Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution.

In the paper Adams, Matthew (2022) Integral, mean and covariance of the simplex-truncated multivariate normal distribution. PLoS One, 17(7), Article number: e0272014. https://eprints.qut.edu.au/233964/, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution.

This dataset consists of all code and results for the associated article.

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.

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 28 percent of the total. The second-largest share was occupied by retail and e-commerce companies, at 26 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.

Model output of Ideal Free Distribution simulationsThe data file contains the model output of the Combined Model. Each row represents one simulation. The first four columns are model parameters that were varied in the simulations (see table A1 in the Appendix for parameter descriptions), and the fifth column is the recorded IFD slope of that simulation. All simulations were run in NetLogo.IFD dryad data Final.csv

Distribution Centre. While all reasonable steps have been taken to ensure the accuracy, completeness and reliability of the information provided, Enemalta assumes no responsibility for any errors, inaccuracies or missing information. In no event shall Enemalta be liable for any direct, indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information being provided.

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 20 percent were leaders in this field. In this respect, Italy scored better than the global average: according to the source, only 12 percent of companies worldwide could be considered leaders in this field.

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.

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.

This is a MaxEnt model map of the global distribution of the seagrass biome. Species occurrence records were extracted from the Global Biodiversity Information Facility (GBIF), United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC) Ocean Data Viewer and Ocean biogeographic information system (OBIS). This map shows the suitable habitats for the seagrass distribution at global scale.

Citation: Jayathilake D.R.M., Costello M.J. 2018. A modelled global distribution of the seagrass biome. Biological Conservation. https://doi.org/10.1016/j.biocon.2018.07.009

Use Constraints: Creative Commons Attribution 4.0 Unported (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/.

Free to (1) copy and redistribute the material in any medium or format, (2) remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

AFSC triennial and NWFSC annual shelf-slope bottom trawl surveys (spatially-restricted)A subset of locations and species obtained from the Alaska Fisheries Science Center triennial shelf survey (1977-2004) and Northwest Fisheries Science Center annual shelf-slope survey (2003-2013). Please see ReadMe.txt for more detailsArchive.csv

In the first half of 2024, healthcare providers reported 252 data breaches in the U.S. healthcare sector, becoming the entity with the highest number of reported breach incidents. As of the time of the reporting, business associates ranked second with the number of reported data breaches.

This dataset includes percent distribution of births for females by age group in the United States since 1933.

The number of states in the reporting area differ historically. In 1915 (when the birth registration area was established), 10 states and the District of Columbia reported births; by 1933, 48 states and the District of Columbia were reporting births, with the last two states, Alaska and Hawaii, added to the registration area in 1959 and 1960, when these regions gained statehood. Reporting area information is detailed in references 1 and 2 below. Trend lines for 1909–1958 are based on live births adjusted for under-registration; beginning with 1959, trend lines are based on registered live births.

SOURCES

NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/).

REFERENCES

1. National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf.

2. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf.

3. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf.

4. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.

5. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf.

6. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.

