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

BackgroundThe typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to vary considerably across Diagnosis Related Groups (DRGs), and to contain markedly high values, in significant proportions. These very long stays are often considered outliers, and thin-tailed statistical distributions are assumed. However, resource consumption and planning occur at the level of medical specialty departments covering multiple DRGs, and when considered at this decision-making scale, extreme LOS values represent a significant component of the distribution of LOS (the right tail) that determines many of its statistical properties.ObjectiveTo build actionable statistical models of LOS for resource planning at the level of healthcare units.MethodsThrough a study of 46, 364 electronic health records over four medical specialty departments (Pediatrics, Obstetrics/Gynecology, Surgery, and Rehabilitation Medicine) in the largest hospital in Thailand (Siriraj Hospital in Bangkok), we show that the distribution of LOS exhibits a tail behavior that is consistent with a subexponential distribution. We analyze some empirical properties of such a distribution that are of relevance to cost and resource planning, notably the concentration of resource consumption among a minority of admissions/patients, an increasing residual LOS, where the longer a patient has been admitted, the longer they would be expected to remain admitted, and a slow convergence of the Law of Large Numbers, making empirical estimates of moments (e.g. mean, variance) unreliable.ResultsWe propose a novel Beta-Geometric model that shows a good fit with observed data and reproduces these empirical properties of LOS. Finally, we use our findings to make practical recommendations regarding the pricing and management of LOS.

REVISED 1/2/2019. SEE UPDATE LINK BELOW. This database contains unit cost information for different components that may be used to integrate distributed photovotaic D-PV systems onto distribution systems. Some of these upgrades and costs may also apply to integration of other distributed energy resources DER. Which components are required and how many of each is system-specific and should be determined by analyzing the effects of distributed PV at a given penetration level on the circuit of interest in combination with engineering assessments on the efficacy of different solutions to increase the ability of the circuit to host additional PV as desired. The current state of the distribution system should always be considered in these types of analysis. The data in this database was collected from a variety of utilities PV developers technology vendors and published research reports. Where possible we have included information on the source of each data point and relevant notes. In some cases where data provided is sensitive or proprietary we were not able to specify the source but provide other information that may be useful to the user e.g. year _location where equipment was installed. NREL has carefully reviewed these sources prior to inclusion in this database. Additional information about the database data sources and assumptions is included in the Unit_cost_database_guide.doc file included in this submission. This guide provides important information on what costs are included in each entry. Please refer to this guide before using the unit cost database for any purpose.

The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Cyclone Mortality Risks and Distribution is a 2.5 minute grid of global cyclone mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality loss. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of cyclone hazard are obtained from the Global Cyclone Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of an approximately equal number of grid cells, providing a relative estimate of cyclone-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

ABSTRACT We build upon an already known but scarcely developed feature of growth theory: the importance of asset distribution in an aggregate production function. We elaborate on a simple model of two individuals, and then generalize its deductions to an extended model of n agents, concluding that perfectly distributed productive capital leads to positive and optimum long-run “endogenous” growth. Recent and classical empirical literature on the topic suggests this interpretation. In addition, we find exploratory panel data evidence that supports our theory of growth and distribution in a set of Latin American countries.

INSPIRE Priority Data Set (Compliant) - Alien species distribution

Returns of the closing price of the Colcap, Bovespa and S&P index and Analysis program for MTAR model of the paper "Bayesian estimation of a multivariate TAR model when the noise process follows a Student-t distribution"

This dataset contains the optimization results using resilience surrogate measures in four chosen cases (i.e., HAN, FOS, PES, MOD). It also includes mechanical reliability calculation results of optimized network layouts obtained by the surrogate measures.

Distribution of diabetic retinopathy data set.

Reptiles make up one-third of tetrapods, however they are often omitted from global conservation analyses. Understanding the determinants of reptile distribution is the foundation for reptile conservation research. We assembled a dataset on the distribution of 231 reptile species (nearly 50% of recorded species in China). We then investigated the association of species range filling (the proportion of observed ranges compared to species potential climate distributions) with climate, range size, topography, and human activity, using three regression methods. At the species level, we found the most primary factors influencing the recent distribution pattern of reptiles across China were the mean annual precipitation (MAP) and the mean annual temperature (MAT). In contrast, human activity came in last. Similarly, at aspatial level, MAP and MAT were still the most important factors. Geographically, the south and east of China support the highest reptile diversity, partially due to high prec..., 614 pieces of literature for reptile occurrence records in China Results of this analysis, , # Data from: Precipitation and temperature primarily determine the reptile distributions in China

https://doi.org/10.5061/dryad.x0k6djhtp

Description of the data and file structure

The first excel offer 614 pieces of literature for reptile occurrence records in China, the second excel offer results of analysis. Our data description is in excel files.

