Representing the dwelling stock as 3D generic tiles estimated from average residential density

AbstractForecasting 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

Residential density classification for sustainable housing development using a machine learning approach

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here we develop a methodology using a Random Forests algorithm (in combination with GIS spatial data processing) to detect and classify the residential neighbourhoods and their spatial characteristics within the region between Oxford and Cambridge, that is, the 'Oxford-Cambridge Arc'. The classification model is based on four pre-defined urban classes, that is, Centre, Urban, Suburban, and Rural for the entire region. The resolution is a grid of 500 m × 500 m. The features for classification include (1) dwelling geometric attributes (e.g. garden size, building footprint area, building perimeter), (2) street networks (e.g. street length, street density, street connectivity), (3) dwelling density (number of housing units per hectare), (4) building residential types (detached, semi-detached, terraced, and flats), and (5) characteristics of the surrounding neighbourhoods. The classification results, with overall average accuracy of 80% (accuracy per class: Centre: 38%, Urban 91%, Suburban 83%, and Rural 77%), for the Arc region show that the most important variables were three characteristics of the surrounding area: residential footprint area, dwelling density, and number of private gardens. The results of the classification are used to establish a baseline for the current status of the residential neighbourhoods in the Arc region. The results bring data-driven decision-making processes to the level of local authority and policy makers in order to support sustainable housing development at the regional scale

Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

Low carbon energy supply technologies are increasingly used at the building and community scale and are an important part of the government decarbonisation strategy. However, with their present state of development and costs, many of these decentralised technologies rely on public subsidies to be financially viable. It is questionable whether they are cost effective compared to other ways of reducing carbon emissions, such as decarbonisation of conventional supply and improving the energy efficiency of dwellings. Previous studies have found it difficult to reliably estimate the future potential of decentralised supply because this depends on the available residential space which varies greatly within a city region. To address this problem, we used an integrated modelling framework that converted the residential density forecasts of a regional model into a representation of the building dimensions and land of the future housing stock. This included a method of estimating the variability of the dwellings and residential land. We present the findings of a case study of the wider south east regions of England that forecasted the impacts of energy efficiency and decentralised supply scenarios to year 2031. Our novel and innovative method substantially improves the spatial estimates of energy consumption compared to building energy models that only use standard dwelling typologies. We tested the impact of an alternative spatial planning policy on the future potential of decentralised energy supply and showed how lower density development would be more suitable for ground source heat pumps. Our findings are important because this method would help to improve the evidence base for strategies on achieving carbon budgets by taking into account how future residential space constraints would affect the suitability and uptakes of these technologies.The research was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) as part of the ReVISIONS Research Grant (EP/F007566/1) and Liveable Cities Programme Grant (EP/J017698). The LUTI model was developed with financial support from the East of England Development Agency for the ReVISIONS project. Ordnance Survey provided MasterMap™ for academic use.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.apenergy.2016.02.09

Green and grey drainage infrastructure: costs and benefits of reducing surface water flood risk

