Category: Crowd Analyser

Enrico Steiger defends PhD on Explorative Spatial & Temporal Human Mobility Analysis from User-Generated Data

today Enrico Steiger successfully defended his PhD thesis about “Explorative Spatial and Temporal Human Mobility Analysis from User-Generated Data” at GIScience Heidelberg University. The massive amount of pervasive, user-generated data creates new possibilities to discover and utilize geographic information. Tied in with this novel role of actively participating users, is a growing research challenge where…

2016-02-05
CrowdAnalyser Talk and Workshop on Big Data and Socioeconomic Analysis by Professor Paul Longley

By the end of November, Professor Paul Longley and his colleague Guy Lansley (both from Department of Geography, University College London) visited our group as part of the last CrowdAnalyser workshop. Both of them gave really inspiring talks. Paul talked about the provenance and use of big data (largely focussing on social media). Guy, in…

2015-12-07
Invitation to 2nd CrowdAnalyser Workshop with speaker Prof. Paul Longley (UCL London)

On behalf of the research training group “CrowdAnalyser” at Heidelberg University (www.uni-heidelberg.de/crowdanalyser), we would like to invite you to participate in our second “CrowdAnalyser Workshop 2015”. The objective of this workshop is to bring together academics and practitioners that are interested in exploring knowledge and developing methods, operational frameworks and systems for spatio-temporal data generated…

2015-11-10
Exploring spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

The investigation of human activity patterns from location-based social networks like Twitter is a promising example of how to infer relationships and latent information for the characterization of urban structures. While there is a growing research body performing spatial analysis on social media data, the high dimensionality, complexity and granularity of social media information still…

2015-10-21
Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data

Detailed knowledge regarding the whereabouts of people and their social activities in urban areas with high spatial and temporal resolution is still widely unexplored. Thus, the spatiotemporal analysis of Location Based Social Networks (LBSN) has great potential regarding the ability to sense spatial processes and to gain knowledge about urban dynamics, especially with respect to…

2015-09-30
Best Paper at ACM SIGSPATIAL GeoCrowd Workshop for our paper on Transport Flow Analysis from Social Media Data

Our paper titled “Explorative Public Transport Flow Analysis from Uncertain Social Media Data” in the GeoCrowd 2014 workshop of the ACM SIGSPATIAL conference has been selected by the workshop organizers as its best paper. In this paper, we propose a framework to detect human mobility transportation hubs and infer public transport flows from unstructured georeferenced…

2015-09-25
Talk “Mixed-Scale Spatial Autocorrelation” in Osnabrück

Last Tuesday, René Westerholt gave a talk about the assessment of spatial autocorrelation within social media data. It was part of the regular colloquium of the Institute for Geoinformatics and Remote Sensing at Osnabrück University. The talk provided an overview of challenges that occur due to the spatially and semantically mixed nature of social media…

2015-07-06
An Advanced Systematic Literature Review on Spatiotemporal Analyses of Twitter Data

Recently an Advanced Systematic Literature Review on Spatiotemporal Analyses of Twitter Data has been conducted and the results have been published online as journal article. This article presents a systematic literature review on the state of research concerning methodologies, applications and use cases of Twitter as a Location-Based Social Network. The proposed systematic literature review…

2015-06-12
Combining Social Media and Authoritative Data towards identifying useful Information for Disaster Management

A Study on the Twitter usage in the River Elbe Flood of June 2013 In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media…

2015-05-15
New ways in analysing multi-scale datasets

Many user-generated datasets (e.g., social media) reflect a number of different phenomena. Consequently, these datasets also comprise very different spatial scales. It goes without saying that this evokes tremendous challenges when conducting spatial statistical analyses of such datasets. When assessing spatial autocorrelation among the observations, for example, classical approaches are usually not appropriate. These were…

2015-05-03
A Local Multi-Scale Spatial Autocorrelation Measure for Social Media Datasets

Some time ago we reported about a new method for assessing spatial autocorrelation among points in social media datasets. A major contribution thereby is the ability of restricting the analysis to specific scales limited by both, an upper and a lower bound. The corresponding paper is now officially available online at IJGIS: Westerholt, R., Resch,…

2015-02-15
A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multi-scale datasets (e.g., social media datasets)

Georeferenced user-generated datasets like those extracted from Twitter and similar social media feeds are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multi-scale nature. Such datasets cannot be properly…

2014-12-30