Exploring how affordable, global people-centred geospatial data products could help planners to evaluate, plan and track progress to UN SDGs 3, 11 and 15

关于环境质量的地理空间信息越来越多。这些新兴数据有许多应用，包括促进可持续和健康的城市规划，鼓励公民意识和更健康的行为，以及跟踪健康和可持续成果的进展。然而，这些数据集的生产是昂贵的，资源密集型和规划人员不习惯处理大型GIS数据集。这严重限制了联合国可持续发展目标11（可持续城市和社区）、3（良好健康和福祉）和15（陆地生命）的实现。因此，没有此类环境数据集的城市在有效论证、规划和跟踪重大城市干预措施和公民行为变化的进展方面能力有限。Tranquil City已开发出一个创新的、以人为本的、具有成本效益的地理空间数据产品包，提供战略性和高分辨率的环境质量和基础设施信息，以通过技术平台促进更健康的行为。我们的方法使用机器学习（ML）技术从“数据丰富”的城市中学习，并训练一个模型，使用全球可用的数据集来预测全球任何城市或地区的许多环境因素（空气质量、噪音、绿色空间质量、水元素、树木覆盖、宁静和健康街道指数）的高分辨率数据，其成本仅为迄今为止使用的标准建模方法的一小部分。这些数据通过我们的授权数据工具包和API进行商业化，以访问集成到面向公众的应用程序中，从而鼓励更健康，更可持续的行为。我们未来的计划是支持规划当局和从业人员为实现可持续发展目标而努力。在Innovate UK的资助下，我们将与当前的客户和利益相关者进行接触，以了解和验证我们为空间规划提供这些数据的最初概念，包括验证，缩放和交付。产出将包括规划者最希望如何使用和处理数据、建模和监测干预措施对可持续发展目标的影响以及如何最好地实施大规模数据生产方法的可行性研究。

More and more geospatial information on environmental quality is becoming available. This emerging data has numerous applications including enabling sustainable and healthy city planning, encouraging citizen awareness and healthier behaviours as well as tracking progress towards healthy and sustainable outcomes. However, the production of these datasets is expensive and resource intensive and planners are not used to dealing with large GIS datasets. This significantly limits the realisation of the UN Sustainable Development Goals 11 (Sustainable Cities and Communities), 3 (Good Health and Wellbeing) and 15 (Life on Land). Cities without such environmental datasets are therefore limited in their ability to effectively justify, plan and track progress of significant urban interventions and citizen behaviour change. Tranquil City has prototyped an innovative, people-centred and cost-effective geospatial data product package that delivers strategic and high-resolution environmental quality and infrastructure information to promote healthier behaviours through tech platforms. Our method uses machine learning (ML) techniques to learn from 'data rich' cities and train a model to use globally available datasets to predict high-resolution data for many environmental factors (air quality, noise, green space quality, water elements, tree cover, Tranquillity and Healthy Streets Indices) in any city or area worldwide at a fraction of the cost of the standard modelling approaches used to date. This data is commercialised via our licensed data toolkits and APIs for access integration into public-facing applications that can encourage healthier, more sustainable behaviours. Our plan for the future is to support planning authorities and practitioners to work toward the SDGs. With Innovate UK's funding, we will engage with current clients and stakeholders to understand and iterate on our initial concepts of offering this data for spatial planning, including validation, scaling and delivery. The outputs will include feasibility studies of how planners would most like to use and handle the data, model and monitor the impact of interventions against SDG targets and how best to implement wide-scale data production methods.