DFG, SPP 1894/1 – Volunteered Geographic Information

Project Lead
Team Member

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.

23.11.2021 14:35
SocialMedia2Traffic bei Fachaustausch Geoinformation “Smarte Region Rhein-Neckar”

Diesen Donnerstag, 25.11.2021, werden wir unser Kooperationsprojekt SocialMedia2Traffic beim Fachaustausch Geoinformation zum Thema “Smarte Region Rhein-Neckar” vorstellen. Aktuelle Verkehrsinformationen sind eine Voraussetzung für Navigationslösungen, um die beste Route und genaue Reisezeiten zu ermitteln. Diese sind momentan jedoch nicht offen verfügbar. Im Projekt SocialMedia2Traffic entwickeln eine Methode, um Verkehrsinformation aus georeferenzierten Social-Media-Daten zu extrahieren und offen […]

22.04.2021 11:09
geoEpi – new DFG research project on spatio-temporal epidemiology of emerging viruses

A couple of viruses are of global interest with respect to human health and well-being. These pathogens include the novel coronavirus SARS-CoV-2, Dengue, Chikungunya, Yellow fever, Zika and Ebola. These viruses show interesting spatio-temporal dynamics. Improved understanding of the driving and moderating factors will help to cope with these pathogens. The recently funded new project […]

15.03.2021 10:00
New mFund project: start of SocialMedia2Traffic – derivation of traffic information from social media data

(deutsche Version siehe unten) Up-to-date traffic information is a prerequisite for navigation solutions to determine the best route and travel time. However, there is no freely available traffic information on a global and federal level. “SocialMedia2Traffic uses freely available data from social media such as Twitter messages,” says Prof. Zipf, “to determine current traffic information […]

08.06.2020 10:10
Exploration of OpenStreetMap Missing Built-up Areas using Twitter Hierarchical Clustering and Deep Learning in Mozambique

Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while often the availability and quality of OSM remains a major concern. The majority of existing […]

05.02.2020 07:10
Detecting OSM Building Facades with Graffiti Artwork Based on Street View Images and Social Media using Deep Learning

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In a newly published paper, we […]

25.06.2019 11:12
Methodological aspects of the spatial analysis of geosocial media feeds: from locations towards places

A new journal article about Methodological aspects of the spatial analysis of geosocial media feeds: from locations towards places has just been published in gis.science Vol 2 2019. It covers some main aspects and findings of the PhD Thesis of our team member Rene Westerholt (now at Warwick UK) and relates those to the analysis of place in […]

05.06.2019 15:22
Understanding human mobility from social media data for epidemic surveillance in urban environment

Vector-born diseases – such as Malaria, Dengue or Zika are serious health hazards in tropical regions. The outbreaks show high temporal and spatial variability. For example, the number of dengue cases in the state of São Paulo increased by 2,124% in the first 11 weeks of 2019 (up to March 16, 229,064 cases were reported), […]

19.10.2018 09:54
An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada

Understanding how citizens interact with transportation system is a key to solving a variety of urban issues in general and traffic congestion in particular. Recently, scholars have put efforts on the pertinent work ranging from developing traffic predictors to understanding human mobility and activity patterns. Multiple types of data have been used, of which crowdsourced […]

04.06.2018 21:45
Successful DFG VGIscience Collaborative Research Week in Heidelberg

Last week about 30 scientists from different insitutions from all across Germany came together in Heidelberg to conduct collaborative research. The research week is the result of an intense collaboration within the DFG Priority Programme VGIscience, which deals with the following topics Information Retrieval and Analysis of VGI: • information extraction (space, time, semantics) • […]

25.04.2018 15:48
Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists

Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. A recently […]

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