41 datasets found
  1. Natural Earth 1:110m Countries

    • kaggle.com
    Updated Mar 14, 2020
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    Anton Poznyakovskiy (2020). Natural Earth 1:110m Countries [Dataset]. https://www.kaggle.com/datasets/poznyakovskiy/natural-earth-1110m-countries/data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 14, 2020
    Dataset provided by
    Kaggle
    Authors
    Anton Poznyakovskiy
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset contains geometry data for the countries of the world together with their names and country codes in various formats. The primary use case is choropleths, color-coded maps. The data can be read as a pandas DataFrame with geopandas and plotted with matplotlib. See the starter notebook for an example how to do it.

    The data was created by Natural Earth. It is in public domain and free to use for any purpose at the time of this writing; you might want to check their Terms of Use.

    Photo by KOBU Agency on Unsplash

  2. f

    Data from: A concentration-based approach to data classification for...

    • tandf.figshare.com
    • figshare.com
    txt
    Updated May 31, 2023
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    Robert G. Cromley; Shuowei Zhang; Natalia Vorotyntseva (2023). A concentration-based approach to data classification for choropleth mapping [Dataset]. http://doi.org/10.6084/m9.figshare.1456086.v2
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Robert G. Cromley; Shuowei Zhang; Natalia Vorotyntseva
    License

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

    Description

    The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme.

  3. USA states GeoJson

    • kaggle.com
    Updated Aug 18, 2020
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    Kate Gallo (2020). USA states GeoJson [Dataset]. https://www.kaggle.com/pompelmo/usa-states-geojson/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 18, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Kate Gallo
    Area covered
    United States
    Description

    Context

    I created a dataset to help people create choropleth maps of United States states.

    Content

    One geojson to plot the countries borders, and one csv from the Census Bureau for the us population per state.

    Inspiration

    I think the best way to use this dataset is in joining it with other data. For example, I used this dataset to plot police killings using the data from https://www.kaggle.com/jpmiller/police-violence-in-the-us

  4. Mapping 2021 Census Data using the Living Atlas

    • lecturewithgis.co.uk
    Updated Apr 30, 2025
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    Esri UK Education (2025). Mapping 2021 Census Data using the Living Atlas [Dataset]. https://lecturewithgis.co.uk/datasets/mapping-2021-census-data-using-the-living-atlas
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    Dataset updated
    Apr 30, 2025
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri UK Education
    Description

    Anyone who has taught GIS using Census Data knows it is an invaluable data set for showing students how to take data stored in a table and join it to boundary data to transform this data into something that can be visualised and analysed spatially. Joins are a core GIS skill and need to be learnt, as not every data set is going to come neatly packaged as a shapefile or feature layer with all the data you need stored within. I don't know how many times I taught students to download data as a table from Nomis, load it into a GIS and then join that table data to the appropriate boundary data so they could produce choropleth maps to do some visual analysis, but it was a lot! Once students had gotten the hang of joins using census data they'd often ask why this data doesn't exist as a prepackaged feature layer with all the data they wanted within it. Well good news, now a lot off it is and it's accessible through the Living Atlas! Don't get me wrong I fully understand the importance of teaching students how to perform joins but once you have this understanding if you can access data that already contains all the information you need then you should be taking advantage of it to save you time. So in this exercise I am going to show you how to load English and Welsh Census Data from the 2021 Census into the ArcGIS Map Viewer from the Living Atlas and produce some choropleth maps to use to perform visual analysis without having to perform a single join.

  5. World shapefile

    • kaggle.com
    Updated Jul 24, 2023
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    Kamile Novaes (2023). World shapefile [Dataset]. https://www.kaggle.com/datasets/kamilenovaes/world-shapefile
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 24, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Kamile Novaes
    License

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

    Area covered
    World
    Description

    This dataset contains a comprehensive collection of geographic shapefiles representing the boundaries of countries and territories worldwide. The shapefiles define the outlines of each nation and are based on the most recent and accurate geographical data available. The dataset includes polygon geometries that accurately represent the territorial extent of each country, making it suitable for various geographical analyses, visualizations, and spatial applications.

    Content: The dataset comprises shapefiles in the ESRI shapefile format (.shp) along with associated files (.shx, .dbf, etc.) that contain the attributes of each country, such as country names, ISO codes, and other relevant information. The polygons in the shapefiles correspond to the land boundaries of each nation, enabling precise mapping and spatial analysis.

