Spatio-Temporal Urban Data Analysis: A Visual Analytics Perspective

A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics

Recent technological innovations have led to an increase in the availability of 3D urban data, such as shadow, noise, solar potential, and earthquake simulations. These spatiotemporal datasets create opportunities for new visualizations to engage experts from different domains to study the dynamic behavior of urban spaces in this under explored dimension. However, designing 3D spatiotemporal urban visualizations is challenging, as it requires visual strategies to support analysis of time-varying data referent to the city geometry. Although different visual strategies have been used in 3D urban visual analytics, the question of how effective these visual designs are at supporting spatiotemporal analysis on building surfaces remains open. To investigate this, in this paper we first contribute a series of analytical tasks elicited after interviews with practitioners from three urban domains. We also contribute a quantitative user study comparing the effectiveness of four representative visual designs used to visualize 3D spatiotemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Participants performed a series of tasks that required them to identify extreme values on building surfaces over time. Tasks varied in granularity for both space and time dimensions. Our results demonstrate that participants were more accurate using plot-based visualizations (linked view, embedded view) but faster using color-coded visualizations (spatial juxtaposition, temporal juxtaposition). Our results also show that, with increasing task complexity, plot-based visualizations perform better in preserving efficiency (time, accuracy) compared to color-coded visualizations. Based on our findings, we present a set of takeaways with design recommendations for 3D spatiotemporal urban visualizations for researchers and practitioners. Lastly, we report on a series of interviews with four practitioners, and their feedback and suggestions for further work on the visualizations to support 3D spatiotemporal urban data analysis.

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web

The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of this research are provided through a geoportal comprised of multiple applications that are related to 3D visualization of cities, ground deformation, land use and land cover and mobility. In a subset of the applications, the datasets handled are considered to be large in volume. The geospatial data were visualized on dynamic and interactive virtual globes to enable visual exploration. The geoportal is available on the web to enable cross-platform access to it. Furthermore, the geoportal was developed employing open standards, free and open source software (FOSS) and open data, most importantly to ensure interoperability and reduce the barriers to access it. The geoportal brings together various datasets, different both in terms of context and format employing numerous technologies. As a result, the existing web technologies for geovisualization and geospatial data processing were examined and exemplary and innovative software was developed to extend the state of the art.