NOTE FOR USERS: For local-level projections, such as at a township and municipal-level, please use the original “2018 Series”. This is the data CMAP recommends be used for planning, grant applications, and other official purposes. CMAP is confident in the updated regional-level population projections; however, the projections for township and municipal level populations appear less reflective of current trends in nearterm population growth. Further refinements of the local forecasts are likely needed.CONTENTS:Filename: ONTO2050OriginalForecastData2018.zipTitle: Socioeconomic Forecast Data, 2018 SeriesThis .zip file contains data associated with the original ON TO 2050 forecast, adopted in October 2018. Includes:Excel file of regional projections of population and employment to the year 2050:CMAP_RegionalReferenceForecast_2015adj.xlsx (94kb)Excel file of local (county, municipality, Chicago community area) projections of household population and employment to the year 2050: ONTO2050LAAresults20181010.xlsx (291kb)GIS shapefile of projected local area allocations to the year 2050 by Local Allocation Zone (LAZ): CMAP_ONTO2050_ForecastByLAZ_20181010.shp (19.7mb)Filename: ONTO2050OriginalForecastDocumentation2018.zipTitle: Socioeconomic Forecast Documentation, 2018 SeriesThis .zip file contains PDF documentation of the original ON TO 2050 forecast, adopted in October 2018. Includes:Louis Berger forecast technical report (2016): CMAPSocioeconomicForecastFinal-Report04Nov2016.pdf (2.3mb)Louis Berger addendum (2017): CMAPSocioeconomicForecastRevisionAddendum20Jun2017.pdf (0.6mb)ON TO 2050 Forecast appendix (2018): ONTO2050appendixSocioeconomicForecast10Oct2018.pdf (2.6mb)Filename: Socioeconomic-Forecast-Appendix-Final-October-2022.pdfTitle: Socioeconomic Forecast Appendix, 2022 SeriesDocumentation & results for the updated socioeconomic forecast accompanying the ON TO 2050 plan update, adopted October 2022. PDF, 2.7mbFilename: RegionalDemographicForecast_TechnicalReport_202210.pdfTitle: 2050 Regional Demographic Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional demographic forecast, developed in coordination with the Applied Population Lab at the University of Wisconsin, Madison. PDF, 1.7mbFilename: RegionalEmpForecast_TechnicalReport_202112.pdfTitle: 2050 Regional Employment Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional employment forecast, developed by EBP and Moody's Analytics. PDF, 0.8mbFilename: CMAPRegionalForecastONTO2050update202209.xlsxTitle: Regional Projections, 2022 SeriesProjections of population and employment to the year 2050, produced for the ON TO 2050 plan update adopted October 2022. 60kbFilename: CMAPLocalForecastONTO2050update202210.xlsxTitle: County and Municipal Projections, October 2022 (2022 Series)Projections of population and employment to the year 2050 at the county and municipal level, produced for the ON TO 2050 plan update adopted October 2022.

Separate tables are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs)There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary.NOTE: Much of the data is from 5-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

This layer contains WorldPop's 100m resolution annual estimates of population density from the year 2000 to 2020. Usage notes: This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population density, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in conjunction with WorldPop's 1-km resolution Population Density layer to create web maps that allow users to pan and zoom to wider areas; this web map contains an example of this combination. The population estimates in this layer are derived WorldPop's total population data, which use a Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042) and converts these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_100m/ImageServer, which was acquired from WorldPop in December 2021.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Indonesia data available from WorldPop here.

In a gold standard map, G, a pixel is in the category if it is above the threshold: x ∈ Gτ if and only if x > τ. Otherwise, x ∉ Gτ. Similarly, the categorization is applied to a candidate map, M. Pixels are classified as true positives (TP), true negatives (TN), false negatives (FN), and false positives (FP) as described in the table. Accuracy profiles are plotted in Fig 6.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Taiwan data available from WorldPop here.

This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics.

This measure answers the question of what is the rate of change for the share of the total city population that is African-American. Calculated the difference of percentage of share over reporting period. Data collected from the U.S. Census Bureau, American Communities Survey (ACS) (1-yr), Race (table B02001), except for 2020 data, which are from the 2020 Decennial Census Count. American Communities Survey is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/6p8t-s826

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Iraq data available from WorldPop here.

ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.AP skills & objectives (CED)Skill 6.A: Identify the scales of analysis presented by maps, quantitative and geospatial data, images, and landscapes.PSO-2.C: Explain how population distribution and density affect society and the environment.PSO-2.F: Explain ways that geographers depict and analyze population composition.Learning outcomesStudents will be able to identify U.S. population data and certain spatial patterns.

