The classification and analysis of regional building stock characteristics using GIS

利用GIS对区域建筑群特征进行分类与分析

• 批准号: EP/E020100/1
• 负责人: Phil Jones
• 金额: $ 21.55万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE020100%2F1
• 关键词: classification; analysis; regional; building; stock

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.

最初为能源和环境预测模型开发的大规模住房存量调查方法已经证明对其用户很有价值。这一模式表明，通过针对某些房屋类型采取适当的节能措施，可以节省大量的资金、能源和碳。现有的调查方法通常需要费力且昂贵的“步行”调查来准确地建立区域的建筑特征; EEP模型建立并使用了这样的方法。这种方法提供了当时无法获得的信息，被应用于尼思港塔尔博特县。总共调查了55，000个住宅（几乎占人口的100%），需要18个人月的投资。事实证明，为这类调查投入人力和时间是进一步采用这些建模技术的障碍。为了更好地利用这些建模方法，有必要开发新的更有效的方法来获取建筑物存量的调查数据。如果这些都可以得到，迹象表明，EEP类型的系统将得到广泛的应用。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。在更大的城市层面上，模式识别的使用已经建立起来;例如，巴尔和巴恩斯利使用OS地图来推断城市土地使用，并通过考虑街道布局模式成功地确定了具有相似建筑年龄的地区。然而，为了达到必要的详细程度，需要提供单个住宅的数据。拟议的方案旨在开发技术，以便对一个地区内的各个住宅进行分类，使用简单模式匹配算法对这一概念进行的初步试验表明，可以通过这种方法确定住房年龄。例如，在2000多个不同年龄的住房样本中，成功地确定了69%的1919年前的住房。在一个有效的系统中，需要更高的成功率。要提高成功率，就需要使用更先进的模式匹配方法和算法，并使用辅助信息，如与道路中心的距离或屋顶形状（例如，可通过卫星图像确定）。因此，本提案旨在寻求提高成功率所需的方法和数据，并确定从不同级别的数据可用性中可能获得的成功率，有效的调查方法应有助于克服在大规模（如国家或地区）采用建筑存量建模的障碍。然而，为了帮助吸收这样一个系统，将征求利益攸关方在这一过程中的意见，以确定这种建模系统的操作和功能特征。据设想，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。一旦开发出成功的住房识别方法，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。