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 |Pages: 28
DOI: 10.4018/IJEPR.2015070101
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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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1. Introduction

1.1. Characterising Urban Resource Consumption Data Sources

Rapid urban population growth and the consequent increase in urban resource demand have put pressure on limited natural resources. Water use has been restricted in many parts of the world (Kenney, Goemans, Klein, Lowrey, & Reidy, 2008; Worthington & Hoffman, 2008) and the broader implications of energy use are now widely recognised (Gram-Hanssen, 2014; Newton & Meyer, 2010; O'Callaghan, Green, Hyde, Wadley, & Upadhyay, 2012). Over the past decade, a nexus between energy and water resources has been increasingly described (Marsh, 2008; Stillwell, King, Webber, Duncan, & Hardberger, 2011; U.S. Department of Energy, 2006) and governments are beginning to take action and devising policies (DHI, 2008; Fisher & Ackerman, 2011; Proust et al., 2007; Stillwell et al., 2011) to target the factors influencing the consumption of both energy and water in various sectors of the economy. In this paper we focus on the residential sector that, in Australia, consumes 11.7% of energy (Syed, Melanie, Thorpe, & Penney, 2007) and 10.7% of non-agricultural water (ABS, 2013).

The influence of dwelling characteristics on the residential consumption of energy and water has emerged as an essential factor in urban resource reduction strategies. For example, separate dwellings consume more residential energy and water than other dwelling types such as semi-detached, or flats and apartments (Crawford & Fuller, 2011; Druckman & Jackson, 2008; Fox, McIntosh, & Jeffrey, 2009; Troy & Holloway, 2004). While social-economic factors are also influential in household resource consumption (Arbués, Villanúa, & Barberán, 2010; Domene, Saurí, & Parés, 2005; Druckman & Jackson, 2008; O'Callaghan et al., 2012), dwelling characteristics are a key element for planners and policy makers to devise urban resource management strategies (Guerra Santin, Itard, & Visscher, 2009). Identifying the most significant dwelling structure factors and estimating the influence of these factors on a household’s energy and water consumption is essential to support policy development. However, the supporting analysis and consequent strategy development requires detailed and high-quality data about the structure of dwellings.

Traditionally, there have been two broad approaches to collecting dwelling structure data: interview-based surveys of users, and spatial data analysis. Acquiring interview data with a large sample size is usually conducted by large organisations. For example, Residential Energy Consumption Surveys (RECS) have been conducted by the US Energy Information Administration (EIA) to collect data about the type and construction year of dwellings (EIA, 2011). Alternatively, spatial analysis has been used by some researchers to extract dwelling structure characteristics. For example Chang, Parandvash et al. (2010) used GIS for geo-coding billing data using linked street addresses. This data was then linked to a tax-lot spatial layer to provide a dataset of dwelling structure characteristics including lot size, building size, building density, and building age.

Satellite images are another spatial resource used to extract dwelling structure data. Siebrits (2012) used high quality satellite images (red-green-blue (RGB) orthophotos) and GIS analysis to identify dwellings with a swimming pool for a sample of 14,233 properties. Furthermore, in a study on identifying determinants of water demand in Phoenix, Arizona, Wentz and Gober (2007) captured the percentage of the garden cover for each census tract from a land cover database classified from a 1998 Landsat TM image using automated satellite image data extraction.

Data about dwelling characteristics can be categorised as either observable or non-observable. Several physical variables, such as insulation status, and many socio-economic variables, such as home ownership and household size, are not observable. However, there are many factors that can be collected by direct observation (Table 1).

Table 1.
Studies on traditional dwelling structure data capture techniques
Dwelling CharacteristicsDwelling Structure Data Capturing Techniques
(Examples)Interview-BasedGIS MethodSatellite Image
ObservableType of dwelling,
Size of dwelling,
Construction year,
Garden type,
Garden style,
Swimming pool presence
(Adua, 2010),(Domene & Saurí, 2006),(Randolph & Troy, 2008), (Newton & Meyer, 2010), (Syme, Shao, Po, & Campbell, 2004), (Liao & Chang, 2002),(Guerra Santin et al., 2009),(Gram-Hanssen, 2014)(Siebrits, 2012), (Chang, Parandvash, & Shandas, 2010),(HousePeters, Pratt, & Chang, 2010)(Siebrits, 2012),(Wentz & Gober, 2007)
Dwelling value
(Adua, 2010),(Liao & Chang, 2002), (Randolph & Troy, 2008), (Newton & Meyer, 2010),(O'Callaghan et al., 2012), (Guerra Santin et al., 2009),(Gram-Hanssen, 2014)

In recent years, due to the high cost of collecting observable data at street level, there has been increasing interest in utilizing online sources to remotely capture such data with which to characterise a sample area. The next section reviews recent experiences with online sources.

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