Spatial Correlations in Social Media Data
Identification and Quantification of Spatial Correlation Structures in Georeferenced Twitter Feeds
Social media feeds are one of the growing numbers of sources of volunteered geographic information. Thereby, over recent years, this kind of data has proven to be a rich source of information for many areas of research. This proposal aims to contribute methodological advancements, whereby we focus on Twitter data. Specifically, we aim to explore novel ways to derive spatial correlation structures within social media feeds. Our work builds upon the mature theory of spatial autocorrelation, which is the traditional way of measuring spatial structure.
The first research question is concerned with integrating the theory of spatial autocorrelation with the geometric stochasticity of tweets. The latter is typically investigated by means of stochastic geometry. We aim to combine principles from both fields in order to derive more accurate correlation structures within tweets. In a first step we investigate the effect of the stochastic geometries on spatial autocorrelation measures. This includes point pattern modelling and a Monte Carlo simulation study. That investigation will provide insights regarding a better interpretation of autocorrelation results. Moreover, the gained knowledge allows detailed insights into the variability of inter-tweet correlations of certain social activities. After this exploratory study, we investigate a measure of spatial autocorrelation that acknowledges the stochasticity of the underlying geometric structure and is thus able to obtain meaningful patterns within social media data.
Secondly we investigate the mutually overlapping character of phenomena that are reflected within the tweets. This overlap is caused by the autonomous behaviour of the users, which report about multiple phenomena simultaneously in space and time. We aim to explore ways of separating relevant tweets from non-relevant ones. This is done by means of Dempster-Shafer theory and Dirichlet processes. The challenge thereby is to disentangle the geometrically overlapping neighbourhoods. In a second step we expand spatial autocorrelation measures towards acknowledging this overlapping character by means of partial autocorrelation functions. This will prevent mixing different phenomena and leads to realistic dependency structures.
While the first two packages focus on the point level, the third aspect addresses suitable aggregation strategies. These strategies involve traditional clustering techniques and indices from point pattern analysis. This allows analysing dependencies between different kinds of compound social activities. Further, aggregating tweets allows investigating the relationship of social processes towards their immediate surroundings. This will be a second step of this work package.
Overall, our research will enable for gaining an increased and detailed understanding of social activities and their respective spatial mechanisms through improved methods allowing to analyse representations of these within socio-technical systems.
We’ve recently finalised the programme of a workshop on “spatial urban analytics with user-generated geographic information”. The event is conjoined with the 2017 International Conference at the Royal Geographical Society in London and is co-chaired by René Westerholt (GIScience Heidelberg). We received methodological as well as empirical contributions, which reflects the breadth of the complex [...]
Last week saw the Workshop on Crowd Assisted Sensing, Pervasive Systems and Communications (CASPer 2017) at the 15th IEEE International Conference on Pervasive Computing and Communications. Here you find some impressions from the event. Alexander Zipf participated as invited panelist at the panel session of CASPer 2017. The panel discusses processing unstructured Big Data and [...]
Several urban studies have been increasingly relying on spatial data provided by Volunteered Geographic Information (VGI) sources. The matching of features across different VGI projects may serve to assess and improve the reliability and completeness of VGI data. In a recent study, we first provide a short discussion on the similarity measures often used for [...]
Crowd assisted sensing and crowdsourcing, as well as their underlying pervasive systems and communications are a fast growing research area and one of the enabling technologies of smart cities and smart infrastructures, as well as important building blocks in healthcare monitoring and vehicular technologies. Crowd assisted sensing (often called participatory sensing) opens new ways for [...]
The aftereffects of disaster events are significant in tourist destinations where they do not only lead to destruction and casualties, but also long-lasting economic harms. The public perception causes tourists to refrain from visiting these areas and recovery of the tourist industry, a major economic sector, to become challenging. To improve this situation, current information [...]
This week Prof. Alexander Zipf presented some recent work of the GIScience Research Group Heidelberg and HeiGIT at the “Münchner GI-Runde” of the “Runde Tisch GIS e.V. Munich”. The overall topic of the presentation was spatio-temporal analysis from user generated geodata such as VGI or AGI (Social Media). Examples included work on OSM quality analytics such [...]
Heidelberg University reports about some of the work of the GIScience research group and at the Heidelberg Institute for Geoinformation Technology (HeiGIT), which is currently being established and core funded by the Klaus Tschira Stiftung. The short reports are available in English and in German. Enjoy! Check some of the Online Services by GIScience & HeiGIT
This week Alexander Zipf was giving an invited keynote presentation at the 18th Geoinfo Conference in Campos do Jordão, São Paulo, Brazil. The GEOINFO conferences aim to bring together leading GIScience and spatial database researchers, to present to the local community a perspective of the state-of-the-art in the area. Past speakers have included Max [...]
Check-in data such as provided by Foursquare is one kind of social media feeds which gained considerable interest over recent years. This interest is partly due to their high degree of semantic detail, given that users check-in at places which are categorized by a relatively well-defined taxonomy. One associated prevalent task, in research as well [...]
Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In a recently published paper (Steiger [...]