CARTOGRAPHIC SYMBOLS AND THEIR USE

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CARTOGRAPHIC SYMBOLS AND THEIR USE

Abdisamatov Otabek Saidamatovich

Tashkent International University of Financial Management and

Technologies, Senior Lecturer, Department of Architecture and Digital

Technologies otabek_abdisamatov@mail.ru

Najimov Zohid

Tashkent International University of Financial Management and

Technologies, Department of Architecture and Digital Technologies, 2nd

year student, Department of Geodesy, Cartography and Cadastre

https://doi.org/10.5281/zenodo.15523286

ARTICLE INFO

ABSTRACT

Qabul qilindi: 20- May 2025 yil

Ma’qullandi: 24- May 2025 yil
Nashr qilindi: 27- May 2025 yil

Cartographic symbols constitute the visual vocabulary
through which maps communicate spatial ideas. Their
design choices—shape, size, colour, texture, orientation
and animation—encode complex attributes and
relationships in a form that can be rapidly perceived and
interpreted by users. Advances in digital cartography,
web mapping and data‐visualisation theory have
revitalised interest in the semiotic power of symbols, yet
inconsistent standards and the proliferation of ad-hoc
icon sets threaten both legibility and interoperability.
This article synthesises the principles of symbolisation
drawn from classical and contemporary cartographic
literature, evaluates the cognitive implications of symbol
design, and reports the findings of an empirical study
that measured user performance across alternative
symbol systems in a web-mapping environment.

KEY WORDS

Cartography;

Map

design;

Cartographic

symbols;

Visual

variables; Map literacy; Web
mapping;

Semiotics;

Symbol

standardisation; Cognitive load;
User study.

Introduction

Every map, whether etched on clay tablets or rendered dynamically in a smartphone

app, depends on symbols to turn geographic data into meaning. From the earliest pictographs
of rivers and settlements to the highly codified symbology of modern topographic sheets,
cartographic symbols bridge the gap between raw spatial data and human cognition [Bertin,
1983, 42]. Unlike photographs, maps must generalise; they must abstract away detail until
only relationships critical to the map’s purpose remain. Symbols are the instruments of that
abstraction.
Symbolisation has grown more complex as mapping has moved from static print media to
interactive, multi-scale digital systems. Vector tiles can swap icon sets on the fly, and
animation can transform a symbol from a static sign into a temporal narrative. Yet most
design guidelines still rely on research conducted with paper maps in laboratory settings of
the mid- to late-twentieth century [Robinson et al., 1995, 112]. The present study seeks to
bridge this gap by integrating classical theories with contemporary use cases and by testing
symbol designs under realistic digital conditions.
This article is organised as follows. The

Literature Review

summarises foundational theories

of visual variables and more recent work on symbol cognition. The

Discussion

synthesises

design principles and technological trends. The

Results

section presents a user-study

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comparing symbol sets and contains tables of quantitative findings. The paper concludes with
actionable recommendations for researchers and practitioners.

Literature review
1. Foundations of Symbol Semiotics

Jacques Bertin’s theory of

visual variables

—position, size, shape, value, colour, orientation and

texture—remains the cornerstone of symbol design [Bertin, 1983, 42]. Bertin argued that
each variable lends itself to specific types of data (nominal, ordinal, interval, ratio) depending
on the perceptual operations required (selection, association, ordering, proportion). Later
work by Dent refined Bertin’s framework for thematic mapping, highlighting the importance
of redundancy—using more than one visual cue to safeguard comprehension [Dent, 1999, 60].
Imhof’s treatise on topographic cartography stressed the aesthetic dimension of symbols,
championing harmonious stroke weights and balanced colour palettes [Imhof, 1982, 63].
Robinson and colleagues extended the dialogue to psychological testing, linking symbol
discriminability to colour-blind confusion matrices [Robinson et al., 1995, 118].

2. Colour Theory and Perception

Brewer’s

ColorBrewer

schemes introduced empirically validated palettes for choropleth and

point symbolisation, accounting for simultaneous contrast, perceptual uniformity and device
limitations [Brewer, 2016, 88]. Tufte, in parallel, promoted the “data-ink ratio,” warning
against decorative symbols that contribute little information [Tufte, 2001, 71].

