In the QuaRCS network, we conduct research on quantitative regional and computational science. We exploit the integration of spatial data science, econometrics, and machine learning to understand and inform the process of sustainable development across subnational regions and countries.
The QuaRCS network is also connected with the UN Sustainable Development Solutions Network (SDSN). As such, it helps mobilize scientific knowledge and technological expertise to promote global sustainable development.
To conduct research across all our projects, we use a modern data science workflow.
To conduct research across all our projects, we implement the latest advances in applied econometrics.
To conduct research acroos various projects, we use various clustering algorithms from the unsupervised manchine learning literature.
Exploratory spatial data analysis (ESDA), exploratory space-time data analysis (ESTDA), spatial dependence, spatial heterogoenity.
Nonparametric density estimation, mixture densities.
Bayesian model averaging.