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

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1. Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population.

2. Here, we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the R package SDLfilter.

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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"

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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.

Blockchain data query: Falcon distribution data

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.

INSPIRE Priority Data Set (Compliant) - Alien species distribution

Since 2005, Fisheries and Oceans Canada has been collecting monitoring data for aquatic invasive species (e.g. https://open.canada.ca/data/en/dataset/8d87f574-0661-40a0-822f-e9eabc35780d, https://open.canada.ca/data/en/dataset/503a957e-7d6b-11e9-aef3-f48c505b2a29, https://open.canada.ca/data/en/dataset/8661edcf-f525-4758-a051-cb3fc8c74423). This monitoring data, as well additional occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict the present-day and future potential distributions of 12 moderate to high risk invasive species on Canada’s east and west coasts. Future distributions were predicted for 2075, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) has also been estimated by summing their occurrence probabilities. This data set includes the occurrence locations of each species, the present-day and future species distribution modeling results for each species, and the estimated species richness. This research has been published in the scientific literature(Lyons et al. 2020). Lyons DA, Lowen JB, Therriault TW, Brickman D, Guo L, Moore AM, Peña MA, Wang Z, DiBacco C. (In Press) Identifying Marine Invasion Hotspots Using Stacked Species Distribution Models. Biological Invasions Cite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Species distribution models and occurrence data for marine invasive species hotspot identification. Published: November 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1bbd5131-8b34-4245-b999-3b4c4259d74f

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.

Distribution of diabetic retinopathy data set.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8676029%2F094ad6be5c855e931da3721967ec333a%2Fminiature-figures-7129617_1280.jpg?generation=1689328141763429&alt=media" alt="">

The World Bank, the World Inequality Database (WID), and the Luxembourg Income Study (LIS) are all sources of data on poverty and inequality. They differ in terms of the income measure they use, the countries they cover, and the frequency of their data updates.

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Description of the Data and file structure

Presab_plus_climate_variables_PROTECTEDAREASONLY_Res10arcminutes.csv, _OpenHabitats.csv, _TropicalForests.csv

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Sharing/Access Information

Other data (i...

This data set is similar to gamma-ray spectroscopy data and is designed for machine-learning data analysis. This dataset is generated by computer.

Scientific Information about Dataset

In gamma-ray spectroscopy, data is generated by capturing the number of emissions within a specific channel range of the radiation emitted by the sample. In scientific data, the sample produces photopeaks exhibiting a Gaussian distribution when statistically examined. A Gaussian distribution (Normal distribution) is a probability distribution dependent on three parameters.

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• x0 : Standard deviation
• σ (sigma)∶ Width of the Gaussian Distribution
• N : Number of the occurrences of the event

for more information: https://en.wikipedia.org/wiki/Normal_distribution

In Gamma Ray Spectroscopy

• x0 = Photopeak location
• σ (sigma) ∝ Detector resolution
• N ∝ Activity of the sample

Co-60 Gamma-ray Spectroscopy Example https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7989877%2F8fad8994bf11dca48657dc3d3e21f628%2Fco60-repc.png?generation=1689324671029928&alt=media" alt="">

Dataset Content

cha_ : Number of radiations captured by the channel from 0 to 2000 with 10 intervals

• cha_5 : Number of radiations captured by the channel between 0-10
• cha_15: Number of radiations captured by the channel between 10-20
• cha_25: Number of radiations captured by the channel between 20-30 . . .
• cha_n: Number of radiations captured by the channel between (n-5)-(n+5)
• x0 : Standard deviation of the Gaussian Distribution
• sigma : Width of the Gaussian Distribution
• N: Number of the emission in the Gaussian Distribution range

Understanding the spatial distribution of individuals is essential for effective species conservation. We investigated the spatio-temporal distribution of Aquila chrysaetos (golden eagle) in Finland using nest surveys, citizen science observations, and environmental data from 1982 to 2021. We extended a popular N-mixture model to estimate population abundance while accounting for the number of recently hatched nestlings, the spatial distribution, and correcting for biases in the observation process, all integrated into a two-stage Bayesian hierarchical model. Results of our model aligned with the previously known north-oriented distribution of A. chrysaetos in Finland, supporting its applicability to A. chrysaetos and other species with similar ecological requirements and life-history traits. Furthermore, we observed the highest densities of successful nests near open landscapes, such as marshes and peat bogs, whereas dense forests, port areas, and areas of high human popula..., , # Multimodal data helps in identifying spatio-temporal patterns and habitat associations of Aquila chrysaetos (Golden Eagle) in Finland

Dataset DOI: 10.5061/dryad.41ns1rnsg

Description of the data and file structure

This repository contains all data, code, and results for the two-stage simulation process described in our study. Each stage is organized into a separate folder with standardized file formats to ensure reproducibility and clarity.

Files and variables

All datasets are structured as matrices, where rows represent spatial sites and columns represent time steps (years). The dataset consists of Stage_I_II.zip folder.

Stage I - Nest counting model

• CountNest.Rdata: Matrix containing the number of nests observed in survey data at each site i and time t.
• Data_1.RData: Matrix of environmental covariates extracted from the CORINE Land Cover map at each site i.
• FMI_Cov.RData: Average temperature data from the Finnish Me...,

About this dataset

Context

This list ranks the 3 cities in the Humphreys County, TN by Jordanian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Jordanian Population: This column displays the rank of city in the Humphreys County, TN by their Jordanian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Jordanian Population: The Jordanian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Jordanian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Humphreys County Jordanian Population: This tells us how much of the entire Humphreys County, TN Jordanian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.