We have a new opening for a Ph.D. position in Mobility Information Engineering. Check here for more details. We are looking forward to your applications!
New IJGIS Paper “Applying Frequent-Pattern Mining and Time Geography to Impute Gaps in Smartphone-Based Human-Movement Data”
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 Presented at Treffpunkt Science City
Prof. Dr. Martin Raubal gave a talk on how green are we on the road (in German) at the Treffpunkt Science City event. The presentation is available online here.
Talk by Prof. Raubal at the Fachtagung Elektromobilität Conference
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).
Ye Hong Joins MIE Lab
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!
Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements
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.
Doctoral Examination of Dominik Bucher
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.
We Welcome Dr. Yanan Xin to the MIE Lab Team
We welcome Dr. Yanan Xin as our newest team member. After successfully completing her PhD at the Pennsylvania State University, she joins the MIE lab in the role of the lead researcher.
Exploring Choices between Internal Combustion Engine Cars and Electric Vehicles
In our recent publication “Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles“, we use a large dataset of people owning both an Internal Combustion Engine (ICE) car as well as an Electric Vehicle (EV) to determine the impacts of various predictor variables on their choices between the two car types. The gained insights may give additional information to assess common uncertainties regarding EVs: “How far can I drive with a fully charged vehicle? How quickly does the battery wear out? Does the reduced range and/or lack of a substantial number of charging stations impact my mobility?”
We find that chocies between ICE cars and EVs are regular considering an individual user, but that it is almost impossible to guess how someone will choose for a given trip if nothing about the person is previously known. This is a strong indication that most trips can easily be performed with any of the two vehicle types, and only individual preferences and circumstances determine the choices.
Doctoral Examination of Christian Sailer
Christian Sailer has successfully defended his doctoral thesis on 25 May online via Zoom about the topic “Enhancing Knowledge, Skills, and Spatial Reasoning through Location-based Mobile Learning”.
Enhancing Knowledge, Skills, and Spatial Reasoning through Location-based Mobile Learning. In a few minutes I will defend my research of the last 6 years with #omleth. Many thanks for the collaboration @Innovedum @projuventute @SATW_ch @OpenDataZH @CeviSchweiz @schabelera pic.twitter.com/84Pf91TSlQ
— ????????? ?????? (@csailer80) May 25, 2020
His research describes how an Location-based Mobile Learning (LBML) system utilizing GIS technology enhances the educational learning outcomes with a special focus on the spatial thinking process. Furthermore, this dissertation describes novel approaches of visual analytics with 2D and 3D map web components to produce new teaching strategies during the activities and new metacognitive strategies to evaluate and reflect the activity.
His presentation includes the study of case studies in universities, vocational schools, and informal education environments using design-based research to develop a mobile-friendly interactive mapping platform and the main study conducted in a secondary school under real conditions. Here, the evaluation focused on the impact of the technology regarding the learning performance and the teaching activities before, during, and after the activity. The results reveal a better cognitive learning outcome in classroom exams when a teaching sequence of several weeks includes an outdoor activity of a double lesson. Moreover, there is potential for enhancing learning beyond the outdoor part to improve spatial reasoning. Long-term self-assessment of the learners, however, resulted in no impact, whether cognitively or affectively. The workload for outdoor teaching compared to classroom teaching is higher mainly due to the profound inspections of the location. The findings and their implications for research and teacher education were discussed in order to corroborate the educational value of LBML to motivate educators using LBML strategies for teaching.