A Longitudinal Multi‑proxy Geospatial Classification of Peri‑urban Transitions Across Community Health Units in Coastal Kenya

Why It Matters

Accurate peri‑urban classification enables targeted health resource allocation and improves surveillance in fast‑growing African regions. Combining satellite‑derived metrics overcomes single‑source limitations, informing policy decisions.

Key Takeaways

• Five satellite proxies capture settlement intensity trends
• Sentinel‑2 built‑up shows highest growth, 111% increase
• Correlations among proxies exceed 0.70, indicating consistency
• Only three CHUs classified as peri‑urban by consensus
• Multi‑proxy method supports dynamic health system planning

Pulse Analysis

Urban expansion across sub‑Saharan Africa is far from uniform, creating a patchwork of rural, peri‑urban, and fully urban zones that often outpace official administrative updates. For ministries of health and donors, this mismatch hampers the allocation of clinics, staff, and disease‑surveillance resources. The recent longitudinal study in Kenya’s Kaloleni‑Rabai Health and Demographic Surveillance System tackles this gap by leveraging openly available satellite products to map settlement dynamics at the Community Health Unit (CHU) level. By treating each CHU as a spatial unit, the researchers provide a granular lens that traditional census data simply cannot match.

The team compiled five independent proxies—VIIRS nighttime lights, Sentinel‑2 built‑up percentage, WorldPop built‑up percentage, WorldPop population density, and the Degree of Urbanization index—and standardized them across ten CHUs from 2017 through 2024. Cumulative changes were striking: nighttime lights rose 62 %, Sentinel‑2 built‑up surged 111 %, WorldPop built‑up increased 34 %, and population density grew 15 %. Pairwise Pearson correlations ranged from 0.71 to 0.96, confirming that despite different data sources, the metrics move in concert. Applying k‑means clustering and a cross‑proxy vote‑scoring system, only three CHUs achieved consensus peri‑urban status, highlighting the method’s discriminating power.

The multi‑proxy framework offers a reproducible, low‑cost tool for ministries seeking to refresh health‑service boundaries as settlements evolve. By combining high‑frequency night‑time light data with high‑resolution land‑cover and demographic layers, planners can detect both rapid construction bursts and slower population shifts, mitigating the floor effects observed in single indices. The approach is scalable to other Kenyan counties and, with minor calibration, to neighboring East African nations facing similar urban‑rural transitions. Ultimately, more accurate peri‑urban classification can improve vaccine outreach, maternal‑child health programs, and disease‑outbreak response, aligning resources with the lived reality of communities.