An Yan

TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.

TL;DR: Equity of spatial access in a novel 'dockless' bike share system that does not that constrain bike pickup and drop-off locations to docking stations in Seattle is explored.

TL;DR: FairST is presented, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility, that can reduce inequity in demand prediction for multiple sensitive attributes, while achieving better accuracy than even state-of-the-art fairness-oblivious methods.

TL;DR: This study examines the challenges and the expectations that civic hackers bring to the use of open government data, building on Gurstein’s theory of barriers to effective use.

TL;DR: A fairness-aware model for predicting demand for new mobility systems that not only reduces the fairness gap by more than 80%, but achieves better accuracy than state-of-the-art but fairness-oblivious methods including LSTMs, ConvLST Ms, and 3D CNN.