Open NABIS ApplicationThe purpose of this information is to show the annual distribution of the silver doryLineage - Document describing the data source used, history of changes, background information about the given layer, and referencesCreator - National Institute of Water and Atmospheric ResearchPublisher - Ministry for Primary IndustriesContributor - Peter McMillan, NIWA, WellingtonCustodian - Ministry for Primary IndustriesJurisdiction of Custodian - New ZealandRights - Crown Copyright Reserved.Subject - Annual distribution of adult silver dory within the waters around New ZealandSource - Multiple sources - see Lineage for detailsLanguage - EnglishRelation - Function - To ensure that fisheries are sustainably used within a healthy aquatic ecosystemLayer Type - biotaAudience - AllMandate - New Zealand Biodiversity StrategyProgress - CompletedMetadata - Compliant with NZGLS and ANZLICLayer InformationSpecies Common Name - Silver dorySpecies Scientific Name - Cyttus novaezealandiae (Arthur 1885)Species Maori Name - N/ASpecies Code - SDOEndangered Status - Not listedSeason - AnnualHabitat Type - Demersal offshoreMinimum Depth - 100 mMaximum Depth - 500 mAccuracyAlert Level - NoneWarnings and Problems - NonePositional Accuracy - 100 kmAttribute Accuracy - Attribute data have been checked against Ministry for Primary Industries guidelines and no significant problems are known.Logical Consistency - Logical consistency has been checked against Ministry for Primary Industries guidelines, and no significant problems are known.Completeness - Completeness of the information layer has been checked against independent descriptions of the distribution of the silver dory and no significant problems are known.Certified/Refereed By - Owen Anderson (NIWA)Certification Date - 01/01/0001CoverageCoverage Name - Coverage: Spatial Northern - Coverage: Spatial Southern - Coverage: Spatial Eastern - Coverage: Spatial Western - Coverage: Temporal Earliest - 13/05/1961Coverage: Temporal Latest - 28/03/2011Distribution Northern - 33.80° SDistribution Southern - 51.22° SDistribution Eastern - 175.23° WDistribution Western - 162.60° EMaintenanceLayer Date Received - 07/02/2007Layer Date Loaded - 31/05/2007Layer Date Renewed - 14/01/2013Metadata Date Received - 07/02/2007Metadata Date Loaded - 31/05/2007Metadata Date Renewed - 14/01/2013Maintenance and Update Frequency - asNeededApproved for Promotion By - Information Management, Ministry for Primary IndustriesApproved for Promotion Date - 30/05/2007

Resetting a stochastic process has been shown to expedite the completion time of some complex task, such as finding a target for the first time. Here we consider the cost of resetting by associating a cost to each reset, which is a function of the distance travelled during the reset event. We compute the Laplace transform of the joint probability of first passage time $t_f$, number of resets $N$ and resetting cost $C$, and use this to study the statistics of the total cost. We show that in the limit of zero resetting rate the mean cost is finite for a linear cost function, vanishes for a sub-linear cost function and diverges for a super-linear cost function. This result contrasts with the case of no resetting where the cost is always zero. For the case of an exponentially increasing cost function we show that the mean cost diverges at a finite resetting rate. We explain this by showing that the distribution of the cost has a power-law tail with continuously varying exponent that depends on the resetting rate. The dataset is related to the upcoming paper John C. Sunil, Richard A. Blythe, Martin R. Evans and Satya N. Majumdar (in submission), 'The Cost of Stochastic Resetting'.

In 2021, 57 percent of the tier 1 data center capacity in the Asia-Pacific region was located in the Greater Tokyo area. In the Asia-Pacific region, tier 1 data centers, which provide lower cost infrastructure, were mostly located in Tokyo, Singapore, Sydney, and Hong Kong.

Open NABIS ApplicationThe purpose of this information is to show the Annual distribution of Lookdown DoryLineage - Document describing the data source used, history of changes, background information about the given layer, and referencesCreator - National Institute of Water and Atmospheric ResearchPublisher - Ministry for Primary IndustriesContributor - Museum of New Zealand Te PapaCustodian - Ministry for Primary IndustriesJurisdiction of Custodian - New ZealandRights - Crown Copyright Reserved.Subject - Annual fish distribution of Lookdown Dory within the waters around New ZealandSource - Multiple sources - see Lineage for detailsLanguage - EnglishRelation - Function - To ensure that fisheries are sustainably used within a healthy aquatic ecosystemLayer Type - biotaAudience - AllMandate - New Zealand Biodiversity StrategyProgress - CompletedMetadata - Compliant with NZGLS and ANZLICLayer InformationSpecies Common Name - Lookdown dorySpecies Scientific Name - Cyttus traversi Hutton 1872Species Maori Name - N/ASpecies Code - LDOEndangered Status - Not listedSeason - AnnualHabitat Type - Demersal offshoreMinimum Depth - 150 mMaximum Depth - 1100 mAccuracyAlert Level - NoneWarnings and Problems - NonePositional Accuracy - 100 kmAttribute Accuracy - Attribute data have been checked against Ministry for Primary Industries guidelines and no significant problems are known.Logical Consistency - Logical consistency has been checked against Ministry for Primary Industries guidelines and no significant problems are known.Completeness - Completeness of the information layer has been checked against independent descriptions of the distribution of Lookdown Dory and no significant problems are knownCertified/Refereed By - Malcolm Francis, National Institute of Water and Atmospheric ResearchCertification Date - 20/09/2010CoverageCoverage Name - New Zealand Exclusive Economic ZoneCoverage: Spatial Northern - 24.00° SCoverage: Spatial Southern - 58.00° SCoverage: Spatial Eastern - 167.00° WCoverage: Spatial Western - 157.00° ECoverage: Temporal Earliest - 02/09/1978Coverage: Temporal Latest - 30/09/2008Distribution Northern - 33.77° SDistribution Southern - 53.65° SDistribution Eastern - 174.30° WDistribution Western - 164.49° EMaintenanceLayer Date Received - 07/10/2003Layer Date Loaded - 10/11/2003Layer Date Renewed - 25/09/2012Metadata Date Received - 07/10/2003Metadata Date Loaded - 10/11/2003Metadata Date Renewed - 25/09/2012Maintenance and Update Frequency - asNeededApproved for Promotion By - Information Management, Ministry for Primary IndustriesApproved for Promotion Date - 30/08/2007

