Anita Graser 🇪🇺🇺🇦🇬🇪Finally finished my #Trajectools presentation for #QGISUC2025 on Monday
May still have to cut here and there to stay within the time limit 😅
Love the visual summary of #SDSC25
No pandas were harmed in the process 😉
MovingPandasEven more human #MovementBehavior research:
Elkin-Frankston et al. (2025). Beyond boundaries: a location-based toolkit for quantifying group dynamics in diverse contexts. Cogn. Research 10, 10 (2025).
https://doi.org/10.1186/s41235-025-00617-6
"We first segmented time periods when the group was in motion by identifying break periods using the stop detection feature from the MovingPandas Python package"
#MovementDataAnalysis #MobilityDataAnalytics #MobilityDataScience #HumanMobility
Beyond boundaries: a location-based toolkit for quantifying group dynamics in diverse contexts - Cognitive Research: Principles and Implications
Existing toolkits for analyzing movement dynamics in animal ecology primarily focus on individual or group behavior in habitats without predefined boundaries, while methods for studying human activity often cater to bounded environments, such as team sports played on defined fields. This leaves a gap in tools for modeling and analyzing human group dynamics in large-scale, unbounded, or semi-constrained environments. Examples of such contexts include tourist groups, cycling teams, search and rescue teams, and military units. To address this issue, we survey existing methods and metrics for characterizing individual and collective movement in humans and animals. Using a rich GPS dataset from groups of military personnel engaged in a foot march, we develop a comprehensive, general-purpose toolkit for quantifying group dynamics using location-based metrics during goal-directed movement in open environments. This toolkit includes a repository of Python functions for extracting and analyzing movement data, integrating cognitive factors such as decision-making, situational awareness, and group coordination. By extending location-based analytics to non-traditional domains, this toolkit enhances the understanding of collective movement, group behavior, and emergent properties shaped by cognitive processes. To demonstrate its practical utility, we present a use case utilizing metrics derived from the foot march data to predict group performance during a subsequent strategic and tactical exercise, highlighting the influence of cognitive and decision-making behaviors on team effectiveness.
New #IOT research using yours truely:
Koszewski et al. (2025). Utilizing IoT Sensors and Spatial Data Mining for Analysis of Urban Space Actors’ Behavior in University Campus Space Design.
https://doi.org/10.3390/s25051393
"Trajectories were processed by the MovingPandas Python library, which offers several valuable processing algorithms"
For the full list of publications we're aware of, check out:
#MovementDataAnalysis #MobilityDataAnalytics #MobilityDataScience
Utilizing IoT Sensors and Spatial Data Mining for Analysis of Urban Space Actors’ Behavior in University Campus Space Design
This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial–temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas—the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors’ behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process of redesigning the WUT campus, but also to support the process of discussion and activation of the community in the development of deliberative democracy and participatory shaping of space in general.
🎉 We're happy to announce the release of #MovingPandas 0.20, now without fiona dependency
For the full changelog see:
https://github.com/movingpandas/movingpandas/releases/tag/v0.20
Freshly forged packages 📦 are available now on conda-forge
New #bicycle 🚲 research #preprint using yours truly:
Skåntorp et al. (2024). Data-driven bicycle driving cycles via mixed-integer programming
"we utilized the #KalmanFilter from the #MovingPandas library"
http://dx.doi.org/10.13140/RG.2.2.19928.10240
For the full list of publications we're aware of, check out:
#MixedIntegerProgramming #MovementDataAnalysis #MobilityDataAnalytics #MobilityDataScience #cycling #ActiveMobility #TrajectoryData
@underdarkGIS if you prefer working directly in #Python, have a look at the #MovingPandas example notebook at https://movingpandas.github.io/movingpandas-website/1-tutorials/10-smoothing-trajectories.html
#GISChat #SDSL2024 #MovementDataAnalysis #MobilityDataAnalytics
New release 🎉
#Trajectools 2.3 brings trajectory generalization, cleaning, and smoothing algorithms to #QGIS
Inspired by #SDSL2024, I've written up the first Trajectools tutorial on #trajectory data preprocessing
http://anitagraser.com/2024/09/21/trajectools-tutorial-trajectory-preprocessing/
#MovementDataAnalysis #MobiltyDataAnalytics #GISChat #MovingPandas
#ChatGPT Data Analyst vs movement data
Today, I took ChatGPT's Data Analyst for a spin. You've probably seen the fancy advertising videos: just drop in a dataset and AI does all the analysis for you?! Let's see ...
http://anitagraser.com/2024/05/30/chatgpt-data-analyst-vs-movement-data/
😅 I should probably leave logo design to the professionals, but this will have to do for now.
At least there is a small #Trajectools page besides the github repo now:
https://anitagraser.com/trajectools/
#QGIS #GISChat #MovementDataAnalysis #MobilityDataAnalytics #MobilityDataScience #Python
