UCL Computer Science


Algorithmic policy-making

Developing a methodology to derive up-to-date poverty indicators at a very fine level of spatio-temporal granularity

Policy making algorithm research project

12 December 2019

Governments often rely on data collected by household surveys and censuses to identify areas in most need of regeneration and development projects.

However, due to the high cost associated with the data collection process, many developing countries conduct such surveys very infrequently and include only a rather small sample of the population, thus failing to accurately capture the current socio-economic status of the country’s population.

We have developed a methodology that relies on ready-available telecommunication data to derive up-to-date poverty indicators, at a very fine level of spatio-temporal granularity. The methodology strives to achieve both accuracy and interpretability, making it more suitable for policy-making than black-box approaches, that sacrifice the latter.

Relevant publications

  • C. Smith-Clarke and L. Capra. “Beyond the baseline: Establishing the value in mobile phone-based poverty estimates”. In 25th International World Wide Web Conference (WWW 2016). Montreal, Canada. April 2016.
  • C. Smith-Clarke, A. Mashhadi and L. Capra. “Poverty on the Cheap: Estimating Poverty Maps Using Aggregated Mobile Communication Networks“. In 32nd ACM Conference on Human Factors in Computing Systems (CHI). Toronto, Canada. April 2014