    Use Cases: This dataset can be utilized in a wide range of applications, including but not limited to:

    • Creating choropleth maps to visualize and analyze various socio-economic indicators by country.
    • Conducting spatial analysis to study population distribution, territorial areas, and geographic trends.
    • Performing geopolitical research and country-level comparisons.
    • Integrating with other datasets to enrich geographic analyses and insights.

    Source: The shapefile data is sourced from reputable and authoritative geographic databases, ensuring its accuracy and reliability for diverse applications.

  6. New York NTA map json reformatted

    • kaggle.com
    zip
    Updated Oct 16, 2019
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    Kosta Tzioumis (2019). New York NTA map json reformatted [Dataset]. https://www.kaggle.com/ktzioumis/new-york-nta-map-json-reformatted
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    zip(1536171 bytes)Available download formats
    Dataset updated
    Oct 16, 2019
    Authors
    Kosta Tzioumis
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    New York
    Description

    NYC Open Data data: Boundaries of Neighborhood Tabulation Areas as created by the NYC Department of City Planning using whole census tracts from the 2010 Census as building blocks. These aggregations of census tracts are subsets of New York City's 55 Public Use Microdata Areas (PUMAs)

    reformatted to add NTA code as ferature.id for use with plotly choropleth

  7. c

    ckanext-mapviews

    • catalog.civicdataecosystem.org
    Updated Jun 4, 2025
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    (2025). ckanext-mapviews [Dataset]. https://catalog.civicdataecosystem.org/dataset/ckanext-mapviews
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    Dataset updated
    Jun 4, 2025
    Description

    The mapviews extension enhances CKAN by adding the capability to display data as interactive maps, including both regular maps and choropleth maps. By utilizing LeafletJS, which offers broad browser compatibility, the extension allows users to visualize datasets geographically. This enhances data exploration and understanding within the CKAN platform. Key Features: Regular and Choropleth Maps: Enables visualization of datasets on maps, offering both standard map views and choropleth maps that represent data variations across geographic regions. LeafletJS Integration: Leverages LeafletJS, a JavaScript library, to create interactive and responsive maps, ensuring compatibility with a wide range of web browsers (IE7+ and modern browsers). GeoJSON Support: Supports GeoJSON format for defining geographical boundaries and features, allowing integration with various GIS data sources. Data Linking: Provides a mechanism to link data from a tabular resource to geographical features in a GeoJSON resource, allowing for data-driven map visualizations. Interactive Filters: Allows filtering of data based on regions clicked on the map. URL Redirection: Can redirect to another page with filters set based on the region clicked, enhancing navigation within a CKAN instance to resources that relate to the region. Integration with CKAN: The extension integrates with CKAN by providing new Resource View types, navigablemap and choroplethmap. These views can be added to resources within CKAN datasets. The extension utilizes CKAN's plugin system, requiring activation via the ckan.plugins configuration setting, and makes use of the Resource View functionality. Benefits & Impact: The mapviews extension provides enhanced data visualization capabilities within CKAN, allowing users to explore and understand spatial data more effectively. The interactive maps, can help reveal patterns, trends, through geographic data. The filtering capabilities further promote data discovery and analysis, enabling the user to examine regional variations in that are represented within the data which may include social, economic, or environmental factors.

  8. Indian City Latitude & Longitude Coordinates

    • kaggle.com
    Updated Jul 16, 2021
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    Arjun Prasad Sarkhel (2021). Indian City Latitude & Longitude Coordinates [Dataset]. https://www.kaggle.com/arjunprasadsarkhel/indian-city-latitude-longitude/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 16, 2021
    Dataset provided by
    Kaggle
    Authors
    Arjun Prasad Sarkhel
    Area covered
    India
    Description

    Context

    I was looking for a dataset that will help me to map all the Major Indian Cities using Geopandas but I couldn't find non. This dataset help me to achieve what I was looking for. This data can be used for choropleth map, foilage map using Geopandas. There was only state value(lat & long), which I found in existing datasets. So I found this dataset.

    Content

    This contains all the Major Cities and their respective Latitude and Longitude Values along with the rounded-off population and the exact population

    Acknowledgements

    Thanks to Simple Maps for making all this data available in one place, you can find the original dataset here:- https://simplemaps.com/data/in-cities

    Inspiration

    You can use this dataset for plotting various features about the Major Indian Cities with the help of Geopandas.

