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.

中文翻译：

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通过社交媒体网络数据发现城市中的活动模式：以伊斯坦布尔为例

随着互联网和通信技术的飞速发展，城市范围内的活动在虚拟环境中产生了反映，这些痕迹使城市动态结构之间的联系变得可见。由移动设备生成的，参与日常生活并与用户活动集成的数据，可提供有关用户在城市中的行为趋势的有价值的信息。这种称为“大数据”的新型数据由大量具有细粒度分辨率的信息组成，还可以通过自下而上的方法帮助人们对城市内的活动模式形成进行推理。这种方法也为开发整体方法铺平了道路。Foursquare API数据库用于收集由位置，地点，类别和访问者计数（签到）功能组成的活动数据。通过在GIS中保存的签入计数和空间统计分析对数据进行映射和加权，以发现研究区域内活动的热点和聚类模式。本文的主要发现是，公民对产品和服务需求的空间分布创造了新兴的城市活动区域的模式。