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@underdarkGIS@fosstodon.orgHappy to announce our latest #preprint short paper 
:
"Towards #eXplainable #AI for #MobilityDataScience"
https://arxiv.org/abs/2307.08461
I'd love to hear your thoughts and experiences with #XAI in #SpatialDataScience
arXiv.orgTowards eXplainable AI for Mobility Data ScienceThis paper presents our ongoing work towards XAI for Mobility Data Science
applications, focusing on explainable models that can learn from dense
trajectory data, such as GPS tracks of vehicles and vessels using temporal
graph neural networks (GNNs) and counterfactuals. We review the existing GeoXAI
studies, argue the need for comprehensible explanations with human-centered
approaches, and outline a research path toward XAI for Mobility Data Science.
If you are in for a longer read, let me recommend: "Towards #MobilityDataScience (Vision Paper)" https://arxiv.org/abs/2307.05717 by M. Mokbel, M. Sakr, L. Xiong, A. Züfle, yours truly and many others
arXiv.orgTowards Mobility Data Science (Vision Paper)Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.