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Investigating the Role of Social Media and Smart Device Applications in Understanding Human-Environment Relationships in Urban Green Spaces

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Investigating the Role of Social Media and Smart Device Applications in Understanding Human-Environment Relationships in Urban Green Spaces INVESTIGATING THE ROLE OF SOCIAL MEDIA AND SMART DEVICE APPLICATIONS IN UNDERSTANDING HUMAN-ENVIRONMENT RELATIONSHIPS IN URBAN GREEN SPACES by HELEN VICTORIA ROBERTS A thesis submitted to the University of Birmingham for the degree of DOCTOR OF PHILOSOPHY School of Geography, Earth and Environmental Sciences College of Life and Environmental Sciences University of Birmingham February 2018 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract Urban green spaces are integral components of urban landscapes and the cultural ecosystem services afforded to human populations by these green spaces are of particular relevance to human and societal well-being. Urban green spaces provide opportunities for human interaction, physical activity and recreation, stress alleviation and mental restoration, economic opportunity, cultural activities and interactions with nature. To understand how these benefits are received by human populations it is vital to understand when and how individuals interact with urban green spaces. The rapid development and uptake of technologies such as smart phones, social networks and apps provides new opportunity to investigate the human interactions occurring in urban green spaces. Using the city of Birmingham as a case study, this thesis aims (i) to demonstrate the utility of data obtained from smart device enabled platforms (social networks and apps) in understanding socio- ecological interactions in urban areas and (ii) to evaluate the utility of these data sources for researchers and policy makers. The successful identification of a range of socio-ecological interaction suggest these data sources provide a viable method of investigating such interactions; however, there remain a number of limitations to consider to ensure they are employed appropriately in research contexts. Acknowledgements The development and production of this thesis was made considerably easier thanks to a number of individuals. I am indebted to my supervisors Professor Jon Sadler and Professor Lee Chapman for their guidance, enthusiasm and optimism, without which the research and production of this thesis would have seemed an impossible task. I would also like to extend thanks to Dr Phil Jones for providing support and encouragement to engage with opportunities beyond those which are standard to PhD research. I gratefully acknowledge the contributions of Dr Bernd Resch and his colleagues who provided the semi-supervised learning algorithm presented in Chapter Six. His expertise and commitment to the project were significant influences in the success of the resulting paper from this chapter. I would also like to thank Simon Bell for obtaining and providing the Netatmo datasets used in Chapter Four. I am grateful to my colleagues in the 225 postgraduate office for their support, advice and friendship. Thank you for providing a welcoming and enjoyable working environment. Finally, I would like to thank my family for their constant support and encouragement in my endeavours, and for believing in me when I struggle to believe in myself. This research was made possible by financial support from an Engineering and Physical Science Research Council (EPSRC) studentship (EP/L504907/1). Table of Contents 1. Introduction…………………………………………………………..……………………1 1.1 The expanding urban landscape…………………………………………………...1 1.2 Ecosystem services and specifically cultural ecosystem services…………………1 1.3 The importance of interdisciplinary research in the urban context………………..4 1.4 The role of smart devices in urban research……………………………………….8 1.5 Aims, objectives and thesis outline………………………………………………10 1.6 Justification of this research in Birmingham……………………………………..11 2. Methods…………………………………………………………………………………...15 2.1 Study location…………………………………………………………………….16 2.2 Twitter……………………………………………………………………………19 2.2.1 Tweet corpus creation and datasets…………………………………….23 2.3 Netatmo…………………………………………………………………………..25 2.3.1 Netatmo dataset………………………………………………………...25 2.4 BetterPoints………………………………………………………………………26 2.4.1 BetterPoints dataset…………………………………………………….27 2.5 Qualitative analysis……………………………………………………………....28 2.6 Statistical analysis………………………………………………………………..29 3. Using Twitter data in urban green space research: A case study and critical evaluation…………………………………………………………………………………30 3.1 Abstract…………………………………………………………………………..30 ` 3.2 Introduction………………………………………………………………………31 3.2.1 Urban green space……………………………………………………...31 3.2.2Crowdsourcing and social networks data……………………………….34 3.3 Methodology……………………………………………………………………..36 3.4 Results……………………………………………………………………………38 3.5 Critique…………………………………………………………………………...44 3.5.1 Advantages of data collection using Twitter…………………………...44 3.5.2 Issues identified with using Twitter……………………………………45 3.5.3 Improving the robustness of a Twitter dataset…………………………47 3.6 Conclusion……………………………………………………………………….48 4. Using Twitter data to investigate seasonal variation in physical activity in urban green space…………………………………….