# Schematizing Crossroads from Abstract Textual Descriptions

Jean-Marie Favreau, Guillaume Touya, Jérémy Kalsron

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Jean-Marie Favreau, Guillaume Touya, Jérémy Kalsron. Schematizing Crossroads from Abstract Textual Descriptions. CompCarto 2022, University of Bonn, Germany, May 2022, Bonn, Germany. [hal-03677334](https://hal.science/hal-03677334)

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Submitted on 24 May 2022

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# Schematizing Crossroads from Abstract Textual Descriptions

Guillaume Touya¹, Jean-Marie Favreau², and Jérémy Kalsron¹,²

¹LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-77420 Champs-sur-Marne, France

²Université Clermont Auvergne, CNRS, Mines de Saint-Étienne, Clermont-Auvergne-INP, LIMOS, F-63000 Clermont-Ferrand, France

May 24, 2022

The use of cartographic representations among people with visual impairments (PVI) is often limited by the lack of available materials. However, two uses have been identified: diagrams made with sticks magnetised to a metal plate (Figure 1) are used by Orientation and Mobility instructors as a discussion aid around complex areas (typically intersections), and more accurate maps made by transcribing adapters are sometimes produced for regular use. While classical variations of the generalisation and stylisation approaches allow for the production of fairly accurate maps [JLCJ21], for example from OpenStreetMap data (figure 2), there are currently no known approaches to producing a more schematic representation, in the manner of the locomotion instructors' magnets.

Mackaness and Reimer define schematized maps as "bereft of detail, emphasising properties such as causality, connectivity, and flow", where abstraction is more important than the correct geographical location [MR14]. Hence, a schematized map seems to be a good way to graphically represent crossroads to PVI: the map should be easy to read by fingers; it should focus on footways (sidewalks, crossings), and identify the dangerous space dedicated to cars with a specific texture.

In order to produce a synthetic map satisfying these objectives, we propose to describe sidewalks by lines, traffic islands by discs, zebra crossings by simple pictograms, and to fill in the road with an easily identifiable texture (Figure 3), taking care to keep a white space between the objects to facilitate their tactile reading.

The production of a schematic representation of a crossroad we propose is based on the representation of the crossroad in an abstract representation model, integrating the different functional elements of a crossroad: branches, lanes, pavements, pedestrian crossings [KFT21]. This abstract model is dedicated to generate a textual/sound description that accompanies the tactile map.

We therefore propose to reconstruct a simplified geometric instance for each element of this abstract representation. A simplified geometric instance of sidewalks (using straight lines along each branch) is registered on the OpenStreetMap data to match the global shape of the crossroad (step 1). A regularization step is then applied to the sidewalk lines to obtain orientations proportional to $\pi/4$, in a similar process to metro map schematization [NW11] (step 2); circular pictograms and zebra pictograms are located at the adjusted location of the traffic islands and crossings (step 3); the road area is reconstructed from the sidewalk lines, and filled with a recognizable texture (step 4); blank spaces are generated using an offset algorithm from sidewalk lines and pictograms (step 5).

We plan to implement these algorithms in the next months and test with PVI if the schematized map is more usable than the accurate tactile maps.

<figure><img src="image_2.png" /><figcaption>Figure 1: Three examples of maps made with magnets</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2: A 2D view of a tactile map (copyright Yuhao Markie Jiang), and the corresponding intersection in OpenStreetMap.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: A manually generated schematised map of the crossroad from Figure 2, based on the textual description and conflated with the geographic data.</figcaption></figure>
# Acknowledgements

This research is part of the ACTIVmap project that was funded by ANR (Agence Nationale de la Recherche) ANR-19-CE19-0005.

# References

[JLCJ21] Y. Jiang, M.-J. Lobo, S. Christophe, and C. Jouffrais. Mapping road crossings for visually impaired people. *Abstracts of the ICA*, 3:127, 2021.

[KFT21] Jérémy Kalsron, Jean-Marie Favreau, and Guillaume Touya. Le carrefour dont vous êtes le héros. La Rochelle, France, May 2021. Published: Spatial Analysis and Geomatics (SAGEO) 2021.

[MR14] William Mackaness and Andreas Reimer. Generalisation in the Context of Schematised Maps. In Dirk Burghardt, Cécile Duchêne, and William Mackaness, editors, *Abstracting Geographic Information in a Data Rich World*, Lecture Notes in Geoinformation and Cartography, pages 299–328. Springer International Publishing, 2014.

[NW11] M. Nöllenburg and A. Wolff. Drawing and Labeling High-Quality Metro Maps by Mixed-Integer Programming. *Visualization and Computer Graphics, IEEE Transactions on*, 17(5):626–641, May 2011.