been published in peer reviewed journals. Therefore, most of the evidence presented below on comparisons across alternative models should be interpreted as tentative priors based on limited evidence from specific contexts.

This review is divided into three sections. The first section begins by very briefly describing some of the many publicly available geospatial indicators. It then reviews selected studies from a rapidly growing literature evaluating the accuracy of small area estimates of wealth and poverty using geospatial data, documenting strong correlations across several studies when compared with census-based estimates. I then briefly touch on three related issues: The sensitivity of accuracy to the nature of the training data; the more limited ability of geospatial data to predict variation across time in welfare than variation across space; and the important distinction between sampled and non-sampled target areas when considering the accuracy of estimates. The second section focuses on comparisons between different types of statistical methods for cross-sectional predictions, including the nature of the geospatial features and different types of models used for prediction. The third section briefly discusses an important recent paper describing how survey and geospatial data were combined to target poor households in Togo (Aiken et al, 2022). The final section concludes with a summary of key points and suggestions for further research.

2. Small area estimates of poverty and wealth with geospatial data a. What type of geospatial features are publicly available?

Geospatial data are typically obtained from satellites, mobile phones, or internet activity. Satellite indicators have a few key advantages over mobile phones and internet activity, however, including the public availability of a large number of indicators, in many cases derived from publicly available imagery provided by the Sentinel 2 and Landsat satellites. Proprietary high-resolution satellite imagery--from companies such as Maxar, Planet, Airbus, and others--can also either be used directly as an input into deep learning models, or as inputs to derive interpretable features such as building footprints, roads, and vehicles. Unlike call detail records, satellite-based indicators typically cover the entire country and therefore avoid selection bias. Call Detail Records (CDR) from mobile phones, in addition to only representing mobile phone users, are also more difficult to obtain for privacy reasons. However, CDR can in some contexts provide more informative indicators such as location information, cell phone behavior, and connection quality and device type. Internet records such as Twitter usage can also be informative (Tonneau et al, 2022). Information from online platforms also suffers from selection bias, however, since only a portion of the population uses it in developing countries, and it is difficult to estimate the extent to which this source of bias affects estimates.

A rich variety of geospatial indicators derived from satellite imagery have become publicly available and can be found in Google Earth Engine, Microsoft Planetary Computer, and other freely accessible websites. These offer access to several climate-related variables as well as a host of predictive features such as night-time lights, land classification, year of switch from pervious to impervious surface, estimates of net primary production, cell phone placement, a wide variety of climate and temperature variables, pollution estimates from the Sentinel 5-P satellite, a variety of soil quality measures, and countless other geospatial indicators. Meta has also publicly released the Relative Wealth Index, based on the pioneering work of Chi et al. (2021). Modeled population estimates from Worldpop, Meta, or 2