100+ datasets found
  1. o

    Geonames - All Cities with a population > 1000

    • public.opendatasoft.com
    • data.smartidf.services
    • +2more
    csv, excel, geojson +1
    Updated Mar 10, 2024
    + more versions
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    (2024). Geonames - All Cities with a population > 1000 [Dataset]. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/
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    csv, json, geojson, excelAvailable download formats
    Dataset updated
    Mar 10, 2024
    License

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

    Description

    All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

  2. n

    A dataset of 5 million city trees from 63 US cities: species, location,...

    • data.niaid.nih.gov
    • search.dataone.org
    • +1more
    zip
    Updated Aug 31, 2022
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    Dakota McCoy; Benjamin Goulet-Scott; Weilin Meng; Bulent Atahan; Hana Kiros; Misako Nishino; John Kartesz (2022). A dataset of 5 million city trees from 63 US cities: species, location, nativity status, health, and more. [Dataset]. http://doi.org/10.5061/dryad.2jm63xsrf
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    zipAvailable download formats
    Dataset updated
    Aug 31, 2022
    Dataset provided by
    The Biota of North America Program (BONAP)
    Harvard University
    Cornell University
    Stanford University
    Worcester Polytechnic Institute
    Authors
    Dakota McCoy; Benjamin Goulet-Scott; Weilin Meng; Bulent Atahan; Hana Kiros; Misako Nishino; John Kartesz
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    United States
    Description

    Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

    Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

    Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

  3. o

    US Cities: Demographics

    • public.opendatasoft.com
    • data.smartidf.services
    • +3more
    csv, excel, json
    Updated Jul 27, 2017
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    (2017). US Cities: Demographics [Dataset]. https://public.opendatasoft.com/explore/dataset/us-cities-demographics/
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    excel, csv, jsonAvailable download formats
    Dataset updated
    Jul 27, 2017
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

  4. A

    ‘Top 100 US Cities by Population’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Top 100 US Cities by Population’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-top-100-us-cities-by-population-e7e5/85aeccd1/?iid=006-941&v=presentation
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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Area covered
    United States
    Description

    Analysis of ‘Top 100 US Cities by Population’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brandonconrady/top-100-us-cities-by-population on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Content

    Data was pulled from a table in the following Wikipedia article: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population I used Microsoft Excel's PowerQuery function to pull the table from Wikipedia. Lists each city, its rank (based on 2020 population), some data on its area, and population in both 2020 and 2010.

    Banner image source: https://unsplash.com/photos/wh-7GeXxItI

    --- Original source retains full ownership of the source dataset ---

  5. d

    Domestic Violence Homicide Incidents in the Ten Largest U.S. Cities

    • catalog.data.gov
    • data.cityofnewyork.us
    • +1more
    Updated Feb 2, 2024
    + more versions
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    data.cityofnewyork.us (2024). Domestic Violence Homicide Incidents in the Ten Largest U.S. Cities [Dataset]. https://catalog.data.gov/dataset/domestic-violence-homicide-incidents-in-the-ten-largest-u-s-cities
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    Dataset updated
    Feb 2, 2024
    Dataset provided by
    data.cityofnewyork.us
    Area covered
    United States
    Description

    This data set contains count data on domestic violence homicide and intimate partner homicide incidents from the 2015 the Federal Bureau of Investigation (FBI) Supplemental Homicide Report (SHR) and population from the SHR.

  6. w

    1000 Largest US Cities By Population With Geographic Coordinates

    • data.wu.ac.at
    Updated May 31, 2017
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    Rich Jones (2017). 1000 Largest US Cities By Population With Geographic Coordinates [Dataset]. https://data.wu.ac.at/schema/public_opendatasoft_com/MTAwMC1sYXJnZXN0LXVzLWNpdGllcy1ieS1wb3B1bGF0aW9uLXdpdGgtZ2VvZ3JhcGhpYy1jb29yZGluYXRlcw==
    Explore at:
    application/vnd.geo+json, xls, json, csv, kmlAvailable download formats
    Dataset updated
    May 31, 2017
    Dataset provided by
    Rich Jones
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset contains information about the 1000 largest US cities by population: population, population growth, geographic coordinates, population rank.

