Global Building Morphology Indicators

GBMI — Understanding the building form around the world in detail

Our triplex project: metrics, open-source software, and open dataset

Characterising and analysing urban morphology is a continuous task in urban data science, environmental analyses, and many other domains. As the availability and quality of data on them have been increasing, buildings have gained more attention. However, tools and data facilitating large-scale studies, together with an interdisciplinary consensus on metrics, remain scarce and often inadequate. We present Global Building Morphology Indicators (GBMI) — a three-pronged contribution addressing such shortcomings:

  1. a comprehensive list of hundreds of building form multi-scale measures derived through a systematic literature review;
  2. a methodology and tool for the computation of these metrics in a database suited for big data and comparative studies, and release the code freely and open-source; and
  3. we carry out the computations using high performance computing, generating a public repository with data quantifying the form of selected urban areas around the world, and demonstrate their value with novel analyses comparing morphological parameters across cities.

GBMI introduces a formalised, structured, modular, and extensible method to compute, manage, and disseminate urban indicators at a large scale and high resolution, while the precomputed dataset facilitates comparative studies. The theory and implementation traverse multiple scales: at the building level, both individual and contextual ones based on encircling buildings by multiple buffers, and aggregations at several hierarchical administrative levels and at multiple grids. Our open dataset, comprising billions of records on a growing scope of urban areas worldwide, is the most comprehensive instance of morphological data parametrising the individual building stock, supporting studies in urban analytics and a range of disciplines.

The nature of our project is open — data and code are released openly, and the project is modular, enabling the addition of new geographies and indicators. Further, the paper has been published as an open access publication.

Paper and attribution

A paper describing the project, and from which the description above was adopted, was published in Computers, Environment and Urban Systems. Please refer to the paper for detailed information, while this website summarises the project and provides the links to the datasets and code.

If you use GBMI in a scientific context, please cite the paper:

@article{2022_ceus_gbmi,
 author = {Biljecki, Filip and Chow, Yoong Shin},
 doi = {10.1016/j.compenvurbsys.2022.101809},
 journal = {Computers, Environment and Urban Systems},
 pages = {101809},
 title = {Global Building Morphology Indicators},
 volume = {95},
 year = {2022}
}

Part 1: Metrics

Despite the omnipresence of urban morphology analyses, especially in domains such as urban microclimate, there is no consensus on the metrics. Based on a systematic review, we put together a structured list of hundreds of indicators that are commonly used to describe the built form quantitatively. Please see the paper for more information about the approach and comprehensive list.

Illustration of contextual aspects computed at the building level based on its surroundings.
Illustration of contextual aspects computed at the building level based on its surroundings.

Part 2: Software

A pillar of our work is a tool for computing these building metrics. The tool, released open-source, is implemented in a database, facilitating large-scale and big data analyses.

Flow of the system. The datasets in the square brackets are those that we have used for generating the dataset, while the software is agnostic supporting other formats and datasets as well. All the computations are performed and kept in the database, which allows easy query and extraction of data. Our export scripts support multiple geospatial formats.
Flow of the system. The datasets in the square brackets are those that we have used for generating the dataset, while the software is agnostic supporting other formats and datasets as well. All the computations are performed and kept in the database, which allows easy query and extraction of data. Our export scripts support multiple geospatial formats.

Part 3: Dataset

We ran the tool on selected cities and countries around the world and produced a series of open datasets. Their download links are listed at the website at different hierarchical levels (e.g. building and grid level). Please go to the GBMI Data Repository to explore more. For more details about the structure, please refer to the paper.

Here are some insights in the produced datasets.

Example of computed indicators at different levels for a part of London. Sources of building and administrative data: (c) OpenStreetMap contributors, GADM.
Example of computed indicators at different levels for a part of London. Sources of building and administrative data: (c) OpenStreetMap contributors, GADM.

A map visualising the spatial pattern of the minimum distance among buildings in high-resolution tiles across Singapore. The photographs are courtesy of Unsplash contributors.
A map visualising the spatial pattern of the minimum distance among buildings in high-resolution tiles across Singapore. The photographs are courtesy of Unsplash contributors.

Hierarchical and structured integration of data. These plots and maps were derived from footprint areas of all buildings in Switzerland, and aggregated at multiple levels.
Hierarchical and structured integration of data. These plots and maps were derived from footprint areas of all buildings in Switzerland, and aggregated at multiple levels.

Demo/Analysis

Analyses taking advantage of numerical indicators parametrising the built form are numerous, as elaborated in the paper, which also contains some examples of using our own dataset for analyses.

Distribution of complexities of building footprints by city.
Distribution of complexities of building footprints by city.

We look forward to seeing what others will come up with.

Licence

All aspects of the project are licenced according to CC BY 4.0. That means that you can use our work for pretty much anything as long as you attribute it (i.e. cite our paper above). The paper has been released under the same licence and it is open access.

Limitations, issues, and future work

Like any other academic software, GBMI is an experimental research prototype, which leaves some opportunity for improvement and future work. For example, computing the contextual indicators for large dense urban areas may take time and we are looking into enhancing this process. Further, input datasets will directly influence the accuracy of the results. While we admire and are grateful for the work of the OpenStreetMap community, some areas are heterogeneous in quality and may impact the generated dataset.

For future work, we hope to add more cities to our collection.

People

Development (software, data)

Yoong Shin Chow

Research (metrics, analysis) & principal investigator

Filip Biljecki

Research group

Urban Analytics Lab, National University of Singapore (NUS)

Acknowledgements

GBMI is made possible by the efforts of many others, primarily developers of PostgreSQL/PostGIS and the OpenStreetMap community.

This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start Up Grant R-295-000-171-133 and by the AWS Cloud Credits for Research.

The illustrations of the metrics are made by our awesome designer April Zhu.

For more information, please see the aforementioned paper.

Yoong Shin Chow
Yoong Shin Chow
Research Assistant
Filip Biljecki
Filip Biljecki
Assistant Professor