Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Summary of basic properties of empirical distributions that are interesting for data mining.

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 file presenting a pore file distribution on a SiC ceramic structure made by mercury intrusion porosimetry method

DOI retrieved: 2023

Percentage of iPhones assembled by contract manufacturers.

Degree centrality plotted on the y-axis with network nodes sorted from largest to smallest on the x-axis. Point colors reflect the networks nodes are in. (Red: data submission; blue: publication; purple: both) The highest degree nodes in 2017 and 2018 (red) may reflect a professionalization of data submission administration. The visualizations were generated with External dataset S1: data submission network graph: https://doi.org/10.7910/DVN/4QUAXY and publication network graph: https://doi.org/10.7910/DVN/YGWKLA.

An interactive chart illustrating Nigeria's growing agent network has become a crucial distribution channel for financial services

United States - Capacity Utilization: Utilities: Electric Power Generation, Transmission, and Distribution (NAICS = 2211) was 67.91770 % of Capacity in May of 2025, according to the United States Federal Reserve. Historically, United States - Capacity Utilization: Utilities: Electric Power Generation, Transmission, and Distribution (NAICS = 2211) reached a record high of 101.47330 in April of 1967 and a record low of 67.91770 in May of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Capacity Utilization: Utilities: Electric Power Generation, Transmission, and Distribution (NAICS = 2211) - last updated from the United States Federal Reserve on July of 2025.

A skewed exponential power distribution, with parameters defining kurtosis and skewness, is introduced as a way to visualize Type II error in normality tests. By varying these parameters a mosaic of distributions is built, ranging from double exponential to uniform or from positive to negative exponential; the normal distribution is a particular case located in the center of the mosaic. Using a sequential color scheme, a different color is assigned to each distribution in the mosaic depending on the probability of committing a Type II error. This graph gives a visual representation of the power of the performed test. This way of representing results facilitates the comparison of the power of various tests and the influence of sample size. A script to perform this graphical representation, programmed in the R statistical software, is available online as supplementary material.

Graph and download economic data for Industrial Production: Utilities: Electric Power Generation, Transmission, and Distribution (NAICS = 2211) (IPG2211SQ) from Q1 1972 to Q1 2025 about power transmission, distributive, electricity, IP, production, industry, indexes, and USA.

The AI Intelligent Visualization Gateway market is experiencing robust growth, driven by the increasing demand for real-time monitoring and control in power distribution systems. The market's expansion is fueled by the need for enhanced grid stability, improved operational efficiency, and proactive maintenance in medium, high, and ultra-high voltage distribution stations. Cloud and edge gateway solutions are playing pivotal roles, with edge gateways offering localized processing for immediate responses and cloud gateways providing comprehensive data analysis and remote management capabilities. The adoption of AI-powered analytics enables predictive maintenance, reducing downtime and optimizing resource allocation. Key players are focusing on developing advanced solutions with enhanced security features and seamless integration capabilities to cater to the growing market demand. We estimate the current market size (2025) to be around $500 million, considering the substantial investments in smart grid technologies and the rising adoption of AI across various sectors. A conservative CAGR of 15% is projected for the forecast period (2025-2033), indicating a significant market expansion to approximately $2 billion by 2033. This growth trajectory is primarily attributed to increasing government initiatives promoting smart grid infrastructure development and the ever-growing focus on improving energy efficiency and reliability. While the market enjoys significant growth potential, certain challenges exist. The high initial investment costs associated with implementing AI-powered gateway solutions and the complexity of integrating these systems into existing infrastructure could hinder adoption in some regions. Cybersecurity concerns related to data transmission and storage within the intelligent gateways also require meticulous attention. Nevertheless, the long-term benefits of improved operational efficiency, reduced maintenance costs, and enhanced grid resilience are likely to outweigh these challenges, driving continued market expansion. Geographic variations in adoption rates are expected, with North America and Asia-Pacific likely leading the market due to significant investments in smart grid technologies and a large number of established players. Continued innovation in AI algorithms, improved data analytics capabilities, and advancements in communication technologies will further stimulate market growth in the coming years.

Data for paper with title "Visualizing sub-organellar distribution of individual lipid species using correlative light and electron microscopy"

This dataset tracks annual distribution of students across grade levels in Visual & Performing Arts High School

This table contains 11 series, with data for years 1997/1998 - 2004/2005 (not all combinations necessarily have data for all years. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Primary intended markets (11 items: Total revenue, film, video and audio-visual distribution and videocassettes wholesaling companies; Total film, video and audio-visual distribution revenue, domestic and exports (foreign clients); Domestic market, film, video and audio-visual distribution revenue; Theatrical distribution revenue; ...).

About this dataset

Context

The dataset tabulates the median household income in Holland. It can be utilized to understand the trend in median household income and to analyze the income distribution in Holland by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Holland, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Holland, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Holland, IN
• Holland, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Holland median household income. You can refer the same here

This table contains 44 series, with data for years 1997/1998 - 2004/2005 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Company size (4 items: Total, all sizes of companies; Small size companies; Medium size companies; Large size companies), Primary intended markets (11 items: Total revenue, film, video and audio-visual distribution and videocassettes wholesaling companies; Total film, video and audio-visual distribution revenue, domestic and exports (foreign clients); Domestic market, film, video and audio-visual distribution revenue; Theatrical distribution revenue; ...).

Italy - Inequality of income distribution was 5.53 in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Italy - Inequality of income distribution - last updated from the EUROSTAT on June of 2025. Historically, Italy - Inequality of income distribution reached a record high of 6.27 in December of 2016 and a record low of 5.27 in December of 2023.

This table contains 12 series, with data for years 1997/1998 - 2004/2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Financial control (3 items: Total Canadian and foreign control; Canadian control; Foreign control) Principal activity (4 items: Total film, video and audio-visual distribution and videocasette wholesaling companies; Film, video and audio-visual distribution and videocassette wholesaling companies; Film, video and audio-visual distribution companies; Videocassette wholesaling companies).

This dataset comprises temporal dynamic graph sequences generated from power grid simulations focused on grid reconfiguration to enhance resilience. The simulations model failure propagation under varying conditions, with nodes assigned distinct failure probabilities. For each time step, the dataset captures the evolution of node states (functional or failed) and features critical to grid operations, such as pv_output, load_profile, load_dispatch, dg_output, loss, and voltage. Node types include sources, normal loads, and nodes with specific equipment like PVs, micro turbines, or shunt capacitors. The dataset is structured to support the training of dynamic graph neural networks, facilitating research on node feature prediction and edge dynamics under failure scenarios. Three distinct configurations are included, providing a robust foundation for modeling power grid resilience.