Subjective or objective measures of street environment, which are more effective in explaining housing prices?

Qiu, Waishan; Zhang, Ziye; Liu, Xun; Li, Wenjing; Li, Xiaojiang; Xu, Xiang; Huang, Xiaokai

doi:10.1016/j.landurbplan.2022.104358

Cited by 99 publications

(82 citation statements)

References 74 publications

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“…SVI data is commonly provided by commercial services such as Google Street View (GSV), and crowdsourced platforms such as Mapillary and KartaView. SVI has enabled and enhanced a wide spectrum of applications in urban-related topics including spatial data infrastructure, public health, urban greenery, transportation, mobility, perception, socioeconomics, and so on (Branson et al, 2018;Cheng et al, 2018;Zhang et al, 2019a;Pelizari et al, 2021;Li et al, 2021;Yao et al, 2021;Inoue et al, 2022;Qiu et al, 2022;Hosseini et al, 2022;Byun and Kim, 2022;Guan et al, 2022).…”

Section: Street View Imagery For Lcz Classificationmentioning

confidence: 99%

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Ignatius

Hou

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Urban heat island (UHI) is considered a serious environmental issue in highly urbanized cities such as Singapore. To better quantify the UHI intensity, the local climate zones (LCZ) classification scheme was adopted to characterize land covers, and describe and compare their thermal performance. There are three commonly used LCZ classification approaches: manual sampling,World Urban Database and Access Portal Tools (WUDAPT) processing method using remote sensing, and geographical information system (GIS)-based method. Based on the current implementation of WUDAPT Level 0 method in the classification work in Singapore, the principal limitations are expounded. To overcome the deficiencies, street view imagery (SVI), which carries substantial urban spatial information, is regarded as a promising data source. This paper reviews the potential of SVI to better estimate certain LCZrelated properties, such as sky view factor (SVF). As it allows a detailed view on the ground objects, SVI opens up the possibility of identifying surface properties such as albedo, as well as anthropogenic heat sources. Although it is not a novel idea, there has been a lack of a comprehensive use of SVI in assisting LCZ classification from the ground up, especially in a high-density city such as Singapore. This paper overviews potential ways to incorporate SVI and identifies challenges such as coarse temporal resolution and spatial coverage constrained to drivable roads.

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Section: Street View Imagery For Lcz Classificationmentioning

confidence: 99%

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Ignatius

Hou

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…• Visual Order: Based on the consistency of physical elements including arrangement of buildings, paving materials, broken glass, character and scale (Ewing et al, 2006, Ewing & Handy, 2009, Griew et al, 2013, Rundle et al, 2011, Qiu et al, 2022 • Aesthetic (Imageability): Based on the distinctness in arrangement of physical elements and whether it captures emotions, impressions and/or attention (Ewing et al, 2006;Ewing & Handy, 2009;Ma et al 2021;Qiu et al, 2021) • Ecology (Greenness): Based on the proportionality between physical elements including vegetation and building facade (Ewing & Handy, 2009;Ma et al, 2021) • Enclosure: Based on the proportionality between vertical height of physical elements and horizontal width of the space (Ewing et al, 2006;Ewing & Handy, 2009;Salesses et al, 2013;Dubey et al, 2016;Ma et al 2021;Qiu et al, 2021) • Complexity: Based on the diversity of physical elements including user numbers, architectural and landscape variety (Ewing et al, 2006;Ewing & Handy, 2009;Salesses et al, 2013;Dubey et al, 2016;Qiu et al, 2021) • Human Scale: Based on the proportionality of between physical elements and humans and the speed that humans move (Ewing et al, 2006;Ewing & Handy, 2009;Salesses et al, 2013;Dubey et al, 2016;Qiu et al, 2021).…”

Section: Urban Design Qualitiesmentioning

confidence: 99%

“…The final image processing was the application of ML in determining if an SVI portrays certain abstract qualities such as order, aesthetic, ecology, enclosure, complexity and human scale (Ewing et al, 2006, Ewing and Handy, 2009, Griew et al, 2013, Qiu et al, 2021, Qiu et al, 2022, Rundle et al, 2011. The ML process of comparing the elements and converting the preferences to street scores used Microsoft's skill based training system, Truskill algorithm, based on a series of surveys of 300 images from a number of university students, details of which can be found in Qiu et al, 2021. Once the images process was complete, the values assigned to each of the SVI were compiled for data manipulation.…”

