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Spatial-Data-Driven Student Characterization in Higher Education
Higher Education is facing disruptive innovation that requires, among other things, provision of a more effective and customized education service to individual students. In the field of Learning Analytics (LA), there has been much effort, in the form ...
Vision Paper: Using Volunteered Geographic Information to Improve Mobility Prediction
Fine-grained real-time movement prediction is becoming increasingly important, with smartphones and vehicles constantly tracking our position and trying to guess our next location to timely provide us with recommendations, traffic forecasts, or driver ...
SHE: Stepwise Heterogeneous Ensemble Method for Citywide Traffic Analysis
Sensored traffic data in modern cities have been collected and applied for various purposes in the domain of intelligent transportation systems (ITS). However, analyzing these traffic data often lacks in priori knowledge due to the dynamics of ...
Extracting Human Mobility Data from Geo-tagged Photos
Photos shared by users on public websites (Flickr, Panoramio) provide a resource for mining behavioural data. When the photos are associated with locations and time stamps, we can reconstruct the trajectories of the users and use the resulting mobility ...
Modeling and reproducing human daily travel behavior from GPS data: A Markov Decision Process approach
Understanding the daily movement of humans in space and time on different granularity levels is of critical value for urban planning, transport management, health care and commercial services. However, population's daily travel behavior data was ...
The impact of MTUP to explore online trajectories for human mobility studies
Social media data which capture long-term personal travel activities as a set of space-time points (time series) become widely used for human mobility study. The space-time points representing individual activities are massive and need aggregation upon ...
Predicting Indoor Crowd Density using Column-Structured Deep Neural Network
This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to ...
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1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News (LENS 2017): Redondo Beach, California, USA - November 7, 2017
The 1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News (LENS 2017) was held in conjunction with the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017). The workshop is intended ...