# The role of map generalisation in the context of thematic multiscale cartography

Paul Bourcier, Guillaume Touya

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Paul Bourcier, Guillaume Touya. The role of map generalisation in the context of thematic multiscale cartography. ICA Workshop on Multiscale Cartography and Sustainability, ICA, Aug 2025, Vancouver (BC), Canada. *hal-05352110*

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# The role of map generalisation in the context of thematic multi-scale cartography

Paul Bourcier<sup>a</sup>, Guillaume Touya<sup>a *</sup>

<sup>a</sup> LASTIG, Univ Gustave Eiffel, IGN, ENSG, Champs-sur-Marne, F-77420, firstname.lastname@ign.fr

* Corresponding author

**Keywords:** sustainable development goals, map generalization, multi-scale cartography, Abstracts of the ICA

**Abstract:**

Maps are complex objects that can be used for a wide variety of purposes. For the general public, one of the most common uses of maps is spatial navigation (route finding, travel planning, etc.). Almost all maps used for this purpose are digital. More specifically, they are multi-scale interactive maps, in which it is possible to modify the view by zooming or panning, and to obtain information on map elements. On the other hand, thematic maps, which provide a spatial understanding of a phenomenon, are less widely used (the need for these maps is less daily), but also less widely distributed in the form of multi-scale maps. Indeed, static maps are still widely used for thematic maps. This may be due to the choices made by cartographers (some may wish to show the spatial distribution of a phenomenon at a single scale), but also to a lack of knowledge about how to construct a multi-scale thematic map, and in particular, how to handle the generalization of these maps (Raposo et al., 2020).

Map generalization is the process of transforming the graphical representation of spatial data in order to reduce the graphical complexity of a map. There are several reasons for this need to reduce graphical complexity, the most common being to improve map legibility. In the case of static maps, cartographic generalization has been widely studied in the context of topographic maps and, to a lesser extent, thematic maps (Raposo et al., 2020). However, cartographic generalization applied to multi-scale maps is not yet very present in the scientific literature. By starting a reflection on this topic, we aim to propose guidelines for the generalization of multi-scale thematic maps. To illustrate our reasoning, we have created a Jupyter Notebook to experiment with manipulating different thematic map generalization techniques (Figure 1). A Jupyter Notebook is an interactive environment combining code, text, images and code results. As map generalization techniques are generally transcribed into algorithms, such an environment is interesting for testing these algorithms, understanding how they work and what visual results they produce.

<figure><img src="image_3.png" /><figcaption>Figure 1. Extract of the Jupyter Notebook, mixing text cells, code cells and code output.</figcaption></figure>

Our Jupyter Notebook invites users to test different generalization techniques applied to a thematic dataset and create multi-scale maps. This dataset consists of the Gini index, at local (French IRIS statistical unit) and international (one value per country) levels. The Gini index is used to measure inequalities in the distribution of a resource (generally disposable income). It is recognized as a key indicator for understanding the Sustainable Development Goal 10 of the United Nations: 'Reduce inequalities within and between countries' (Kraak et al., 2020). We believe that the multi-scale thematic map is useful in the context of understanding this indicator and its spatial distribution. Indeed, the question of
inequalities arises at several levels, at international level (comparison of countries), but also at regional and local ones (do inequalities exist within a country, a city, etc.). Three generalization algorithms are presented in this Jupyter Notebook: aggregation in a regular grid, aggregation in administrative units and kernel density estimation (Figure 2). These three algorithms take a set of point data as input and produce a continuous graphical representation as output. The first two algorithms work in the same way: they calculate the average value of points in each cell/administrative unit. Kernel density estimation (also known as a heatmap) is different, as it calculates an estimated density of points based on distance (weighted by point values).

<figure><img src="image_4.png" /><figcaption>Figure 2. Generalization algorithms presented in the Jupyter Notebook. (a) raw point data (b) aggregation into regular grid (c) aggregation into administrative units (d) kernel density estimation</figcaption></figure>

The data flow follows the following pattern: (1) Import and manipulation of geographic data files via the Geopandas library. (2) If necessary, transformation of data geometries via the Shapely library. (3) Use of the map generalization algorithm itself, with the CartAGen library<fnref n="1" /> or directly with Geopandas. (4) Generation of multi-scale maps using Pydeck and Ipyleaflet, two libraries enabling web mapping in a Python environment. The CartAGen tool is specifically dedicated to map generalization. It was developed at IGN France (the French NMA) to facilitate the use of map generalization.

Concerning the generalization of multi-scale thematic maps, our Jupyter Notebook proposes two approaches. The first is to use a single generalization technique for all scales where data is to be generalized, in order to maintain the consistency of the graphical representation across scales. If we take the example of regular grid aggregation, the only aspect that varies with scale is the size of the grid cells (the larger they are, the more generalized the map). The second approach is to mix generalization methods. The idea is that some techniques are more appropriate than others, depending on the scale. At the regional level, for example, it may be more interesting to aggregate data into administrative units than into a regular grid, as this makes it easier to compare territories.

In conclusion, this demonstration highlights several questions concerning the generalization of multi-scale thematic maps. On the one hand, there are questions about the approach to generalizing the representation of thematic data itself. Indeed, is it preferable to use a single technique for each scale, in order to limit abstraction gaps between scales, or to mix generalization techniques across scales? And if you mix techniques, is it more efficient to let the map reader choose between several modes of representation (via interactive buttons, for example)? Or should this remain the cartographer's choice? On the other hand, there are questions about the integration of topographic data into these maps. As the map generalization method presented here produces a continuous representation, the basemap that serves as a localization facilitator is hidden. In this context, how can we display thematic data without altering the map's spatialization capacity too much? In addition to modifying the opacity of the thematic data layer, it might be worth exploring options such as adding spatial anchors like place names, landmarks or roads above the thematic layer.

## Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 101003012, LostInZoom).

## References

Kraak, M.-J., Roth, R. E., Ricker, B., Kagawa, A. and Le Sourd, G., 2020. *Mapping for a sustainable world*. United Nations, New York, USA.

Raposo, P., Touya, G. and Bereuter, P., 2020. A Change of Theme: The Role of Generalization in Thematic Mapping. *ISPRS International Journal of Geo-Information* 9(6), pp. 371. Publisher: MDPI.

<footnote n="1">¹https://cartagen.readthedocs.io/en/latest/</footnote>