Abstract
Forecasting the variability of dwellings and residential land is important for estimating the future potential of environmental technologies. This paper presents an innovative method of converting average residential density into a set of one-hectare 3D tiles to represent the dwelling stock. These generic tiles include residential land as well as the dwelling characteristics. The method was based on a detailed analysis of the English House Condition Survey data and density was calculated as the inverse of the plot area per dwelling. This found that when disaggregated by age band, urban morphology and area type, the frequency distribution of plot density per dwelling type can be represented by the gamma distribution. The shape parameter revealed interesting characteristics about the dwelling stock and how this has changed over time. It showed a consistent trend that older dwellings have greater variability in plot density than newer dwellings, and also that apartments and detached dwellings have greater variability in plot density than terraced and semi-detached dwellings. Once calibrated, the shape parameter of the gamma distribution was used to convert the average density per housing type into a frequency distribution of plot density. These were then approximated by systematically selecting a set of generic tiles. These tiles are particularly useful as a medium for multidisciplinary research on decentralized environmental technologies or climate adaptation, which requires this understanding of the variability of dwellings, occupancies and urban space. It thereby links the socioeconomic modeling of city regions with the physical modeling of dwellings and associated infrastructure across the spatial scales. The tiles method has been validated by comparing results against English regional housing survey data and dwelling footprint area data. The next step would be to explore the possibility of generating generic residential area types and adapt the method to other countries that have similar housing survey data.
Highlights
There has been an increasing emphasis on understanding the building stock and how to reduce the consumption of energy and production of waste (Kohler & Hassler, 2002)
The residential land areas in the Land Use Interaction and Social Accounting model (LUISA) regional forecasting model were based on Generalized Land Use Database (GLUD) residential land which as explained earlier, has broadly consistent metrics with English House Condition Survey (EHCS) and so the average densities per zone of the LUISA model could be converted directly into plot densities
This paper has presented a new innovative method of analyzing housing survey data to explore the variability of dwellings plot sizes
Summary
There has been an increasing emphasis on understanding the building stock and how to reduce the consumption of energy and production of waste (Kohler & Hassler, 2002). This reliance on mapping limits the capability to forecast the future urban form In another example, a regional scale macro-model was linked to an UrbanSim model (Waddell et al, 2003), which simulated neighborhoods as 2.25 ha grid cells chosen from a set of 25 development types further defined by a range of residential units and non-residential floor space to create typical contiguous urban areas. A regional scale macro-model was linked to an UrbanSim model (Waddell et al, 2003), which simulated neighborhoods as 2.25 ha grid cells chosen from a set of 25 development types further defined by a range of residential units and non-residential floor space to create typical contiguous urban areas These models aim to represent the actual land parcels and this leads to difficulties matching the data sources which makes the models very resource intensive to create and operate over large areas within a macro-modeling framework. It is expected that this paper will be of interest for spatial modeling and urban simulation, for forecasting the impacts of building scale interventions such as sustainable technologies and climate change mitigation
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