Random Location Generator

Generate a random place on Earth — anywhere on the sphere, on land only, or inside a specific country. Free tool. Mathematically honest equal-area uniform distribution, batches up to 100 points, optional seed for reproducibility, CSV and GeoJSON export.

Random points done correctly — equal-area on the sphere

Most "random location generator" sites use the simplest possible math: pick a random latitude between -90° and +90°, pick a random longitude between -180° and +180°. Done. This is wrong.

Earth is a sphere, not a flat rectangle. The area of a 1° latitude × 1° longitude cell shrinks as you approach the poles, by a factor of cos(latitude). If you sample latitude uniformly, you generate points proportionally to lines of latitude — but each line at high latitude wraps a much smaller circumference than the equator. Result: random points pile up near the North and South Poles, with proportionally too few near the equator. Common online tools have this exact bug; a "random place on Earth" produced by a naive generator is roughly 4× more likely to land at 70°N latitude than at the equator.

The fix is to sample sin(latitude) uniformly, not latitude itself. The formula: lat = asin(2u − 1) × 180/π where uis uniform in [0, 1]. The asin compensates for the cos(lat) area element. The result is genuinely uniform by surface area on the sphere — and it is the math we use.

How to use the random location generator

Five steps. Defaults are sensible — most users only set the mode and tap Generate.

1. Pick a mode. Anywhere on the sphere returns a uniformly distributed random point on Earth — about 71% of which will land in the ocean. Land only re-rolls until the point falls inside a country boundary. In a country lets you pick any of 195 countries and generates a point inside its polygon.
2. Set how many points. The default is 1; the maximum is 100 per batch. For travel-planning style use cases (one random destination), 1 is right; for dataset seeding or geocaching challenges, 5–25 is typical; for stress-testing and analysis, go up to 100.
3. Optionally set a seed. A seed makes the result reproducible: the same seed + same settings always produce the same points. Useful for sharing ("here are the 5 random spots, seed = trip-2026"), for testing, or for repeatable runs in research and education. Leave the seed empty for true Math.random output.
4. Generate. Tap the green Generate button. Pins drop on the map; the result list shows each location with country flag, country name, and lat/lng. The map auto-frames all pins.
5. Re-roll, copy, or export. Tap "Re-roll" to generate another batch with the same settings (uses a fresh internal seed). Tap "Reveal place" on any card to fetch the human-readable place name from OpenStreetMap. Click any pin on the map to see its details. Export the whole batch as CSV or GeoJSON.

The three modes — and when to use each

What people use this tool for

Seven recurring patterns we see in the analytics.

How the tool actually works

1. Sphere sampling

Two uniform random numbers u, v ∈ [0, 1]. Latitude: lat = asin(2u − 1) × 180/π (range -90° to +90°, weighted equal-area). Longitude: lng = -180 + v × 360 (uniform). The math is one of the standard Marsaglia methods for sphere-uniform sampling.

2. Land-only filter (rejection sampling)

Generate a sphere-uniform point. Run a point-in-polygon test against all 241 country features in Natural Earth’s 1:50,000,000 cultural-vectors set. If the point falls inside a country polygon, accept it. If not (ocean / unmapped), retry up to 250 times. Empirically the first attempt succeeds ~29% of the time, so the average count of attempts for a single accepted point is about 3.4. Even at 100 batch size, total runtime is well under a second.

3. In-a-country sampling

Compute the bounding box of the country polygon (minimum and maximum lat/lng). Sample a point inside the bounding box using the same equal-area latitude transform within the latitude band. If the point falls inside the country polygon, accept; otherwise reject and retry up to 600 times. For most countries, 1–3 attempts succeed; for awkwardly shaped countries (Chile, Norway, Indonesia), 5–15 is typical.

4. Pseudorandom number generation

With no seed: native Math.random() (different on every page-load). With a seed: we hash the seed string with the 53-bit cyrb53 hash, then feed the result into mulberry32 — a small, statistically robust 32-bit PRNG. Same seed + same settings = same points, every time. Different seeds produce statistically independent point sets.

5. Reverse geocoding (on demand)

We do not auto-call OpenStreetMap Nominatim for every result — that would burn the public rate limit on batch generations. Instead, you can tap "Reveal place" on any single card to enrich that one point with a human-readable name (city, region, nearest feature). The country flag and country name are always shown, since those come from the polygon test and are free.

SimpleMapLab vs other random location generators

Honest comparison with the major free random location tools online. Each tool wins different scenarios — the table is a feature checklist, not a value judgement.

The pole bias bug — why most random location tools are wrong

A test we ran on three popular "random GPS coordinates" sites: generated 10,000 points each, plotted them on a globe. The naive sites all show the same pattern — points pile up in two visible blobs at the North and South Poles, with the equatorial belt looking suspiciously empty.

The math reason: if you pick latitude uniformly, the probability of being in the latitude band [70°, 80°] is the same as [0°, 10°] — namely 1/18. But the surface area of [70°, 80°] is dramatically smaller than [0°, 10°] (by a factor of 1/cos(70°) − 1/cos(80°) ≈ 5.7×). So the same number of points are squeezed into 5.7× less area near the pole — much higher density.

Visually: a "random place on Earth" produced by a naive generator overrepresents Russia (the country closest to the pole) by a factor of ~3 vs its actual surface area share. Antarctica is wildly overrepresented. The Pacific Ocean is underrepresented because most of it is near the equator.

Our fix is the asin(2u − 1)transformation. After this transformation, a random point is genuinely uniform over the sphere’s surface area — Russia comes up in proportion to its actual share, the equator gets its fair turn, and the South Pacific is honestly represented.

Which country comes up most often in "Land only" mode?

For a uniformly distributed point on land, the probability of any country is its share of the total land area. The biggest land areas (and their approximate land-area share):

• Russia — 17.1 million km² (≈11.4% of all land)
• Antarctica — 14.0 million km² (≈9.4%, but excluded from most "country" lookups since it is governed by treaty, not as a country)
• Canada — 9.98 million km² (≈6.7%)
• USA — 9.83 million km² (≈6.6%)
• China — 9.60 million km² (≈6.4%)
• Brazil — 8.51 million km² (≈5.7%)
• Australia — 7.69 million km² (≈5.1%)
• India — 3.29 million km² (≈2.2%)
• Argentina — 2.78 million km² (≈1.9%)
• Kazakhstan — 2.72 million km² (≈1.8%)

Generate 100 land-only points, and on average ~50 will fall in those top ten countries. The remaining ~50 are spread across the other 185 countries. Vatican City (0.49 km²) has a 0.0000003% chance per random pick — you would need to generate ~300 million points before one lands inside it.

Related tools and resources

For a single point picked by area or population: Find Cities in Radius and Find ZIP Codes in Radius. For the country at any GPS point: What Country Am I In?. For elevation at a random point: Elevation Finder. For time zone at a random point: Time Zone Finder. For drawing distance circles around a random pick: Map Radius Tool. For the antipode of a random point: Antipode Finder. For coloring a map with a random country pick: Color a Map.

Frequently asked questions

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