Section: Image Processingmentioning

confidence: 99%

Data-Driven Evaluation of Streets to Plan for Bicycle Friendly Environments: A Case Study of Brisbane Suburbs

Toohey¹,

Nguyen²,

Vilppola³

et al. 2022

CAADRIA Proceedings

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Empirical cycling data from across the world illustrates the many barriers that car-dependent cities face when implementing cycling programs and infrastructure. Most studies focus on physical criteria, while perception criteria are less addressed. The correlations between the two are still largely unknown. This paper introduces a methodology that utilises computer vision analysis techniques to evaluate 15,383 Google Street View Images (SVI) of Brisbane City against both physical and perception cycling criteria. The study seeks to better understand correlations between the quality of a street environment and an urban area's 'bicycle-friendliness'. PSPNet Image Segmentation is utilised against SVIs to determine the percentage of an image corresponding with objects and the environment related to specific cycling factors. For physical criteria, these images are then further analysed by Masked RCNN processes. For perception criteria, subjective ranking of the images is undertaken using Machine Learning (ML) techniques to score images based on survey data. The methodology effectively allows for current findings in cycling research to be further utilised in combination via computer visioning (CV) and ML applications to measure different physical elements and urban design qualities that correspond with bicycle-friendliness. Such findings can assist targeted design strategies for cities to encourage the use of safer and more sustainable modes of transport.

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“…• Visual Order: Based on the consistency of physical elements including arrangement of buildings, paving materials, broken glass, character and scale , Griew et al, 2013, Rundle et al, 2011, Qiu et al, 2022 • Aesthetic (Imageability): Based on the distinctness in arrangement of physical elements and whether it captures emotions, impressions and/or attention • Ecology (Greenness): Based on the proportionality between physical elements including vegetation and building facade • Enclosure: Based on the proportionality between vertical height of physical elements and horizontal width of the space Dubey et al, 2016; • Complexity: Based on the diversity of physical elements including user numbers, architectural and landscape variety Dubey et al, 2016; • Human Scale: Based on the proportionality of between physical elements and humans and the speed that humans move Dubey et al, 2016;.…”

Section: Urban Design Qualitiesmentioning

confidence: 99%

“…The final image processing was the application of ML in determining if an SVI portrays certain abstract qualities such as order, aesthetic, ecology, enclosure, complexity and human scale , Griew et al, 2013, Qiu et al, 2022, Rundle et al, 2011. The ML process of comparing the elements and converting the preferences to street scores used Microsoft's skill based training system, Truskill algorithm, based on a series of surveys of 300 images from a number of university students, details of which can be found in Once the images process was complete, the values assigned to each of the SVI were compiled for data manipulation.…”

Section: Image Processingmentioning

confidence: 99%

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

2022

CAADRIA Proceedings

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The work collected in these volumes has been produced during extraordinary circumstances, as the Covid-19 pandemic has continued to impact working and living conditions. Papers have been written, reviewed and edited throughout lockdowns, challenging family situations and health crises. The high number of submissions and quality of the papers is a testament to the resilience and strength of the CAADRIA community.The papers in this publication have been selected through a two-stage, doubleblind peer review process. All reviews and papers have been evaluated by the Paper Selection Committee. After receiving an initial 488 abstract submissions, a final set of 150 full papers has been selected for publication. We thank all authors for their contributions to the process and congratulate the accepted paper authors.The papers in these proceedings are specifically selected for their contribution to this year's conference theme, following the conference organisers' call for authors to position their work in relation to the United Nations Sustainable Development Goals (SDGs). As the world is experiencing the increasing impacts of climate change, there is an urgent need to reflect on the potential of the latest research in architecture, urbanism and construction to address these global challenges.Our special thanks go out to our more than 180 international reviewers, who volunteered their valuable time and effort. We also thank the organising team in Sydney for developing the 2022 CAADRIA Conference as a strong collaborative effort between academic institutions, industry and practice. We thank Gabriel Wurzer for his support during the proceedings' final production stage, and for facilitating their publication via the open access database CumInCAD.org. Finally, we sincerely thank the CAADRIA community for offering us the honour to serve as members of the paper selection committee for Post-Carbon, the 27 th International CAADRIA Conference 2022.

show abstract

Subjective or objective measures of street environment, which are more effective in explaining housing prices?

Cited by 99 publications

References 74 publications

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Data-Driven Evaluation of Streets to Plan for Bicycle Friendly Environments: A Case Study of Brisbane Suburbs

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

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