It is now estimated that in the UK alone 3.2 million properties are at risk of surface water flooding – an increase of almost half a million from ten years ago – and it is expected that this problem will increase further under current climatic changes and urbanisation. Sustainable Drainage Systems (SuDS) seek to reduce flooding from surface water without relying on conventional piped sewer networks by restoring the pre-development hydrological conditions of an area through mimicking natural drainage processes. As their behaviour is more complex compared to their traditional, greyer counterparts, there is still incomplete understanding of their performance during intense rainfall. Research to-date has focused on the optimisation of their design at an infrastructure-scale for achieving hydrological benefits, and a growing number of case studies into their inclusion in small, neighbourhood developments. However, an understanding of the influence of external factors on SuDS behaviours and the additional range of co-benefits SuDS may provide are also important for the design of effective systems, whilst an appreciation of their potential role at greater scales will allow a more informed consideration of drainage alternatives in larger-scale developments. Thus, this thesis investigated how built form influences SuDS’ performance and how the inclusion of SuDS in regional-scale developments may contribute to wider environmental goals. To analyse the effect of urban built form, a range of 1 hectare urban tiles were developed to represent different housing typologies, urban densities and SuDS implementations under current design principles drawn from The SuDS Manual (CIRIA 2015). The rainfall-runoff model Stormwater Management Model (SWMM) was used to simulate storm events of varying magnitudes and the resultant hydrographs analysed. These tiles were then applied to a proposed regional development spanning five counties in south-east England, the Oxford-Cambridge Arc, under eight different scenarios of urban development, and the length of pipes required to connect such developments estimated. Finally, a methodology was developed to further assess these regional-scale urban development designs for their potential contributions to green infrastructure (GI) networks. The designs were assessed against four goals for GI provision: 1. Ecosystem Services; 2. Ecological Status; 3. Ecological Connectivity; 4. Proximity to the Population. Each of these goals was assessed using existing approaches which utilised readily available datasets to allow for widespread application of the methodology. It was found that the differences in impermeable surface areas as a result of different built form designs influenced peak and total runoff volumes from a storm event, both with and without the inclusion of SuDS, although to what extent was dependent upon the SuDS infrastructure(s) employed and their overall implementation. Notably, in some urban designs, a lower proportional implementation of a SuDS infrastructure at a higher development density saw greater reductions in peak and total runoff volumes than a higher proportional implementation at a lower development density. These proportions were of available surface type for SuDS (e.g. roof area for green roofs). More dense urban configurations provide greater potential surface area for their construction. The spatial arrangement of these built form elements, however, also proved an important consideration due to the spatial variation of external landscape characteristics (such as soil type and slope) which also impact runoff dynamics. The spatial arrangement of these built form elements, however, also proved an important consideration due to the spatial variation of external landscape characteristics (such as soil type and slope) which also impact runoff dynamics. In such a way, the developed approach proves particularly useful, as by combining a tile approach for designing urban developments with rainfall-runoff modelling, the methodology allowed for these landscape and built form elements to be readily varied and scrutinised at both local and regional scales. Investigation of pipe requirements found that for all housing typologies the use of SuDS could reduce the minimum required pipe diameter, although not consistently for all SuDS designs. Different spatial development approaches also resulted in different required pipe network lengths. Given that current guidelines permit high cost as a justification for not constructing SuDS in developments, such findings suggest that financial savings could be found elsewhere with a well-designed SuDS system. When considering co-benefit provision, the inclusion of SuDS consistently saw greater GI provision scores, although it is worth noting that urban spaces presented opportunities for GI provision even without. When considering individual GI elements, this is particularly clear. For ecosystem services, very few SuDS designs were able to score higher than the pre-developed state, and these occurred only where existing land cover was poorly-scoring. Once again, the specific SuDS infrastructure(s) employed played a strong role in determining which co-benefits were provided, and to what extent. By their nature, infrastructure-based SUDS, for example, require less free space in a development, and as such can help minimise loss of undeveloped land in an urban area (or provide more room for compact development to help reduce overall sprawl). Whilst this indicates that SuDS choice is an important component in achieving the specific aims of a development project, interactions between different SuDS infrastructures and/or elements of a development design highlights the need for trade-offs to be understood and adequately balanced in resultant designs. From this research, four key considerations for urban planners arise when designing new development involving SuDS infrastructure. First, the location and layout of the development; second, the choice of housing typology; third, the comparison of multiple SuDS infrastructure and combinations; fourth, the opportunities posed by urban space in providing GI (even without SuDS)

A Multi-Scale Flexible Framework for Urban Modelling

Ph. D. ThesisThe configuration of urban areas, and of infrastructures which serve them is central to managing the urbanisation process. Integrated assessment frameworks aim to inform decisions regarding planning, policy, and design to coordinate projects across sectors. Development of such models poses a number of challenges; (i) scenario generation, (ii) intelligibility to stakeholders, (iii) validity, (iv) control and feedback, (v) execution time, (vi) data requirements, (vii) uncertainties and, (viii) flexibility/reusability. This research has developed a multi-scale flexible framework which disaggregates projected regional employment to ward-level population, and further to rasterised development. This comprises; (i) transport network generalised cost, (ii) cost composition, (iii) spatial interaction incorporating transport accessibility, (iv) development zoning, (v) multi-criteria evaluation of development suitability, and (vi) cellular development. The framework is generically implemented, each model being specified in terms of inputs, outputs, and parameters. Modellinkage is via input/output chaining, providing the opportunity to experiment with alternative solutions. Execution is flexible/configurable to perform multiple model runs whilst varying parameters and propagating metadata through stages. Python controls execution flow, C++ provides performance, PostgreSQL manages data, and QGIS assists input/output. The framework is deployed in baseline scenarios for London and Innsbruck, and in more detailed scenario/uncertainty exploration for London. The framework’s utility is judged by criteria corresponding to the above challenges and is found to be favourable, with performance, flexibility and uncertainty support as key attributes. The framework executes models for London in ~52 seconds on modest hardware (1.6GHz, 8GB). This involves costweighted Dijkstra - 4 transport networks (~42s), cost composition and accessibility conversion (~4s), spatial interaction - 633 wards (~2s), rasterised 4-hectare development zones (~1s), 7 criteria development suitability evaluation (~1s), and cellular development - 100m scale (~2s). Combinatorial uncertainties are accommodated by a flexible, modular structure which promotes reuse, and records run configuration as well as model parameters in chained metadat