Correlating Projected and Surveyed Population using Google Building Footprints in Jigawa State, Nigeria: Targeting Zero Dose Children Aligned with SDG Goal 3

Authors

Yakubu Joel Cherima, Zubairul Islam, Ugo Uwadiako Enebeli, Uchenna Stephen Nwokenna, Fiyidi Mikailu, Rejoice Kaka Hassan, Kebiru Umoru, Eziyi Iche Kalu, Yonwul Jacqueline Dakyen

Abstract

This study evaluates the accuracy of projected population data and its implications for estimating zero-dose children in Jigawa State, Nigeria. The specific objectives were to correlate projected and surveyed population data using Google Building Footprints and assess the gap in zero-dose children across various settlement types. A quantitative approach was employed, utilizing data from Google’s Building Footprints, Copernicus Human Settlement, Sentinel Hub, OpenStreetMap, and NASA Earth data. A geolocated household survey was conducted, covering 1,600 buildings across 27 LGAs using stratified random sampling. Essential demographic and socio-economic data were collected. Ordinary Least Squares (OLS) regression analysis was performed to validate projected population data, and paired t-tests compared population estimates for rural and urban areas. For zero-dose children estimation, a model was developed incorporating settlement types, NDVI, time distance to health facilities, and nightlight data. The OLS regression analysis demonstrated a strong correlation between projected and surveyed data (coefficient = 0.823, p < 0.01) with an Adjusted R-Squared of 0.95. However, paired t-tests indicated significant discrepancies, with the projected data overestimating rural populations and underestimating urban populations. The total zero-dose children were estimated at 113,143 (32% of Target Population) based on projected data and 91,415 (26% of Target Population) based on survey data. Addressing these discrepancies is vital for effective public health planning and resource allocation, particularly in targeting interventions for zero-dose children in rural areas of Jigawa State. The findings emphasize the importance of localized adjustments and continuous monitoring to reflect actual demographic changes and improve healthcare accessibility

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