Exploring Patterns of Socio-spatial Interaction in the Public Spaces of City through Big Data

[EN] Research on socio-spatial aspect of cities has never been so vibrant and exciting. The form of urban life is changing and evolving with new advancements in communication and technology. Digital communication and social media has reshaped the way people as the actors of society interact with each other and with the network of city. New social networks and widespread of mobile devises can be used to create and reinforce existing social ties. Mobile devises also change the role of citizens from consumers into producers of data; they are the new reporters, photographers, videographers of everyday life. This production creates large quantities of data known as the “Big Data”. Big data has opened up many doors for researchers to investigate new aspects of cities. This paper aims to explore how people access urban public spaces through social media by taking the parameter of distance and physical proximity into account. We tried to investigate if different levels of accessibility effects the way people interact with space through social media. Through this process the study explored different socio-spatial patterns in the city that are being affected by social media. The research data was collect in two layers of Nicosia in Northern Cyprus: first, the geo-tagged social media data was collected from the target group, and it was located on the map. Twitter as a microblogging medium was selected for data collection due to its public nature, geo-tagged abilities, and manageable short content. Second, degrees of accessibility in local and global scale were calculated using Space Syntax. The data was analyzed using regression analysis, scatter plot, and outlier detention. The result shows various patterns in correlation of interactions between society and space; it illustrates the importance of exploring the outliers when reading big data on the city. The result shows clear importance of local accessibility even when social media is the effective variable.Iranmanesh, A.; Alpar Atun, R. (2018). Exploring Patterns of Socio-spatial Interaction in the Public Spaces of City through Big Data. En 24th ISUF International Conference. Book of Papers. Editorial Universitat Politècnica de València. 1127-1135. https://doi.org/10.4995/ISUF2017.2017.5254OCS1127113

Addressing the Sparsity of Location Information on Twitter

Micro-blogging services such as Twitter have gained enormous popularity over the last few years leading to massive volumes of user generated content. In combination with the proliferation of smart-phones, this information is generated live from a multitude of content contributors. Interestingly, the content and timestamp of tweets is not the only information that can produce useful knowledge. The location information of users is of great significance since it can be utilized in a variety of applications such as emergency identification, tracking the spread of a disease and advertising. Unfortunately, information regarding location is very rare since many users do not accurately specify their location, and fewer posts have geographic coordinates. In this work, we aim to confront this data sparsity issue. Utilizing Twitter’s social graph and content, we are able to obtain users from a specific location. We optimize our method to work with minimum amount of queries considering the large volume of data in such settings. We also provide a mechanism for geo-locating a tweet within a city and present the qualitative enrichment in our data, achieved by our method