Abstract
This paper analyzes success public spaces (specifically plazas) in the urban fabric of the city of Murcia, Spain. Two approaches were adopted. Firstly, the city was visualized as a complex network whose nodes represent plazas. A centrality algorithm was applied to determine the importance of each node. Secondly, data sets were used from social networks Foursquare and Twitter, which provide different types of data as well as user profiles. Foursquare data indicates user preferences of urban public spaces, while in this respect Twitter offers less specific user generated data. Both perspectives have facilitated two rankings based on the most visited plazas in the city. The results enabled a comparative study to determine the potential differences or similarities between both approaches.
Highlights
The city is a complex system where a large amount of information and data are generated and used to determine its characteristics
Field work in the city of Murcia we describe the data source representing the information from the physical context of the city
We have studied the set of places that exist in the city center of Murcia from two perspectives
Summary
The city is a complex system where a large amount of information and data are generated and used to determine its characteristics. Twitter is a very popular social-networking and micro-blogging service which was launched in 2006, and within nine years had 300 million registered users worldwide, sending an average of 340 million tweets per day This is a huge data source and the analysis of these data can provide useful insights into the places and situations in which users engage. We extracted and analyzed the data sets provided by the social networks Foursquare and Twitter, especially those related to the outdoors and recreation category where the word plaza appears. The selection of Murcia as a case study is based on the fact that Murcia is Spain’s fourth city in terms of the amount of activity on Foursquare (www.puromarketing.com/ 16/15391/comousan-espanoles-foursquare.html) Data from this social network is analysed.
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