Cities have long been producers and consumers of ‘big data’ whether it be about its population, economy, transport networks, flows of people along with the impacts of climate change on the built and natural environment. Citizens create much of this data, carrying out everyday transactions, mostly without their knowledge or informed consent. Big data can be derived from a variety of data stores: social media, consumer sites, search engines, smart phone apps, smart utility meters, credit card transactions, CCTV, etc. and whilst big data offers many as-yet-unexploited opportunities for smart cities, the risks to individual privacy and freedom also be taken seriously.
Cities can benefit hugely from all of this data if they have the methods, tools and techniques to properly interrogate, analyse and interpret this data in meaningful ways. Manchester's Data Science Institute are at the forefront of developing such methods through our urban data science theme to support cities in fully utilising the new opportunities that exist within data science to support cities and urban areas in understanding this emerging area.
The Spatial Policy and Analysis (SPA) Laboratory provides a home for staff and students across The University of Manchester engaged with urban and regional spatial policy research. Their research draws both on quantitative and qualitative methodologies, and their work has developed a variety of innovative methods of analysing large datasets.
Their ongoing projects cover a range of urban and regional policy initiatives, including territorial spatial planning, spatial analysis, decision support systems and web-based public participation toolkits using Geographic Information Science (GISc) methods.
Cecilia is the Executive Director of the SPA Laboratory. Her research has spanned housing, urban regeneration and regional development, with professional experience in both the UK and China. She uses quantitative methods to analyse very large data sets, in projects such as Eco-Urbanisation she uses GIS to coordinate and analyse the data which is generated. The project attempt to identify innovative practices and effective strategies to manage and plan for sustainable urbanisation in China through the interactive process between urban development, resource consumption, and environmental impacts.
Richard is the Deputy Director of the SPA Laboratory. Richard's research focuses on the role of technology and spatial data analysis in supporting all forms of the planning and development process. His research brings together three areas of work: Smart Cities, Public Participation GIS, and Planning Support Systems. For the ESRC funded project, commute-flow, Richard developed 9 classification groups and 40 sub-groups using GIS to analyse the data. Commute-flow forms part of the ESRC's Secondary Data Analysis Initiative programme which has developed an online toolkit for better planning of transport infrastructure.
Nuno is a lecturer in urban planning and design. His main research interests focus on the use of quantitative approaches to urban planning, namely on: decision support in planning, urban simulation, land-use and transport interaction and big data in urban studies. He was the recipient of the Breheny Prize for the best paper in 2010 in Environment and Planning B: Planning and Design, a leading academic journal in the field of urban modeling and urban planning. Nuno has a PhD in planning by the School of Architecture of Barcelona, BarcelonaTech, in Spain, and holds two Master degrees in planning (University of Porto, Portugal, and BarcelonaTech, Spain). He is also a charted Civil Engineer (University of Coimbra, Portugal).
Lei is a Hallsworth China Political-Economy Fellow within the laboratory, his research interests include spatial development in China and its sustainable challenges in the process of rapid urbanisation and industrialisation. The research analysis uses eastern China as the empirical test bed to allow meaningful analysis of the dynamic process of change. He collects land use change data and social-economic data to detect the spatial restructuring process and its mechanisms in the Yangtze River Delta. To analyse the data, GIS-based statistical methods and spatial modelling are used.