A comparison of in situ and GIS landscape metrics for residential satisfaction modeling

Sahraoui, Yohan; Youssoufi, Samy; Foltête, Jean-Christophe

doi:10.1016/j.apgeog.2016.07.011

Cited by 19 publications

(12 citation statements)

References 66 publications

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“…There is a considerable body of research on residential satisfaction related to other elements, such as urban/building features and design process (e.g. user participation in the design process) [142][143][144][145][146][147][148][149][150][151]. In the present study, this variable can indicate how increasing levels of thermal discomfort affect the overall satisfaction with the dwelling.…”

Section: Independent Variablesmentioning

confidence: 80%

Seasonal Thermal Sensation Vote – An indicator for long-term energy performance of dwellings with no HVAC systems

Cóstola

Carreira

Fernandes

et al. 2019

Energy and Buildings

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Dwellings with no heating, ventilation and air conditioning (HVAC) systems are commonly found in many countries. The long-term thermal performance of these buildings can be assessed based on hourly data of occupant thermal discomfort integrated over the required timespan (e.g. total degree hours of discomfort per year). This approach can be easily applied when simulation is adopted in the assessment, but field studies using this approach are rare as they would require complex, costly and long measurement/survey campaigns. This paper addresses the challenges on conducting field studies on long-term thermal performance of dwellings with no HVAC system by introducing a novel performance indicator: the Seasonal Thermal Sensation Vote (S-TSV). S-TSV adopts the standard 7-point thermal sensation scale and is based on the perceived overall thermal sensation recalled by the user of the building for specific seasons and times of day. The new performance indicator is not intended to replace existing ones, but to complement them in the understanding of the complex thermal performance processes taking place in buildings with no HVAC. S-TSV was applied in a field study targeting a small sample of dwellings in Brazil. Results demonstrate the capabilities of S-TSV to describe trends in buildings performance in this sample. S-TSV also assisted on the identification of relationships between such performance and some independent variables addressed in this field study (e.g. windows operation, footwear and income), considering a threshold of p-values < 0.05 on the chi-square statistic test.

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Section: Independent Variablesmentioning

confidence: 80%

Seasonal Thermal Sensation Vote – An indicator for long-term energy performance of dwellings with no HVAC systems

Cóstola

Carreira

Fernandes

et al. 2019

Energy and Buildings

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“…The PixScape software facilitates such modeling, while allowing access to numerous functions and visibility metrics and providing original approaches such as tangential analysis and multi‐resolution computation. Initial experiments with this software (Sahraoui, Clauzel et al, ; Sahraoui, Youssoufi et al, ; Hilal, Joly, Roy, & Vuidel, ) provided an opportunity to test the relevance of the software’s original visibility metrics. In fact, we highlighted the capacity of tangential landscape metrics to explain landscape preferences and residential satisfaction.…”

Section: Conclusion and Further Developmentmentioning

confidence: 99%

“…By characterizing landscapes on the basis of digital spatial data, they can be used to convert the visual perception of the physical environment into quantifiable criteria by means of landscape indicators (Sang, Ode, & Miller, 2008). This modeling approach brings advantages for in situ landscape evaluation because it can be performed systematically for large areas (Sahraoui, Youssoufi, & Foltête, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Another issue about visibility analysis is how to characterize the visual content in the form of metrics. This content can be used, for example, to assess landscape quality (Martín, Ortega, Otero, & Arce, 2016) and to evaluate individuals' landscape preferences by crossing visibility analyses with empirical data expressing stated preferences (e.g., Dramstad, Tveit, Fjellstad, & Fry, 2006;Sahraoui, Clauzel, & Foltête, 2016;Sahraoui, Youssoufi, et al, 2016) or revealed preferences (Cavailhès et al, 2009;Joly et al, 2009;Tenerelli, Püffel, & Luque, 2017). To generate visibility metrics, current applications involve a sequence of processes and often involve combining GIS software to define viewsheds with specific software to compute landscape metrics, as for example, in Tenerelli et al (2017) with Fragstats.…”

Section: Introductionmentioning

confidence: 99%

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Integrated GIS software for computing landscape visibility metrics

Sahraoui

Vuidel

Joly

et al. 2018

Transactions in GIS

Self Cite

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As an important component of the quality of the living environment, landscape is increasingly addressed in terms of its visual dimension. In contrast to the point‐like character of in situ observations and photographic analyses, the modeling of landscape visibility from digital data has the advantage of scanning geographical space in a systematic way. However, the tools currently available for visibility modeling are limited to the mapping of viewsheds. They require complementary operations for a complete landscape assessment, including direct and easy computation of landscape metrics. Furthermore, none of those tools integrates recent technical advances to better characterize the visible landscape by tangential vision (i.e., from ground level as opposed to vision by viewshed from above). Starting from this, PixScape software proposes to integrate a large set of functions for modeling landscape visibility while remaining interfaced with GIS software. This software can be used to perform a complete landscape assessment by computing a wide range of original landscape metrics. It performs tangential analysis in addition to viewshed analysis, which can produce more realistic outcomes. Because landscape visibility analysis over large areas implies significant computation time, the software also integrates a multi‐resolution process intended to speed up calculations while also taking into account the cognitive abilities of human vision.

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“…Due to the increasing urban population and the high prices of land, living in apartment buildings has become common nowadays (António et al, 2014;Ghuzlan & Al-Omari, 2016). The quality of apartments is a significant factor influencing overall residential satisfaction and satisfaction with the residential area (Boumová & Zdráhalová, 2016;Sahraoui et al, 2016). In 2010, the results of a survey in Korea showed that, among various residential properties, 59% of the population has apartments as their main place of residence.…”

mentioning

confidence: 99%

Visual Description of the Indoor Space of Real Estate in Crowd-Sourcing Environments

Haghbayan

Malek

Tashayo

2020

Real Estate Management and Valuation

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Emerging the power of collective intelligence through crowdsourcing could create a clear path for visualizing real estate as well. One of the Crowdsourcing applications is describing the indoor space of a real estate. This paper aims to describe real estate in the context of spatial crowdsourcing. Qualitative and quantitative criteria were used in this study to describe the real estate space, topological relationships, directional relations, color, location, dimensions, and height as qualitative criteria. Quantitative criteria were selected as the dimensions and height. The proposed model was evaluated by two groups: those who had never seen the real estate and others that had already seen the same real estate. We implemented a website called SAMA1 to evaluate the proposed model with crowdsourcing data using online collaborative tools. SAMA is using tools, such as a sketch plan, photo, text, virtual tour, and visual descriptions. To evaluate SAMA, we compared it with four representative commercial websites, and the impact of the tools was precisely examined. The obtained results demonstrate that the proposed model can be utilized to visually describe the indoor space of real estate in crowd-sourcing environments.

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A comparison of in situ and GIS landscape metrics for residential satisfaction modeling

Cited by 19 publications

References 66 publications

Seasonal Thermal Sensation Vote – An indicator for long-term energy performance of dwellings with no HVAC systems

Seasonal Thermal Sensation Vote – An indicator for long-term energy performance of dwellings with no HVAC systems

Integrated GIS software for computing landscape visibility metrics

Visual Description of the Indoor Space of Real Estate in Crowd-Sourcing Environments

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