Geography, Coordinates, and Computational Mapping in the Islamic World: Spatial Data, Cartography, and Algorithmic Reasoning

Last Updated June 22, 2026

Geography, Coordinates, and Computational Mapping in the Islamic World examines how geographic knowledge became calculable through coordinates, routes, tables, descriptions, measurements, instruments, astronomical reference points, and mapmaking traditions. In medieval Islamic-world scholarship, geography was not only a descriptive art. It was also a computational practice: locating places, comparing distances, organizing climates, correcting inherited data, plotting regions, estimating routes, coordinating calendars and directions, and making the earth legible through mathematical representation.

This article treats mapping as algorithmic reasoning. A map is not merely a picture of territory. It is a structured representation built from choices about projection, scale, orientation, coordinates, categories, names, routes, coastlines, rivers, cities, climates, and audiences. A coordinate list is not merely a catalogue. It is a data structure for locating places. A route description is not merely travel writing. It can encode sequences, distances, constraints, and decision points.

The importance of Islamic-world computational mapping is that it shows geography as a bridge between mathematics, astronomy, travel, administration, trade, empire, scholarship, and visual reasoning. Geographic calculation drew on inherited Greek materials, Indian and Persian knowledge, merchant and traveler reports, astronomical measurements, administrative needs, and original correction. It transformed location into data and territory into a procedural field of representation.

This article introduces mathematical geography, coordinate lists, al-Khwārizmī’s geographical work, Ptolemaic inheritance and correction, latitude and longitude, climates, routes, travel knowledge, cartographic tables, al-Idrīsī, regional mapping, qibla calculation, astronomical reference points, spatial data, projection, scale, direction, administrative geography, commercial networks, manuscript transmission, and the computational logic of maps. It argues that mapping belongs in algorithmic history because it converts the earth into structured data, procedures, and visual reasoning.

Why Computational Mapping Matters

Computational mapping matters because maps are not simple mirrors of the world. They are constructed representations. They decide what counts as a place, how a place is located, how distances are measured, how directions are organized, how regions are classified, and how spatial knowledge is transmitted. Every map contains procedures of selection, transformation, simplification, and interpretation.

In Islamic-world geography, maps and geographic texts often combined mathematics, astronomy, travel reports, administrative needs, trade routes, pilgrimage routes, climatological zones, and inherited coordinate systems. This mixture makes mapping a powerful case study in algorithmic reasoning: it takes complex reality and turns it into structured, usable representation.

Computational mapping is the art of making location, direction, and territory procedurally knowable.

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Geography as Data Structure

A geographic text can function as a data structure. It may list places, coordinates, distances, directions, boundaries, rivers, mountains, seas, cities, routes, and regions. The entries are organized so that a reader can retrieve, compare, and use spatial information.

This is especially important for algorithmic history because a coordinate list resembles a spatial database before digital databases. It stores records. Each record can contain a place name, latitude, longitude, region, feature type, route connection, or descriptive note. The data can be copied, corrected, rearranged, projected, or visualized.

When geography is organized into structured records, the map becomes computational before it becomes digital.

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Coordinates and the Mathematics of Place

Coordinates turn place into mathematical relation. Instead of describing a city only by story, direction, or political belonging, a coordinate system locates it within a grid. Latitude and longitude create a way to compare places, compute distances approximately, organize maps, and connect geography with astronomy.

Coordinates are algorithmic because they make spatial questions calculable. Which place is farther east? Which lies within a climate band? How might a coastline be drawn from ordered points? How can a table of places be converted into a map? A coordinate system supplies a common frame for these operations.

Coordinates do not merely describe places; they create a mathematical infrastructure for spatial reasoning.

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Al-Khwārizmī and the Description of the Earth

Al-Khwārizmī’s geographical work is important because it shows the same procedural imagination that appears in arithmetic and algebra applied to the earth. His Kitāb Ṣūrat al-Arḍ, often rendered as the Book of the Description/Image of the Earth, organized geographic data in relation to inherited Ptolemaic geography while revising and extending spatial knowledge.

For algorithmic history, the key point is not simply that al-Khwārizmī wrote about geography. It is that geography could be organized as coordinate data, corrected, tabulated, transmitted, and potentially plotted. This is a form of computational representation: places become entries, entries become coordinates, coordinates become a basis for mapping, and mapping becomes a procedure for representing the world.

