Geographically disaggregated poverty data are vital for better understanding development issues and ensuring development efforts are directed to the places where they are most needed. Poverty has traditionally been measured by data on consumption, income, or assets. However, recent advances in computing power and the emergence of new methods has made it increasingly feasible to produce reliable, cost-effective, and timely poverty maps by extracting features from novel data sources such as satellite imagery, call detail records, and internet connectivity indicators. This paper explores the methodological implications of using both traditional and novel data sources to generate poverty maps. Specifically, it examines the applications of (i) survey and census data
(ii) Global System for Mobile Communications, smartphone, and Wi-Fi indicators
(iii) call detail records
(iv) daytime and nighttime remote sensing imagery
and (v) the Survey of Well-being via Instant and Frequent Tracking for poverty mapping. Each section provides a brief overview of the data requirements, methodology, and applicability considerations of the data source under consideration. In addition, the paper discusses the usefulness and limitations of each approach in the field of evaluation, providing concrete examples of poverty maps created from each of the listed data sources.