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The population rate of change of Van Buren County, TN was 0.51% 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 Van Buren County, TN

  • API

    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

  • API

    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2021-11-12T14:15:42.000Z

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

  • API

    2010 Census/ACS Detailed Block Group Data

    data.kcmo.org | Last Updated 2021-11-12T14:22:17.000Z

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

  • API

    Point In Time Homeless Survey Data

    data.sonomacounty.ca.gov | Last Updated 2019-07-12T18:26:35.000Z

    The County of Sonoma conducts an annual homeless count for the entire county. The survey data is derived from a sample of about 600 homeless persons countywide per year. The resulting information is statistically reliable only for the county as a whole, not for individual locations. The exception is the City of Santa Rosa, where the sample taken within the city is large enough to be predictive of the overall homeless population in that city.

  • API

    Travel Decision Survey 2019

    data.sfgov.org | Last Updated 2020-01-31T22:45:39.000Z

    **Please refer to the downloadable XLSX attachment (http://bit.ly/SFMTATravelSurvey2019) for the complete dataset, metadata, and instructions for use.** This workbook provides data and data dictionaries for the SFMTA 2019 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.2: Mode Share target of 80% sustainable travel by 2030. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 801 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis. The survey period for this survey is as follows: 2019: May - August 2019 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.3% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: • n = 801(Total Sample). Margin of error = +/- 3.3% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%