Al-Khwārizmī’s geography shows place becoming structured data.

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Ptolemy, Correction, and Transmission

Islamic-world geography did not emerge in isolation. It inherited, translated, criticized, corrected, and transformed earlier materials, especially Ptolemaic geography. Ptolemy’s coordinate-based approach provided a powerful framework, but inherited frameworks require evaluation. They may contain distortions, missing information, inaccurate distances, or assumptions that need correction.

Correction is algorithmic work. It compares data against observation, travel knowledge, astronomical measurement, or alternative reports. It identifies error, revises values, reorganizes records, and transmits improved representations. This is close to what modern data work calls cleaning, validation, calibration, and model revision.

Transmission is not passive preservation. It is computational maintenance across cultures and media.

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Climates, Zones, and Regional Organization

Medieval geography often organized the inhabited world through climates, zones, or regional divisions. These classifications could be connected with latitude, environmental variation, astronomy, political geography, travel knowledge, or textual tradition. A climate system reduces the world’s complexity by grouping places into meaningful bands or regions.

This is algorithmic because classification changes what can be compared. A city can be located not only by coordinate, but by zone. A route can pass through regions. A map can be organized by bands. A table can be sorted by climate and longitude. Regional organization supplies an indexing system for spatial knowledge.

Zones and regions turn geography into a searchable, comparable, and administrable structure.

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Routes, Distance, and Network Reasoning

Geography is not only about points. It is also about movement between points. Trade, pilgrimage, diplomacy, military planning, postal systems, administration, scholarship, and travel all required knowledge of routes, distances, stages, hazards, ports, roads, and connections.

Routes are algorithmic because they are ordered paths. A route description may say where to begin, which stage follows, how far to travel, what constraint matters, and what destination comes next. A route network turns geography into a graph: places are nodes, roads or sea lanes are edges, and distances or risks are weights.

Route knowledge is geographic computation over networks of places.

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Al-Idrīsī and Cartographic Synthesis

Al-Idrīsī’s twelfth-century geographical work is one of the most famous examples of medieval cartographic synthesis. Working in the court of Roger II of Sicily, he compiled geographic knowledge from texts, reports, routes, and learned traditions into a major map and descriptive geography. The significance is not only visual. It is computational: information from many sources had to be compared, selected, organized, and represented.

Cartographic synthesis requires procedures. Reports must be reconciled. Regions must be divided. Routes must be compared. Coastlines must be shaped. Descriptions must be linked to places. A map must choose what to include and how to arrange it. This is spatial reasoning at scale.

Al-Idrīsī’s work reminds us that mapping is a procedure for synthesizing evidence, not only drawing outlines.

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Qibla, Timekeeping, and Directional Calculation

Geography in Islamic contexts was often connected to religious, astronomical, and institutional needs. Qibla calculation required directional reasoning toward Mecca. Prayer-time calculation required local solar relations. Calendrical questions required timekeeping. These practices connected geography with spherical reasoning, astronomy, instruments, tables, and practical methods.

Directional calculation is algorithmic because it asks for a procedure: given a location, determine a direction. The input is a place. The output is an orientation. The method may involve geometry, astronomy, tables, instruments, or approximate rules. The result is applied in practice.

Directional calculation shows geography as applied spatial computation.

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Projection, Scale, and Map Choice

Every map transforms the earth. A curved surface must be represented on a flat page, a manuscript folio, a diagram, a sectional map, or a circular image. Projection and scale are not decorative choices. They determine what kind of distortion is tolerated, what purpose the map serves, and what kinds of reasoning the map supports.

Algorithmically, projection is a transformation from one coordinate system to another. Scale is a compression rule. Orientation is a convention. Omission is a filtering decision. Classification is a layer. A map is therefore a bundle of transformations.

Maps are algorithms of attention: they compute what can be seen, compared, and used.

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Administration, Commerce, and Spatial Order

Mapping matters institutionally because territory matters. Trade routes, tax districts, roads, ports, cities, frontiers, pilgrimage routes, water systems, agricultural zones, and administrative regions all require spatial knowledge. Geography can therefore serve commerce, governance, scholarship, travel, and planning.

This does not make geography neutral. Spatial representation can support navigation and learning, but it can also support control, taxation, military movement, extraction, boundary-making, and administrative power. Computational mapping must therefore be studied as both knowledge and institution.

