QOREgeo

Quantum-Powered Spatial Intelligence for Python.

"The Pandas of Spatial Data Science."

QOREgeo is the only Python GIS library that works everywhere Python works — Windows, Mac, Linux, Raspberry Pi, AWS Lambda — with zero C++ dependencies and a roadmap to quantum spatial algorithms via QORE OS.

Why QOREgeo?

$ pip install geopandas
ERROR: Failed building wheel for GDAL
note: This error originates from a subprocess.
error: legacy-install-failure

GeoPandas requires GDAL, a C++ library that fails to install on Windows 40% of the time.
QOREgeo fixes the root cause — not the symptoms.

$ pip install qoregeo
Successfully installed qoregeo-1.0.0

Done in 2 seconds.

Installation

pip install qoregeo

Requirements: Python 3.8+ · Zero external dependencies

Quick Start

from qoregeo import GeoEngine

geo = GeoEngine()
geo.load("cities.csv")

# ── Distance ─────────────────────────────────────────
delhi  = (28.6139, 77.2090)
mumbai = (19.0760, 72.8777)

km = geo.distance(delhi, mumbai)
print(km)          # 1153.54

miles = geo.distance(delhi, mumbai, unit="miles")
print(miles)       # 716.84

# ── Compass bearing ───────────────────────────────────
direction = geo.bearing(delhi, mumbai)
print(direction)   # South-Southwest

degrees = geo.bearing(delhi, mumbai, as_degrees=True)
print(degrees)     # 202.4

# ── Geofencing ────────────────────────────────────────
zone = geo.buffer(delhi, radius=10, unit="km")

inside = geo.point_in_polygon(mumbai, zone)
print(inside)      # False

close  = (28.65, 77.22)
inside = geo.point_in_polygon(close, zone)
print(inside)      # True

# ── Filter by radius ──────────────────────────────────
nearby = geo.filter_by_radius(28.61, 77.20, radius=400)
print(nearby.count())  # 3

# ── Find nearest ──────────────────────────────────────
result = geo.nearest(delhi)
print(result["feature"]["properties"]["name"])  # Delhi
print(result["distance"])                        # 0.0

# ── Filter by property ────────────────────────────────
mh = geo.filter("state", "Maharashtra")
print(mh.count())  # 2

# ── Export map ────────────────────────────────────────
geo.map("output.html", title="India Cities")

# ── Heatmap ───────────────────────────────────────────
geo.heatmap("heat.html", intensity_col="population")

# ── Save data ─────────────────────────────────────────
geo.save("output.geojson")
geo.save("output.csv")

Method Chaining

Every method returns self, enabling Pandas-style pipelines:

from qoregeo import GeoEngine

(GeoEngine()
    .load("all_stores.csv")
    .filter("state", "Maharashtra")
    .filter_by_radius(19.07, 72.87, radius=100)
    .save("results.geojson")
    .map("results.html", title="Mumbai Stores")
)

API Reference

GeoEngine()

The single entry point for all operations.

Loading Data

geo.load(path, lat_col=None, lng_col=None, encoding='utf-8-sig')

Load from .csv or .geojson. Auto-detects latitude/longitude columns.

geo.load("data.csv")
geo.load("data.geojson")
geo.load("data.csv", lat_col="y_coord", lng_col="x_coord")

Auto-detected column names for latitude:
latitude, lat, y, ylat, lat_deg, geo_lat, point_lat

Auto-detected column names for longitude:
longitude, lng, lon, long, x, xlong, lng_deg, geo_lng, point_lng

geo.load_data(features)

Load raw GeoJSON Feature dicts (no file needed).

geo.load_data([
    {
        "type": "Feature",
        "geometry": {"type": "Point", "coordinates": [77.20, 28.61]},
        "properties": {"name": "Delhi"}
    }
])

Geometry

geo.distance(point_a, point_b, unit='km') → float

Haversine great-circle distance.

geo.distance((28.61, 77.20), (19.07, 72.87))           # 1153.54
geo.distance((28.61, 77.20), (19.07, 72.87), unit='m') # 1153540.0

geo.bearing(point_a, point_b, as_degrees=False) → str | float

Compass direction from A to B.

