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Virtual Identification of Dwelling Characteristics Online for Analysis of Urban Resource Consumption

Virtual Identification of Dwelling Characteristics Online for Analysis of Urban Resource Consumption

Maryam Saydi, Ian Bishop, Abbas Rajabifard
Copyright: © 2015 |Volume: 4 |Issue: 3 |Pages: 28
ISSN: 2160-9918|EISSN: 2160-9926|EISBN13: 9781466680210|DOI: 10.4018/IJEPR.2015070101
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MLA

Saydi, Maryam, et al. "Virtual Identification of Dwelling Characteristics Online for Analysis of Urban Resource Consumption." IJEPR vol.4, no.3 2015: pp.1-28. http://doi.org/10.4018/IJEPR.2015070101

APA

Saydi, M., Bishop, I., & Rajabifard, A. (2015). Virtual Identification of Dwelling Characteristics Online for Analysis of Urban Resource Consumption. International Journal of E-Planning Research (IJEPR), 4(3), 1-28. http://doi.org/10.4018/IJEPR.2015070101

Chicago

Saydi, Maryam, Ian Bishop, and Abbas Rajabifard. "Virtual Identification of Dwelling Characteristics Online for Analysis of Urban Resource Consumption," International Journal of E-Planning Research (IJEPR) 4, no.3: 1-28. http://doi.org/10.4018/IJEPR.2015070101

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Abstract

The impact of dwelling structure on residential energy and water consumption is important in urban resource management. This paper introduces Virtual Identification of Dwelling Characteristics Online (VIDCO) as a novel technique to assess dwelling characteristics. Using both aerial and street level views from Google mapping products, exterior dwelling characteristics were captured for 50 random dwellings in each of 40 Postal Areas. VIDCO saved the time and cost of travelling to the widely spread suburbs and provided data that could not be attained in-field. Three approaches to validity checking were used. First, comparison of dwelling type with data from Australian Bureau of Statistics (ABS) showed that outer suburb areas had higher agreement than inner city areas. Second, the homogeneity of the data was assessed to indicate whether the sampling rate was appropriate. The results were mixed. Third, the degree to which key variables -such as presence of swimming pools- affected residential energy and water demand, as determined by linear regression, was consistent with other studies.

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