Open NABIS ApplicationThe purpose of this information is to show the Annual distribution of Smooth SkateLineage - Document describing the data source used, history of changes, background information about the given layer, and referencesCreator - National Institute of Water and Atmospheric ResearchPublisher - Ministry for Primary IndustriesContributor - Museum of New Zealand Te PapaCustodian - Ministry for Primary IndustriesJurisdiction of Custodian - New ZealandRights - Crown Copyright Reserved.Subject - Annual fish distribution of Smooth Skate within the waters around New ZealandSource - Multiple sources - see Lineage for detailsLanguage - EnglishRelation - Function - To ensure that fisheries are sustainably used within a healthy aquatic ecosystemLayer Type - biotaAudience - AllMandate - New Zealand Biodiversity StrategyProgress - CompletedMetadata - Compliant with NZGLS and ANZLICLayer InformationSpecies Common Name - Smooth skateSpecies Scientific Name - Dipturus innominatus (Garrick and Paul 1974)Species Maori Name - Pakaurua, WaewaeSpecies Code - SSKEndangered Status - Not threatenedSeason - AnnualHabitat Type - DemersalMinimum Depth - 0 mMaximum Depth - 1160 mAccuracyAlert Level - NoneWarnings and Problems - NonePositional Accuracy - 100 kmAttribute Accuracy - Attribute data have been checked against Ministry for Primary Industries guidelines and no significant problems are known.Logical Consistency - Logical consistency has been checked against Ministry for Primary Industries guidelines and no significant problems are known.Completeness - Completeness of the information layer has been checked against independent descriptions of the distribution of Smooth Skate and no significant problems are knownCertified/Refereed By - Malcolm Francis, National Institute of Water and Atmospheric ResearchCertification Date - 20/09/2010CoverageCoverage Name - New Zealand Exclusive Economic ZoneCoverage: Spatial Northern - 24.00° SCoverage: Spatial Southern - 58.00° SCoverage: Spatial Eastern - 167.00° WCoverage: Spatial Western - 157.00° ECoverage: Temporal Earliest - 01/01/1974Coverage: Temporal Latest - 30/09/2008Distribution Northern - 31.36° SDistribution Southern - 54.75° SDistribution Eastern - 174.15° WDistribution Western - 166.54° EMaintenanceLayer Date Received - 13/11/2003Layer Date Loaded - 21/11/2003Layer Date Renewed - 25/09/2012Metadata Date Received - 13/11/2003Metadata Date Loaded - 21/11/2003Metadata Date Renewed - 25/12/2012Maintenance and Update Frequency - asNeededApproved for Promotion By - Information Management, Ministry for Primary IndustriesApproved for Promotion Date - 30/08/2007

Microsatellite genotype data for Allocasuarina humilisGenotype data for 27 Allocasuarina humilis populations assayed for 8 microsatellite markersAllocasuarina humilis microsatellite data.csvNexus file for Allocasuarina humilis cpDNA haplotype dataDataset consists of 25 cpDNA haplotypes sampled from 27 populations of Allocasuarina humilis. Haplotypes were based on analysis of rpl32-trnL, trnQ-rps16 and trnG sequences. Haplotype names are consistent with those used in the Journal of Biogeography publication.Allocasuarina_humilis_nexus.txt