Discovering Activity Patterns in the City by Social Media Network Data: a Case Study of Istanbul

Abstract

With the rapid developments in internet and communication technologies, activities take within the city create a reflection in virtual environments and these traces make visible the relation ties of the city’s dynamic structure. The data generated by mobile devices that take part in everyday life and become integrated with the user’s activities gives valuable information about users’ behavioural trends in the city. This new type of data, called ‘Big Data’ that consist of huge amounts of information with a fine-grained resolution, also help people to make reasoning about the activity pattern formations within the city, with a bottom-up approach. This approach also paves the way for developing a holistic approach. This study aims to discover and analyse the activity patterns of the parts of historical districts of Istanbul by evaluating the data generated from location-based social networks. Foursquare API database is utilised to collect activity data that consist of location, venue, category, and visitor counts (check-in) features. The data mapped and weighted with the check-in counts and spatial statistics analyses held in GIS to discover hotspot and cluster patterns of the activities within the study area. The main finding of the paper is that the spatial distribution of citizens’ demand for products and services creates patterns of emerging urban areas of activity.