Hard and Soft Data Integration in Geocomputation: Mixed Methods for Data Collection and Processing in Urban Planning


With the fast development and popularity of Information and Communication Technology (ICT) and the Internet of Things (IoT), there has been a boom in spatial data in recent years. Such data flow with large volume, high velocity and big variety is becoming a hotspot for urban studies and planning research (Batty, 2012; Geertman et al., 2017). The multisource new data environment differentiates itself from conventional census and survey data with its high resolution, wide coverage and timeliness, which opens up vast opportunities for the study of spatial dynamics and Planning Support Science. Planning Support Science will therefore see the enhancement of its action, efficiency levels and scope by having access to quasi-immediate dynamics with the availability of quasi-live and live data. In addition, Planning Support Science will be able to seamlessly act in quasi-real time, complementing global and disaggregated data with very local and regional scale data and dynamics and allowing the decision maker and the Planning Support Science expert to operate with a specific portfolio of actions with increasing levels of confidence.

Handbook on Planning Support Science. Edward Elgar Publishers. Geertman, S. and Stillwell, J. (Ed)
Haifeng Niu
Haifeng Niu
Research Associate

I am a Research Associate at the Lab of Interdisciplinary Spatial Analysis, Department of Land Economy, University of Cambridge, currently leading spatial analysis work in the European Union’s Horizon 2020-funded project Emotional Cities(WP4). My expertise includes urban big data mining, spatial data science, geo-visualisation, and urban sensing and modelling. I have a strong interest in how the intersection of machine learning & AI and urban big data better supports urban planning, policy-making and smart management.