Automatic icon placement approach for improved association & walkability on city wayfinding maps

Abstract

With the evolution of cartography from hand-drawn to digital maps, the task of icon placement has
become increasingly complex. Nowadays there is a need to automate this process to produce high
quality results in less time. This study aimed to optimize icon placement on city wayfinding
maps, addressing challenges of placing icons in high-density areas and maintaining a strong
association between icons and their actual locations.
To achieve this, a two-stage approach was employed. Initially, a grid search algorithm was
developed to search approximate icon locations. It was implemented by placing icons sequentially
in the least disturbing position. After defining three quality metrics of disturbance, legibility and
association, the grid algorithm’s performance was evaluated based on them and two default
parameteres (searchGroundDistance and p.orgX, p.orgY). Eighteen experiments were conducted
for the grid algorithm’s performance and the best-performing combination, particularly in the
association metric, was chosen for further refinement. Subsequently, the Non-dominated Sorting
Genetic Algorithm II (NSGA-II) was implemented to further optimize these placements, with
respect to the three metrics and the city wayfinding guidelines set by T-Kartor.
The outcome of this optimization process was a Pareto front of non-dominant solutions, with the
one excelling in association chosen as the best, given the study's focus on improving this metric.
In that way the resulting placements not only aligned with T-Kartor's guidelines but also
significantly improved walkability on the maps by balancing association, disturbance, and
legibility.
This method offers a systematic approach to producing high-quality urban wayfinding maps,
enhancing user navigation. The study marks a significant contribution to cartography, addressing
important challenges and a previously under-investigated area of icon placement. Future research
could explore improving the computational efficiency of the optimization algorithm, considering
different icon shapes rather than square, and developing a user-customizable plugin for varied
optimization preferences.

Popular Abstract

In the age of digital transformation, the art of map-making has come a long way. Gone are the days
of hand-drawn maps; today, we have intricate digital maps that guide us through the bustling streets
of our cities. But have you ever wondered how those little icons on the map, such as landmarks,
restaurants and bus stops, are placed and why their placement is important for navigation? It is not
as simple as it seems, especially in high density urban areas where every inch of space matters.
This research delves into city wayfinding maps, with a focus on icon placement. Historically, this
was a time-consuming manual task. The challenge? Ensuring that the icons are placed in a way that
they do not obscure background map features, do not overlap with themselves and text labels on
the map, and accurately represent their real-world locations. These are the main metrics that
evaluate the map’s quality.
To tackle this challenge, a two-step approach was adopted. Initially, a grid search algorithm was
used to place the icons in positions that caused the least disturbance on the map. Then, using a
method called multi-objective optimization, these positions were optimized with respect to the
three quality metrics. Finally, the optimized result that prioritized the association between icons
and their real-world references, was chosen as the best one for this study.
The outcome? A set of icon positions on city wayfinding maps that are both visually appealling and
user-friendly. These optimized positions align with the guidelines set by the cartographic company
T-Kartor, while they resect the three metrics. More importantly, the main focus on association
enhances the user’s walking experience, making navigation in complex cities a breeze.
This research offers a significant contribution to the field of icon placement, which received less
investigation in recent years. Of course, no research is without its challenges. Future work can look
into making the process even faster, accommodating different icon shapes, and creating a plugin
that allows users to customize the level of optimization they want based on their preferences.

• author: Apostolidou, Sofia
• supervisor: Rachid Oucheikh
• organization: Dept of Physical Geography and Ecosystem Science
• course: NGEM01 20231
• year: 2023
• type: H2 - Master's Degree (Two Years)
• subject: Earth and Environmental Sciences
• keywords: Physical Geography, Ecosystem Analysis, Cartography, Icon placement, High density, City wayfinding maps, Quality metrics, Association, Disturbance, Legibility, Cartographic guidelines, Grid algorithm, Evaluation, Multi-objective optimization, NSGA-II, Pareto front, Walkability, Geomatics
• publication/series: Student thesis series INES
• report number: 631
• language: English
• id: 9139960
• date added to LUP: 2023-10-13 09:13:51
• date last changed: 2023-10-13 09:13:51