Haakon is a Master's student building a dataset that captures changes in Africa's buildings over time, measuring height, area, volume, count, and density down to ADM3 and ADM4 levels, in order to estimate economic and demographic growth. In the future, he hopes to use the dataset to analyse infrastructure damage, examine the effect of armed conflict on development, strengthen the growth proxy with NTL-data, estimate climate emissions, and map migration patterns. He also plans to extend the dataset to Southeast Asia and Latin America, and to PRIO-GRID and other spatial units.

Haakon's interests lie broadly in data and quantitative methods, including Bayesian inference, time-series analysis, machine learning, and geospatial analysis, applied to development, conflict, and environmental change.

Technical skills:

• R
• GIS
• Some Python, Machine Learning and SQL

Languages:

• Norwegian (mother tongue)
• English (fluent)
• French (B1-B2)