  9. NFIP Community Layer No Overlaps Whole

    • catalog.data.gov
    • gimi9.com
    Updated Jun 7, 2025
    + more versions
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    FEMA/Resilience/Federal Insurance Directorate (2025). NFIP Community Layer No Overlaps Whole [Dataset]. https://catalog.data.gov/dataset/nfip-community-layer-no-overlaps-whole
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    Dataset updated
    Jun 7, 2025
    Dataset provided by
    Federal Emergency Management Agencyhttp://www.fema.gov/
    Description

    This dataset is flattened and multicounty communities are unsplit by county lines. Flattened means that there are no overlaps; larger shapes like counties are punched out or clipped where smaller communities are contained within them. This allows for choropleth shading and other mapping techniques such as calculating unincorporated county land area. Multicounty cities like Houston are a single feature, undivided by counties. This layer is derived from Census, State of Maine, and National Flood Hazard Layer political boundaries.rnrnThe Community Layer datasets contain geospatial community boundaries associated with Census and NFIP data. The dataset does not contain personal identifiable information (PII). The Community Layer can be used to tie Community ID numbers (CID) to jurisdiction, tribal, and special land use area boundaries.rnrnA geodatabase (GDB) link is Included in the Full Data section below. The compressed file contains a collection of files that can store, query, and manage both spatial and nonspatial data using software that can read such a file. It bcontains all of the community layers/b, not just the layer for which this dataset page describes. rnThis layer can also be accessed from the FEMA ArcGIS viewer online: https://fema.maps.arcgis.com/home/item.html?id=8dcf28fc5b97404bbd9d1bc6d3c9b3cfrnrnrnCitation: FEMA's citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

  10. d

    Data from: CrimeMapTutorial Workbooks and Sample Data for ArcView and...

    • catalog.data.gov
    • icpsr.umich.edu
    • +1more
    Updated Mar 12, 2025
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    National Institute of Justice (2025). CrimeMapTutorial Workbooks and Sample Data for ArcView and MapInfo, 2000 [Dataset]. https://catalog.data.gov/dataset/crimemaptutorial-workbooks-and-sample-data-for-arcview-and-mapinfo-2000-3c9be
    Explore at:
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justice
    Description

    CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

  11. H

    Foreign-born Population in Boston Area Communities, 1870–2010

    • dataverse.harvard.edu
    Updated May 11, 2018
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    Marilynn S. Johnson (2018). Foreign-born Population in Boston Area Communities, 1870–2010 [Dataset]. http://doi.org/10.7910/DVN/IC42Z8
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 11, 2018
    Dataset provided by
    Harvard Dataverse
    Authors
    Marilynn S. Johnson
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    1870 - 2010
    Area covered
    Boston Metropolitan Area
    Description

    This dataset underlies a choropleth map of Boston area communities in which areas are shaded according to the percentage of the population that was foreign-born during each decade. The data was drawn from the US Census of Population, as well as the American Community Survey.

  12. p

    Pedestrian Network - Dataset - CKAN

    • ckan0.cf.opendata.inter.prod-toronto.ca
    Updated Aug 29, 2019
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    (2019). Pedestrian Network - Dataset - CKAN [Dataset]. https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/pedestrian-network
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    Dataset updated
    Aug 29, 2019
    Description

    The Pedestrian Network (pednet) was created by the DAV team at the City of Toronto, and it is based on the sidewalk inventory from Transportation Services, Toronto road centrelines, and manual collection from aerial imagery. Pednet is integrated with centerline intersections, traffic signals, pedestrian crosswalks and crossovers, traffic signal data from Transportation Services as well as other City of Toronto datasets. Pednet was built using a variety of open source libraries such as NetworkX, Pandana, Quantum GIS, and Space syntax, as well as production mapping tools from ESRI’s ArcPro/ArcMap. The project source code can be found on DAV’s GitHub account here, which includes the semi-automated offsetting method from the Sidewalk Inventory and the analytical procedures undertaken. Pednet is a data model resembling a network graph (edges and nodes) weighted by linear distance. Shortest routes were calculated from every building centroid in the city to the nearest nth amenity at the maximum distance of 5000m. Walk times were calculated in the nearest minutes, using the prescribed 1.0m/per-second velocity used by Transportation services. Two separate versions of pednet were created in this iteration of the project: 1) using actual linear distances as network weights, and 2) where crosswalks were “extended” by 20% of their length to impose additional impedance to their distances and walk times. For every address within the City of Toronto, the walk times were calculated to various amenities like schools, libraries, hospitals, supermarkets, TTC stops and convenience stores see Section 3. Walk times were assigned to individual addresses as attributes. We then aggregated all these walk times to the census tract level and calculated the minimum, maximum, standard deviation, median, and average walk times. We used these aggregated values to both: 1) relate walkability measures to Statistics Canada Census data for socio-demographic analysis, as well as 2) the building footprints, pednet centerlines, and census tract area boundaries to be used in choropleth maps contained within the following sections.

