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

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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.