Integration of Remote Sensing Data and Official Statistics: Spatial Analysis of Environmental, Social, And Basic Access Dimensions on Infant Mortality in Eastern Indonesia, 2022
DOI:
https://doi.org/10.26630/rj.v20i1.5551Keywords:
Infant Mortality Rate, Eastern Indonesia, GWRAbstract
In 2022, Eastern Indonesia continued to experience a relatively high infant mortality rate (IMR) compared to the national average, with several regions in Papua categorized as having high IMR levels. This situation highlights the urgency of identifying the underlying determinants. Various dimensions, including environmental, social, and access-to-basic-services factors, are important to investigate. However, since environmental variables are not available in official statistical data, remote sensing data were utilized as an alternative source. This study integrates official statistical data and remote sensing data to identify the determinants of IMR. The remote sensing data were obtained through Google Earth Engine script processing. Preliminary modeling indicated the presence of spatial heterogeneity; therefore, a Geographically Weighted Regression (GWR) approach was employed to examine the local variation in the effects of each independent variable. Based on the significance mapping of model parameters, substantial spatial variation in IMR was observed across Eastern Indonesia. The GWR results indicate that environmental variables were generally not the dominant factors contributing to higher IMR, as most local coefficients reflected infrastructure disparities. The only environmental variable that consistently showed a significant effect was Land Surface Temperature (LST). In contrast, social conditions and access to basic services played a more substantial role in explaining variations in IMR across Eastern Indonesia. Barriers to healthcare access, low levels of welfare, and limited infrastructure were identified as the main factors contributing to the high infant mortality rates in Eastern Indonesia in 2022.
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