Spatial knowledge is never only spatial. It organizes action.

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Manuscripts, Copying, and Spatial Transmission

Geographic knowledge traveled through manuscripts, maps, tables, route books, administrative records, travel writing, astronomical handbooks, teaching traditions, and later translations. Copying a map or coordinate list is not simple reproduction. It can introduce errors, corrections, regional adaptations, omissions, and reinterpretations.

Spatial transmission is computational because data must survive movement across media. Names change. Coordinates shift. Routes are updated. Maps are redrawn. New places are added. Older claims are corrected. A geographic manuscript is therefore a versioned spatial artifact.

Every copied map is also a computational event: data is preserved, transformed, or lost.

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From Medieval Coordinate Lists to Modern GIS

Modern geographic information systems use coordinates, layers, projections, metadata, databases, routes, networks, distance functions, and visualization tools. Medieval Islamic-world geography is not GIS in the modern sense, but it belongs to the deeper history of spatial computation because it organized geographic knowledge into structured, reusable, and transformable forms.

The continuity lies in the computational problem: how do we represent the earth so that people can reason about location, movement, region, and distance? The tools have changed dramatically. The underlying representational challenge remains familiar.

Computational mapping has always depended on the same core question: how should the world be represented so that action becomes possible?

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Origin Stories and Careful Interpretation

Geography, coordinates, and mapping in the Islamic world should not be turned into a single-origin story. Islamic-world geography drew from Greek, Persian, Indian, Arabic, Syriac, local, maritime, administrative, and traveler knowledge. It also produced original corrections, syntheses, maps, route systems, instruments, and textual traditions. The point is not to replace one origin myth with another. The point is to trace how spatial knowledge was translated, tested, reorganized, and used.

Careful interpretation also avoids treating maps as neutral. Maps simplify, center, omit, classify, and persuade. A coordinate list may look objective, but it depends on measurement methods, reference points, transmission, and selection. Computational mapping must always be read as both mathematics and worldview.

A careful history treats mapping as mathematical, institutional, cultural, and computational at once.

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Mathematics, Computation, and Modeling

A place record can be modeled as:

A route can be modeled as:

A distance approximation between nearby coordinate points can be represented as:

A mapping pipeline can be written as:

These formulas use modern notation to make spatial procedure visible. They are interpretive models, not claims that medieval authors used this exact symbolic form.

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Python Workflow: Computational Mapping Procedure Map

The Python workflow below creates a dependency-light interpretive map of geography, coordinates, and computational mapping in the Islamic world. It scores themes by spatial representation, coordinate structure, procedural clarity, institutional use, correction awareness, transmission importance, and modern resonance, then writes reproducible CSV and JSON outputs.

# geography_coordinates_computational_mapping_map.py
# Dependency-light workflow for mapping geography and coordinates as computational reasoning.

from __future__ import annotations

from dataclasses import asdict, dataclass
from pathlib import Path
from statistics import mean
import csv
import json
from datetime import datetime, timezone

ARTICLE_ROOT = Path(__file__).resolve().parents[1]
TABLES = ARTICLE_ROOT / "outputs" / "tables"
JSON_DIR = ARTICLE_ROOT / "outputs" / "json"


@dataclass(frozen=True)
class ComputationalMappingConfig:
    article: str = "geography_coordinates_and_computational_mapping_in_the_islamic_world"
    core_threshold: float = 0.80
    high_spatial_threshold: float = 0.86


def timestamp_utc() -> str:
    return datetime.now(timezone.utc).isoformat()


def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    if not rows:
        path.write_text("", encoding="utf-8")
        return
    fieldnames = sorted({key for row in rows for key in row.keys()})
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore")
        writer.writeheader()
        writer.writerows(rows)


def write_json(path: Path, payload: object) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")


