### Traffic and Urban Planning - SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations, Dongyu Liu, Di Weng, Yuhong Li, Jie Bao, Yu Zheng, Huamin Qu, Yingcai Wu - SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories, Shamal AL-Dohuki, Farah Kamw, Ye Zhao, Chao Ma, Yingyu Wu, Jing Yang, Xinyue Ye, Fei Wang, Xin Li, Wei Chen
### SmartAdP Identifying the optimal billboard locations using massive trajectory data. ![](./assets/smartadp-1.png)
#### Backgrounds - Finding befitting areas to place billboards. - Selecting proper locations in the specified areas. - Evaluating a solution and convincing customers. - Providing customers with multiple solutions.
#### Tasks and Models **Spatio-temporal distribution and location recommendation**: Road network data, GPS trjectory and POI data. MongoDB indexes. Two interactive queries. ![](./assets/smartadp-2.png)
#### Tasks and Visual Designs - **Results' assessment**: Locations and solutions, distributions and detailed information. - **Solution comparison, classification and ranking**: A quick overview of the solutions can obtained by grouping solutions, ranking can help planners quickly find the desired solutions.
#### Tasks and Visual Designs **Glyph Design** ![smartadp_visdes](./assets/smartadp-3.png)
#### Case studies - Exploring the solution space - Exploring the distribution of target trajectories - Comparing solution areas for billboard placements - Improving and editing solutions - Finding the optimal solution - Weekday and weekend - Dispersed Strategy vs Clustered Strategy - Domain Expert Interview
#### Case studies ![smartadp_visdes](./assets/smartadp-5.png) ![](./assets/smartadp-6.png)
### SemanticTraj - Typical approach to explore taxi trajectory data: Require users to select and brush geospatial region on a map: tedious and time consuming - The method: manage visualize taxi trajectory data in an intuitive, semantic rich and efficient means.
#### Components - Textualization transformation process - Text search engine with a corpus of taxi documents - Semantic labels and meta-summaries of results are integrated with a set of visualizations - Feedback from domain experts and a preliminary user study to evaluate the visual system ![](./assets/semantictraj-1.png)
#### Visual Exploration: semantic trips search ![](./assets/semantictraj-2.png)
#### Visual Exploration: study individual trajectory ![](./assets/semantictraj-3.png)
#### Provide shuttle buses for tourists ![](./assets/semantictraj-4.png)
#### Study traffic information over streets ![](./assets/semantictraj-5.png)
## vis.cs.kent.edu - [NY Taxi Trip](http://vis.cs.kent.edu/NYTaxi/): Data of taxi trips. Taxi trip refers to a taxi carrying its passengers from their origin (pickup) location to destination (dropoff) location. - [Porto Taxi Trip](http://vis.cs.kent.edu/PortoTrajectories/): Real-world mobility data of taxi trajectories. Taxi trajectory is the recording of the positions of a taxi at specific space-time domain, for a given taxi and a given time interval, it is presented as a sequence of geometric location in 2D spatial system (xi, yi, ti).
## Posters and Arts Program
### Poster: Analyzing Hillary Clinton’s Emails ![](./assets/poster-1.png)
### Poster: Towards Combining Mobile Devices for Visual Data Exploration ![](./assets/poster-2.png)
### Workshop on Visualization in Practice **vega**: Vega is a visualization grammar. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format, and generate views using HTML5 Canvas or SVG. ![](./assets/poster-3.png)

cf. city flows

### Urban Radiance, East Asia 1992-2013 ![](./assets/frame_31-1.jpg)

THE END

@hijiangtao

2016.11