We welcome Yatao Zhang to our team as a PhD student at the Singapore-ETH Centre. Yatao received his Master of Science degree in Cartography and Geographical Information System from Wuhan University, and a Bachelor of Science degree in Geographical Information Science from Sun Yat-sen University in China.
The book chapter titled Geosmartness for Personalized and Sustainable Future Urban Mobility by Prof. Dr. Martin Raubal, Dr. Dominik Bucher, and Henry Martin from the book Urban Informatics is now available online. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making.
A new paper titled Applying Frequent-Pattern Mining and Time Geography to Impute Gaps in Smartphone-Based Human-Movement Data by Dr. Pengxiang Zhao, Dr. David Jonietz, and Prof. Dr. Martin Raubal is published in the International Journal of Geographical Information Science (IJGIS).
Abstract: Though GPS-based human trajectory data have been commonly used in travel surveys and human mobility studies, missing data or data gaps that are intrinsically relevant to research reliability remain a critical and challenging issue. This study proposes a novel framework for imputing data gaps based on frequent-pattern mining and time geography, which allows for considering spatio-temporal travel restrictions during imputation by evaluating the spatio-temporal topology relations between the space-time prisms of gaps and corresponding frequent activities or trips. For the validation, the proposed framework is applied to raw GPS trajectories that were collected from 139 participants in Switzerland. In the case study, the temporal and spatio-temporal gaps are artificially generated by randomly choosing activities and trips from the trajectory data. Through comparing the mobility indicators (i.e. duration and distance) calculated from raw data, imputed data, and data with gaps, we quantitatively evaluate the performance of the proposed method in terms of Pearson correlation coefficients and deviation. We further compare the framework with the shortest path interpolation method based on the generated spatio-temporal gaps. The comparison results demonstrate the performance and advantage of the proposed method in imputing gaps from GPS-based human movement data.
The figure above shows a hypothetical gap with its Space-Time Prism and two potential candidate trips.
Prof. Raubal gave a talk at the Fachtagung Elektromobilität Conference on Geographic information Analysis for Future Electric Mobility: Smart Charging and Energy Saving Potentials (in German).
We welcome Ye as our new team member! He holds a B.Sc. in GIS and remote sensing from Sun Yat-sen University, China, and an M.Sc. in Geomatics from ETH Zurich. His work will focus on understanding human mobility behavior and develop models for mobility prediction.
Read more here!
Category : research
In our recently published 2021 GIScience paper “Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements“, we study the problem of how to quantify the spatial autocorrelation of samples that were recorded at different points in space and time. Such data is commonly produced by mobile sensors, e.g., the temperature sensors of cars, but also by social phenomena such as location checkins of people. As not only the sensors move, but also the phenomena (potentially) change over time, an autocorrelation value such as Moran’s I can either be inaccurate or impossible to compute due to a lack of data. Instead, we propose to weigh the contribution of different samples based on an empirically projected uncertainty. This weakening of the impact of uncertain samples leads to more stable estimations of spatial autocorrelation quantifications such as Moran’s I.
Category : Uncategorized
Dominik Bucher has successfully defended his doctoral thesis titled “Spatio-Temporal Information and Communication Technologies Supporting Sustainable Personal Mobility” on 21 September 2020.
His research revolved around the question of how smartphone-tracked individual mobility data can be analyzed and utilized to support people in transitioning towards sustainable mobility usage. To this purpose, analysis methods (that identify transport modes, extract preferences and context, and detect changes in behavior), routing algorithms (that put an emphasis on personalized inter-modal transport involving a large number of transport options) and communication strategies (based on research on motivation and persuasion and evaluated using the large-scale mobility study GoEco!) were presented. Overall, if individual mobility data is used to give people eco-feedback and alternative route options in a timely manner, it is successful in helping people think about and adopt more sustainable mobility styles.