  13. A

    ‘Malaria in Africa’ 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). ‘Malaria in Africa’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-malaria-in-africa-083b/9093d805/?iid=011-324&v=presentation
    Explore at:
    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
    Africa
    Description

    Analysis of ‘Malaria in Africa’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lydia70/malaria-in-africa on 28 January 2022.

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

    Context

    Africa, the world's second-largest continent, a continent with a wide array of vibrant cultures each with its own deep history, continent number 2 of largest population, and the continent is home to wonderful wildlife you can spot when you go on safari! Let's focus on Africa in this dataset.

    Malaria is a common disease in Africa. The disease is transmitted to humans through infected mosquito bites. Although you can take preventive measures against malaria, it can be life-threatening. This dataset includes the malaria cases in African countries, the incidence at risk, and data on preventive treatments against malaria.

    Content

    This dataset includes data on all African countries from 2007 till 2017. Each country has a unique ISO-3 country code, and the dataset includes the latitude and longitude point of each country as well. The dataset includes the cases of malaria that have been reported in each country and each year, as well as data on preventive measures that have been taken to prevent malaria.

    Acknowledgements

    The data on the incidence of malaria, malaria cases reported, and preventive treatments against malaria have been retrieved from the world bank open data source.

    Inspiration

    Each country has a unique ISO-3 country code. You can use the ISO-3 code to create choropleth maps and in the geospatial analysis. In addition, the dataset includes latitude and longitude points for each country.

    Drinking water safety and sanitation include a risk factor for malaria. Can improved drinking water facilities and preventive measures decrease the risk of malaria infection?

    Check out my notebook submission, feel free to copy the kernel for your analysis: https://www.kaggle.com/lydia70/notebook-malaria-in-africa The notebook submission includes geospatial analysis with plotly.

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

  14. Synthetic population for USA_ALABAMA

    • zenodo.org
    bin, pdf, zip
    Updated Jul 16, 2024
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    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie; Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie (2024). Synthetic population for USA_ALABAMA [Dataset]. http://doi.org/10.5281/zenodo.6505866
    Explore at:
    pdf, zip, binAvailable download formats
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie; Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie
    License

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

    Area covered
    United States, Alabama
    Description

    Synthetic populations for regions of the World (SPW) | Alabama

    Dataset information

    A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

    License

    CC-BY-4.0

    Acknowledgment

    This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

    Contact information

    Henning.Mortveit@virginia.edu

    Identifiers

    Region nameAlabama
    Region IDusa_140002904
    Modelcoarse
    Version0_9_0

    Statistics

    NameValue
    Population4768478
    Average age37.8
    Households1933164
    Average household size2.5
    Residence locations1933164
    Activity locations398709
    Average number of activities5.7
    Average travel distance65.0

    Sources

    DescriptionNameVersionUrl
    Activity template dataWorld Bank2021https://data.worldbank.org
    Administrative boundariesADCW7.6https://www.adci.com/adc-worldmap
    Curated POIs based on OSMSLIPO/OSM POIshttp://slipo.eu/?p=1551 https://www.openstreetmap.org/
    Household dataIPUMShttps://international.ipums.org/international
    Population count with demographic attributesGPWv4.11https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11

    Files description

    Base data files (usa_140002904_data_v_0_9.zip)

    FilenameDescription
    usa_140002904_person_v_0_9.csvData for each person including attributes such as age, gender, and household ID.
    usa_140002904_household_v_0_9.csvData at household level.
    usa_140002904_residence_locations_v_0_9.csvData about residence locations
    usa_140002904_activity_locations_v_0_9.csvData about activity locations, including what activity types are supported at these locations
    usa_140002904_activity_location_assignment_v_0_9.csvFor each person and for each of their activities, this file specifies the location where the activity takes place

    Derived data files

    FilenameDescription
    usa_140002904_contact_matrix_v_0_9.csvA POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.

