Zoomless Maps:
External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale

In this project we develop visualization concepts and algorithms for label placement used in mobile devices with small screens (e.g. smartphone, smartwatch). In the following, we present four different labeling approaches for five different cities and further a preliminary expert study.

Calgary, Las Vegas, Montreal, Pittsburgh, Toronto, Expert Study

The application scenario is as follows: A user searches for restaurants with his mobile device and these should be labeled with their star rating and the category of the restaurant (e.g. burger, pizza, etc.). In order to sustain orientation within the city, all restaurants should be labeled without zooming in. We present four different methods for the placement of the labels:

Internal labeling
(internal)

This method places the labels of all feature points at the same time in the map. Each label is located in the top-left position of the feature point. There is no restriction for intersecting labels.

Multi-page boundary labeling
(mpl)

This labeling concept distributes the labels over six pages such that each page consists of a boundary labeling. The user can navigate through the pages by using the navigation buttons.

Sliding boundary labeling
(sliding)

This labeling concept arranges the labels in a single row that can be continuously slid along the bottom side of the map. Only the labels directly below the map are displayed to the user.

Stacking boundary labeling
(stacking)

This labeling concept creates five stacks of labels below the map. The labels are distributed such that each label belongs to exactly one stack. The topmost label of the stack is connected to its point feature via a leader. When the user clicks on the stack the topmost label is pushed underneath the bottommost label. Hence, the second topmost label moves up and is then connected to its point feature.

We obtained the data of the restaurants by Yelp, the map tiles by Stamen Design, under CC BY 3.0, and the map data by OpenStreetMap, under ODbL. For the visualization we use the implementation provided by Open Layers, under 2-clause BSD License.