Abstract
Recent advances in modelling the internal structure of cities have primarily been applied to cities in developed countries, where rich data at a fine geographical scale is typically available. However, the majority of the world’s urban population lives in cities in developing countries where data from traditional sources is scarce. In this paper we show how quantitative urban models can be estimated in such data-sparse environments using data from Dhaka. Building on recent work using increasingly available mobile phone data to estimate commuting costs, we show how newly available satellite data on building heights can be used to estimate the housing supply elasticity. With this key model parameter, we can also estimate the price of land and floor space in each location of the city, which are prices that are usually difficult to directly observe for cities in developing countries. To illustrate how the calibrated model can be used for policy analysis we consider model counterfactuals in which the housing supply elasticity is increased by 25% and a radial road is constructed through the city.
Invited by: Alessandro Sforza
Local Organizer: Massimiliano Onorato