  Deep learning methods are being increasingly used for urban traffic
prediction where spatiotemporal traffic data is aggregated into sequentially
organized matrices that are then fed into convolution-based residual neural
networks. However, the widely known modifiable areal unit problem within such
aggregation processes can lead to perturbations in the network inputs. This
issue can significantly destabilize the feature embeddings and the predictions,
rendering deep networks much less useful for the experts. This paper approaches
this challenge by leveraging unit visualization techniques that enable the
investigation of many-to-many relationships between dynamically varied
multi-scalar aggregations of urban traffic data and neural network predictions.
Through regular exchanges with a domain expert, we design and develop a visual
analytics solution that integrates 1) a Bivariate Map equipped with an advanced
bivariate colormap to simultaneously depict input traffic and prediction errors
across space, 2) a Morans I Scatterplot that provides local indicators of
spatial association analysis, and 3) a Multi-scale Attribution View that
arranges non-linear dot plots in a tree layout to promote model analysis and
comparison across scales. We evaluate our approach through a series of case
studies involving a real-world dataset of Shenzhen taxi trips, and through
interviews with domain experts. We observe that geographical scale variations
have important impact on prediction performances, and interactive visual
exploration of dynamically varying inputs and outputs benefit experts in the
development of deep traffic prediction models.
