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.

这项研究的目的是探索如何使用来自手机的新形式的空间大数据来研究城市人与自然的互动。虽然绿地的健康和福祉越来越被认识到，但在过去一年半的时间里，由于新冠肺炎的限制，绿地的重要性变得更加重要。这些同样的限制也可能扩大了获得绿地的不平等，从而加剧了健康不平等。手机数据有可能更好地了解人类在城市自然空间中的行为，但作为一种新的数据形式，它们也包含潜在的偏见。这个项目考察了我们如何克服这些偏见，并使用这些数据来更好地理解城市地区的人与自然的互动。这个特别的应用也可以被视为许多其他潜在应用的测试案例或演示，这些应用涉及人口流动性的细粒度分析。背景：人与自然的动态对我们的城市来说很重要，即使在“正常”的时代也是如此。在大流行时期，自然空间的预期效益被放大，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。在大流行期间，大自然一直是许多人身心喘息和滋养的源泉，封锁规定加强了我们对当地公园和绿地的欣赏。这种与自然区域更多的接触很可能形成这个时代的持久遗产之一。然而，大流行施加的限制(特别是对公共交通的限制)可能加剧了绿地准入和使用方面的现有不平等。传统上，抽样调查是了解绿地利用情况的最常用工具。对于提供对自然空间的偏好和社会规范以及总体使用的变化的高水平图景，它仍然很重要。然而，样本量的限制意味着它不能详细了解不同类型场地的使用变化或随时间的变化，以应对相对短暂的行动限制。答复率参差不齐或抽样战略中的弱点也可能在结果中造成偏差。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图景，因为他们难以应对游客数量增加或大流行导致的其他使用变化带来的压力。移动电话数据凭借可用数据量大、覆盖范围广以及提供的空间和时间细节，提供了巨大的潜力。然而，这些数据的产生过程往往相当不清楚，而且它们还可能包含人口覆盖率方面的偏见，这对它们提供的情况产生了影响。我们需要密切关注数据的质量，并了解不同的商业提供商之间的质量可能会有何不同。建议的研究：我们将直接解决手机数据中的偏见和代表性问题。我们使用的所有数据都是经过身份识别的(即所有姓名、电话号码或其他个人标识都已被删除)，但我们可以使用每个移动电话的移动来推断用户居住的地区，从而推断数据在地理和社会上的代表性。然后，我们可以对数据进行调整或加权，以尝试在必要时提供更具代表性的图景。手机数据可以从不同的提供商那里获得许可，但对于商业运营商之间的数据差异几乎一无所知。我们通过比较来自两个不同手机数据提供商的数据来探索这一点。通过我们增强的数据集，我们将探索大流行不同阶段绿地使用模式的变化。我们还将研究在谁使用不同类型的网站、人们使用网站的频率和距离方面的社会不平等。

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