    Validation and measures files

    FilenameDescription
    usa_140002904_household_grouping_validation_v_0_9.pdfValidation plots for household construction
    usa_140002904_activity_durations_{adult,child}_v_0_9.pdfComparison of time spent on generated activities with survey data
    usa_140002904_activity_patterns_{adult,child}_v_0_9.pdfComparison of generated activity patterns by the time of day with survey data
    usa_140002904_location_construction_0_9.pdfValidation plots for location construction
    usa_140002904_location_assignement_0_9.pdfValidation plots for location assignment, including travel distribution plots
    usa_140002904_usa_140002904_ver_0_9_0_avg_travel_distance.pdfChoropleth map visualizing average travel distance
    usa_140002904_usa_140002904_ver_0_9_0_travel_distr_combined.pdfTravel distance distribution
    usa_140002904_usa_140002904_ver_0_9_0_num_activity_loc.pdfChoropleth map visualizing number of activity locations
    usa_140002904_usa_140002904_ver_0_9_0_avg_age.pdfChoropleth map visualizing average age
    usa_140002904_usa_140002904_ver_0_9_0_pop_density_per_sqkm.pdfChoropleth map visualizing population density
    usa_140002904_usa_140002904_ver_0_9_0_pop_size.pdfChoropleth map visualizing population size

  15. Synthetic population for SWE

    • zenodo.org
    • explore.openaire.eu
    bin, csv, pdf, zip
    Updated Jul 16, 2024
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    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie; Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie (2024). Synthetic population for SWE [Dataset]. http://doi.org/10.5281/zenodo.6503529
    Explore at:
    pdf, zip, csv, binAvailable download formats
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie; Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie
    License

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

    Description

    Synthetic populations for regions of the World (SPW) | Sweden

    Dataset information

    A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

    License

    CC-BY-4.0

    Acknowledgment

    This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

    Contact information

    Henning.Mortveit@virginia.edu

    Identifiers

    Region nameSweden
    Region IDswe
    Modelcoarse
    Version0_9_0

    Statistics

    NameValue
    Population9143037.0
    Average age40.8
    Households3820873.0
    Average household size2.4
    Residence locations3820873.0
    Activity locations1440586.0
    Average number of activities5.8
    Average travel distance49.3

    Sources

    DescriptionNameVersionUrl
    Activity template dataWorld Bank2021https://data.worldbank.org
    Administrative boundariesADCW7.6https://www.adci.com/adc-worldmap
    Curated POIs based on OSMSLIPO/OSM POIshttp://slipo.eu/?p=1551 https://www.openstreetmap.org/
    Population count with demographic attributesGPWv4.11https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11

    Files description

    Base data files (swe_data_v_0_9.zip)

    FilenameDescription
    swe_person_v_0_9.csvData for each person including attributes such as age, gender, and household ID.
    swe_household_v_0_9.csvData at household level.
    swe_residence_locations_v_0_9.csvData about residence locations
    swe_activity_locations_v_0_9.csvData about activity locations, including what activity types are supported at these locations
    swe_activity_location_assignment_v_0_9.csvFor each person and for each of their activities, this file specifies the location where the activity takes place

    Derived data files

    FilenameDescription
    swe_contact_matrix_v_0_9.csvA POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.

    Validation and measures files

    FilenameDescription
    swe_household_grouping_validation_v_0_9.pdfValidation plots for household construction
    swe_activity_durations_{adult,child}_v_0_9.pdfComparison of time spent on generated activities with survey data
    swe_activity_patterns_{adult,child}_v_0_9.pdfComparison of generated activity patterns by the time of day with survey data
    swe_location_construction_0_9.pdfValidation plots for location construction
    swe_location_assignement_0_9.pdfValidation plots for location assignment, including travel distribution plots
    swe_swe_ver_0_9_0_avg_travel_distance.pdfChoropleth map visualizing average travel distance
    swe_swe_ver_0_9_0_travel_distr_combined.pdfTravel distance distribution
    swe_swe_ver_0_9_0_num_activity_loc.pdfChoropleth map visualizing number of activity locations
    swe_swe_ver_0_9_0_avg_age.pdfChoropleth map visualizing average age
    swe_swe_ver_0_9_0_pop_density_per_sqkm.pdfChoropleth map visualizing population density
    swe_swe_ver_0_9_0_pop_size.pdfChoropleth map visualizing population size

  16. Synthetic population for USA_NEWMEXICO

    • zenodo.org
    bin, pdf, zip
    Updated Jul 16, 2024
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    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie; Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie (2024). Synthetic population for USA_NEWMEXICO [Dataset]. http://doi.org/10.5281/zenodo.6505934
    Explore at:
    pdf, zip, binAvailable download formats
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie; Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie
    License

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

    Area covered
    New Mexico, United States
    Description

    Synthetic populations for regions of the World (SPW) | New Mexico

    Dataset information

    A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

    License

    CC-BY-4.0

    Acknowledgment

    This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

    Contact information

    Henning.Mortveit@virginia.edu

    Identifiers

    Region nameNew Mexico
    Region IDusa_140002890
    Modelcoarse
    Version0_9_0

    Statistics

    NameValue
    Population2056390
    Average age37.2
    Households833667
    Average household size2.5
    Residence locations833667
    Activity locations160317
    Average number of activities5.7
    Average travel distance76.2

    Sources

    DescriptionNameVersionUrl
    Activity template dataWorld Bank2021https://data.worldbank.org
    Administrative boundariesADCW7.6https://www.adci.com/adc-worldmap
    Curated POIs based on OSMSLIPO/OSM POIshttp://slipo.eu/?p=1551 https://www.openstreetmap.org/
    Household dataIPUMShttps://international.ipums.org/international
    Population count with demographic attributesGPWv4.11https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11

    Files description

    Base data files (usa_140002890_data_v_0_9.zip)

    FilenameDescription
    usa_140002890_person_v_0_9.csvData for each person including attributes such as age, gender, and household ID.
    usa_140002890_household_v_0_9.csvData at household level.
    usa_140002890_residence_locations_v_0_9.csvData about residence locations
    usa_140002890_activity_locations_v_0_9.csvData about activity locations, including what activity types are supported at these locations
    usa_140002890_activity_location_assignment_v_0_9.csvFor each person and for each of their activities, this file specifies the location where the activity takes place

    Derived data files

    FilenameDescription
    usa_140002890_contact_matrix_v_0_9.csvA POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.

    Validation and measures files

    FilenameDescription
    usa_140002890_household_grouping_validation_v_0_9.pdfValidation plots for household construction
    usa_140002890_activity_durations_{adult,child}_v_0_9.pdfComparison of time spent on generated activities with survey data
    usa_140002890_activity_patterns_{adult,child}_v_0_9.pdfComparison of generated activity patterns by the time of day with survey data
    usa_140002890_location_construction_0_9.pdfValidation plots for location construction
    usa_140002890_location_assignement_0_9.pdfValidation plots for location assignment, including travel distribution plots
    usa_140002890_usa_140002890_ver_0_9_0_avg_travel_distance.pdfChoropleth map visualizing average travel distance
    usa_140002890_usa_140002890_ver_0_9_0_travel_distr_combined.pdfTravel distance distribution
    usa_140002890_usa_140002890_ver_0_9_0_num_activity_loc.pdfChoropleth map visualizing number of activity locations
    usa_140002890_usa_140002890_ver_0_9_0_avg_age.pdfChoropleth map visualizing average age
    usa_140002890_usa_140002890_ver_0_9_0_pop_density_per_sqkm.pdfChoropleth map visualizing population density
    usa_140002890_usa_140002890_ver_0_9_0_pop_size.pdfChoropleth map visualizing population size

  17. f

    Spatial polygon data used for Fig 2.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Dec 5, 2024
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    Chongsuvivatwong, Virasakdi; Chumchuen, Kemmapon; Wichaidit, Wit (2024). Spatial polygon data used for Fig 2. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001358357
    Explore at:
    Dataset updated
    Dec 5, 2024
    Authors
    Chongsuvivatwong, Virasakdi; Chumchuen, Kemmapon; Wichaidit, Wit
    Description

