Understanding use of urban greenspaces through big data

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

Funders: ESRC

Partners: Greenspace Scotland, Glasgow City Council

Background:

The interaction between humans and nature has always been crucial for urban environments. During the pandemic, the benefits of urban greenspaces have become even more apparent as they have played a vital role in enhancing the health and wellbeing of urban communities, providing a source of physical and mental solace for many. However, access to urban greenspaces is not equally available to everyone. The Covid-19 pandemic may have worsened existing inequalities in access to and use of greenspaces.

Traditionally, questionnaires have been the primary method for understanding interactions with urban greenspaces. While they are essential for grasping broad changes in preferences and social norms, they fall short in providing the near real-time insights needed at the local level for effective management. Surveys, despite their wide socio-demographic reach, often lack detailed spatial-temporal information and may not capture individual behaviors over extended periods. Therefore, this project aims to leverage new sources of spatial big data, particularly mobile phone data, to gain a high-resolution understanding of greenspace use and inform management decisions effectively.

Objectives:

The overall aim of this project is to incorporate mobile phone data to better understand human interactions with urban greenspaces and how this dynamic has changed for different sectors of the population throughout the Covid-19 pandemic.

Specific objectives include:

1. Assess Geographic Representativeness: Evaluate the geographic representativeness of mobile phone data for mobility research using advanced techniques.

2. Explore Use and Accessibility: Investigate the use and accessibility of urban greenspaces by different population segments and how Covid-19 restrictions have impacted these dynamics.

3. Impact of Greenspace Quality: Identify how greenspace quality and other site-specific characteristics affect usage among different socio-demographic groups.

Through a case study of urban greenspaces in the Glasgow City Region, we will collaborate with key stakeholders to develop and test novel methodologies incorporating mobile phone data. The goal is to enhance urban greenspace management and planning, benefiting researchers and practitioners interested in urban mobility and spatial big data, and supporting research with potential public benefits.

Ongoing Findings:

To address potential biases and uncertainties in the socio-demographic representativeness of mobile phone data, we developed a novel approach to assess these factors using two large independent mobile phone application datasets, Huq and Tamoco, each containing three years of data for a large and diverse city-region (Glasgow, Scotland) home to over 1.8 million people. By incorporating high-resolution land use data into our home location detection methods, we tested the representativeness of the data across multiple dimensions.

Our findings provide greater confidence in using mobile phone application data for research and planning. Both datasets show good representativeness compared to the known population distribution, achieving better population coverage than traditional random sample surveys. More importantly, our approach provides an improved benchmark for assessing the quality of similar data sources in the future.

For more details, you can read the full paper here: ScienceDirect.

Lead:

Michael Sinclair

Team:

Luning Li, Nick Bailey, Qunshan Zhao, Richard Mitchell

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