Global Building Morphology Indicators

Biljecki, Filip; Chow, Yoong Shin

doi:10.1016/j.compenvurbsys.2022.101809

Cited by 55 publications

(13 citation statements)

References 125 publications

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“…In Python, those include for example the libraries Momepy 47 , GeoPandas 36 , Shapely 44 , PySAL 48 , and OSMnx 49 . One can also find relevant open-source code developed in other studies 37 , 50 . Refer to the documentation of these libraries for example of metrics and workflows.…”

Section: Usage Notesmentioning

confidence: 99%

EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings

et al. 2023

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Building stock management is becoming a global societal and political issue, inter alia because of growing sustainability concerns. Comprehensive and openly accessible building stock data can enable impactful research exploring the most effective policy options. In Europe, efforts from citizen and governments generated numerous relevant datasets but these are fragmented and heterogeneous, thus hindering their usability. Here, we present eubucco v0.1, a database of individual building footprints for ~202 million buildings across the 27 European Union countries and Switzerland. Three main attributes – building height, construction year and type – are included for respectively 73%, 24% and 46% of the buildings. We identify, collect and harmonize 50 open government datasets and OpenStreetMap, and perform extensive validation analyses to assess the quality, consistency and completeness of the data in every country. eubucco v0.1 provides the basis for high-resolution urban sustainability studies across scales – continental, comparative or local studies – using a centralized source and is relevant for a variety of use cases, e.g., for energy system analysis or natural hazard risk assessments.

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Section: Usage Notesmentioning

confidence: 99%

EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings

et al. 2023

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“…Buildings share an intimate relationship with road networks, and are as much as networks, indicative of the hierarchical processes inherent in urban systems. On that note, the increasing availability and accessibility of large-scale, high-quality global building morphology indicators and footprints present unique opportunities to improve our multi-scalar understanding of complex urban networks (Biljecki and Chow, 2022). Another area for further research includes bridging gaps in data distribution and inequality.…”

Section: Discussionmentioning

confidence: 99%

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Yap

Chang

Biljecki

2022

Environment and Planning B: Urban Analytics and City Science

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Planning for active mobility satisfies many fundamental tenets of good urban design and planning. However, planning for active mobility is a complex endeavour due to numerous local, place-based factors that influence active mobility decisions. Recent advancements in urban data research have demonstrated the effectiveness of deep learning methods in evaluating active mobility potential for urban environments. At present, the incorporation of semantic information from deep learning models and street view imagery into spatio-temporal contexts remains a challenge. In particular, knowledge extraction from deep learning models remains an open question for urban planning and decision-making. Towards this issue, we propose a functional deep learning and network science workflow that employs open data from OpenStreetMap and Mapillary to assess factors affecting active mobility decisions and route planning. We demonstrate the generalisability of our analytical workflow through two case studies focusing on urban greenery in Nerima city (Japan) and urban visual complexity in Pasir Ris town (Singapore). Our results reveal clear patterns of heterogeneity in urban streetscapes and identify unevenness in street infrastructure provision based on destination types. Using this information, we propose specific areas for design intervention to improve active mobility planning. Our workflow is applicable for a diverse range of use cases making it relevant to a wide range of stakeholders, not limited to, urban researchers, policy makers and urban planners.

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“…To support other use cases, we provide an open source python package where users can generate euclidean buffers of arbitrary distance. As an example, building footprint proportion corresponds to the ratio between the building area and the buffered area around each node 19 .…”

Section: Methodsmentioning

confidence: 99%

“…94 Total Perimeter Yes Yes Decimal m 2 Tikhonova & Beirao 95 Mean Perimeter Yes Yes Decimal m 2 Litardo et al . 96 Perimeter St. dev Yes Yes Decimal m 2 Biljecki & Chow 19 Complexity Mean Yes Yes Decimal m 2 Basaraner & Cetinkaya 97 Complexity St. dev Yes Yes Decimal m 2 Labetski et al . 98 No.…”

Section: Data Recordsmentioning

confidence: 99%

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Yap,

Biljecki

2023

Sci Data

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Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset’s strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research.

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Global Building Morphology Indicators

Cited by 55 publications

References 125 publications

EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings

EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

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