Developing and exploring methods to understand human-nature interactions in urban areas using new forms of big data

利用新形式的大数据开发和探索理解城市地区人与自然相互作用的方法

• 批准号: ES/W012979/1
• 负责人: Michael Sinclair
• 金额: $ 30.26万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FW012979%2F1
• 关键词: Developing; exploring; methods; understand; human

The aim of this research is to explore how new forms of spatial big data from mobile phones can be used to examine urban human-nature interactions. While the health and well-being benefits of greenspace have been increasingly recognised, they have taken on even greater significance over the last year and a half due to the Covid-19 restrictions. These same restrictions may also have widened inequalities in access to greenspace, and hence contributed to widening health inequalities. Mobile phone data have the potential to provide a better understanding of human behaviour in urban natural spaces but, as a novel form of data, they also contain potential biases. This project examines how we might overcome these biases and use these data to better understand human-nature interactions in urban areas. This particular application can also be seen as a test case or demonstrator for many other potential applications involving the fine-grained analysis of population mobility.Background:The human-nature dynamic is important for our cities even in 'normal' times. In a time of pandemic, the perceived benefits of natural spaces are amplified, with greenspace playing an even greater role in promoting the health and well-being of our urban societies. Nature has been a source of physical and mental respite and nourishment for many during the pandemic, with lockdown rules heightening our appreciation for local parks and greenspaces. This increased engagement with natural areas may well form one of the enduring legacies of this time. However, the restrictions imposed by the pandemic (notably on public transport) may have exacerbated existing inequalities on access to and use of greenspace. Traditionally, the sample survey is the most common tool for understanding the use of greenspace. It remains important for providing a high-level picture of changes in preferences and social norms towards nature spaces as well as overall usage. However, limitations of sample size mean it cannot provide detailed understanding of changes in the use of different kinds of sites or variations over time in response to relatively short-lived restrictions on movement. Uneven response rates or weaknesses in sampling strategies may also introduce biases in results. For greenspace managers, surveys cannot provide the kind of site-specific spatiotemporal picture needed to inform strategies for investment and management as they struggle to cope with the pressures of increased visitor numbers or other changes in use causes by the pandemic. Mobile phone data offer enormous potential by virtue of the volume of data available, the wide population coverage and the spatial and temporal detail provided. However, the processes by which these data are produced are often rather unclear and they may also contain biases in population coverage which impact on the picture they provide. We need to pay close attention to the quality of the data and understand how this quality may vary between the different commercial providers.Proposed research:We will address the issues of bias and representativeness in mobile phone data directly. All the data we use are deidentified (i.e. all names, phone numbers or other personal identifiers have been removed), but we can use the movements of each mobile phone to infer which area a user lives in and hence how geographically and socially representative the data are. We can then adjust or weight the data to try to provide a more representative picture if necessary. Mobile phone data can be licensed from different providers yet almost nothing is known about how data vary between commercial operators. We explore this by comparing data from two different providers of mobile phone data. With our enhanced datasets, we will explore variations in the patterns of greenspace usage across the different stages of the pandemic. We will also examine social inequalities in who uses different kinds of sites, how often and how far people travel to do so.

本研究的目的是探索如何从移动的手机空间大数据的新形式可以用来检查城市人与自然的相互作用。虽然绿地的健康和福祉效益日益受到认可，但由于COVID-19的限制，它们在过去一年半中变得更加重要。同样的限制也可能扩大了在获得绿地方面的不平等，从而加剧了健康方面的不平等。移动的电话数据有可能更好地了解城市自然空间中的人类行为，但作为一种新型数据形式，它们也包含潜在的偏见。该项目探讨了我们如何克服这些偏见，并利用这些数据来更好地了解城市地区的人与自然的相互作用。这个特殊的应用程序也可以被看作是一个测试案例或示威者为许多其他潜在的应用程序，涉及人口mobility.Background的细粒度分析：人与自然的动态是很重要的，我们的城市，即使在“正常”的时候。在大流行时期，自然空间的感知效益被放大，绿地在促进我们城市社会的健康和福祉方面发挥着更大的作用。在疫情期间，大自然一直是许多人身心放松和营养的来源，封锁规则提高了我们对当地公园和绿地的欣赏。这种与自然领域的日益接触很可能成为这个时代的持久遗产之一。然而，这一流行病所施加的限制（特别是对公共交通的限制）可能加剧了在获得和使用绿地方面的现有不平等。传统上，抽样调查是了解绿地使用情况的最常用工具。它对于提供对自然空间的偏好和社会规范以及整体使用的变化的高层次画面仍然很重要。然而，由于样本量的限制，它无法详细了解不同类型的场地使用情况的变化，或因相对短暂的行动限制而产生的变化。答复率不均衡或抽样战略薄弱也可能导致结果出现偏差。对于绿地管理者来说，调查无法提供为投资和管理战略提供信息所需的特定地点的时空情况，因为他们正在努力科普游客人数增加或大流行病造成的其他使用变化的压力。移动的电话数据因其数据量大、人口覆盖面广以及所提供的空间和时间细节而具有巨大潜力。然而，这些数据的制作过程往往不甚明确，而且在人口覆盖面方面也可能存在偏差，影响到数据所提供的情况。我们需要密切关注数据的质量，并了解不同商业提供商之间的质量差异。建议的研究：我们将直接解决移动的电话数据中的偏见和代表性问题。我们使用的所有数据都是去识别的（即所有姓名、电话号码或其他个人标识符都已被删除），但我们可以使用每个移动的手机的移动来推断用户居住的区域，从而推断数据的地理和社会代表性。然后，我们可以调整或加权数据，以便在必要时提供更具代表性的图片。移动的电话数据可以从不同的提供商获得许可，但几乎不知道商业运营商之间的数据如何变化。我们通过比较来自两个不同的移动的电话数据提供商的数据来探索这一点。我们将利用增强的数据集，探索疫情不同阶段绿地使用模式的变化。我们还将研究谁使用不同类型的网站，人们旅行的频率和距离的社会不平等。

项目成果

Estimating greenspace visitation using Digital Footprints Data: A collaboration with Glasgow City Council to aid in Open Space policy and operations

使用数字足迹数据估算绿地访问量：与格拉斯哥市议会合作，协助制定开放空间政策和运营

• DOI: 10.31219/osf.io/3xzsv
• 发表时间: 2023
• 作者: Sinclair M

Assessing the socio-demographic representativeness of mobile phone application data

• DOI: 10.1016/j.apgeog.2023.102997
• 发表时间: 2023-09
• 期刊: Applied Geography
• 影响因子: 4.9
• 作者: Michael Sinclair;Saeed Maadi;Qunshan Zhao;Jinhyun Hong;A. Ghermandi;N. Bailey