Settlement context, globally.

Why DEGURBA?

DEGURBA considers not only population density but also connected cells, cluster population, distance, permanent land, and smoothing. That makes it useful for settlement context, urban/rural stratification, sampling, service-area analysis, and contextual map styling. This library uses 1-km grid as the master source.

Results

class_share is the dominant source-class area share inside the T3 cell—not confidence. mixed marks a source boundary. A finer triangle represents that boundary; it does not improve on the source's 1 km analytical resolution.

The Level-1 source hierarchy puts water in its residual “rural grid cell” domain. Use surface to distinguish water, land, mixed coast, and unknown/nodata.

Python

pip install settlementcheck

from settlementcheck import SettlementCheck
sc = SettlementCheck()
r = sc.classify(24.7536, 59.4370)
print(r.settlement_class)
# sc.check(...) lazily reads optional local details.

JavaScript

npm install settlementcheck

import { SettlementCheck } from "settlementcheck";
const sc = await SettlementCheck.fromFile();
const r = sc.classify(24.7536, 59.4370);
// await sc.checkAsync(...) loads optional details.