Free and open source urbanism: Software for urban planning practice

Yap, Winston; Janssen, Patrick; Biljecki, Filip

doi:10.1016/j.compenvurbsys.2022.101825

Cited by 37 publications

(19 citation statements)

References 112 publications

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“…On the bright side, all software and models used in our workflow are mature software with good documentation, clear installation instructions and easy-to-follow tutorials. Nonetheless, there is a need for more integrative software solutions that recognise the multidimensional nature of urban problems (Yap et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Subject to data availability, SVI-based studies can be implemented for any time period/interval and image collection for target sites can be completed within hours. Such analytical methods provide a powerful mechanism to monitor, detect and compare urban change over time and address current gaps for evidence-based planning (Yap et al, 2022).…”

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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“…Another motivation for data selection is to continue supporting the open source eco-system. Free and open source projects have been one of the main contributors of planning technologies innovation in the last decade 54 . Recent breakthrough technologies such as the ChatGPT series were almost entirely developed on open source technologies and information 55 .…”

Section: Methodsmentioning

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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“…Design and planning schemes can be strongly influenced and even optimized by large data visualization technologies, and they are increasingly used in the urban planning industry (Cao et al, 2020). Urban analytical tools are likely to continue to evolve and thus new ways in which urban theories can be understood and modeled can be discovered (Yap et al, 2022). However, managers and urban planners need support to handle data efficiently and to be able to acquire the skills needed to navigate these new data sources, software or platforms (Chackraborty et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Supporting the Process of Designing and Planning Heritage and Landscape by Spatializing Data on a Single Support Platform. Case Study: Romania

Gârjoabă

Crăciun²

2022

RREM

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Data handling is a general objective of education, regardless of the educational level (middle school, high school, university or postgraduate) and branch. Being at the intersection between the humanities and the exact sciences, the field of design requires a continuous summation and overlapping of information from specialists. Obtaining data is essential for perceiving the current situation, but also for adapting the solution proposed in the planning process to the given situation, to the particularities and main characteristics of the context. How difficult it is to obtain information, but especially their overlap and correlation to obtain indicators specific to target areas, depends in most cases on the experience of the specialist in the field, but for a recent graduate, the training received during the years of university training is perhaps his most important support. However, there may be data that are predominantly available during university training and data that can be obtained predominantly outside the academic environment (financial data, communication with public institutions). The paper describes a proposal for a support platform to assist the process of architectural design, urban planning and landscaping in Romania, but which can also support adjacent studies and analyzes. The purpose of the platform is to support the design and planning process of heritage and landscape, architecture and urban planning, by simplifying the data acquisition process and also directing the design/planning process to a long-term perspective, which is based on resilient solutions for natural and built heritage and for the conservation of the local cultural landscape.

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Free and open source urbanism: Software for urban planning practice

Cited by 37 publications

References 112 publications

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

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

Supporting the Process of Designing and Planning Heritage and Landscape by Spatializing Data on a Single Support Platform. Case Study: Romania

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