Digital twin for the built environment

建筑环境的数字孪生

• 批准号: 10022996
• 金额: $ 26.24万
• 依托单位: FF CLARION LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10022996
• 关键词: Digital; twin; built; environment

Commercial property investment analysis is time-consuming and judgment based. In traditional analysis, all the data is sourced, cleansed and interpreted manually. Comparative analysis is incomplete, as data is often sparse, leading to subjective, biased decisions. Non-traditional data, such as social referencing, mobility and key local factors, is rarely taken into account. Valuable historic deal pipeline and existing portfolio insights get ignored or lost in the email chains. Only a fraction of potential data is used, limiting risk analysis depth and quality.Built AI is creating an AI-powered platform that will enable real estate investors to make the best data-driven investment decisions. Our cloud-based SAAS platform provides investors with a better way to make property decisions, through a combination of street-level data insights, fast & powerful financial modelling and machine learning that leverages historical datasets for dynamic learning.In this project, BuiltAI will conduct research to build a novel data platform and analytics tools. The technology will offer an advanced ML-based data analytics tool that will allow property buyers, owners and tenants to get far more information, e.g. hyper-local, to make optimal use of their commercial property.We will provide users with the most up to date analytics on locations, buildings, tenants in the UK, with a focus on five of the major urban centres, and include other value drivers such as footfall, demographics, supply, demand, transportation, etc. The BuiltAI platform will enable users to consider any property, identify the critical data insights in that location and for that property type. In particular, BuiltAI will provide granular data insights at a 'street level' that gives users a deeper understanding of the market than is currently available.This research will accelerate UK economic development in built environments and improve the investment in and use of real estate space, to meet the needs of a post-covid UK.

商业地产投资分析是耗时和判断的基础。在传统的分析中，所有数据都是手动获取、清理和解释的。比较分析是不完整的，因为数据往往是稀疏的，导致主观的，有偏见的决定。非传统数据，如社会背景、流动性和关键的当地因素，很少得到考虑。有价值的历史交易渠道和现有的投资组合见解被忽略或丢失在电子邮件链中。仅使用了一小部分潜在数据，限制了风险分析的深度和质量。人工智能正在创建一个基于人工智能的平台，使真实的房地产投资者能够做出最佳的数据驱动投资决策。BuiltAI的SAAS平台基于云计算，通过结合街道级数据洞察、快速而强大的金融建模和利用历史数据集进行动态学习的机器学习，为投资者提供更好的房地产决策方式。在这个项目中，BuiltAI将进行研究，以建立一个新的数据平台和分析工具。该技术将提供一个先进的基于ML的数据分析工具，将允许购房者，业主和租户获得更多的信息，例如超本地化，以最佳利用他们的商业物业。我们将为用户提供最新的位置，建筑物，租户的分析，重点是五个主要城市中心，并包括其他价值驱动因素，如客流量，BuiltAI平台将使用户能够考虑任何物业，识别该位置和该物业类型的关键数据见解。特别是，BuiltAI将提供“街道层面”的精细数据洞察，让用户比目前更深入地了解市场。这项研究将加速英国建筑环境的经济发展，并改善对真实的房地产空间的投资和使用，以满足后新冠肺炎英国的需求。