Studying structural and functional connectivities of human cerebral cortex has drawn significant interest and effort recently. A fundamental and challenging problem arises when attempting to measure the structural and/or functional connectivities of specific cortical networks: how to identify and localize the best possible regions of interests (ROIs) on the cortex? In our view, the major challenges come from uncertainties in ROI boundary definition, the remarkable structural and functional variability across individuals and high nonlinearities within and around ROIs. In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on their learned fiber shape models from multimodal task-based functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. In the training stage, shape models of white matter fibers are learnt from those emanating from the functional ROIs, which are activated brain regions detected from task-based fMRI data. In the prediction stage, functional ROIs are predicted in individual brains based only on DTI data. Our experiment results show that the average ROI prediction error is around 3.94 mm, in comparison with benchmark data provided by working memory and visual task-based fMRI. Our work demonstrated that fiber bundle shape models derived from DTI data are good predictors of functional cortical ROIs.

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Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

Digital Elevation Models (DEMs) have been successfully used in a large range of environmental issues. Several methods such as digital contour interpolation and remote sensing have allowed the generation of DEMs, some of which are now freely available for almost the entire globe. The Soil and Water Assessment Tool (SWAT) is a widely used semi-distributed model operating at the watershed level and has previously been shown to be very sensitive to the quality of the input topographic information. The objective of this study was to evaluate the impact of DEMs generated from different data sources, respectively DLG5m (local Digital Line Graph, 5 m interval), ASTER30m (1 arc-s ASTER Global DEM Version 1, approximately 30 m resolution), and SRTM90m (3 arc-s SRTM Version 4, approximately 90 m resolution), on SWAT predictions for runoff, sediment, total phosphor (TP) and total nitrogen (TN). Eleven resolutions, from 5 m to 140 m, were considered in this study. Results indicate that the predictions of TPs and TNs decreased substantially with coarser resampled resolution. Slightly decreased trends could be found in the predicted sediments when DEMs were resampled to coarser resolutions. Predicted runoffs were not sensitive to resampled resolutions. The predicted outputs based on DLG5m were more sensitive to resampled resolutions than those based on ASTER30m and SRTM90m. At original resolutions, the predicted outputs based on ASTER30m and SRTM90m were similar, but the predicted TNs and TPs based on ASTER30m and SRTM90m were much lower than the one based on DLG5m. For the predicted TNs and TPs, which were substantially sensitive to DEM resolutions, the output accuracies of SWAT derived from ASTER30m and SRTM90m could be improved by down-scaled resampling, but they could not improve on finer DEM (DLG5m) at the same resolution. This study helps GIS environmental model users to understand the sensitivities of SWAT to DEM resolution, and choose feasible DEM data for environmental models