Kaveh is a lead data scientist at the Data Science Campus.
He obtained a PhD in Urban and Transport Modelling from the University of Cambridge, using advanced statistical and machine learning techniques to understand travel patterns and their temporal and spatial variations.
Kaveh has worked research projects throughout on understanding urban issues, from travel and health to climate change, through digital urban modelling in the UK and across different geographic areas of the world.
Prior to joining the Campus, Kaveh worked to a combined policy-academic career, as transport modelling team leader at the Department for Transport (DfT) and Department for Environment, Food and Rural Affairs’ Joint Air Quality Unit. He was also a Research Associate in urban analysis at the University of Cambridge.
Kaveh also has experience in private sectors’ research and policy units and consultancies, working as senior Transport Modeller for different global consultants (including WSP Group). For example he managed the Department for Transport’s “National Transport Model”, and APRIL, which models the impact of transport policies in London.
We demonstrate the use of a range of freely available anonymised and aggregated novel datasets to estimate visitation counts to natural areas.
Read more on A data science approach to estimate the use of natural spaces: a feasibility study
We have produced an alternative estimation of the census travel to work matrices annually from 2012 to 2021, bridging the 10-year gap. This report looks at the technical specification of the gravity model, with the summary of input data and initial results.
Read more on Technical report: Estimation of travel to work matrices
Using novel modelling approaches, we have produced an alternative estimation of the travel to work matrices annually between 2012 and 2021, bridging the 10-year gap between Census data.
Read more on Estimation of travel to work matrices
ONS Data Science Campus (DSC) and Defra’s Spatial Data Science team developed a novel solution for estimating the number of visitors to natural spaces across England.
Read more on Using open-source data to measure our engagement with the natural environment
Understanding and monitoring the major influences on COVID-19 infection numbers in communities is essential to inform policy making and evaluate the impact of non-pharmaceutical interventions. We have developed a community-level analysis by assembling a large set of static and dynamic data for England.
Read more on Use of hybrid data to understand the community-level influences on coronavirus (COVID-19) incidence