2015
,
Müller, Marc
,
Schulz, Daniel
,
Mock, Michael
,
Hecker, Dirk
A Bluetooth sensor network was built up in the city of Bonn to measure Bluetooth MAC-addresses. The results of the acquired data are separated on a macro level and mobility patterns. We have collected nearly 5 million data points from 14 distinct stationary sensors over a period of 1 month and recognized over 85.000 unique devices. We show that the data is sufficiently dense to detect commuter patterns based on a Fourier analysis. In addition, we discuss limitations found in the dataset and present lessons learned.
2008
,
Scheider, Simon
,
May, Michael
,
Rösler, Roberto
,
Schulz, Daniel
,
Hecker, Dirk
In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.