Multi-ring buffers, K‑nearest neighbors, full distance matrices, density heatmaps & interactive map. All computed live with great-circle precision, entirely in your browser.
Multi-ring buffers, K‑nearest neighbors, full distance matrices, density stats & interactive map.
Real computed figures from your coordinates — no simulation.
Proximity analysis is a core spatial technique that measures how close geographic features sit to one another. In a GIS, every feature is stored as a point, line, or polygon, and proximity analysis answers a simple but powerful question: what lies within a given distance of what? A retailer might want every competitor store within ten kilometers of a proposed site. An ecologist might want how far each nesting site sits from the nearest water source. A city planner might need every parcel within a flood buffer along a river. All three are proximity problems, solved with the same underlying spatial math.
Buffers are zones drawn at a fixed distance around a feature. Buffer a point and you get a circle; buffer a line and you get a corridor; buffer a polygon and you get an expanded outline. This tool supports multiple concentric buffer rings around any reference point, so you can see how points fall into different distance bands at a glance — e.g. 5 km, 10 km, 20 km.
KNN analysis identifies, for every point in a dataset, the K closest other points and their distances. It powers logistics routing, retail cannibalization studies, and epidemiological cluster detection. The average nearest-neighbor distance across the dataset also produces a clustering signal: values much smaller than random suggest clustering, while larger values suggest dispersion.
Density converts a count of features into a rate per unit area, making it possible to compare regions of different sizes fairly. Counting ten crime reports in a one‑block radius means something very different from ten spread across a county. This tool computes points per square kilometer inside a search radius around any point, using the actual circular area formula (π × r²) rather than a flat‑grid estimate.
A distance matrix is a square table showing the pairwise great‑circle distance between every point and every other point. It is the foundation for clustering, route optimization, and spatial autocorrelation tests. This tool generates a full or triangular matrix that you can copy or export directly.
Every calculation runs client‑side with production‑grade spatial formulas.
Draw 1–5 concentric buffer rings around any point and see exactly which points fall into each band.
Configure K from 1–20 and get the closest neighbors with exact great‑circle distances for every point.
Full pairwise distance matrix with triangular option — the foundation for clustering and routing.
Points‑per‑km² computed from real circular area math, with color‑coded visualization.
Batch‑upload points from any CSV with header detection. No manual entry for large datasets.
Copy results or download as CSV, JSON, or GeoJSON — ready for QGIS, ArcGIS, or your own scripts.
All computation happens locally in your browser. Your coordinates are never uploaded anywhere.
Switch instantly between a map‑room dark theme and a bright paper‑map light theme.
Four steps from raw coordinates to a finished spatial answer.
Enter a label + latitude + longitude for each location — or import a CSV in one click.
Pick NN, buffer, density, or distance matrix. Configure K, radius, or ring distances.
The tool applies Haversine geometry live. Buffer rings, NN connections, and density colors render instantly on the map.
Copy the output, or download as CSV, JSON, or GeoJSON to drop into your GIS or spreadsheet.
Buffer asks "which points are within a fixed distance of a reference point?" K‑nearest neighbor asks "for each point, which K other points are closest, and how far are they?" Buffer is threshold‑based; KNN is rank‑based.
Every distance comes from the Haversine formula, which computes great‑circle distance on a sphere approximating Earth. It's accurate to within ~0.1% for most real‑world distances and is the same method used in professional GIS.
Yes — use the CSV import button. The tool reads latitude/longitude columns automatically (it looks for "lat", "latitude", "lon", "longitude" in headers). There's no server upload; everything stays in your browser.
CSV (tabular), JSON (structured), and GeoJSON (standard GIS interchange). GeoJSON is ideal for loading into QGIS, ArcGIS, or any web‑mapping library.
Proximity analysis is one of dozens of free geospatial and developer utilities built for real, live results.