The classification and analysis of regional building stock characteristics using GIS

最初为能源与环境预测（EEP）模型开发的大规模住房存量调查方法已经被证明对其用户有价值。该模型表明，通过针对特定类型的房屋采取适当的能效措施，它可以产生可观的财务、能源和碳节约。它还允许用户，例如地方当局，针对那些最需要的地区。现有的测量方法通常需要费力和昂贵的“步行”测量，以准确地建立一个地区的建筑特征；建立了EEP模型并使用了这种方法。这种方法提供了当时无法获得的信息，并应用于下塔尔伯特港县。总共调查了55,000个住宅（几乎是人口的100%），需要18个人力月的投资。这类调查在人力和时间上的投入已被证明是进一步采用这些建模技术的障碍。为了更多地利用这些建模方法，需要开发新的更有效的方法来获取建筑存量的调查数据。如果这些成为可用的，迹象表明，EEP类型的系统将找到广泛的应用。提出了一种将模式识别算法应用于数字地图分析的新方法。在较大的城市一级已经建立了模式识别的使用；例如，Barr和Barnsley使用OS地图来推断城市土地使用情况，并通过考虑街道布局模式成功地识别出具有相似建筑年龄的区域。然而，为了达到必要的详细程度，需要单个住宅水平的数据。拟议的方案寻求发展技术，以便对一个地区内的个别住宅进行分类。使用简单的模式匹配算法对这一概念进行的初步试验表明，住房年龄可以通过这种方法确定。例如，在2000多个不同年龄的住宅样本中，69%的1919年以前的住房被成功识别出来。在工作系统中需要更高的成功率。提高成功率将需要使用更先进的模式匹配方法和算法，并使用辅助信息，例如与道路中心的距离或屋顶形状（例如可以从卫星图像确定）。因此，本提案旨在寻求提高成功率所需的方法和数据，并从不同级别的数据可用性中确定可能的成功率。有效的调查方法应有助于克服在非常大的范围内（例如县或地区）采用建筑存量模型的障碍。然而，为了帮助采用这种系统，将寻求过程中利益相关者的意见，以确定这种建模系统的操作和功能特征。设想，一旦试验、演示和建立，这些技术可以应用于建筑环境的其他方面。一旦成功地开发了住房识别方法，设想该方法可以扩展到开发其他建筑建模部门的调查技术，例如非住宅建筑、绿色空间分类、洪水风险、街道照明、工业过程和运输分析。

The large scale housing stock survey methods originally developed for the Energy and Environment Prediction (EEP) model have already proven valuable to its users. This model has shown that it can produce considerable financial, energy, and Carbon savings through the targeting of energy efficiency measures appropriate to certain house types. It also allows the user, e.g. a local authority, to target those areas with the greatest need.Existing survey methods usually require a laborious and expensive 'walk by' survey to establish, accurately, the built characteristics of an area; the EEP model established and uses such a method. This method, which provided information that was then otherwise unobtainable, was applied to the county of Neath Port Talbot. A total of 55,000 dwellings were surveyed (nearly 100% of the population), requiring an investment of 18 man-months. This investment in manpower and time for such surveys has proved to be a barrier to the further uptake of these modelling techniques. In order to allow greater access to these modelling methods, there is a need to develop new more efficient methods for acquiring survey data of building stock. Should these become available, the indications are that EEP type systems would find wide-scale application. A new method, applying pattern recognition algorithms to the analysis of digital maps, is proposed. At a larger urban level the use of pattern recognition has been established; Barr and Barnsley, for instance, used OS maps to infer urban land use and successfully identified areas with similar built ages by considering street layout patterns. However in order to achieve the level of detail necessary, data at the level of an individual dwelling is required. The proposed programme seeks to develop techniques in order to classify individual dwellings within an area.An initial trial of the concept, using simple pattern matching algorithms, has shown that housing age can be identified by such an approach. For instance, in a sample of over 2000 dwellings of varying age 69% of all pre-1919 housing were successfully identified. Higher success rates will be required in a working system. Improvements in the success rate will require the use of more advanced pattern matching methods and algorithms and the use of supporting information, such as distance from the road centre or roof form (as may be determined from satellite imagery for instance). This proposal therefore aims to seek methods and data required to improve the success rate, and to establish the likely success rate available from various levels of data availability.An efficient survey method should help to overcome the barriers to the uptake of building stock modelling on a very large scale (e.g. county or regional). However to aid in uptake of such a system, views of stakeholders in the process will be sought in order to define the operational and functional characteristics of such a modelling system.It is envisioned that, once trialled, demonstrated, and established, the techniques could be applied to other aspects of the built environment. Once successful methods for the identification of housing are developed it is envisioned that the method could be extend to develop survey techniques for other building modelling sectors for example non-domestic buildings, green space classification, flood risk, street lighting, industrial processes and transport analysis.