Global Urban Centres (GUC)

Database Title

Global Urban Centres (GUC)

Overview

This dataset contains the geographic coordinates (latitude and longitude) of 3,402 city centres worldwide, manually identified and compiled by Paul Kilgarriff beginning in 2021. It represents approximately two months of systematic manual work to establish reliable functional city centre locations for global urban research. The dataset addresses a significant gap in publicly available data on functional city centre locations at global scale, particularly for cities in the developing world where standard administrative centre data is often unreliable or unavailable.

The Global Urban Centres dataset is, to the author's knowledge, one of the most comprehensive manually verified collections of functional city centre coordinates currently available for public use. Unlike automated or algorithmically generated coordinate datasets, every location in this database was individually inspected and verified by Paul Kilgarriff using a consistent and documented methodological framework.

Version History

The original version of this dataset was compiled by Paul Kilgarriff in 2021 and made available to research collaborators prior to this formal Zenodo publication. Subsequent versions incorporated additional cities, methodological refinements and data cleaning processes. This Zenodo record represents the formal public release of that work and establishes the complete, authoritative methodological record of its development and provenance. The dataset has been used in collaborative urban research contexts since 2021.

Background and Motivation

The study of urban population density and intra-urban spatial structure requires reliable anchor points from which radial and distance based analyses can be conducted. The functional centre of a city, typically its central business district or historic administrative core, serves as this anchor point in a wide range of urban research methodologies.

Despite the importance of this data, no comprehensive, manually verified global dataset of functional city centres existed at the time of compilation in 2021. Existing coordinate sources either relied on automated geocoding of city names, which frequently returns geometric centroids or administrative boundaries rather than functional centres, or were limited in geographic scope to specific regions or city size thresholds.

This dataset was compiled specifically to address that gap, providing researchers with a reliable, methodologically consistent set of functional city centre coordinates suitable for global scale urban analysis.

Data Sources

The city list was primarily derived from the United Nations World Urbanization Prospects (WUP) datasets, with a focus on two specific populations. The first comprises cities with populations of 300,000 inhabitants or more. The second comprises all major urban agglomerations globally, ensuring comprehensive geographic coverage including smaller urban centres in underrepresented regions.

Initial coordinate identification used visual inspection of publicly available mapping platforms including OpenStreetMap and Google Maps. These platforms were used to determine the functional centre of each city through direct visual assessment of urban morphology, building density, administrative infrastructure and landmark presence.

Methodology and Selection Hierarchy

The city centres were identified using a specific hierarchical decision-making framework developed by Paul Kilgarriff to ensure consistency and replicability across diverse urban systems worldwide. This methodology represents the core intellectual contribution of this dataset and was applied systematically across all 3,402 cities in the database.

The need for a hierarchical and region-specific framework arose from the significant variation in urban morphology, administrative structures and available landmark data across different world regions. A single universal criterion such as city hall location, which works well in most European and North American cities, is frequently inappropriate or unavailable in cities across Asia, Africa, the Middle East and Latin America. The methodology developed here accounts for this variation systematically.

The selection priority followed these criteria:

Primary: The City Hall or equivalent local government administrative building. This was used as the primary indicator of the functional urban core wherever it was clearly located within the central urban area and verifiable through mapping platforms.

Secondary: If a city hall was not available, was unidentifiable on available mapping platforms, or was clearly located outside the functional urban core, a central building such as the Central Post Office, main railway station or principal plaza or square was used as the functional centre indicator.

Regional Specificity (India): For cities in India, the key train station or central post office was selected as the functional centre. This decision reflects the historical and functional centrality of railway infrastructure in Indian urban development, where major railway stations frequently anchor the commercial and administrative core of the city.

Regional Specificity (Middle East and North Africa): For cities in the Middle East and North Africa region, the Bazaar or central mosque was used as the most reliable indicator of the functional urban core. These landmarks reflect the historic organisation of urban space in the region and consistently correspond to areas of highest urban density and activity.

Regional Specificity (Africa): For cities in sub-Saharan Africa, the central marketplace or City Hall was used as the primary centre indicator, reflecting the role of market infrastructure as the functional anchor of urban activity in many African cities.

Regional Specificity (South America): For cities in South America, the Plaza Central, municipal hall or mayor's house was used where identifiable. The central plaza tradition in Latin American urban planning, derived from Spanish colonial urban design principles, provides a consistent and reliable functional centre indicator across the region.

Regional Specificity (China): For cities in China, a large central government building identified through satellite imagery was used as the functional centre indicator, reflecting the organisation of Chinese urban space around administrative infrastructure.

All city centre coordinates were manually identified and recorded by Paul Kilgarriff through individual inspection of each urban area using publicly available mapping platforms. Where the functional centre was ambiguous or unclear, additional verification was performed using building footprint data from aerial photography and population density indicators available through mapping platforms. The rationale for each centre selection was recorded at the time of compilation and is reflected in the decision_method variable included in the dataset.

Quality and Verification

Each coordinate in this dataset was individually assessed and verified through direct visual inspection. The manual verification process involved cross-referencing multiple mapping sources to confirm the functional centrality of the selected location. Locations identified as uncertain at the time of compilation were flagged for additional review.

