The increasing availability of movement data and rapid advancements in artificial intelligence have opened new frontiers in movement pattern analysis with implications for a wide range of domains such as urban planning, transportation management, public health, pandemic management, disaster response, and wildlife conservation. The sensitive nature and heterogeneity of novel movement data, however, create technical and methodological challenges around data governance and open movement analytics. This article explores these challenges and identifies key opportunities for the geography discipline to drive analytical advances in human mobility research and open science. In this vision, the geographic perspective is emphasized in future developments of artificial intelligence for movement research for its ability to enrich mobility analyses with spatial and temporal context. Strategies for effective governance of large-scale mobility data are discussed, with special attention to fostering open science and reproducible research practices to ensure transparency and transferability in this rapidly evolving field. ... Read more

Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks. ... Read more