Our team member René Westerholt recently held a joint session with Dr Guibo Sun from Hong Kong University. The session on “spatial urban analytics” was part of the Geography colloquium at Harvard University. Both talks were dealing with methodological issues. Thereby, René emphasised on technical issues in the spatial analysis of social media data. Dr [...]
Tag Archive 'Spatial Autocorrelation'
recently we were happy to receive the notification that two new research projects will be funded by the Deutsche Forschungsgemeinschaft (DFG) the German main funding agency for fundamental research.
Both projects belong to the DFG Priority Programme “Volunteered Geographic Information: Interpretation, Visualisation and Social Computing“ (SPP 1894).
The two projects are:
A:) IntrisicOSMquality: A framework for measuring [...]
This Friday - the hottest day of the year so far - Lucy Waruguru Mburu from the GIScience Research Group Heidelberg University successfully defended her PhD thesis on Criminal Geographic Profiling methods.
Her thesis is entitled: “ A Framework for Prediciting Criminal Behavior and Area-Specific Crime Risk through Retrospective Analysis of Geographic Data“. Complemented [...]
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 [...]
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 [...]
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, B., [...]