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Exploiting big data to understand access to greenspace in the UK

Exploiting big data to understand access to greenspace in the UK
利用大数据了解英国绿色空间的使用情况
批准号:
2737544
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:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
翻译
绿色空间为人类提供了宝贵的利益;生态系统服务。例如,英国有超过62,000个城市绿地,估计为附近居民提供价值约1300亿英镑的ES。这些好处可以细分为不同的ES:例如粮食生产(每年1.14亿英镑),碳封存(3300万英镑),空气过滤(2.11亿英镑),冷却(1.66亿英镑),噪音缓解(1400万英镑),改善身体健康(44亿英镑)和其他文化服务(21亿英镑)。虽然一般来说对ES进行了大量研究,但它们通常被视为静态的,人们对ES的空间过程了解甚少。例如,为了获得这些特定的环境服务,人们需要能够进入城市景观中的绿地。平均而言,在GB中,每公顷功能绿地有1.4个接入点,平均城市物业在200米半径内有4.6公顷的绿色空间。有人可能认为,距离绿地是其使用的主要贡献者。然而,人们如何在景观尺度上接触自然,目前尚不清楚。该项目将通过结合人类流动和行为生态学的理论来解决这一知识差距,以了解“人的运动生态学”。使用智能手机数据的研究表明,超过50%的人类运动是由于日常生活,人口显示出每小时,每天和每周的运动模式。结合行为生态学模型(特别是觅食模型,包括:边际价值定理,理想自由分布和中心地理论),我们将考虑如何通过“人类觅食”进入绿地,因为人们寻找从自然中受益的机会。例如,与其他动物一样,人类可能会表现出复杂的“觅食”行为,这是简单的距离度量无法捕捉的。这将为人们如何获得一系列ES提供更广泛的见解,并将运动生态学思想应用于ES的关键问题。
英文摘要
Greenspaces provide valuable benefits to people; ecosystem services (ES). For example, there are over 62,000 urban greenspaces in GB, estimated to provide ES worth ~£130bn to those living nearby. These benefits can be broken down to different ES: e.g. food production (£114M per year), carbon sequestration (£33M), air filtration (£211M), cooling (£166M), noise mitigation (£14M), improved physical health (£4.4bn) and other cultural services (£2.1bn).While much research is done on ES in general, they are usually treated as static, and the spatial process by which people access ES are poorly understood. For example, to receive these specific ES, people need to be able to access greenspaces within the urban landscape. On average, in GB there are 1.4 access points per hectare of functional greenspace, with the average urban property having 4.6 hectares of green space within a 200-meter radius. One may think that distance to greenspace is the main contributor to its usage. However, how people access nature at a landscape scale is currently not known. This project will address this knowledge gap by combining theories from human mobility and behavioural ecology to understand the "movement ecology of people". Studies using smartphone data show that >50% of human movement is due to routine and that populations show hourly, daily and weekly movement patterns. Combined with behaviour ecology models (particularly foraging models, including: Marginal Value Theorem, Ideal Free Distribution, and Central Place Theory), we will consider how greenspaces are accessed via "human foraging" as people search the landscape for opportunities to benefit from nature. For example, as with other animals, humans may show complex "foraging" behaviour that simple distance metrics cannot capture. This will provide broader insights into how people access a range of ES, and develop a novel use of movement ecology ideas to the critical issue of ES.
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