  7. e

    LinkedIn US city ID's

    • datarepository.eur.nl
    txt
    Updated May 31, 2023
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    Paul Kievits (2023). LinkedIn US city ID's [Dataset]. http://doi.org/10.25397/eur.19932221.v1
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    txtAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Erasmus University Rotterdam (EUR)
    Authors
    Paul Kievits
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    United States
    Description

    This is a very small but useful dataset if you are ever looking to get jobs for a certain US city in LinkedIn. It contains a list of US cities and states and it's corresponding LinkedIn ID (which is usually externally hidden).

    The cities list was retreived from here: https://github.com/kelvins/US-Cities-Database and the names of the ciiadjusted to match the name used in LinkedIn (which could differ in subtle ways).

    Some cities do not have an ID, this is because the city is either too small or because there was a difference in the name on LinkedIn which I did not detect (human error). If you ever run in to one of these feel free to enhance this dataset.

  8. N

    cities in Bibb County Ranked by Native American Population // 2025 Edition

    • neilsberg.com
    csv, json
    Updated Jan 24, 2025
    + more versions
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    Neilsberg Research (2025). cities in Bibb County Ranked by Native American Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/cities-in-bibb-county-al-by-native-american-population/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jan 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Alabama, Bibb County
    Variables measured
    Native American Population, Native American Population as Percent of Total Population of cities in Bibb County, AL, Native American Population as Percent of Total Native American Population of Bibb County, AL
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    This list ranks the 7 cities in the Bibb County, AL by American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by Native American Population: This column displays the rank of cities in the Bibb County, AL by their American Indian and Alaska Native (AIAN) population, using the most recent ACS data available.
    • cities: The cities for which the rank is shown in the previous column.
    • Native American Population: The Native American population of the cities is shown in this column.
    • % of Total cities Population: This shows what percentage of the total cities population identifies as Native American. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total Bibb County Native American Population: This tells us how much of the entire Bibb County, AL Native American population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

  9. 500 Cities: Local Data for Better Health, 2019 release

    • catalog.data.gov
    • healthdata.gov
    • +5more
    Updated Jun 28, 2025
    + more versions
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    Centers for Disease Control and Prevention (2025). 500 Cities: Local Data for Better Health, 2019 release [Dataset]. https://catalog.data.gov/dataset/500-cities-local-data-for-better-health-2019-release
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    Dataset updated
    Jun 28, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    This is the complete dataset for the 500 Cities project 2019 release. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2017, 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017, 2012-2016 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures (all teeth lost, dental visits, mammograms, pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) from the 2016 BRFSS that are the same in the 2019 release as the previous 2018 release. More information about the methodology can be found at www.cdc.gov/500cities.

  10. K

    US Places (Population 50K-100K)

    • koordinates.com
    csv, dwg, geodatabase +6
    Updated Feb 1, 2001
    + more versions
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    US Bureau of Transportation Statistics (BTS) (2001). US Places (Population 50K-100K) [Dataset]. https://koordinates.com/layer/22835-us-places-population-50k-100k/
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    dwg, geodatabase, pdf, mapinfo mif, kml, shapefile, geopackage / sqlite, csv, mapinfo tabAvailable download formats
    Dataset updated
    Feb 1, 2001
    Dataset authored and provided by
    US Bureau of Transportation Statistics (BTS)
    Area covered
    Description

    This data set includes cities in the United States, Puerto Rico and the U.S. Virgin Islands. These cities were collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information. This is a revised version of the December, 2003, data set.

    This layer is sourced from maps.bts.dot.gov.

  11. World Cities

    • hub.arcgis.com
    • data.lojic.org
    • +5more
    Updated Jun 30, 2013
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    Esri (2013). World Cities [Dataset]. https://hub.arcgis.com/datasets/esri::world-cities/about
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    Dataset updated
    Jun 30, 2013
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This world cities layer presents the locations of many cities of the world, both major cities and many provincial capitals.Population estimates are provided for those cities listed in open source data from the United Nations and US Census.

