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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

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Exploring the high-resolution mapping of gender-disaggregated development indicators.

C Bosco^1,2,3, V Alegana^4,2, T Bird^4,2

¹WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK c.bosco@soton.ac.uk.

Journal of the Royal Society, Interface

|April 7, 2017

Summary

This summary is machine-generated.

Spatial interpolation methods can map gender-disaggregated development indicators at high resolution. While promising for female literacy, accuracy varies significantly by country and indicator, necessitating careful validation for targeted resource allocation.

Keywords:

development indicators geo-statistics geographic information system mapping

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Area of Science:

Geospatial analysis
Development indicators
Health equity

Background:

National/subnational development indicators can mask crucial inequities, particularly for rural populations.
Targeting limited resources effectively requires understanding geographical variations in health and wealth.
Spatial interpolation offers potential for high-resolution mapping of demographic and health data.

Purpose of the Study:

To assess the accuracy of spatial interpolation methods for generating gender-disaggregated, high-resolution maps.
To evaluate the predictability of literacy, stunting, and modern contraceptive use across diverse settings.
To compare Bayesian geostatistical and machine learning approaches for mapping socio-economic indicators.

Main Methods:

Utilized geolocated Demographic and Health Surveys (DHS) cluster data and geospatial covariates.
Applied Bayesian geostatistical and machine learning modeling across four low-income countries.
Generated 1x1 km resolution maps with uncertainty estimates for key gender-disaggregated indicators.

Main Results:

Achieved high predictive accuracy (up to 75% explained variance) for female literacy in Nigeria and Kenya.
Demonstrated moderate accuracy (50-70% explained variance) for several other socio-economic indicators.
Observed significant variability in mapping accuracy (2-30% explained variance) across countries and variables, with both methods showing limitations.

Conclusions:

Spatial interpolation shows potential for creating detailed maps to support geographically stratified decision-making and monitor development goals.
Substantial variations in accuracy highlight challenges in universal application across diverse countries and indicators.
Emphasized the critical importance of rigorous validation and uncertainty quantification when applying these methods.