Dr Ana Basiri
Lecturer in Spatial Data Science and Visualisation
Centre for Advanced Spatial Analysis
Faculty of the Built Environment
- Joined UCL
- 8th Dec 2017
My research activities focus on developing novel solutions based on the idea of ‘indicative data science’. Indicative data science is a set of tools, techniques and the mindset that considers gaps, unavailability, and the uncertainty of data as a useful source of data.
An application of this can be extracting the 3D map of cities based on the blockage of signals coming from GPS (or other similar Global Navigation Satellite Systems (GNSS), e.g. EU's Galileo). Patterns of blockage, reflection, and attenuation of the GNSS signals can be extracted using spatio-temporal statistical, machine learning, and AI techniques. from crowd-sourced GNSS raw data, contributed by the volunteers through the crowdsourcing framework of the project. This provides a ubiquitous and free of charge 3D mapping service for a wide range of applications including emergency services, positioning and navigation in urban canyons and indoors, energy consumption modelling, and drone and autonomous vehicles navigation.
In the era of big data, open data, social media and crowdsourced data when “we are drowning in data”, gaps and unavailability may indicate some hidden problems or reasons. Also, the datasets may have some quality, uncertainty, representativeness and bias issues associated with them. In this regard, the indicative data science can provide a set of (theoretical and applied) techniques and tools to understand the data better.
For this, I collaborate with world-leading academic and industrial partners, including Ordnance Survey, Uber, Alan Turing Institute, and engage with the public, policymakers and government.