  12. N

    cities in Dunn County Ranked by White Population // 2025 Edition

    • neilsberg.com
    csv, json
    Updated Jan 24, 2025
    + more versions
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    Neilsberg Research (2025). cities in Dunn County Ranked by White Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/cities-in-dunn-county-nd-by-white-population/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Jan 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Dunn County, North Dakota
    Variables measured
    White Population, White Population as Percent of Total White Population of Dunn County, ND, White Population as Percent of Total Population of cities in Dunn County, ND
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    This list ranks the 4 cities in the Dunn County, ND by White population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by White Population: This column displays the rank of cities in the Dunn County, ND by their White population, using the most recent ACS data available.
    • cities: The cities for which the rank is shown in the previous column.
    • White Population: The White population of the cities is shown in this column.
    • % of Total cities Population: This shows what percentage of the total cities population identifies as White. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total Dunn County White Population: This tells us how much of the entire Dunn County, ND White population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

  13. N

    Florida City, FL Age Group Population Dataset: A Complete Breakdown of...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Florida City, FL Age Group Population Dataset: A Complete Breakdown of Florida City Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4522fba4-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Florida City, Florida
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Florida City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Florida City. The dataset can be utilized to understand the population distribution of Florida City by age. For example, using this dataset, we can identify the largest age group in Florida City.

    Key observations

    The largest age group in Florida City, FL was for the group of age 15 to 19 years years with a population of 1,187 (9.28%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Florida City, FL was the 80 to 84 years years with a population of 79 (0.62%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Florida City is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Florida City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Florida City Population by Age. You can refer the same here

  14. N

    Kenneth City, FL Age Group Population Dataset: A complete breakdown of...

    • neilsberg.com
    csv, json
    Updated Sep 16, 2023
    + more versions
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    Neilsberg Research (2023). Kenneth City, FL Age Group Population Dataset: A complete breakdown of Kenneth City age demographics from 0 to 85 years, distributed across 18 age groups [Dataset]. https://www.neilsberg.com/research/datasets/708ca8b0-3d85-11ee-9abe-0aa64bf2eeb2/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Sep 16, 2023
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Florida, Kenneth City
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Kenneth City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Kenneth City. The dataset can be utilized to understand the population distribution of Kenneth City by age. For example, using this dataset, we can identify the largest age group in Kenneth City.

    Key observations

    The largest age group in Kenneth City, FL was for the group of age 60-64 years with a population of 504 (9.99%), according to the 2021 American Community Survey. At the same time, the smallest age group in Kenneth City, FL was the 10-14 years with a population of 102 (2.02%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Kenneth City is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Kenneth City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Kenneth City Population by Age. You can refer the same here

  15. N

    Broad Top City, PA Age Group Population Dataset: A Complete Breakdown of...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
    Share
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    Neilsberg Research (2025). Broad Top City, PA Age Group Population Dataset: A Complete Breakdown of Broad Top City Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/451385b3-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Pennsylvania, Broad Top City
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Broad Top City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Broad Top City. The dataset can be utilized to understand the population distribution of Broad Top City by age. For example, using this dataset, we can identify the largest age group in Broad Top City.

    Key observations

    The largest age group in Broad Top City, PA was for the group of age 60 to 64 years years with a population of 45 (10.47%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Broad Top City, PA was the 35 to 39 years years with a population of 12 (2.79%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Broad Top City is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Broad Top City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Broad Top City Population by Age. You can refer the same here

  16. d

    Digital data sets describing metropolitan areas in the conterminous US

    • catalog.data.gov
    • data.usgs.gov
    • +2more
    Updated Oct 5, 2024
    + more versions
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    U.S. Geological Survey (2024). Digital data sets describing metropolitan areas in the conterminous US [Dataset]. https://catalog.data.gov/dataset/digital-data-sets-describing-metropolitan-areas-in-the-conterminous-us
    Explore at:
    Dataset updated
    Oct 5, 2024
    Dataset provided by
    U.S. Geological Survey
    Area covered
    Contiguous United States, United States
    Description

    This data set describes metropolitan areas in the conterminous United States, developed from U.S. Bureau of the Census boundaries of Consolidated Metropolitan Statistical Areas (CMSA) and Metropolitan Statistical Areas (MSA), that have been processed to extract the largest contiguous urban area within each MSA or CMSA.