………………………………………….50 4.1 Abstract…………………………………………………………………………..51 4.2 Introduction………………………………………………………………………52 4.3 Methodology……………………………………………………………………..56 4.3.1 Study Area……………………………………………………………...56 4.3.2 Measurement of physical activity using Twitter……………………….56 4.3.3 Measurement of meteorological variables………………………..……58 4.3.4 Statistical Analysis……………………………………………………..59 4.4 Results……………………………………………………………………………60 4.4.1 The influence of season on received physical activity tweets………….61 4.4.2 The influence of meteorological variables on received physical activity tweets…………………………………………………………………..64 4.4.3 The influence of park characteristics and organised sports events on received physical activity tweets………………………………………65 4.5 Discussion………………………………………………………………………..67 4.6 Evaluation of the use of Twitter data…………………………………………….71 4.7 Conclusion………………………………………………………………………..74 5. The value of Twitter data for determining the emotional responses of people to urban green spaces: a case study and critical evaluation………………………….………….76 5.1 Abstract…………………………………………………………………………..78 5.2 Introduction………………………………………………………………………79 5.2.1 Green space and well-being……………………………………............79 5.2.2 Approaches to investigating associations between green space and well- being…………………………………………………………………...82 5.3 Methodology……………………………………………………………………..85 5.3.1 Study Area……………………………………………………………...85 5.3.2 Tweet corpus creation………………………………………………….86 5.3.3 Annotation……………………………………………………………...87 5.3.4 Analysis………………………………………………………………...88 5.4 Results and Discussion…………………………………………………………...88 5.4.1 Positivity, negativity and neutrality of tweet responses………………..88 5.4.2 Emotional responses and thematic analysis……………………………93 5.5 Evaluation of using Twitter data to gather information about emotional responses to urban green spaces……………………………………………………………97 5.6 Conclusion………………………………………………………………………100 6. Investigating the emotional responses of individuals to urban green space using Twitter data: a critical comparison of three different methods of sentiment analysis…………………………………………………………………………………..102 6.1Abstract………………………………………………………………………...103 6.2 Introduction…..………………………………………………………………..103 6.2.1Twitter, sentiment analysis and urban green space……………………103 6.2.2 Methods of sentiment analysis………………………………………..107 6.3 Methodology…………………………………………………………………..108 6.3.1 Case study location……………………………………………………108 6.3.2 Tweet corpus creation………………………………………………...109 6.3.3 Manual annotation…………………………………………………….109 6.3.4 Fully automated annotation…………………………………………...111 6.3.5 Graph-based semi-supervised learning annotation……………………111 6.3.6 Analysis……………………………………………………………….113 6.4 Results………………..........................................................................................113 6.4.1 Assignment of the tweets into positive, neutral and negative categories……………………………………………………………………113 6.4.2 Inter-method reliability……………………………………………….116 6.4.3 Quality control using emojis………………………………………….117 6.4.4 Assignment of tweets into discrete emotion categories………………118 6.5 Discussion………………………………………………………………………119 6.5.1 Comparison of the outputs of manual, automated and semi-automated analysis……………………………………………………………………...119 6.5.2 Implications of these findings for urban planners…………………….123 6.6 Conclusion………………………………………………………………………124 7. Using crowdsourced geospatial data to explore the dynamic nature of human interactions with urban green spaces: an emerging opportunity?..............................127 7.1 Abstract…………………………………………………………………………128 7.2 Introduction……………………………………………………………………..129 7.3 Datasets…………………………………………………………………………132 7.4 Results and discussion…………………………………………………………..134 7.4.1 Spatial and temporal variation in urban green space use……………..134 7.4.2 Assessment of ecosystem service provision…………………………..140 7.4.3 Theorisations of urban mobilities……………………………………..142 7.4.4 Methodological implications………………………………………….144 7.5 Conclusion………………………………………………………………………146 8. Synthesis and general discussion………………….…………………………………...148
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