The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The United States recorded a trade deficit of 78.31 USD Billion in July of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Urban centres shape societies, but there is no systematic global approach analysing how countries are organized around multiple urban centres. We advance understanding by delineating 6,100 city–regions worldwide using a novel framework classifying 30,000 urban centres into four tiers and mapping their nested catchment areas based on travel time accessibility. Our results show extensive interconnectedness among urban centres and with their surrounding areas, with 3.2 billion people having physical access to multiple tiers with 1-hour travel time, rising to 4.7 billion for 3-hours travel time. Importantly, among people living in or near towns and small cities, access to intermediate cities is far greater than to large cities. This highlights the essential role intermediate cities play in engaging surrounding populations. For the first time, city–regions around the world are identified systematically, showing great diversity in how societies are organized across urban tiers, depending on geography and national income. The associated spatial dataset is a powerful tool for regional planning, economic development, and natural resource management.

Motivation

Estimating the properties of galaxies, and even where they are, is a challenging process. The Rubin Observatory, a sky survey telescope located in Chile, will once it becomes operational image tens of billions of astronomical objects, the vast majority of which will be galaxies that have never been imaged before. Analyses of these data will require sophisticated methodologies, ones that will allow us to first determine where the galaxies are (i.e., how far away they are), and then conditional on the distance, how massive they are. Given galaxy distance and mass data, we can test theories of how the Universe evolves, by comparing simulated galaxy data with these data.

The Buzzard-V1.0 simulation was used to generate a realistic sample of Rubin Observatory data. In this dataset are measurements for 111,172 galaxies. Developers used these data to benchmark, e.g., methods for estimating galaxy distance. Here, we can assume the distance has been estimated well, and use these data to try to model galaxy mass as a function of brightness and distance.

Data

The dataset contains measures of magnitude and magnitude uncertainty in six astronomical bands (u for ultraviolet, g for green, r for red, i for infrared, and z and y for two additional infrared bands). Magnitude is a logarithmic measure of brightness, with an increase of 5 representing a decrease in brightness by a factor of 100, and with a value of zero being represented (roughly) by how the star Vega appears in the night sky. In addition, there is a redshift measured for each galaxy; it represents by how much light from the galaxy is stretched (by the expansion of Universe) as it travels to us. Thus higher redshifts represent larger distances. The last measurement is log.mass, which is the base-10 logarithm of the galaxy stellar mass in units of solar mass; for instance, log.mass = 10 means that the galaxy has a mass 10 billion times that of the Sun.

Variable Description

• u Galaxy magnitude in Rubin u band (320.5-393.5 nm)
• g Galaxy magnitude in Rubin g band (401.5-551.9 nm)
• r Galaxy magnitude in Rubin r band (552.0-691.0 nm)
• i Galaxy magnitude in Rubin i band (691.0-818.0 nm)
• z Galaxy magnitude in Rubin z band (818.0-923.5 nm)
• y Galaxy magnitude in Rubin y band (923.8-1084.5 nm)
• u.err Uncertainty for u-band magnitude
• g.err Uncertainty for g-band magnitude
• r.err Uncertainty for r-band magnitude
• i.err Uncertainty for i-band magnitude
• z.err Uncertainty for z-band magnitude
• y.err Uncertainty for y-band magnitude
• log.mass Galaxy stellar mass (log-base-10 solar masses)
• redshift Galaxy redshift

Questions

As noted above, the idea here is to learn a statistical association between measures of magnitude and distance, and galaxy mass.

One wrinkle here that analysts can exploit is that the data contain standard error estimates for the magnitudes (though not for redshift, for which, in practice, the error would be ).

References

Schmidt, Malz, Soo, Almosallam, Brescia, Cavuoti, Cohen-Tanugi, Connolly, DeRose, Freeman, Graham, Iyer, Jarvis, Kalmbach, Kovacs, Lee, Longo, Morrison, Newman, Nourbakhsh, Nuss, Pospisil, Tranin, Wechsler, Zhou, Izbicki, (The LSST Dark Energy Science Collaboration). “Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)”. Monthly Notices of the Royal Astronomical Society 499, December 2020, pages 1587–1606. https://doi.org/10.1093/mnras/staa2799

Acknowledgement

Foto from unsplash

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