  17. N

    Big Stone City, SD Age Group Population Dataset: A Complete Breakdown of Big...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
    Share
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    Neilsberg Research (2025). Big Stone City, SD Age Group Population Dataset: A Complete Breakdown of Big Stone City Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/45111e75-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    Big Stone City, South Dakota
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Big Stone City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Big Stone City. The dataset can be utilized to understand the population distribution of Big Stone City by age. For example, using this dataset, we can identify the largest age group in Big Stone City.

    Key observations

    The largest age group in Big Stone City, SD was for the group of age 75 to 79 years years with a population of 115 (18.88%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Big Stone City, SD was the 5 to 9 years years with a population of 3 (0.49%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

    Age groups:

    • Under 5 years
    • 5 to 9 years
    • 10 to 14 years
    • 15 to 19 years
    • 20 to 24 years
    • 25 to 29 years
    • 30 to 34 years
    • 35 to 39 years
    • 40 to 44 years
    • 45 to 49 years
    • 50 to 54 years
    • 55 to 59 years
    • 60 to 64 years
    • 65 to 69 years
    • 70 to 74 years
    • 75 to 79 years
    • 80 to 84 years
    • 85 years and over

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Big Stone City is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Big Stone City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for Big Stone City Population by Age. You can refer the same here

  18. f

    A unified and validated traffic dataset for 20 U.S. cities

    • figshare.com
    zip
    Updated Aug 31, 2024
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    Xiaotong Xu; Zhenjie Zheng; Zijian Hu; Kairui Feng; Wei Ma (2024). A unified and validated traffic dataset for 20 U.S. cities [Dataset]. http://doi.org/10.6084/m9.figshare.24235696.v4
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 31, 2024
    Dataset provided by
    figshare
    Authors
    Xiaotong Xu; Zhenjie Zheng; Zijian Hu; Kairui Feng; Wei Ma
    License

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

    Area covered
    United States
    Description

    Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

  19. d

    500 Cities: City Boundaries

    • catalog.data.gov
    • healthdata.gov
    • +5more
    Updated Feb 3, 2025
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    Centers for Disease Control and Prevention (2025). 500 Cities: City Boundaries [Dataset]. https://catalog.data.gov/dataset/500-cities-city-boundaries
    Explore at:
    Dataset updated
    Feb 3, 2025
    Dataset provided by
    Centers for Disease Control and Prevention
    Description

    This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

  20. T

    Vital Signs: Population – by city

    • data.bayareametro.gov
    Updated Oct 6, 2021
    + more versions
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    California Department of Finance (2021). Vital Signs: Population – by city [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Population-by-city/2jwr-z36f
    Explore at:
    application/rssxml, tsv, csv, application/rdfxml, xml, kmz, application/geo+json, kmlAvailable download formats
    Dataset updated
    Oct 6, 2021
    Dataset authored and provided by
    California Department of Finance
    Description

    VITAL SIGNS INDICATOR Population (LU1)

    FULL MEASURE NAME Population estimates

    LAST UPDATED October 2019

    DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

    DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

    California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

    U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

    U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

    U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

    CONTACT INFORMATION vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

    Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

    Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

    Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

    The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

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(2024). Geonames - All Cities with a population > 1000 [Dataset]. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/

Geonames - All Cities with a population > 1000

Explore at:
14 scholarly articles cite this dataset (View in Google Scholar)
csv, json, geojson, excelAvailable download formats
Dataset updated
Mar 10, 2024
License

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

Description

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

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