'NA' value in the results represent so that there's not not applicable.

Sharing/Access information

The data that support the findings of this study are also available in supplementary material of this paper, refer 10.1111/ecog.07005.

Profiles of salinity, temperature, turbidity, and particle size distribution were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center in South San Francisco Bay. Data were collected at depth intervals ranging between 0.5 and 2 m (depending on total water depth); sensors remained at each depth for 1-2 minutes. Each profile was collected from surface to bed, and the near-surface region was sampled again at the end of the profile to check steady-state conditions. Profiles were collected on 4 days: June 22, July 21, and December 3 of 2021, and on January 4, 2022 (UTC). Data files are grouped by season (summer or winter) and by instrument (CTD or LISST). No LISST data were collected in the winter. Users are advised to assess data quality carefully.

Sample of MATs for research into MAT communication with Communities

An annotated check-list is given of Crinoidea species occurring deeper than 2000 m in the seas bordering Europe.

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'.

Context

Our target was to predict gender, age and emotion from audio. We found audio labeled datasets on Mozilla and RAVDESS. So by using R programming language 20 statistical features were extracted and then after adding the labels these datasets were formed. Audio files were collected from "Mozilla Common Voice" and “Ryerson AudioVisual Database of Emotional Speech and Song (RAVDESS)”.

Content

Datasets contains 20 feature columns and 1 column for denoting the label. The 20 statistical features were extracted through the Frequency Spectrum Analysis using R programming Language. They are: 1) meanfreq - The mean frequency (in kHz) is a pitch measure, that assesses the center of the distribution of power across frequencies. 2) sd - The standard deviation of frequency is a statistical measure that describes a dataset’s dispersion relative to its mean and is calculated as the variance’s square root. 3) median - The median frequency (in kHz) is the middle number in the sorted, ascending, or descending list of numbers. 4) Q25 - The first quartile (in kHz), referred to as Q1, is the median of the lower half of the data set. This means that about 25 percent of the data set numbers are below Q1, and about 75 percent are above Q1. 5) Q75 - The third quartile (in kHz), referred to as Q3, is the central point between the median and the highest distributions. 6) IQR - The interquartile range (in kHz) is a measure of statistical dispersion, equal to the difference between 75th and 25th percentiles or between upper and lower quartiles. 7) skew - The skewness is the degree of distortion from the normal distribution. It measures the lack of symmetry in the data distribution. 8) kurt - The kurtosis is a statistical measure that determines how much the tails of distribution vary from the tails of a normal distribution. It is actually the measure of outliers present in the data distribution. 9) sp.ent - The spectral entropy is a measure of signal irregularity that sums up the normalized signal’s spectral power. 10) sfm - The spectral flatness or tonality coefficient, also known as Wiener entropy, is a measure used for digital signal processing to characterize an audio spectrum. Spectral flatness is usually measured in decibels, which, instead of being noise-like, offers a way to calculate how tone-like a sound is. 11) mode - The mode frequency is the most frequently observed value in a data set. 12) centroid - The spectral centroid is a metric used to describe a spectrum in digital signal processing. It means where the spectrum’s center of mass is centered. 13) meanfun - The meanfun is the average of the fundamental frequency measured across the acoustic signal. 14) minfun - The minfun is the minimum fundamental frequency measured across the acoustic signal 15) maxfun - The maxfun is the maximum fundamental frequency measured across the acoustic signal. 16) meandom - The meandom is the average of dominant frequency measured across the acoustic signal. 17) mindom - The mindom is the minimum of dominant frequency measured across the acoustic signal. 18) maxdom - The maxdom is the maximum of dominant frequency measured across the acoustic signal 19) dfrange - The dfrange is the range of dominant frequency measured across the acoustic signal. 20) modindx - the modindx is the modulation index, which calculates the degree of frequency modulation expressed numerically as the ratio of the frequency deviation to the frequency of the modulating signal for a pure tone modulation.