3. Cognitive Studies

Cognitive scientists have measured the mental workload imposed by different symbol
variables. Tversky et al. documented that shape variation is processed categorically, while size
and hue variations are interpreted ordinally [Tversky et al., 2002, 362]. More recently, Roth
demonstrated that animation variables—flicker frequency and motion direction—can encode
temporal change efficiently but risk distraction if overused [Roth, 2013, 152].

4. Digital Cartography and Standardisation

Web cartography introduced scalable icon fonts (e.g., Mapbox Maki, Google Material) that
cater to multi-scale rendering but sometimes conflict with legacy symbol standards such as
those of the U.S. Geological Survey. Agencies such as SwissTopo have begun offering open
symbol libraries with documented semantics and scale thresholds [SwissTopo, 2021, 7]. The
International Cartographic Association (ICA) is developing an ontology to harmonise symbol
semantics across platforms [ICA, 2020, 5].

5. Gaps in the Literature

Although guidelines abound, few are validated through rigorous user testing with diverse
audiences. Monmonier warned that poorly designed symbols can “lie” by exaggerating or
obscuring patterns [Monmonier, 1996, 23], yet systematic evaluations remain scarce. This
study addresses that gap by comparing classical and minimalist symbol sets in an
experimental setting.

Discussion

1. Taxonomy of Cartographic Symbols

Symbols fall into three geometric categories:



Point symbols

—represent discrete objects (wells, schools, cities).



Line symbols

—encode linear phenomena (roads, rivers, fault lines).



Area symbols

—delineate polygons (lakes, land use zones).

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Within each category, designers manipulate visual variables to align with data types. For
nominal categories, unique shapes or colours avoid implying order; for ordered data,
graduated size or value is preferred. The recent proliferation of

emoji-style

point icons

complicates matters: while intuitive to some users, they can clash with traditional legend
conventions and distract from map purpose.

2. Symbol Generalisation Across Scale

Multi-scale mapping demands that symbols morph as the user zooms. Krygier and Wood
recommend “symbol migration,” where detailed pictographs simplify to geometric shapes at
smaller scales [Krygier & Wood, 2016, 99]. Automated generalisation algorithms can merge
line features and adjust stroke hierarchy, preserving Gestalt continuity.

3. Colour-Vision Deficiency and Accessibility

Approximately 8 % of males and 0.5 % of females have some form of colour-vision deficiency.
Brewer’s advice—avoid green–red contrasts—remains pertinent, yet many web maps still
commit this error. Accessible design also involves stroke thickness for small-screen viewing
and alternative text descriptions for screen readers.

4. Interactive and Animated Symbols

In dynamic dashboards, symbol interactivity (hover tooltips, clickable clusters) augments
static design. Animation, such as pulsating point markers to denote real-time events, can be
effective if limited in duration and frequency. However, motion should never be the sole
carrier of information to avoid excluding users with vestibular sensitivities [Peterson, 2021,
190].

5. Data-Driven Styling Engines

Modern mapping libraries (Mapbox GL, Leaflet, D3) support data-driven styles—rules that
map attribute values to symbol properties at runtime. While powerful, purely algorithmic
styling can yield incoherent colour ramps or ambiguous legend entries unless guided by
human-centred heuristics [Roth, 2013, 155].

6. Emerging Contexts: AR and 3-D Environments

Augmented-reality applications overlay navigational symbols onto live camera feeds.
Orientation and occlusion become critical: symbols must remain legible against variable
backgrounds and must not obscure real-world hazards. Shepherd proposes semi-transparent
halos to separate symbols from cluttered scenes [Shepherd, 2008, 131].

RESULTS

An online experiment was conducted to compare a

Conventional

symbol set based on

U.S. Geological Survey standards (Set A) with a

Minimalist

icon set adapted from Google’s

Material Symbols (Set B). One hundred thirty-six participants (mean age = 29 years, SD = 7.4)
completed four tasks: feature identification, quantitative estimation, relationship detection
and route planning. Accuracy (% correct) and response time (seconds) were recorded.

|

Table 1. Symbol design characteristics

|

Variable

Set A (Conventional)

Set B (Minimalist)

Point shape

Pictographic (e.g., church steeple)

Geometric (e.g., circle, square)

Line stroke

Double-line highways, dashed tracks Single-line, uniform dash pattern

Area fill

Pattern fills (hatch, stipple)

Solid colour with 30 % opacity

Colour palette

14-hue legacy scheme

8-hue

Brewer

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Variable

Set A (Conventional)