def computational_mapping_themes() -> list[dict[str, object]]:
    return [
        {"theme_id": "coordinates_as_spatial_data", "spatial_representation": 0.98, "coordinate_structure": 0.98, "procedural_clarity": 0.88, "institutional_use": 0.86, "correction_awareness": 0.90, "transmission_importance": 0.90, "modern_resonance": 0.96},
        {"theme_id": "al_khwarizmi_geographical_tables", "spatial_representation": 0.94, "coordinate_structure": 0.96, "procedural_clarity": 0.88, "institutional_use": 0.84, "correction_awareness": 0.92, "transmission_importance": 0.94, "modern_resonance": 0.94},
        {"theme_id": "routes_as_network_reasoning", "spatial_representation": 0.90, "coordinate_structure": 0.78, "procedural_clarity": 0.92, "institutional_use": 0.94, "correction_awareness": 0.82, "transmission_importance": 0.88, "modern_resonance": 0.94},
        {"theme_id": "climates_and_regional_classification", "spatial_representation": 0.88, "coordinate_structure": 0.84, "procedural_clarity": 0.86, "institutional_use": 0.88, "correction_awareness": 0.82, "transmission_importance": 0.88, "modern_resonance": 0.90},
        {"theme_id": "al_idrisi_cartographic_synthesis", "spatial_representation": 0.96, "coordinate_structure": 0.84, "procedural_clarity": 0.88, "institutional_use": 0.90, "correction_awareness": 0.88, "transmission_importance": 0.94, "modern_resonance": 0.92},
        {"theme_id": "qibla_and_directional_calculation", "spatial_representation": 0.90, "coordinate_structure": 0.88, "procedural_clarity": 0.94, "institutional_use": 0.96, "correction_awareness": 0.86, "transmission_importance": 0.88, "modern_resonance": 0.94},
        {"theme_id": "projection_scale_and_map_choice", "spatial_representation": 0.94, "coordinate_structure": 0.90, "procedural_clarity": 0.86, "institutional_use": 0.86, "correction_awareness": 0.90, "transmission_importance": 0.84, "modern_resonance": 0.96},
    ]


def score_theme(row: dict[str, object], config: ComputationalMappingConfig) -> dict[str, object]:
    mapping_score = mean([
        float(row["spatial_representation"]),
        float(row["coordinate_structure"]),
        float(row["procedural_clarity"]),
        float(row["institutional_use"]),
        float(row["correction_awareness"]),
        float(row["transmission_importance"]),
        float(row["modern_resonance"]),
    ])

    if mapping_score >= config.core_threshold and float(row["spatial_representation"]) >= config.high_spatial_threshold:
        interpretive_status = "core_computational_mapping_thread"
    elif mapping_score >= config.core_threshold:
        interpretive_status = "major_computational_mapping_thread"
    else:
        interpretive_status = "supporting_computational_mapping_thread"

    return {
        "theme_id": row["theme_id"],
        "spatial_representation": round(float(row["spatial_representation"]), 6),
        "coordinate_structure": round(float(row["coordinate_structure"]), 6),
        "procedural_clarity": round(float(row["procedural_clarity"]), 6),
        "institutional_use": round(float(row["institutional_use"]), 6),
        "correction_awareness": round(float(row["correction_awareness"]), 6),
        "transmission_importance": round(float(row["transmission_importance"]), 6),
        "modern_resonance": round(float(row["modern_resonance"]), 6),
        "mapping_score": round(mapping_score, 6),
        "interpretive_status": interpretive_status,
    }


def interpretation_cautions() -> list[dict[str, str]]:
    return [
        {"caution": "do_not_treat_maps_as_mirrors", "meaning": "Maps are constructed representations shaped by projection, scale, selection, and purpose."},
        {"caution": "do_not_treat_coordinates_as_context_free", "meaning": "Coordinates depend on reference systems, measurement practices, copying, and correction."},
        {"caution": "do_not_reduce_islamic_geography_to_preservation", "meaning": "Islamic-world geography inherited, corrected, extended, and synthesized earlier materials."},
        {"caution": "do_not_ignore_routes_and_networks", "meaning": "Geographic computation includes movement, sequence, distance, and connection."},
        {"caution": "do_not_project_modern_gis_backward", "meaning": "Medieval coordinate lists and maps are spatial-computational artifacts, but not modern GIS."},
    ]


def main() -> None:
    config = ComputationalMappingConfig()
    themes = computational_mapping_themes()
    scored = [score_theme(row, config) for row in themes]
    cautions = interpretation_cautions()

    summary = {
        "article": config.article,
        "timestamp_utc": timestamp_utc(),
        "themes_reviewed": len(scored),
        "core_threads": sum(1 for row in scored if row["interpretive_status"] == "core_computational_mapping_thread"),
        "major_threads": sum(1 for row in scored if row["interpretive_status"] == "major_computational_mapping_thread"),
        "supporting_threads": sum(1 for row in scored if row["interpretive_status"] == "supporting_computational_mapping_thread"),
        "mean_mapping_score": round(mean(float(row["mapping_score"]) for row in scored), 6),
        "cautions": len(cautions),
        "interpretation": "Computational mapping should be studied as spatial algorithmic reasoning: coordinates, routes, regions, projections, correction, transmission, and institutional use.",
    }

    write_csv(TABLES / "computational_mapping_themes.csv", themes)
    write_csv(TABLES / "computational_mapping_map.csv", scored)
    write_csv(TABLES / "interpretation_cautions.csv", cautions)
    write_csv(TABLES / "computational_mapping_summary.csv", [summary])

    write_json(JSON_DIR / "computational_mapping_config.json", asdict(config))
    write_json(JSON_DIR / "computational_mapping_map.json", scored)
    write_json(JSON_DIR / "interpretation_cautions.json", cautions)
    write_json(JSON_DIR / "computational_mapping_summary.json", summary)

    print("Geography, coordinates, and computational mapping map complete.")
    print(TABLES / "computational_mapping_summary.csv")


if __name__ == "__main__":
    main()

This workflow turns geographic mapping into a reproducible interpretive artifact: spatial representation, coordinate structure, procedural clarity, institutional use, correction awareness, transmission importance, modern resonance, mapping score, and historical caution are documented together.

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R Workflow: Computational Mapping Diagnostics

The R workflow reads the generated CSV outputs, summarizes computational mapping themes, visualizes theme dimensions, and writes an additional diagnostic table.

# geography_coordinates_computational_mapping_summary.R
args <- commandArgs(trailingOnly = FALSE)
file_arg <- grep("^--file=", args, value = TRUE)

if (length(file_arg) > 0) {
  script_path <- normalizePath(sub("^--file=", "", file_arg[1]), mustWork = TRUE)
  article_root <- normalizePath(file.path(dirname(script_path), ".."), mustWork = TRUE)
} else {
  article_root <- getwd()
}

setwd(article_root)

tables_dir <- file.path(article_root, "outputs", "tables")
figures_dir <- file.path(article_root, "outputs", "figures")
dir.create(tables_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(figures_dir, recursive = TRUE, showWarnings = FALSE)

map_path <- file.path(tables_dir, "computational_mapping_map.csv")
summary_path <- file.path(tables_dir, "computational_mapping_summary.csv")

if (!file.exists(map_path)) {
  stop(paste("Missing", map_path, "Run the Python workflow first."))
}

mapping_map <- read.csv(map_path, stringsAsFactors = FALSE)
summary <- read.csv(summary_path, stringsAsFactors = FALSE)

png(file.path(figures_dir, "computational_mapping_dimensions.png"), width = 1200, height = 850)
score_matrix <- t(as.matrix(mapping_map[, c("spatial_representation", "coordinate_structure", "procedural_clarity", "institutional_use", "correction_awareness", "transmission_importance", "modern_resonance")]))
barplot(score_matrix,
        beside = TRUE,
        names.arg = mapping_map$theme_id,
        las = 2,
        ylim = c(0, 1),
        ylab = "Interpretive Score",
        main = "Geography, Coordinates, and Computational Mapping Dimensions")
legend("bottomright",
       legend = rownames(score_matrix),
       cex = 0.72,
       bty = "n")
grid()
dev.off()

png(file.path(figures_dir, "mapping_score_by_theme.png"), width = 1000, height = 750)
barplot(mapping_map$mapping_score,
        names.arg = mapping_map$theme_id,
        las = 2,
        ylab = "Mapping Score",
        main = "Computational Mapping Score by Theme")
grid()
dev.off()

r_summary <- data.frame(
  themes_reviewed = summary$themes_reviewed[1],
  core_threads = summary$core_threads[1],
  major_threads = summary$major_threads[1],
  supporting_threads = summary$supporting_threads[1],
  mean_mapping_score = summary$mean_mapping_score[1],
  cautions = summary$cautions[1],
  diagnostic_note = "Computational mapping should be studied as spatial algorithmic reasoning: coordinates, routes, regions, projections, correction, transmission, and institutional use."
)

write.csv(r_summary, file.path(tables_dir, "r_computational_mapping_diagnostic_summary.csv"), row.names = FALSE)
print(r_summary)

The R layer makes the interpretive structure visible: coordinates, al-Khwārizmī’s geography, routes, climate zones, al-Idrīsī, qibla, projection, scale, correction, and transmission can be examined as related but distinct dimensions of computational mapping.

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GitHub Repository

The companion repository contains reproducible workflows, synthetic interpretive data, outputs, calculators, documentation, and multilingual examples for this article.

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A Practical Method for Studying Computational Mapping

A careful study of computational mapping should ask how spatial knowledge is represented, transformed, transmitted, and used. The method below treats maps and coordinate lists as structured artifacts.

This method treats geography as data, procedure, image, text, and institution at once.

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Common Pitfalls

The first pitfall is treating maps as neutral pictures. The second is treating coordinates as context-free facts. The third is treating Islamic-world geography only as preservation of Ptolemy rather than correction, synthesis, and extension. The fourth is projecting modern GIS backward too directly.

Computational mapping becomes clearer when maps are read as procedures, not just images.

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Why Computational Mapping Belongs in Algorithmic Reasoning

Geography, coordinates, and computational mapping in the Islamic world belong in algorithmic reasoning because they show how place becomes data and how territory becomes representation. Coordinates locate. Routes sequence. Zones classify. Projections transform. Tables store. Corrections revise. Maps synthesize. Institutions use the result.

This history broadens the meaning of computation. Computation is not only arithmetic, algebra, or machine execution. It is also the disciplined organization of spatial knowledge so that people can reason about where things are, how they connect, how far apart they lie, and how they can be represented.

The lesson for modern systems is direct. Every geospatial model depends on reference frames, data quality, projection, scale, classification, documentation, and institutional purpose. Medieval Islamic-world mapping reminds us that spatial computation has always been both mathematical and cultural. AI belongs in the toolkit, not in control.

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Related Articles

• Astronomical Tables, Calendars, and Algorithmic Prediction
• Al-Kindī, Cryptanalysis, and Statistical Reasoning
• Al-Khwārizmī, Algorism, and the Procedural Imagination
• Hindu-Arabic Numerals and the Transmission of Positional Calculation
• Algorithms in Climate, Energy, and Infrastructure

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Further Reading

• Harley, J.B. and Woodward, D. (eds.) (1992) The History of Cartography, Volume 2, Book 1: Cartography in the Traditional Islamic and South Asian Societies. Chicago: University of Chicago Press.
• King, D.A. (1999) World-Maps for Finding the Direction and Distance to Mecca. Leiden: Brill.
• Kennedy, E.S. (1983) ‘Mathematical geography’, in Rashed, R. (ed.) Encyclopedia of the History of Arabic Science. London: Routledge.
• Edson, E. and Savage-Smith, E. (2004) Medieval Views of the Cosmos. Oxford: Bodleian Library.
• al-Idrīsī (trans. 1970) Opus Geographicum. Naples and Rome: Istituto Universitario Orientale.
• MacTutor History of Mathematics (n.d.) ‘Cartography’. University of St Andrews.
• Library of Congress (2021) ‘The Islamic World Map of 1154’.

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References

• Edson, E. and Savage-Smith, E. (2004) Medieval Views of the Cosmos. Oxford: Bodleian Library.
• Harley, J.B. and Woodward, D. (eds.) (1992) The History of Cartography, Volume 2, Book 1: Cartography in the Traditional Islamic and South Asian Societies. Chicago: University of Chicago Press.
• Kennedy, E.S. (1983) ‘Mathematical geography’, in Rashed, R. (ed.) Encyclopedia of the History of Arabic Science. London: Routledge.
• King, D.A. (1999) World-Maps for Finding the Direction and Distance to Mecca. Leiden: Brill.
• Library of Congress (2021) ‘The Islamic World Map of 1154’. Available at: https://blogs.loc.gov/loc/2021/08/the-islamic-world-map-of-1154/.
• Library of Congress (2022) ‘Al-Idrisi’s Masterpiece of Medieval Geography’. Available at: https://blogs.loc.gov/maps/2022/01/al-idrisis-masterpiece-of-medieval-geography/.
• MacTutor History of Mathematics (n.d.) ‘Cartography’. University of St Andrews. Available at: https://mathshistory.st-andrews.ac.uk/HistTopics/Cartography/.
• Rapoport, Y. and Savage-Smith, E. (eds.) (2018) An Eleventh-Century Egyptian Guide to the Universe: The Book of Curiosities. Leiden: Brill.

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