geo.bearing((28.61, 77.20), (19.07, 72.87))                    # 'South-Southwest'
geo.bearing((28.61, 77.20), (19.07, 72.87), as_degrees=True)   # 202.4

geo.buffer(center, radius, unit='km', num_points=64) → dict

Create a circular buffer polygon (GeoJSON Polygon).

zone = geo.buffer((28.61, 77.20), radius=10)            # 10 km radius
zone = geo.buffer((28.61, 77.20), radius=10, unit='m')  # 10 metre radius

geo.point_in_polygon(point, polygon) → bool

Test if a point is inside a polygon (ray-casting).

zone = geo.buffer((28.61, 77.20), radius=50)
geo.point_in_polygon((28.65, 77.22), zone)   # True
geo.point_in_polygon((19.07, 72.87), zone)   # False

geo.nearest(point, unit='km') → dict

Find the nearest loaded feature to a point.

Returns: {"feature": ..., "distance": float, "index": int}

result = geo.nearest((28.61, 77.20))
print(result["distance"])                         # 0.12
print(result["feature"]["properties"]["name"])    # 'Delhi'

Filtering

geo.filter(column, value) → GeoEngine

Filter by property value (case-insensitive string match).

mh = geo.filter("state", "Maharashtra")

geo.filter_by_radius(lat, lng, radius, unit='km') → GeoEngine

Filter features within a radius. Results sorted nearest-first. Injects _distance property.

nearby = geo.filter_by_radius(28.61, 77.20, radius=400)
for f in nearby.get_features():
    print(f["properties"]["name"], f["properties"]["_distance"])

Data Access

Visualisation

geo.map(output_path, title, zoom, center) → GeoEngine

Interactive Leaflet.js marker map, saved as a standalone HTML file.

geo.map("output.html")
geo.map("output.html", title="My Cities", zoom=6)

geo.heatmap(output_path, title, intensity_col, zoom, center) → GeoEngine

Density heatmap with optional intensity weighting.

geo.heatmap("heat.html")
geo.heatmap("heat.html", intensity_col="sales_volume")

Saving

geo.save(path) → GeoEngine

Save to .geojson, .json, or .csv.

geo.save("output.geojson")
geo.save("output.csv")

Error Messages

QOREgeo errors are designed to teach, not confuse:

❌  QOREgeo — Column Not Found
────────────────────────────────────────────────
File: 'stores.csv'
Could not find a 'lat' column.

Columns in your file:
    'store_id', 'latitude', 'longitude'

Fix it:
    geo.load('stores.csv', lat_col='latitude', lng_col='longitude')

Real-World Examples

Delivery Zone Analysis

from qoregeo import GeoEngine

geo = GeoEngine().load("customers.csv")

branches = {
    "Delhi HQ":   (28.6139, 77.2090),
    "Mumbai Hub": (19.0760, 72.8777),
}

for name, coords in branches.items():
    nearby = geo.filter_by_radius(*coords, radius=50)
    print(f"{name}: {nearby.count()} customers within 50 km")

Restaurant Geofencing

from qoregeo import GeoEngine

geo = GeoEngine()
restaurant = (28.6315, 77.2167)   # Connaught Place, Delhi
zone = geo.buffer(restaurant, radius=5)   # 5 km delivery zone

for order in incoming_orders:
    inside = geo.point_in_polygon(order["location"], zone)
    status = "DELIVER" if inside else "TOO FAR"
    print(status)

Ambulance Dispatch

from qoregeo import GeoEngine

geo = GeoEngine().load("hospitals.csv")
patient = (28.6139, 77.2090)

result = geo.nearest(patient)
print(f"Nearest hospital: {result['feature']['properties']['name']}")
print(f"Distance: {result['distance']} km")

# Show nearby hospitals on a map
geo.filter_by_radius(*patient, 5).map("nearby_hospitals.html")

Roadmap

Contributing

1. Fork the repo at github.com/bosekarmegam/qoregeo
2. Create a branch: git checkout -b feature/your-feature
3. Add tests for new functionality
4. Run pytest — all tests must pass
5. Open a pull request

License

MIT © 2025 ArcGX TechLabs Private Limited
Built by Suneel Bose K

Links

• 📦 PyPI: pypi.org/project/qoregeo
• 🐙 GitHub: github.com/bosekarmegam/qoregeo
• 🐛 Issues: github.com/bosekarmegam/qoregeo/issues
• ✉️ Contact: suneelbosekarmegam@gmail.com