The dataset underwent iterative quality review during the compilation process. Cities where the functional centre was particularly difficult to identify, due to polycentric urban structures, coastal geography, or limited mapping data availability, received additional scrutiny. The decision_method variable documents the landmark type used for each city, providing full transparency regarding the basis for each coordinate selection.

Data Processing

Following the manual collection and verification phase, the raw spatial and attribute data underwent a programmatic cleaning pipeline to standardise formatting, remove duplicates and correct text encoding issues in international city and country names. This processing was applied solely to metadata and text fields and did not alter any coordinate data.

Specific processing steps included:

Parsing complex text strings to extract and standardise key landmark metadata from compilation notes.

Programmatically standardising the decision_method variable based on extracted keywords such as mosque, city hall, rail station and marketplace to ensure consistent categorical classification.

Deduplicating coordinate pairs to ensure a strict one to one city to centre ratio across the database.

Applying Unicode normalisation to correct text encoding artifacts in international city and country names, ensuring consistent and accurate representation of non-Latin character sets.

Data Format and Variables

The data is provided in CSV format. The dataset includes the following variables:

consol_id: A unique standard identifier for each city entry.

city_name: The name of the city.

country: The country in which the city is located.

population: UN population estimate for the city derived from the World Urbanization Prospects dataset.

latitude: The latitude coordinate of the identified functional city centre in decimal degrees.

longitude: The longitude coordinate of the identified functional city centre in decimal degrees.

decision_method: A standardised categorical variable documenting the landmark type used to identify the functional city centre, reflecting the hierarchical methodology described above.

Intended Use and Applications

This dataset is intended to support a wide range of urban research applications requiring reliable functional city centre coordinates at global scale. Primary applications include but are not limited to the following areas.

Radial and monocentric analysis of urban population density, land use and spatial structure using the city centre as the anchor point for distance based calculations.

Urban scaling research examining how properties of cities evolve with population size, where consistent centre location is critical for cross-city comparability.

Global comparative urban studies requiring a standardised and methodologically consistent set of city centre reference points across diverse geographic and cultural contexts.

Transportation and accessibility research where functional city centre location serves as the primary destination or origin point.

Urban policy and planning research requiring reliable spatial reference points for cities in data-scarce regions.

Limitations

Users should be aware of the following limitations when using this dataset.

Coordinate Accuracy Disclaimer: While every effort has been made to identify and verify the functional centre of each city in this dataset, the author cannot guarantee the accuracy of every individual coordinate. At a dataset scale of 3,402 cities spanning all world regions, some coordinates may be imprecise or may not fully reflect the current functional centre of their respective city. This is particularly likely in the following circumstances. First, for smaller cities in data-scarce regions where mapping platform coverage was limited at the time of compilation. Second, for cities with polycentric or highly dispersed urban structures where no single functional centre is clearly identifiable. Third, for cities that have undergone significant urban development or administrative changes since the time of compilation in 2021. Researchers are advised to verify individual coordinates independently where precision is critical to their analysis. The author accepts no liability for any inaccuracies in individual coordinate entries and welcomes corrections and refinements from the research community.

Temporal Coverage: The dataset reflects the state of available mapping data at the time of compilation in 2021. For some cities, particularly in data-scarce regions, the mapping information available at that time may have been incomplete or imprecise. Urban development and administrative changes occurring after 2021 are not reflected in this version of the dataset.

Functional Centre Subjectivity: The functional centre concept, while methodologically consistent within this dataset, is inherently somewhat subjective for polycentric cities or cities with highly dispersed urban structures. The decision_method variable provides full transparency regarding the basis for each selection, allowing users to assess the appropriateness of individual coordinate choices for their specific research applications.

Population Threshold Coverage: The dataset focuses on cities meeting the UN WUP population thresholds described above and does not claim comprehensive coverage of all urban centres globally. Smaller urban centres below the population thresholds applied are not represented.

Mapping Platform Dependency: Coordinate identification relied on publicly available mapping platforms including OpenStreetMap and Google Maps. The accuracy and completeness of these platforms varies by region and may have affected coordinate precision in areas with limited mapping coverage at the time of compilation.

Historical Context and Provenance

This dataset was developed by Paul Kilgarriff in 2021 to support research on global urban spatial structure. The compilation represented a substantial investment of manual effort, estimated at approximately two months of systematic work, to identify, verify and record functional city centre coordinates for over three thousand cities worldwide.

The methodological framework described in this record was developed specifically for this dataset and reflects considered decisions about how to define functional urban centres consistently across the full diversity of global urban systems. These decisions, including the region-specific selection criteria, represent original intellectual contributions to the methodology of global urban data compilation.

An earlier version of this dataset was shared with research collaborators in 2021 prior to this formal Zenodo publication. This Zenodo record establishes the complete and authoritative public record of the dataset, its methodology, and its provenance.

Citation

Researchers making use of this dataset in published work are requested to cite it formally as follows:

Kilgarriff, P. (2026). Global Urban Centres (GUC) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.18705750