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Urban data and decision-making: emerging technologies and built environment design

Urban data and decision-making: emerging technologies and built environment design
城市数据和决策:新兴技术和建筑环境设计
批准号:
2283072
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
未结题
起止时间:
2019 至 --

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中文摘要
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英文摘要
The research explores the relationship between urban data and built environment decision-making, in terms of policy, planning and design. It focuses on how contemporary urban data innovations, approaches and technologies (e.g. crowdsourcing, big data mining, in-person co-design workshops, IoT, sensors) can provide insight and add value to decision-making processes by informing designs/policy, post-occupancy evaluation or future scenario modelling, and any subsequent impact on user experience, environmental, health and well-being outcomes. For example, by creating pedestrian/cycle-friendly cities, promoting social interaction in public space, or increasing greenspace provision. This will help close a gap, better connecting what can now be learnt about the city, with the practical application of built environment decision-making.Aims: Explore how urban data is already used in cities built environment decision-making worldwide.Investigate the impact on decision-making, including health, well-being, user experience outcomes.Explore the future potential for urban data to add value to urban planning, policy or design decisions, focussing on user experience.Disseminate learnings The research will be innovative. PhD outputs will enable a more effective positive feedback loop between what is built, and lessons learned from what was built previously. Exploration of live data streams and simulated models will support more agile and responsive decision-making. Learnings will feed into industry partner - Connected Places Catapults Future of Planning programme. Outputs: Toolkit: summarising 10 global case studies. Successes/lessons learnt. Sharing practical knowledge about applying urban data techniques. Distributed via website/mailing-list of academics, professionals, citizens, including research participants.Framework: highlighting recommended data-driven techniques/metrics built environment decision-makers could utilise to maximise user experience, health, well-being outcomes.Conference: sharing framework/toolkit with public, academics, professionals. Speakers: Connected Places Catapult, case study participants, urban data/design experts, researchers.Website: featuring open source toolkit, framework, conference talks, case studies. Sharing learnings with those delivering built environment public services/policy/initiatives worldwide (professionals, citizen groups). Research impact will include adding to academic knowledge, as well as improving the effectiveness of future urban policy/design decisions. This is particularly valuable given the costs of physical infrastructure changes and long-term city-wide impacts of planning policy. The PhD would help decision-makers take practical steps to use urban user experience data, improving societal (health/well-being) and economic outcomes (better targeting finite public funds/services).
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
复杂数据下半参数转换模型及其在老年慢性病发展中的应用研究
  • 批准号:
    72101261
  • 项目类别:
    青年科学基金项目(C类)
  • 资助金额:
    30.0万元
  • 批准年份:
    2021
  • 负责人:
    孙韬
  • 依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: