2017-2021 - STATES [Dataset]. https://hub.arcgis.com/maps/USCensus::language-spoken-at-home-2017-2021-states
    Explore at:
    Dataset updated
    Mar 24, 2023
    Dataset authored and provided by
    US Census Bureau
    Area covered
    Description

    This layer shows Language Spoken at Home. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with Limited English Speaking Status. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B16004, DP02, S1601, S1602Data downloaded from: CensusBureau's API for American Community Survey Date of API call: February 16, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

  17. a

    STATES

    • hub.arcgis.com
    Updated Feb 5, 2024
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    US Census Bureau (2024). STATES [Dataset]. https://hub.arcgis.com/datasets/5413d242f5ad4855a9b2e8fbe431cbd9
    Explore at:
    Dataset updated
    Feb 5, 2024
    Dataset authored and provided by
    US Census Bureau
    Area covered
    Description

    This layer shows Language Spoken at Home. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with Limited English Speaking Status. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B16004, DP02, S1601, S1602Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

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ESRI (2019). Percent of Population with Limited Ability to Speak English [Dataset]. https://data.amerigeoss.org/dataset/percent-of-population-with-limited-ability-to-speak-english
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Percent of Population with Limited Ability to Speak English

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esri rest, htmlAvailable download formats
Dataset updated
Jul 24, 2019
Dataset provided by
Esrihttp://esri.com/
Description
This map shows the percent of population with a limited ability to speak English by census tract. Search to your community and investigate the top language needs in nearby census tracts.


*DATA AS OF 2011-2015*
Data Source: U.S. Census Bureau's American Community Survey 5-year estimates, 2011-2015, Table B16001.

Complete list of all languages available in this data set (29):
Spanish or Spanish Creole; French (including Patois, Cajun); French Creole; Italian; Portuguese; German; Yiddish; Greek; Russian; Polish; Serbo-Croatian; Armenian; Persian; Gujarati; Hindi; Urdu; Chinese; Japanese; Korean; Mon-Khmer, Cambodian; Hmong; Thai; Laotian; Vietnamese; Tagalog; Navajo; Hungarian; Arabic; Hebrew. Those who have limited English ability and speak other languages are included in the percentage depicted in the map, but other languages will not appear in the ranked list or in the table.

Accompanying feature layer and viewing app are also available.
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