Granular socioeconomic data assists digital nomads make informed decisions about their destinations and helps policymakers address disparities and foster growth.

Addressing data gaps, investing in human resources, and using technologies provide policymakers with insights to make informed climate-related decisions.

Analysis of geographically granular data is essential to identify vulnerable areas and optimize resource distribution.

Leverage data integration, administrative data, and other targeted methods for data collection and analysis.

Leverage machine learning and satellite imagery for informed resource allocation to enhance road quality and address development challenges.

Pseudo-panel methods using repeated cross-sectional surveys, which are less costly and easier to do, may offer a solution to this problem.

Crises like a pandemic underscore the importance of alternative sources of high-quality and timely data in developing effective measures.

Statisticians turn to digital solutions and nontraditional methods for faster data collection, processing, and dissemination to inform crisis response.

Poverty maps derived from satellite images helped target the most vulnerable households in pandemic-affected areas in the Philippines.

A study examines the feasibility of applying computer vision techniques to satellite data of the Philippines and Thailand to produce poverty maps.