Acknowledgements

Gender and Age Audio Data Souce: Link: https://commonvoice.mozilla.org/en Emotion Audio Data Souce: Link : https://smartlaboratory.org/ravdess/

MASTERS THESIS: In 1982 (the researcher) initiated a study to examine any differences in the distribution and abundance between mountain and Tahoe sucker and to determine how these two similar species coexist in Sagehen Creek. Early in the study (she) noticed that the relative abundance of these species had shifted dramatically when compared to earlier reports. IN addition to Sagehen Creek, four nearby streams were surveyed in 1982 to locate both species. At all sites mountain sucker were either absent or rare and only 10 individuals were eventually collected. The apparent scarcity of mountain sucker was perplexing; early records had shown mountain sucker were not only present and abundant but were also more numerous than the associated Tahoe sucker at these sites. (She) expanded the study to address the following objectives: 1. To determine if significant shifts in the relative abundance of suckers have occurred in Sagehen Creek since early surveys, 2. To determine if similar shifts have occurred elsewhere, 3. To distinguish differences in the distribution and abundance between the two species of sucker in Sagehen Creek where both species still exist, and 4. To determine how mountain and Tahoe sucker manage to coexist in a relatively small segment of stream.

A sieve analysis (or gradation test) is a practice or procedure commonly used in civil engineering to assess the particle size distribution (also called gradation) of a granular material.

As part of the Sediment Analysis and Geo-App (SAGA) a series of data processing web services are available to assist in computing sediment statistics based on results of sieve analysis. The Calculate Percentile service returns one of the following percentiles: D5, D10, D16, D35, D50, D84, D90, D95.

Percentiles can also be computed for classification sub-groups: Overall (OVERALL), <62.5 um (DS_FINE), 62.5-250um (DS_MED), and > 250um (DS_COARSE)

Parameter #1: Input Sieve Size, Percent Passing, Sieve Units.

• Semi-colon separated. ex: 75000, 100, um; 50000, 100, um; 37500, 100, um; 25000,100,um; 19000,100,um
• A minimum of 4 sieve sizes must be used. Units supported: um, mm, inches, #, Mesh, phi
• All sieve sizes must be numeric

Parameter #2: Percentile

• Options: D5, D10, D16, D35, D50, D84, D90, D95

Parameter #3: Subgroup

• Options: OVERALL, DS_COARSE, DS_MED, DS_FINE
• The statistics are computed for the overall sample and into Coarse, Medium, and Fine sub-classes
• Coarse (> 250 um) DS_COARSE
• Medium (62.5 – 250 um) DS_MED
• Fine (< 62.5 um) DS_FINE
• OVERALL (all records)

Parameter #4: Outunits

• Options: phi, m, um

This service is part of the Sediment Analysis and Geo-App (SAGA) Toolkit.

Looking for a comprehensive user interface to run this tool?
Go to SAGA Online to view this geoprocessing service with data already stored in the SAGA database.

This service can be used independently of the SAGA application and user interface, or the tool can be directly accessed through http://navigation.usace.army.mil/SEM/Analysis/GSD

The structure of liquid foam is generally considered random and isotropic. However, when foam flows past a set of wires, an inhomogeneous liquid fraction distribution, or layering, can be observed within the bulk. This dataset presents neutron radiography data of foam flowing past a set of thin metal wires. During the experiments, the gas flow rate and bubble size were varied. Additionally, a dataset for foam flow past a single wire is included for reference.

The folder includes initial data for the manuscript "Generating structured foam via flowing through a wire array".

Folder includes:

01_scripts scripts used for the data processing
02_rawdata Initial neutron imaging data (.tif images)
03_evaluation folder with MATLAB scripts used for data analysis

LABBOOK Experimental labbook explaining the experimental sequence.
Protocol The Neutron imaging protocol with the data of neutron source and image resolution

The data processing is shown for the O1 bubble generator. It includes:
1. MASK_... script used to define the cell walls and determine the mask, used further for the liquid fraction calculation.
2. N13_INIT... scritps to define normalised image, which further used to determine liquid fraction distribution
3. POST_BOT... scripts used to postprocess the data: define Liquid fraction distribution and DFT of those distributions.

Note:

1. The data were analysed at two positions: bottom (0) and top (100), meaining at the wire grid and 100 mm downstream the grid. To this end, mask should be calculated also for the top part of the nozzle, if needed, as shown in the presented examples.

2. The data for the empty cell were calculated for the foam flow through the cell with a single thin wire. The data were extracted
in the ROI before the wire (run 553-557).

3. Data processing was performed as suggested in https://doi.org/10.1371/journal.pone.0210300