Mateo is a Doctoral Researcher at Centre for Advanced Spatial Analysis through the Alan Turing Institute doctoral programme. Mateo is interested in the application of data science and artificial intelligence on the built environment.

He is an architect with a MSc in Smart Cities and Urban Analytics from UCL, and works at the architectural practice Foster + Partners where he implements novel frameworks and methods to understand cities and urbanisation to inform design strategies.

Previously, Mateo worked as a spatial algorithm reseacher at SignalBox, developing positioning and context detection algorithms for transport networks using realtime transport data and mobile signals.

His interests revolve around cities as systems of interaction, particularly how urban dynamics shape and are shaped by collective behaviour. His research seeks to understand and model hidden relationships between connectivity, complexity, and resilience for coupled socio-physical systems.

You can find my full resume here.


Analyzing Transport Networks

Interactive Web tool to explore centrality measures of the metro systems of London, New York, Chicago, and Santiago de Chile. The time-weighted graphs were constructed using publicly available GTFS data and visualized using javascript. Click on the image to view the tool, along with a more indepth description.


placeSpace is an interactive online platform that enables users to visualize and explore amenity location and distribution in London. The platform originates from the need to interpret and assess the complexity of cities in terms of agglomeration economies.


Our collective activities in the city unfold in time and space in a repeating fashion. These cyclic patterns of actions and interactions create a particular rythm, an urban ‘Pulse’ influenced by socio-cultural and physical aspects of our environment.

Increasingly people use online networks to interact, and even though these interactions are virtual, they take place in physical space. By retreiving the digital traces they leave behind we can visualize these cyclic spatio-temporal patterns in a city.