# 3. Statistical methods for prediction

- a. Interpretable features predict at least as well as deep learning from imagery

One aspect in which the existing literature on geospatial data fusion has diverged involves the nature of the geospatial indicators used. While several studies obtain predictions directly from imagery using deep learning techniques such as CNNs, others instead generate predictions from interpretable features such as land classification types, night-time light luminosity, building density, and so on. When using interpretable features, predictions can be obtained using linear models – which may be regularized – or a tree-based machine learning algorithm such as extreme gradient boosting. It is possible to use both, as demonstrated in Lee and Braithwaithe (2022), but using deep learning entails several additional costs. Deep learning models are complex and effectively a “black box” to users. Furthermore, they require specialized skills to understand and deploy, and thousands of training data points to perform well. On the other hand, the number of EAs in survey data are typically less than five hundred. Pre-trained CNNs can help circumvent the need for more data, but little is currently understood about how the specific nature of the architecture or pre-training affects prediction accuracy or bias.

A few existing studies shed light on the relative predictive power of deep learning and interpretable features. As noted above, Babenko et al. (2017) compare the predictive power of poverty predictions obtained from a CNN, as well as with those obtained from land classification, and both together. The correlation with the benchmark measure of truth, derived from the census, was equally correlated with the direct CNNs and land classifications, and the correlation improved moderately when both were included.

Engstrom et al. (2022) directly compare CNN-based estimates of headcount poverty rates with featurebased estimates in Sri Lanka. A variety of features were used, including roof type, shadows, cars, and road types. In that context, feature-based prediction is more accurate, with an R<sup>2</sup> of 0.61 and a mean absolute error of 3.2 pp, as opposed to an R<sup>2</sup> of 0.39 and a mean absolute error of 5.5 pp for the CNNbased estimates.

Ayush et al. (2022) also derive several interpretable features such as trucks, maritime vessels, vehicles, aircraft, etc. They then compare predictions obtained from these features in a gradient boosting model with those obtained from a CNN trained to nightlights data, as in Jean et al. (2016). When predicting average per capita consumption across villages, the out of sample R<sup>2</sup> of the interpretable features is 0.54, as compared with 0.41 when using the CNN trained to night-time lights. While this is a lower bound measure of accuracy of the CNN, since it was trained to night-time lights, it is consistent with Engstrom et al. (2022).

Finally, as noted above, Lee and Braithewaite (2022) employ an innovative approach that first uses interpretable features to predict the DHS wealth index, and then supplements that with poverty predicted from a deep learning model. This approach, however, shows limited benefits from adding direct CNN estimates to feature-based estimates for specific countries, with increases in R<sup>2</sup> of only about 0 to 2 points across four of the five countries. The exception is South Africa, where there is an increase 13