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Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic, a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City, captured over the period of a year. Work- ing in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods, and temporal analysis of unfolding urban development. 

The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower domain experts to explore multiple data sets, at different time and space resolutions. Since these systems rely on computationally-intensive spatial aggregation queries that slice and summarize the data over different regions, an important challenge is how to attain interactivity. While traditional pre-aggregation approaches support interactive exploration, they are unsuitable in this setting because they do not support ad-hoc query constraints or polygons of arbitrary shapes. To address this limitation, we have recently proposed Raster Join, an approach that converts a spatial aggregation query into a set of drawing operations on a canvas and leverages the rendering pipeline of the graphics hardware (GPU). By doing so, Raster Join evaluates queries on the fly at interactive speeds on commodity laptops and desktops. In this demonstration, we showcase the efficiency of Raster Join by integrating it with Urbane and enabling interactivity. Demo visitors will interact with Urbane to filter and visualize several urban data sets over multiple resolutions. 

Advances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory-efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.