Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities. 

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A survey of urban visual analytics: Advances and future directions

Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada

A POIs based method for determining spatial distribution of urban fire risk

Videos embedded with spatial coordinates, especially when combined with additional expert insights, offer the potential to acquire fine-scale multi-time period contextualized data for a variety of ...

GeoVisuals: a visual analytics approach to leverage the potential of spatial videos and associated geonarratives

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.

Interactive Visual Exploration of Human Mobility Correlation Based on Smart Card Data

In modern cities, service providers want to identify the regions that are hard to reach from multiple fire stations, a citizen wants to meet with friends in a restaurant close to everyone, and administrators want to find whether an area far from two bus stations needs a new one. Such tasks involve studying the dynamic accessibility of the urban structures over multiple geospatial and temporal constraints, which is an important topic in geographical sciences and urban transportation. In this paper, we present a new computational model and a visualization system that help domain users to interactively study the jointly constrained accessible regions, street segments, and Points of Interest (POIs). In particular, Urban Structure Accessibility Visualization system is built upon a new Min–Max Joint Set model, where specifically designed set operations not only represent the accessible regions but also compute the minimum and maximum access times to urban structures from the joint constraints. The computation and visualization are supported by a new graph model that accommodates the real-world dynamic traffic situation and the geographical settings of urban street segments and POIs. The visualization system allows the users to conveniently construct and manage accessible regions and visually explore the urban structures inside them.

Urban Structure Accessibility Modeling and Visualization for Joint Spatiotemporal Constraints

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Urban Structure Accessibility Modeling and Visualization for Joint Spatiotemporal Constraints