What 45,716 Meteorite Landings Look Like (Live NASA Data)

Disclosure: QuantumLayers, the analytics platform used to load the data and build every chart and statistic in this article, is our own product, operated by the team behind Lurika. This post relies on it throughout and doubles as a demonstration of what it can do. The underlying data is public and comes entirely from NASA’s open meteorite catalogue; QuantumLayers is the tool we used to analyze it.

Here is a fact to start with: many meteors flare green as they fall, because the nickel and magnesium in the rock glow that color under the heat of entry. Most burn up completely. The ones that survive the trip, humans have been quietly cataloguing for more than a thousand years. To see the whole record at once, we loaded NASA’s complete catalogue of known meteorite landings into a public dataset on QuantumLayers and charted it. The tally: 45,716 meteorites, with entries dating as far back as the year 860.

The dataset is marked public, and every chart below is drawn live: when this page loads, your browser calls QuantumLayers’ public chart endpoint and renders whatever the dataset currently holds, with no API key, no login, and no hard-coded numbers. Each figure also links to its own standalone ql-chart page on QuantumLayers.

A record twelve centuries long, and then it explodes

For most of history, a meteorite entered the record only when someone happened to witness it and keep the rock. That makes for a slow trickle: a handful a year through the 1800s, rising gently into the 1900s. Then something changes sharply after 1976. Counts leap from dozens a year into the hundreds and thousands. This is not the sky suddenly getting busier. It is us: organized expeditions began sweeping the blue ice of Antarctica and the world’s hot deserts, where dark meteorites stand out against pale ground and survive for millennia. The single biggest year in the catalogue is 2003.

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Figure 1: Meteorites recorded per year from 1800 onward, fetched live from QuantumLayers. Source: NASA.

▶ Open the live ql-chart on QuantumLayers

Nearly all of them are the same kind of rock

Meteorites get sorted into hundreds of classes, but the catalogue is overwhelmingly made of just a few. The codes that dominate, L6, H5, L5, H6 and H4, are all varieties of ordinary chondrite: stony leftovers from the birth of the solar system. Type L6 alone is close to a quarter of everything classified. The exotic material people picture, iron meteorites, fragments of Mars or the Moon, glassy achondrites, is all in here too, but it sits almost entirely inside the thin “Other” slice.

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Figure 2: Share of meteorites by class, top eight, fetched live. L and H codes are ordinary chondrites. Source: NASA.

▶ Open the live ql-chart on QuantumLayers

Seen falling, or found long after

Every meteorite in the catalogue is one of two kinds. A fall is one somebody actually watched streak down and then recovered. A find is one discovered on the ground later, sometimes centuries after it landed. Witnessed falls are vanishingly rare: only 1,107 of the tens of thousands on record were ever seen to arrive. Everything else was simply found. That one fact shapes almost everything else about the data, including where the meteorites appear to come from.

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Figure 3: Meteorites seen to fall versus found later, fetched live. Source: NASA.

▶ Open the live ql-chart on QuantumLayers

What the statistics reveal

Charts show the shape; the statistics pin it down. We ran QuantumLayers’ automated statistical analysis across every numeric column in the dataset, testing distributions, outliers and group effects. Three findings stood out.

The typical meteorite would fit in your palm. The median mass is just 48.8 grams, roughly the weight of a golf ball, and the middle half of all meteorites weigh between 11 and 288 grams. Yet the distribution is one of the most lopsided we have ever charted, with a skewness of 56.9, because a long tail of giants runs all the way up to 60,000,000 grams. That single 60 tonne outlier is Hoba, the largest known intact meteorite, still lying where it landed in Namibia. QuantumLayers flagged about 16% of masses as statistical outliers; here that is not bad data, it is the real span of things that fall out of the sky.

▶ Open the live ql-chart on QuantumLayers

Where a meteorite is found says more about us than about space. QuantumLayers’ analysis of variance found that whether a rock was seen to fall or merely found has a large, highly significant effect on its latitude (Cohen’s d ≈ 1.47, p < 0.0001). Witnessed falls are scattered across the inhabited latitudes where people are around to see them. Finds pile up at the extremes: the median latitude across the whole dataset sits near 72 degrees south, deep inside Antarctica. The rocks are not choosing where to land; the pattern is a map of where humans go looking.

The geography of discovery shifted over time. Year has a large, significant effect on latitude as well (η² ≈ 0.55, p < 0.0001). In plain terms, the map moved. For most of history meteorites were recorded across the populated northern hemisphere, and then, in step with that post-1976 surge, the record swung hard toward the far south as Antarctic collection took over. The when and the where of this dataset are telling the same story.

How this was built

No spreadsheets, no manual downloads. NASA’s catalogue was connected as a public dataset in QuantumLayers, which ingested and typed every column. Each chart above is rendered with Chart.js, populated at page load by a single POST to QuantumLayers’ ql_get_chart_data endpoint. Because the dataset is public, that call needs no authentication: the browser gets back a ready-to-draw Chart.js config and paints it. The “Open the live ql-chart” links point at QuantumLayers’ hosted ql-chart page, which renders the same visualization from nothing but URL parameters. Want a different cut, mass by class, falls by decade, a scatter of every landing site? The data is public; go explore.

Data: NASA open data, Meteorite Landings, compiled from The Meteoritical Society. Counts and masses reflect the dataset at request time.




This article is part of an ongoing series in which we analyze public datasets using QuantumLayers, our own data-analysis platform. Each post takes an openly available dataset, loads it into QuantumLayers, and shares what the charts and statistics reveal.