Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this paper, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain (OSGB): MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment

Transductive Learning for Spatial Data Classification

Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the nave Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented

Land cover maps for environmental modeling at multiple scales

As described in the ECOCHANGE proposal, Task01.02.02 “Map production and aggregation”, two major products are generated within this WP. Firstly, land cover maps at high spatial resolutions will be produced for the European Union and for the reference years of 1960, 1990 and 2000. Secondly, thematic and spatial aggregated products will be derived at coarser spatial resolutions in order to synthesize the fragmentation and variability within coarser cells for biodiversity assessment and modelling. The name of the official deliverable is D01.02.01 “Land cover maps for environmental modelling at multiple scales” and includes this report, the digital land cover products and an interactive website to view the data at all thematic and spatial scales

Mapping underground assets in the UK: Project Iceberg. Work Package 1, market research into current state of play and global case studies

Project Iceberg is an exploratory project undertaken by Future Cities Catapult, British Geological Survey (BGS) and Ordnance Survey (OS). The project aims to address the serious issue of the lack of information about the ground beneath our cities and the un-coordinated way in which the subsurface space is managed. Difficulties relating to data capture and sharing of information about subsurface features are well understood by some sectors and have been explored in previous research and industry reports, many of which are highlighted in this report. This study does not replicate past work, but rather reviews outcomes and explores the barriers to wider uptake of subsurface management systems within integrated city management. The long-term goal is to help increase the viability of land for development and de-risk future investment through better management of subsurface data. To help achieve this, our study aims to enable a means to discover and access relevant data about the ground’s physical condition and assets housed within it, in a way that is suitable for modern, data driven decision-making processes. The project considers both physical infrastructure i.e. underground utilities and natural ground conditions i.e. geological data and is divided into three different work packages: Work Package 1: Market research and analysis Work Package 2: Data operation systems and interoperability for a subsurface data platform Work Package 3: Identification of use cases for a subsurface data platform This report summarises the findings of work package 1 and identifies the following key findings and recommendations. There is substantial potential for commercialisation of data tools and data services using an integrated surface-subsurface data platform, which would support, for example, urban planning, redevelopment, infrastructure assessments and street works. Realising the full benefit of these opportunities relies on the sharing of data beyond statutory undertakers, albeit with suitable controls in place. Statutory undertakers do not necessarily have the national overview, capability or remit to develop an integrated platform. Stakeholders acknowledge that incomplete subsurface information means that land value is not being protected or worse, is being diminished and that organisations are incurring 6 indirect costs due to project delays and requirements for additional surveys. However, the direct costs of obtaining subsurface data and the indirect costs incurred because of incomplete access to subsurface data is largely unknown. Amendments to existing and introduction of new data standards (PAS 128 and PAS 256) make provision for more consistent and accurate data capture of buried utilities. Sharing of more accurate utility data will be facilitated and links to building information models and smart city standards will be more explicit. However, currently, storage of data and the integrity of data stores is not being addressed consistently at national level. There is a currently a lack of national standard that addresses commercial sensitivities and security risks concerning subsurface data sharing that can potentially guide “the right people getting access to the right and comprehensive set of data, at the right time without fear that parts of it have been redacted or manipulated” Investment in research and innovation to support the development of tools to identify the location of buried infrastructure has been successful and new systems are being brought to the market that will enable more accurate mapping of underground infrastructure. Precedents have been set for the sharing of underground utility data of national importance – exemplar projects, such as the VAULT and Greater Manchester Open Data Infrastructure Map (GMODIN), demonstrate successful collaboration across the utility sector to generate an integrated utility infrastructure map. Meanwhile adoption of AGS data formats by the ground investigation community has led to large-scale sharing of geotechnical data. National scale sharing of buried utility data has only been demonstrated in Scotland, largely driven by nationalised utilities. Upscaling of exemplar projects across the UK needs prioritising. The National Infrastructure Commission, Infrastructure Projects Authority and Digital Built Britain should take leadership of the development of an integrated data framework that combines surface and subsurface data. Future legislation and standards may be required to ensure the accurate and standardised capture and supply of buried infrastructure data. The benefits and business opportunities that may be delivered through an integrated data framework that embeds subsurface data are not sufficiently highlighted to stakeholders. Thus, the incentives and business drivers to collaborate on a subsurface data platform need to be better illustrated. Project Iceberg WP3 goes some way to addressing this but further work is needed

