Document Type
Article
Department
Population Health (East Africa); Institute for Human Development
Abstract
Background Urbanization is spatially heterogeneous in many sub-Saharan African settings. This complicates community health planning when rural and peri-urban labels are applied informally or updated inconsistently. We assessed whether multiple publicly available geospatial proxies could characterize settlement dynamics and support Community Health Unit (CHU) level classification within the Kaloleni Rabai Health and Demographic Surveillance System (KRHDSS) in coastal Kenya.
Methods We conducted a longitudinal ecological analysis across 10 CHUs in Kilifi County with annual observations from 2017 to 2024. Five geospatial proxy indicators of settlement intensity were summarized within CHU boundaries: Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights radiance, Sentinel-2 built-up area percentage, WorldPop built-up area percentage, WorldPop population density, and Degree of Urbanization urban percentage. We quantified year-to-year and cumulative change, assessed concordance using pooled descriptive correlations, CHU-level summary correlations, within-CHU centered correlations, and change correlations, and derived an exploratory consensus classification using k-means clustering applied separately to standardized CHU-level median proxy values, followed by cross-proxy vote scoring. Robustness was assessed using alternative temporal summaries, proxy-exclusion analyses, leave-one-CHU-out checks, temporal resampling, joint k-means clustering, and principal component analysis.
Results The analytic dataset comprised 80 CHU-year observations per proxy with no missing raw proxy values. Degree of Urbanization showed a pronounced floor effect with 37 zero observations. Median cumulative percent change from 2017 to 2024 was 62.25% for nighttime lights, 110.74% for Sentinel-2 built-up area, 34.14% for WorldPop built-up area, and 14.67% for population density. Sentinel-2 built-up area showed substantial interannual variability, with 41.0% of valid year-to-year transitions negative. Pooled raw-value correlations ranged from 0.710 to 0.955, but these estimates were treated as descriptive because the 80 CHU-year observations were repeated within 10 CHUs. CHU-level median correlations ranged from 0.736 to 0.967, while within-CHU centered and change correlations showed weaker temporal agreement. Cross-proxy vote scoring classified 3 of the 10 CHUs as consensus peri-urban; these three remained stable under the main proxy-exclusion and temporal-summary sensitivity analyses.
Conclusions A multi-proxy geospatial approach can provide a reproducible exploratory framework for characterizing CHU settlement context in transitional settings. In this setting, it identified a stable core of higher-intensity CHUs while also highlighting proxy dependence, temporal volatility, and threshold-related floor effects. The approach should be interpreted as a complementary spatial stratification requiring local calibration and validation before direct use in routine health-system planning.
Publication (Name of Journal)
International Journal of Health Geographics
DOI
https://doi.org/10.1186/s12942-026-00483-5
Recommended Citation
Makena, P.,
Oluoch, F.,
Iseme, R.,
Gudda, F.,
Keter, A.,
Das, J.,
Bhutta, Z.,
Ngugi, A.
(2026). A longitudinal multi‑proxy geospatial classification of peri‑urban transitions across community health units in coastal Kenya. International Journal of Health Geographics, 1-32.
Available at:
https://ecommons.aku.edu/eastafrica_fhs_mc_popul_health/224
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.