cycle and walking path
Spatialising residential neighbourhoods

and catchment area of public amenities using urban big data and spatial analytics - culture, time, and identify

City Governance
Oxford OX


In early 20th Century, the planning methods is to use Euclidean Distance to measure neighborhood catchment area. By the late 20th Century, network distance measure gained its popularity. These catchment areas measures are based on the assumption that people are using the closest facilities that they have access to. However, actual catchment areas can be affected by many other factors such as social capital cultivation, cultural adaptation, climate and seasonal variation, and park function and facilities provided, etc. The actual catchment areas are difficult to measure with tracking devices and individual travel-activity data.


Since the turn of the millennium, the emergence of big data provides inspiring potentials to examine urban mobility at low cost and a large scale such as social media and crowdsourcing data (Cao et al, 2015; Zhai et al, 2019), Internet of Things (IoT), traffic and vehicular data, and mobile application data (Evans-Cowley and Kitchen, 2011). Among the variety of big data, real time location data has been used to aid a variety of planning activities and research objectives.

Our research will apply mobile signalling data containing positional information to delimit urban amenities’ service areas. We will use real time location of mobile signalling data instead of mobile base station, to improve accuracy of delimit urban amenities catchment area and accuracy of identifying facility user home locations. We will also conduct longitudinal studies using weekdays and weekends data across four seasons of the year.