The population rate of change of District of Columbia was 1.80% in 2018.

Population

Population Change

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

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Demographics and Population Datasets Involving District of Columbia

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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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    Provisional Death Counts for Coronavirus Disease (COVID-19): Weekly State-Specific Data Updates

    data.cdc.gov | Last Updated 2020-07-08T19:37:34.000Z

    This report provides a weekly summary of deaths with coronavirus disease 2019 (COVID-19) by select geographic and demographic variables. In this release, counts of deaths are provided by the race and Hispanic origin of the decedent. Topics will be added to the release as they become available. These provisional counts are based on a current flow of mortality data in the National Vital Statistics System. National provisional counts include deaths occurring within the 50 states and the District of Columbia that have been received and coded as of the date specified. Data shown on this page may be incomplete and will likely not include all deaths that occurred during a given time period, especially for the more recent time periods. Data on this page are revised weekly and may increase or decrease as new and updated death certificate data are received from the states by NCHS. COVID-19 death counts shown here may differ from other published sources, as data currently are lagged by an average of 1–2 weeks. Weighted population distributions more accurately reflect race/ethnic distributions of the geographic locations where COVID outbreaks are occurring (see below for the methods used to calculate weighted percentages). The weighted population distributions ensure that the population estimates and percentages of COVID-19 deaths represent comparable geographic areas, in order to provide information about whether certain racial and ethnic subgroups are experiencing a disproportionate burden of COVID-19 mortality. See Table 2 below for unweighted populations. Estimated distributions of COVID-19 deaths and population size by race and Hispanic origin The percentages of COVID-19 deaths by race and Hispanic origin were calculated by dividing the number of COVID-19 deaths for each race and Hispanic origin group by the total number of COVID-19 deaths. Percentages may not sum to 100 due to rounding. The distribution of deaths involving COVID-19 by race/ethnicity should not be compared to the race/ethnicity distribution of the U.S. population because COVID-19 deaths are concentrated in certain geographic locations where the racial and ethnic population distribution differs from that of the United States overall. Additionally, COVID-19 deaths are concentrated in certain areas within states, and it is therefore not appropriate to compare the percent of COVID-19 deaths by race/ethnicity to the racial/ethnic population distribution of a given state. To make the estimated population distribution more comparable to the geographic areas where COVID-19 deaths are occurring, weighted population distributions are provided in this report. The weighted population distributions were calculated as follows. County-level population counts by race and Hispanic origin were multiplied by the corresponding total count of COVID-19 deaths by county (of residence). These weighted counts were then summed to the state (or national) level. The percentage of the population within each race and Hispanic origin group by state (or for the U.S.) was then estimated using these weighted counts. Counties with no COVID-19 deaths received a weight of zero, and thus do not contribute to the weighted population totals. Population counts for counties with large numbers of COVID-19 deaths are upweighted proportional to their numbers of COVID-19 deaths. These weighted population distributions ensure that the population estimates and percentages of COVID-19 deaths represent comparable geographic areas, in order to provide information about whether certain racial and ethnic subgroups are experiencing a disproportionate burden of COVID-19 mortality. For example, assume that 75% of the total number of COVID deaths occurred in a single county, County X, while the other 25% of COVID deaths occurred in County Y, and all other counties reported zero deaths. The weighted population counts for County X would contribute 75% of the total population counts, while the population counts for Count

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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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    Census Demographics 2010

    data.baltimorecity.gov | Last Updated 2017-02-06T04:44:33.000Z

    BNIA-JFI analyzed data from the Census to provide greater understandingof the socioeconomic and demographic characteristics of the residents of the City and its neighborhoods . BNIA-JFI also used this data as denominators for many of the Vital Signs indicators allowing for data to be normalized and rates to be computed. Census data analyzed by BNIA-JFI is grouped into the following categories: population, race and ethnicity; households and families; and income.

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    Census Demographics (2010-2014)

    data.baltimorecity.gov | Last Updated 2017-02-06T04:55:23.000Z

    Census data are frequently used throughout Vital Signs as denominators for normalizing many other indicators and rates. The socioeconomic and demographic indicators are grouped into the following categories: population, race/ethnicity, age, households, and income and poverty.

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    NCHS - Leading Causes of Death: United States

    data.cdc.gov | Last Updated 2020-06-05T17:31:08.000Z

    This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES 1. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. 2. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

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    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2013-02-08T20:03:40.000Z

    basic characteristics of people and housing for individual 2010 census block groups

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    Labor Force Demographic Characteristics by Commuting Mode Split: 2012 - 2016

    data.cambridgema.gov | Last Updated 2019-09-17T17:16:51.000Z

    This data set provides demographic and journey to work characteristics of the Cambridge Labor Force by primary mode of their journey to work. Attributes include age, presence of children, racial and ethnic minority status, vehicles available, time leaving home, time spent traveling, and annual household income. The data set originates from a special tabulation of the American Community Survey - the 2012 - 2016 version of the Census Transportation Planning Products (CTPP). The Cambridge Labor Force consist of all persons who live in Cambridge who work or are actively seeking employment. For more information on Journey to Work data in Cambridge, please see the full 2015 report (https://www.cambridgema.gov/~/media/Files/CDD/FactsandMaps/profiles/moving_forward_20150930.ashx?la=en).

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    NYSERDA Low- to Moderate-Income New York State Census Population Analysis Dataset: Average for 2013-2015

    data.ny.gov | Last Updated 2019-11-15T22:30:02.000Z

    How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

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    Concentrations of Protected Classes from Analysis of Impediments

    data.austintexas.gov | Last Updated 2019-07-29T17:26:04.000Z

    A new component of fair housing studies is an analysis of the opportunities residents are afforded in “racially or ethnically concentrated areas of poverty,” also called RCAPs or ECAPs. An RCAP or ECAP is a neighborhood with significant concentrations of extreme poverty and minority populations. HUD’s definition of an RCAP/ECAP is: • A Census tract that has a non‐white population of 50 percent or more AND a poverty rate of 40 percent or more; OR • A Census tract that has a non‐white population of 50 percent or more AND the poverty rate is three times the average tract poverty rate for the metro/micro area, whichever is lower. Why the 40 percent threshold? The RCAP/ECAP definition is not meant to suggest that a slightly‐lower‐than‐40 percent poverty rate is ideal or acceptable. The threshold was borne out of research that concluded a 40 percent poverty rate was the point at which a neighborhood became significantly socially and economically challenged. Conversely, research has shown that areas with up to 14 percent of poverty have no noticeable effect on community opportunity. (See Section II in City of Austin’s 2015 Analysis of Impediments to Fair Housing Choice: http://www.austintexas.gov/sites/default/files/files/NHCD/Reports_Publications/1Analysis_Impediments_for_web.pdf) This dataset provides socioeconomic data on protected classes from the 2008-2012 American Community Survey on census tracts in Austin’s city limits and designates which of those tracts are considered RCAPs or ECAPs based on these socioeconomic characteristics. A map of the census tracts designated as RCAPs or ECAPs is attached to this dataset and downloadable as a pdf (see below).