# 1. Introduction

Using geospatial data as auxiliary data for small area estimation is an old idea. Proof of concept was initially demonstrated thirty-five years ago by Battese, Harter, and Fuller (1988), who combined survey data with early imagery from the Landsat satellite to predict the area under corn and soybean production in 11 counties in Iowa. That paper is widely cited in the field of small area estimation statistics, with nearly 1,100 cites on Google Scholar as of May 2023. But the paper is better known for another seminal contribution, as it was the first to develop and apply the well-known nested-error unitlevel model, with a conditional random effect specified at the target area level, to estimate means for small areas. From 1988 to about 2015, economists and statisticians devoted considerable effort to refining this model in various ways, with a particularly important innovation introduced by Molina and Rao (2010) to estimate indicators other than means, such as poverty headcount rates, using simulation techniques. In the meantime, the publication of Elbers, Lanjouw, and Lanjouw (2003), which used a slightly different unit-level model, popularized the use of small area estimation at the World Bank. Nonetheless, until relatively recently virtually all applications during this time used census or other administrative data as auxiliary data, ignoring geospatial data as a potential source of auxiliary data from which surveys could “borrow strength” to improve the measurement of socioeconomic data.

Geospatial data was rediscovered as a potential source of auxiliary data in the mid 2010s, as advances in computing power and storage enabled geospatial data to become publicly available at a wide scale; as surveys began to be regularly implemented on tablets that collect geocoordinates; and as a new generation of data scientists, economists, and statisticians discovered the potential of geospatial data to improve socioeconomic measurement. This, in turn, sparked interest in combining survey and satellite indicators for the purposes of small area estimation. Using appropriate methods for this type of “data fusion” is important because small area poverty estimates have implications for the targeting and evaluation of public interventions and can shed light on economic geography more generally. At the same time, in part because of recent advances in machine learning algorithms, different disciplines and authors have taken very different methodological approaches to combining geospatial data and survey data for the purposes of small area estimation.

This paper provides a non-technical review of selected evidence from this relatively new literature. It builds on two recent reviews (Burke, 2021, McBride et al, 2022) but focuses exclusively on the small area estimation of wealth and poverty, devoting particular attention to differences in statistical methodology across studies. In particular, it ignores some of the excellent recent work on agricultural crops and yields (Lobell et al, 2020, Erciulescu et al, 2019), labor (Merfeld et al, 2022), and other indicators. There is now a robust literature documenting that estimates of wealth and poverty derived from survey and geospatial data are correlated with benchmarks derived from surveys or censuses. The strength of these correlations varies widely and depends on a myriad number of factors, including the country context, the method used for prediction, the target area for prediction, the exact indicator being predicted, the choice of geospatial variables, and the nature of the training and evaluation data.

Because the literature is relatively new, no consensus has yet emerged around the optimal prediction method in different contexts. Furthermore, comparisons of alternative prediction methods in the same geographic context remains rare, and some of the few examples of these comparisons have not yet 1