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

The table reports for each dataset: the reference to the journal article/book where the study was published, the type of data (LBSN stands for Location Based Social Networks, CDR for Call Detail Record), the number of individuals (or vehicles in the case of car/taxi data) involved in the data collection, the duration of the data collection (M → months, Y → years, D → days, W → weeks), the minimum and maximum length of spatial displacements, the shape of the probability distribution of displacements with the corresponding parameters, the temporal sampling, the shape of the distribution of waiting times with the corresponding parameters. Power-law (T), indicates a truncated power-law. The table can also be found at http://lauraalessandretti.weebly.com/plosmobilityreview.html.

Network and loading data for a real-world distribution network in the North-East of England.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset contains the historical Unidata Internet Data Distribution (IDD) Global Observational Data that are derived from real-time Global Telecommunications System (GTS) reports distributed via the Unidata Internet Data Distribution System (IDD). Reports include surface station (SYNOP) reports at 3-hour intervals, upper air (RAOB) reports at 3-hour intervals, surface station (METAR) reports at 1-hour intervals, and marine surface (BUOY) reports at 1-hour intervals. Select variables found in all report types include pressure, temperature, wind speed, and wind direction. Data may be available at mandatory or significant levels from 1000 millibars to 1 millibar, and at surface levels. Online archives are populated daily with reports generated two days prior to the current date.

Water-quality data for groundwater samples collected from 4,824 sites, and ancillary data and information on sampled wells and principal aquifers, were used to assess the occurrence and distribution of strontium in U.S. groundwater from 32 principal aquifers. This data release includes one tab-delimited text file detailing these data. Table 1. Chemical data from the U.S. Geological Survey National Water Information System and ancillary data considered for assessment of strontium concentration in U.S. groundwater.

This dataset provides supporting information for the species distribution data used in the associated manuscript. Collections of five non-native fish species were made by a number of institutions, and several capture techniques were used. This dataset also includes number of individuals of each species captured at each locality.

Statistical distribution of social media creators and influencers in the Education category

This dataset includes the data visualization scripts that are part of the second chapter in the PhD thesis Landscapes of Trade, the used (open) data, and resulting plots. There is also one figure of Chapter 1 and one figure of Chapter 7 included. Proprietary data used to calculate some of the numbers in Chapter 2 are not included in this repository.

The set includes two zipped work folders:

The folder 'Datavisualization' includes: a README file, two R scripts to produce plots and numbers used in the publication, along with underlying data folder and export folder.

The folder Gateway Factor includes: a README file, two R scripts to treat the data and produce the regression analysis as shown in Chapter 2, with underlying data folder and export folder.

Composite liquid and ice particle size distributions as measured by several instruments that were onboard the NRC Convair 580 aircraft for the ICICLE (In-Cloud ICing and Large-drop Experiment) campaign. The flights were out of Rockford, Illinois and took place over the western Great Lakes and surrounding plains region. The data are in NetCDF format.

Organizations in the services industry were the most common targets of leaks of confidential data in the database format in Russia in 2023, having accounted for ** percent of the total. The second-largest share was occupied by retail and e-commerce companies, at ** percent of data theft cases.

This statistic shows the breakdown of space in the global data center market, based on the type of data center, in terms of operational square feet. In 2015, enterprise data centers accounted for 76 percent of the square footage of the data center market worldwide.

When facing data protection challenges, the majority of Italian companies were well-equipped in 2018. More than half of the interviewed companies had already adopted good data protection measures, while ** percent were leaders in this field. In this respect, Italy scored better than the global average: according to the source, only ** percent of companies worldwide could be considered leaders in this field.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov. This visualization provides data that can be used to illustrate potential differences in the burden of deaths due to COVID-19 by race and ethnicity.

This dataset includes the rock distribution data on lunar surface as reported in an article by Yuan Li and Bo Wu (Analysis of Rock Abundance on Lunar Surface from Orbital and Descent images Using Automatic Rock Detection).

This repository contains datasets and code with a novel numerical approach aimed at finding a distribution network expansion plan (DNEP) that prevents future congestion and voltage issues. This approach is tested using the modified IEEE 33-bus network. Electricity demand and PV production data for a leap year with a 1-minute resolution was generated using the CREST model from the Loughborough University and is provided as a dataset of future high-load and high-production scenario.

The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

Small changes in estimates between years should be treated with caution as they may not be statistically significant.

Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

Repository that contains alerts that will be sent to SSA employees when certain conditions exist, to inform them of work that needs to be done, is being reviewed, or has been completed.

Statistical distribution of social media creators and influencers in the Pets category

Readers can process the codes in go and R languages. * Go can be downloaded at https://go-language.org/. * R can be downloaded at https://www.r-project.org/. * If readers want to process the codes to repeat the work in our case study, please load the file "r_codes.r" in the folder "case study" using R language, which contains all the documentation for the other files under this folder. Then, readers can run the R codes and process the data with the help of the notes in this script. * If readers want to process the codes to repeat the work in our simulation study of parameter estimation, please load the file "r_codes2.r" in the folder "/simulation study/parameter estimation" using R language, which contains all the documentation for the other files under this folder. Then, readers can run the R codes and process the data with the help of the notes in this script. * If readers want to process the codes to repeat the work in our simulation study of animal mov...