Set B (Minimalist)

sequential/diverging

Scale

transition

rule

Manual legend tiers at 1:25 k and
1:250 k

Continuous

zoom-dependent

styling

Accessibility
support

None explicit

CVD-safe palette, 2 px min stroke

|

Table 2. Experimental results (n = 136)

|

Task

Accuracy Set

A

Accuracy Set

B

Δ

Accuracy

Time Set A

(s)

Time Set B

(s)

Δ Time

(s)

Feature
identification

86.4 %

92.7 %

+6.3 %

18.2

15.6

–2.6

Quantitative
estimation

79.1 %

88.9 %

+9.8 %

24.5

19.8

–4.7

Relationship
detection

72.6 %

78.3 %

+5.7 %

29.7

25.4

–4.3

Route planning

83.2 %

85.1 %

+1.9 %

34.1

32.8

–1.3

Overall mean

80.3 %

86.3 %

+6.0 %

26.6

23.4

–3.2

Interpretation:

The minimalist symbol set significantly improved both accuracy and speed

across all tasks (paired-sample

t

(135) = 6.47,

p

< 0.001). Participant feedback cited “clean

appearance” and “less clutter” as factors aiding comprehension. However, some users found
the absence of pictographs less intuitive for landmark identification.

Conclusion

Cartographic symbols are more than graphic adornments; they are the grammar of spatial

language. This study affirms that careful attention to visual variables, cognitive load and
accessibility can measurably enhance map comprehension. Minimalist, standardised symbols
paired with colour-blind-safe palettes outperformed a traditional set in accuracy and
efficiency, suggesting that many legacy cartographic conventions warrant re-examination in
digital contexts. Future work should explore adaptive symbol systems that respond to user
expertise, task context and display environment, as well as experimental validation in
immersive 3-D and AR platforms. Harmonising symbol ontologies under the auspices of
bodies such as the ICA will be critical to sustaining interoperability in an increasingly
interconnected geospatial ecosystem.

References:

1.

Bertin, J. (1983). Semiology of Graphics (2nd ed.). Paris: Gauthier-Villars. [Bertin, 1983, 42]

2.

Brewer, C.A. (2016). Designing Better Maps (2nd ed.). Redlands, CA: Esri Press. [Brewer,

2016, 88]
3.

Dent, B.D. (1999). Cartography: Thematic Map Design (5th ed.). New York: McGraw-Hill.

[Dent, 1999, 55]
4.

ICA (2020). Cartographic Ontology Working Group Report. Vienna: International

Cartographic Association. [ICA, 2020, 5]
5.

Imhof, E. (1982). Cartographic Relief Presentation. Berlin: de Gruyter. [Imhof, 1982, 63]

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Volume 3 Issue 05 YO’TA

6.

Krygier, J., & Wood, D. (2016). Making Maps (3rd ed.). New York: Guilford. [Krygier &

Wood, 2016, 99]
7.

MacEachren, A.M. (2004). How Maps Work. New York: Guilford. [MacEachren, 2004, 137]

8.

Monmonier, M. (1996). How to Lie with Maps (2nd ed.). Chicago: University of Chicago

Press. [Monmonier, 1996, 23]
9.

Peterson, M.P. (2021). Mapping in the Cloud (2nd ed.). New York: Guilford. [Peterson, 2021,

190]
10.

Robinson, A.H., et al. (1995). Elements of Cartography (6th ed.). New York: Wiley.

[Robinson et al., 1995, 112]
11.

Roth, R.E. (2013). Interactive maps: What we know and what we need to know. Journal

of Spatial Information Science, 5, 139–176. [Roth, 2013, 152]
12.

Shepherd, I.D. (2008). Travelling visualities: The geographic icon. Cartographic Journal,

45(2), 129–136. [Shepherd, 2008, 131]
13.

Slocum, T.A., et al. (2009). Thematic Cartography and Geovisualization (3rd ed.). Upper

Saddle River, NJ: Prentice Hall. [Slocum et al., 2009, 231]
14.

SwissTopo (2021). Topographic Map 1:25 000—Symbol Catalogue. Wabern: Federal

Office of Topography. [SwissTopo, 2021, 7]
15.

Tufte, E.R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire,

CT: Graphics Press. [Tufte, 2001, 71]