    In June 2022, Thailand legalized recreational cannabis. Currently, cannabis is now the most consumed drug. Cannabis usage can increase inflammatory responses in the respiratory tract. Sharing of cannabis waterpipes has been linked to increased tuberculosis risks. Using a national in-patient databank, we aimed to 1) describe the spatiotemporal correlation between cannabis-related and tuberculosis hospital admissions, and 2) compare the rate of subsequent pulmonary tuberculosis admission between those with prior admissions for cannabis-related causes and those without. Both admission types were aggregated to the number of admissions in monthly and provincial units. Temporal and spatial patterns were visualized using line plots and choropleth maps, respectively. A matched cohort analysis was conducted to compare the incidence density rate of subsequent tuberculosis admission and the hazard ratio. Throughout 2017–2022, we observed a gradual decline in tuberculosis admissions, in contrast to the increase in cannabis-related admissions. Both admissions shared a hotspot in Northeastern Thailand. Between matched cohorts of 6,773 in-patients, the incidence density rate per 100,000 person–years of subsequent tuberculosis admissions was 267.6 and 165.9 in in-patients with and without past cannabis-admission, respectively. After adjusting for covariates, we found that a cannabis-related admission history was associated with a hazard ratio of 1.48 (P = 0.268) for subsequent tuberculosis admission. Our findings failed to support the evidence that cannabis consumption increased pulmonary tuberculosis risk. Other study types are needed to further assess the association between cannabis consumption and pulmonary tuberculosis.

  18. o

    Synthetic population for JOR

    • explore.openaire.eu
    Updated Apr 30, 2022
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    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie (2022). Synthetic population for JOR [Dataset]. http://doi.org/10.5281/zenodo.6503397
    Explore at:
    Dataset updated
    Apr 30, 2022
    Authors
    Abhijin Adiga; Hannah Baek; Stephen Eubank; Przemyslaw Porebski; Madhav Marathe; Henning Mortveit; Samarth Swarup; Mandy Wilson; Dawen Xie
    Description

    Synthetic populations for regions of the World (SPW) | JordanDataset informationA synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics). LicenseCC-BY-4.0 AcknowledgmentThis project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541). Contact informationHenning.Mortveit@virginia.edu Identifiers Region name Jordan Region ID jor Model coarse Version 0_9_0 Statistics Name Value Population 5723567.0 Average age 23.5 Households 1235755.0 Average household size 4.6 Residence locations 1235755.0 Activity locations 131978.0 Average number of activities 6.4 Average travel distance 44.5 Sources Description Name Version Url Activity template data World Bank 2021 https://data.worldbank.org Administrative boundaries ADCW 7.6 https://www.adci.com/adc-worldmap Curated POIs based on OSM SLIPO/OSM POIs http://slipo.eu/?p=1551 https://www.openstreetmap.org/ Household data DHS https://dhsprogram.com Population count with demographic attributes GPW v4.11 https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 Files descriptionBase data files (jor_data_v_0_9.zip) Filename Description jor_person_v_0_9.csv Data for each person including attributes such as age, gender, and household ID. jor_household_v_0_9.csv Data at household level. jor_residence_locations_v_0_9.csv Data about residence locations jor_activity_locations_v_0_9.csv Data about activity locations, including what activity types are supported at these locations jor_activity_location_assignment_v_0_9.csv For each person and for each of their activities, this file specifies the location where the activity takes place Derived data files Filename Description jor_contact_matrix_v_0_9.csv A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model. Validation and measures files Filename Description jor_household_grouping_validation_v_0_9.pdf Validation plots for household construction jor_activity_durations_{adult,child}_v_0_9.pdf Comparison of time spent on generated activities with survey data jor_activity_patterns_{adult,child}_v_0_9.pdf Comparison of generated activity patterns by the time of day with survey data jor_location_construction_0_9.pdf Validation plots for location construction jor_location_assignement_0_9.pdf Validation plots for location assignment, including travel distribution plots jor_jor_ver_0_9_0_avg_travel_distance.pdf Choropleth map visualizing average travel distance jor_jor_ver_0_9_0_travel_distr_combined.pdf Travel distance distribution jor_jor_ver_0_9_0_num_activity_loc.pdf Choropleth map visualizing number of activity locations jor_jor_ver_0_9_0_avg_age.pdf Choropleth map visualizing average age jor_jor_ver_0_9_0_pop_density_per_sqkm.pdf Choropleth map visualizing population density jor_jor_ver_0_9_0_pop_size.pdf Choropleth map visualizing population size

  19. Global Refugees Dataset 1951-2015

    • kaggle.com
    Updated Aug 13, 2023
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    Abu Talha (2023). Global Refugees Dataset 1951-2015 [Dataset]. https://www.kaggle.com/datasets/talhabu/global-refugee-trends-and-displacement-statistics
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 13, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Abu Talha
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This comprehensive dataset presents the global refugee landscape by providing a detailed overview of refugee and displacement statistics from various countries and territories over a span of time. With a total of 107,980 rows and 11 columns, this dataset delves into the complexities of forced migration and human displacement, offering insights into the movements of refugees, asylum-seekers, internally displaced persons (IDPs), returned refugees and IDPs, stateless individuals, and other populations of concern.