An approach to modeling and forecasting real estate residential property market

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThis thesis aims to provide an approach to real estate residential modeling and forecasting covering property types’ correlation, time series attributes within a region or a city, and socio-economic attributes of preferred real estate locations. The thesis covers residential estate markets and concentrates on property types, while previous studies that have considered country wide house price indices. There is a gap identified in the literature in the need to study correlations between property types within a region or a city and whether they will provide diversification benefits for real estate investors such as risk reduction per unit of returns. The thesis concentrates on property type seasonality in addition to modeling time series attributes within a region or city instead of real estate index seasonality. This thesis the first to combine modern information systems techniques such as geographic information systems (GIS) with socio-economic factors to help understanding causal relationships that can be used to forecast real estate prices. The results show that it is more achievable to forecast real estate prices within a city than for the real estate market of the entire country. The GIS and socio-economic modeling results show that higher property prices are awarded to real estate with more green spaces, residents with higher disposable incomes, lower council tax bands, fewer tax benefits claimants, and better health services. Previous studies have examined real estate price indices at the macro level (the general, all real estate house price indices). There has not been a study that examines real estate price forecasts by property types within a city. The contribution of this thesis is its focus on time series analysis as well as causal modeling within a city with the objective of providing a better understanding of the dynamics of real estate price changes

Urban metabolism and land use modeling for urban designers and planners: A land use model for the Integrated Urban Metabolism Analysis Tool

Predicting the resource consumption in the built environment and its associated environmental consequences (urban metabolism analysis) is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. There is a critical need for a single integrated framework to analyze the consequences of urban growth and eventually predict the impacts of sustainable policies on the urbanscape. This dissertation presents the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) – an analytical framework that simulates urban metabolism by integrating urban subsystems in a single comprehensive computational environment. It reviews the existing literature on urban sustainability, urban metabolism, as well as introducing the general framework for IUMAT. IUMAT uses three separate models for quantifying environmental impacts of land-use transition, consumption of resources, and transportation. This work outlines the development of IUMAT Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict land use change patterns. By using Density-Based Spatial Clustering and normal equations, this dissertation introduces a method for generating building-form variables from Light Detection and Ranging (LIDAR) data, which can be used as a new determinant factor in land-use change modeling. The proposed Land-use Model, within IUMAT or other analytical models, can be useful to local planning officials in understanding the complexity of land-use change and developing enhanced land-use policies

The geographical differences and similarities of radon affected areas in England

The geographical distribution of radon gas is very uneven. The gas occurs naturally in all buildings at concentrations which can vary from below the United Kingdom national average of 20 Bq m(^-3) to more than 2,000 Bq m(^-3). Five counties have been identified by the NRPB as 'Affected Areas' where more than 1% of homes have radon levels in excess of the current Action Level of 200 Bq m(^-3) (Miles et al., 1992). These counties are Cornwall, Devon, Somerset, Derbyshire and Northamptonshire. The level of radon gas in buildings is largely dependent on the underlying geology but geology does not always provide a full answer as to why spatial variations in radon occur. The implication of land capability on indoor radon levels in the five Affected Areas has been assessed using ARC/INFO and in Northamptonshire die influence of social factors (population density, social class and the proportion of households consisting only of pensioners) has been analysed. There are some similarities in the results for the Affected Areas (especially between the counties located in the south-west of die country) as well as some striking differences (for example, the relationship between urban areas and radon levels differs in all the Affected Areas). Results in Somerset and Northamptonshire are strongly influenced by one or more dominant radon category or land capability grade. In general, higher radon levels are associated with poor quality agricultural land and, in Northamptonshire, with high population density at ward level. The areas of Northamptonshire which have above average proportions in social classes I and II (1991 Census) are more likely to be associated with low radon levels (at district level), whereas areas with high proportions of households consisting only of pensioners tend to be associated with areas where more than 10% of homes are above the Action Level (at ward level)

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond