Architect & Spatial Data Analyst. He holds a Master of Research in advanced spatial analysis and visualisation from CASA, the leading research centre of Bartlett, UCL, collaborating with prof Michael Batty, sir Alan Wilson and Dr Martin Austwick. He is the creator of the Spatially Transformed Social Networks (STSN) model which is an agent-based simulator that analyses data of social graphs and transforms them into spatial structures. His research is published in international conferences such as the SCCS2014 and journals. As a teenager he wanted to change the world. But until now, the only thing he managed to change was his cell phone. At least he did it several times.
The key approach to this research is the focus of urban design process on the utilisation of social structures' spatial and visual analytics. Social networks, as graphs and therefore as data concerning interactive relationships, can constitute a reliable tool for urban and architectural design in a self-organisation logic of space. Within the theory of complexity, new ideas on networks have been developed ranging from their form to their visualisation. Also, efforts have been made for the modelling of social structures' translation into spatial correlations (e.g. Spatially Transformed Social Networks model). However, these attempts do not shed enough light to the following crucial questions:
Is there any link between the effectiveness of social networks in terms of information diffusion in their graphs and the effectiveness of urban space that the members of these networks utilise or create in terms of functionality?
Is it feasible the establishment of a new, singular and coherent indicator for urban space effectiveness?
In other words, the interest of the Doctoral Thesis is concentrated to the link of information with the natural space through social networks. The term effectiveness related to social networks is referred to the kind, the velocity and the accuracy of information while in the part of spatial functionality is mainly referred to quantitative characteristics of urban environment, such as spatial distribution, crowd behaviour or the morphology and typology of public space.
Case Study & types of data
In the core of interest is the smart development of degraded urban areas via informal procedures of space self-organisation. The target is the production of effective knowledge about the relation between the growth management and ecosystems planning and the mobility and density of vulnerable or not social structures. The Thesis take into consideration the three basic dimensions of this kind of urban environments:
1. The properties of natural objects (buildings, topology of street network etc).
2. Social and census properties of social networks. In a social network the position of each member in the graph is stronger than the individual properties of that member.
3. The way by which space is perceived by the members of these social networks.
In order to link these three fields the research should be focused on and analyse the following types of data:
1. Data of daily routine movements and commuting (e.g. position data from GPS and location based services technologies).
2. Data of natural objects distribution in urban space (e.g. street networks, buildings density etc).
3. Census data (properties, land uses, educational level).
Taking into account the parameters above, research could shed light on how building types or position of urban services in the city are connected to spatial distribution of population’s social demographic properties. The knowledge of anatomy of degraded urban ecosystems in overlapping layers (spatial, economical and social) would allow the control of urban effectiveness through the smart distribution of crowd behaviour. What is possible today will be routine tomorrow.