    Columns in the dataset:

    • Year: The year of data collection.
    • Country / territory of asylum/residence: The host country or territory where refugees seek asylum or residence.
    • Origin: The country of origin from which refugees are fleeing.
    • Refugees (incl. refugee-like situations): The number of refugees, including those in refugee-like situations.
    • Asylum-seekers (pending cases): The count of individuals seeking asylum whose cases are pending.
    • Returned refugees: The number of refugees who have returned to their country of origin.
    • Internally displaced persons (IDPs): The count of people who have been displaced within their own country due to conflict or other reasons.
    • Returned IDPs: The number of internally displaced persons who have returned to their previous locations.
    • Stateless persons: Individuals who do not have the legal recognition of any country.
    • Others of concern: Additional populations in need of humanitarian assistance.
    • Total Population: The sum of all above categories, representing the overall population affected by displacement. The dataset serves as a valuable resource for studying global refugee trends, assessing the impact of conflicts and crises on displacement, and understanding the challenges faced by vulnerable populations worldwide.

    Visualization Ideas: Time Series Analysis: Plot the trends in different refugee populations over the years, such as refugees, asylum-seekers, IDPs, returned refugees, etc. Geographic Analysis: Create heatmaps or choropleth maps to visualize refugee flows between different countries and regions. Origin and Destination Analysis: Show the top countries of origin and the top host countries for refugees using bar charts. Pie Charts: Visualize the distribution of different refugee populations (refugees, asylum-seekers, IDPs, etc.) as a percentage of the total population. Stacked Area Chart: Display the cumulative total of different refugee populations over time to observe changes and trends.

    Data Modeling and Machine Learning Ideas: Time Series Forecasting: Use machine learning algorithms like ARIMA or LSTM to predict future refugee trends based on historical data. Clustering: Group countries based on similar refugee patterns using clustering algorithms such as K-Means or DBSCAN. Classification: Build a classification model to predict whether a country will experience a significant increase in refugee inflow based on historical and socio-political factors. Sentiment Analysis: Analyze social media or news data to determine the sentiment around refugee-related topics and how it correlates with migration patterns. Network Analysis: Construct a network graph to visualize the connections and interactions between countries in terms of refugee flows.

    These visualization and modeling ideas can provide meaningful insights into the global refugee crisis and aid in decision-making, policy formulation, and humanitarian efforts.

  20. f

    Additional file 1 of Using Kano diagrams to display the most cited article...

    • figshare.com
    xlsx
    Updated Jun 1, 2023
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    Po-Hsin Chou; Yu-Tsen Yeh; Wei-Chih Kan; Tsair-Wei Chien; Shu-Chun Kuo (2023). Additional file 1 of Using Kano diagrams to display the most cited article types, affiliated countries, authors and MeSH terms on spinal surgery in recent 12 years [Dataset]. http://doi.org/10.6084/m9.figshare.14100424.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    figshare
    Authors
    Po-Hsin Chou; Yu-Tsen Yeh; Wei-Chih Kan; Tsair-Wei Chien; Shu-Chun Kuo
    License

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

    Description

    Additional file 1: Dataset used in this study.

Share
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Anton Poznyakovskiy (2020). Natural Earth 1:110m Countries [Dataset]. https://www.kaggle.com/datasets/poznyakovskiy/natural-earth-1110m-countries/data
Organization logo

Natural Earth 1:110m Countries

Country shapefiles for choropleth maps

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 14, 2020
Dataset provided by
Kaggle
Authors
Anton Poznyakovskiy
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

This dataset contains geometry data for the countries of the world together with their names and country codes in various formats. The primary use case is choropleths, color-coded maps. The data can be read as a pandas DataFrame with geopandas and plotted with matplotlib. See the starter notebook for an example how to do it.

The data was created by Natural Earth. It is in public domain and free to use for any purpose at the time of this writing; you might want to check their Terms of Use.

Photo by KOBU Agency on Unsplash

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