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Dr Ana Basiri
CASA 1st Floor
90 Tottenham Court Road
Tel: 020 3108 3904
  • Lecturer in Spatial Data Science and Visualisation
  • Centre for Advanced Spatial Analysis
  • Faculty of the Built Environment

I am a Lecturer in Spatial Data Science and Visualisation at UCL's Center for Advanced Spatial Analysis (CASA), and a UKRI's Future Leaders Fellow. At CASA I am the Equal Opportunity Liaison Officer and also organise seminar series of the Department. Since my PhD, I have secured over a million pound in research funding as a PI and have been involved with several projects as the Co-Investigator. I have had several awards and prizes, including Women Role Model in Science by Alexander Humboldt and European Commission Marie Curie Alumni. I have (co-) chaired several seminars and international conferences, including International Conference on Localisation and GNSS 2017, gave keynote/invited talks, and served as the editorial board/team of some high impact journals including IET Smart city and International Journal Geographical Information Science. I have published more than 40 peer-reviewed journal papers and book chapters, more than 30 conference papers, supervised 5 PhD and 21 MSc/MRes students.

Research Themes
Research Summary

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.

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