The European Directive 2002/49/EC on the assessment and management of environmental noise sets out a common approach for all Member States of the European Union to avoid, prevent or reduce as a matter of priority the harmful effects of exposure to noise in the environment. It has been transposed into French law by ordinance, ratified by the Law of 26 October 2005 and is now included in the Environmental Code. This approach is based on noise exposure mapping, population information and implementation of the Environmental Noise Prevention Plan (PPBE) at the local level. Articles L572-1 to L572-11 and R572-1 to R572-11 of the Environmental Code define the competent authorities to adopt noise maps and environmental noise prevention plans. As regards the major road and rail infrastructure of the national network, the noise maps and the PPBEs are adopted by the Prefect, in accordance with the conditions laid down in the circular of 7 June 2007 concerning the drawing up of noise maps and plans for the prevention of environmental noise and by the instruction of 23 July 2008.

In the light of the circular of 7 June 2007 on the preparation of noise maps and environmental noise prevention plans, noise maps are to be drawn up for large infrastructure and in large agglomerations. The following are concerned: — roadways used by more than 8200 vehicles/d — railways with more than 82 train crossings/d — agglomerations with a population of more than 100 000 inhabitants

Exceedance maps of limit values or type C map: maps representing, for the year in which the maps are drawn up, the areas where the limit values in Lden (day) and Ln (night) are exceeded (Article L 572-6 of the Environmental Code).

The accuracy, recall, and precision for the population classifications shown in the header and illustrated in Fig 6.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Pakistan data available from WorldPop here.

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Notes from product: II. Notes on China 2000 and 2010 Population Census Data In order to guide you to use the data correctly, provide you some explanations as follows: (l) Census time: 0:00AM of November 1, 2000 and 2010 as the reference time for the census. (2) The 2000 and 2010 population census covered all persons who hold the nationality of, and have permanent residing place in the People's Republic of China. During the census, each person was enumerated in his/her permanent residing place. The following persons should be enumerated in their permanent residing place: a) Those who reside in the townships, towns and street communities and have their permanent household registration there. b) Those who have resided in the townships, towns and street communities for more than 6 months but the places of their permanent household registration are elsewhere. c) Those who have resided in the townships, towns and street communities for less than 6 months but have been away from the place of their permanent household registration for more than 6 months. d) Those who live in the townships, towns and street communities during the population census while the places of their household registration have not yet settled. e) Those who used to live in the townships, towns and street communities but are working or studying abroad during the census and have no Permanent household registration for the time being. (3) Two types of questionnaires (long form and short form) were used for the 2000 and 2010 population census. The short form contains items that reflect the basic situation of the population, while the long form include all short form items plus other items such as migration, education, economic activities, marriage and family, fertility , housing , etc. . According to the National Bureau of Statistics of China, the households for the Long Form survey were selected by a random sampling program. The data included in this product are from 100% Short Form survey.(4) Results in this publication are based on the processing of data directly from enumeration without any adjustment. It is therefore worthwhile to notice the following: a. Data in the publication do not include population not enumerated in the Census. b. Data in the publication do not include the servicemen of the People's Liberation Army. c. The post-enumeration sample survey indicates an undercount of 1.81% in 2000 Census and 0.12% in 2010 Census. III. Notes on the China Province GIS Maps for the 2000 and 2010 Population Census Data (1) The China Province GIS map were developed for the 2000 and 2010 population Census data, which covered all 31 municipalities, provinces and autonomous regions of China, except for Taiwan, Hong Kong and Macao. (2) The population data came from the 5th and 6th China Population Census surveyed in 2000 and 2010. The GIS data is based on the national digital map (1:1 million) developed by the National Geographic Information Center of China (NGCC), including rives, roads, residential area and administrative boundaries.(3) The China province GIS maps are developed for matching 2000 and 2010 China population Census data, which should only be used as references for research or education instead of used as official maps. The distributor is not responsible for the accuracy of the those maps if the maps are used for business or other purposes.

The strategic noise maps, in accordance with European Directive 2002/49/EC of 25 June 2002, allow for the overall assessment of exposure to noise in the environment. They allow a representation of noise levels, but also to enumerate the exposed population, quantify the nuisances, and then draw up action plans. The type “c” map represents the isophone curves of the areas where the Lden exceeds the value of 68 dB(A) for the road network. Lden 68 dB(A): area where the Lden (Level day, evening, night) exceeds the value of 68 dB(A) DB(A) is a unit of measurement expressing a level of intensity (decibel) weighted according to the physiological characteristics of the human ear. Reference: prefectural Decree of 25 May 2011 MAXIMUM CONSULTATION SCALE: 1/25 000 th C_COMMUNE_Labels C_ROUTES_Labels C_L_LIMITE_LDen_68_S_037_Values_dBInf C_COMMUNES_IMPACTEES_C_LDEN68 C_DEPARTMENT C_COMMUNE C_ROUTES

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Sri Lanka data available from WorldPop here.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.