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The population count of San Juan, PR was 331,165 in 2018.

Population

Population Change

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Demographics and Population Datasets Involving San Juan, PR

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    NYCHA Resident Data Book Summary

    data.cityofnewyork.us | Last Updated 2020-02-08T00:56:30.000Z

    Contains resident demographic data at a summary level as of January 1, 2019. The Resident Data Book is compiled to serve as an information source for queries involving resident demographic as well as a source of data for internal analysis. Statistics are compiled via HUD mandated annual income reviews involving NYCHA Staff and residents. Data is then aggregated and compiled by development. Each record pertains to a single public housing development.

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    Projections 2040 by Jurisdiction

    data.bayareametro.gov | Last Updated 2019-05-01T23:00:49.000Z

    Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for jurisdictions in the nine county San Francisco Bay Area region.

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    CPI 1.1 Texas Child Population (ages 0-17) by County 2013-2022

    data.texas.gov | Last Updated 2023-01-30T16:22:35.000Z

    As recommended by the Health and Human Services Commission (HHSC) to ensure consistency across all HHSC agencies, in 2012 DFPS adopted the HHSC methodology on how to categorize race and ethnicity. As a result, data broken down by race and ethnicity in 2012 and after is not directly comparable to race and ethnicity data in 2011 and before. The population totals may not match previously printed DFPS Data Books. Past population estimates are adjusted based on the U.S. Census data as it becomes available. This is important to keep the data in line with current best practices, but may cause some past counts, such as Abuse/Neglect Victims per 1,000 Texas Children, to be recalculated. Population Data Source - Population Estimates and Projections Program, Texas State Data Center, Office of the State Demographer and the Institute for Demographic and Socioeconomic Research, The University of Texas at San Antonio. Current population estimates and projections data as of December 2020. Visit dfps.state.tx.us for information on all DFPS programs.

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    AmeriCorps Member Race and Ethnicity National Figures

    data.americorps.gov | Last Updated 2021-02-06T01:05:53.000Z

    This dataset represents the percent distribution of AmeriCorps member terms which started their service in calendar year 2019 by race and ethnicity. This report excludes AmeriCorps Seniors volunteers. Included are percentage distributions from the United States Census Bureau's 2010-2019 State Population Characteristics dataset.

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    COVID-19 Cases by Population Characteristics Over Time

    data.sfgov.org | Last Updated 2023-06-08T12:02:36.000Z

    <strong>A. SUMMARY</strong> This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups. Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable. <strong>B. HOW THE DATASET IS CREATED</strong> Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. <u><a href="https://sf.gov/information/covid-19-data-questions">Learn more</a></u>. Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19. Data notes on each population characteristic type is listed below. <u> Race/ethnicity</u> * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups. <u> Gender</u> * The City collects information on gender identity using <a href="https://www.sfdph.org/dph/files/PoliciesProcedures/COM5_SexGenderGuidelines.pdf">these guidelines</a>. <strong>C. UPDATE PROCESS</strong> Updates automatically at 05:00 AM Pacific Time each day. Redundant runs are scheduled at 07:00 AM and 09:00 AM in case of pipeline failure. Dataset will not update on the business day following any federal holiday. <strong>D. HOW TO USE THIS DATASET</strong> Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a <a href="https://data.sfgov.org/d/cedd-86uf">view based on the San Francisco Population and Demographic Census dataset</a>. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date. New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed. This data may not be immediately available for recently reported cases. Data updates as more information becomes available. To explore data on the total number of cases, use <u><a href="https://data.sfgov.org/COVID-19/COVID-19-Cases-Over-Time/gyr2-k29z">the COVID-19 Cases Over Time dataset</a></u>. <strong>E. ARCHIVED DATA</strong> Certain population characteristics that were once included in this dataset are no

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    Demographics For Unincorporated Areas In San Mateo County

    datahub.smcgov.org | Last Updated 2018-10-25T21:45:46.000Z

    Demographics, including median income, total population, race, ethnicity, and age for unincorporated areas in San Mateo County. This data comes from the 2012 American Community Survey 5 year estimates DP03 and DP05 files. They Sky Londa area is located within two Census Tracts. The data for Sky Londa is the sum of both of those Census Tracts. Users of this data should take this into account when using data for Sky Londa.

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    Social Vulnerability Index for Virginia by Census Tract, 2018

    data.virginia.gov | Last Updated 2023-05-22T14:49:26.000Z

    "ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event. SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking." For more see https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html

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    COVID-19 Deaths by Population Characteristics Over Time

    data.sfgov.org | Last Updated 2023-06-08T12:02:01.000Z

    <strong>A. SUMMARY</strong> This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. Deaths are included on the date the individual died. Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups. Data is lagged by five days, meaning the most date included is 5 days prior to today. All data update daily as more information becomes available. <strong>B. HOW THE DATASET IS CREATED</strong> COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate. Data on the population characteristics of COVID-19 deaths are from: * Case interviews * Laboratories * Medical providers These multiple streams of data are merged, deduplicated, and undergo data verification processes. It takes time to process this data. Because of this, data is lagged by 5 days and death totals for previous days may increase or decrease. More recent data is less reliable. Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths. Data notes on each population characteristic type is listed below. <u> Race/ethnicity</u> * We include all race/ethnicity categories that are collected for COVID-19 cases. <u>Gender</u> * The City collects information on gender identity using <u><a href="https://www.sfdph.org/dph/files/PoliciesProcedures/COM5_SexGenderGuidelines.pdf">these guidelines</a></u>. <strong>C. UPDATE PROCESS</strong> Updates automatically at 05:00 AM Pacific Time each day. Redundant runs are scheduled at 07:00 AM and 09:00 AM in case of pipeline failure. Dataset will not update on the business day following any federal holiday. <strong>D. HOW TO USE THIS DATASET</strong> Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a <a href="https://data.sfgov.org/d/cedd-86uf">view based on the San Francisco Population and Demographic Census dataset</a>. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date. New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed. This data may not be immediately available for more recent deaths. Data updates as more information becomes available. To explore data on the total number of deaths, use <u><a href="https://data.sfgov.org/COVID-19/COVID-19-Deaths-Over-Time/g2di-xufg">the COVID-19 Deaths Over Time dataset</a></u>. <strong>E. ARCHIVED DATA</strong> Certain population characteristics that were once included in this dataset are no longer being reported publicly. An archived copy of these data can be found at this dataset here: <a href="https://data.sfgov.org/d/w6fd-iq9e">ARCHIVED: COVID-19 Deaths by Population Characteristics Over Time</a>. The archived dataset contains data on the following population characteristics that are no longer being reported publicly: <UL><LI>Skilled Nursing Facility Occupancy <LI>Sexual orientation <LI>Comorbidities <LI>Homelessness <LI>Single Room Occupancy (SRO) tenancy <LI>Transmission Type</UL> <strong>F. CHANGE LOG</strong> <UL><LI>6/6/2023 - data on deaths by transmission type have been removed. See section ARCHIVED DATA for more detail. <LI>5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single roo

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    Bronx Zip Population and Density

    bronx.lehman.cuny.edu | Last Updated 2012-10-21T14:06:17.000Z

    2010 Census Data on population, pop density, age and ethnicity per zip code

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    Indicators of Anxiety or Depression Based on Reported Frequency of Symptoms During Last 7 Days

    data.cdc.gov | Last Updated 2023-05-17T14:14:18.000Z

    The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,