ITEM METADATA RECORD
Title: The Role of Data Visualization in Urban Studies
Other Titles: De rol van data visualisatie in stedelijke studies
Authors: Chua Yung Hwa, Alvin
Issue Date: 8-Nov-2016
Abstract: As large volumes of data become increasingly available, new opportunities arise to harness their potential for urban studies. Data visualization is vital for this purpose, particularly in tackling fuzzy problems that require an explorative process of discovery to re-frame. This research investigates how data visualization can be used to harness big data for domain specific challenges in urban studies. To address this question, I demonstrate the utility of data visualization in four different case studies and developed a framework to compare their outcomes from design to application. Based on this comparison, I show how data visualization facilitates discovery, contemplation and presentation amongst other roles. Reflecting on my experience from this research, I discuss the factors to consider when developing data visualizations for particular roles in urban studies, and share my thoughts on the need for novel visualization techniques.
Case Study 1. “BinSq” is a novel gridded dot density mapping technique designed to visualize voluminous geographic datasets. The technique was applied to a large dataset of language referenced, geotagged Twitter data to reveal ethnic communities in Brussels. This work contributes to data visualization research and urban studies.
Case Study 2. In “Mapping Cilento: Using Geotagged Social Media Data to Characterize Tourist Flows in Southern Italy”, a novel analytical technique was developed to characterize tourist flows with geotagged Twitter data. The resulting insights from analysis extended the existing understanding of tourist movements in Southern Italy.
Case Study 3. In “Who Is Really Using FixMyStreet? Studying the extent of Socio-Demographic Inequality in Crowdsourced Civic Participation on FixMyStreet Brussels”, visual analysis of geotagged data from a web-based civic participation platform led to several statistically significant findings which, reveal the extent of socio-demographic inequality on that platform.
Case Study 4. In “What Public Transit API Query Logs Tell Us About Travel Flows”, visualizations of query logs from a route planning API provided experts in transportation studies with an alternative perspective of public transit demand in Belgium. The insights obtained were published in a news article and eventually paved the way for discussions with public transport service providers.
Table of Contents: Abstract i
Contents vii
List of Figures xiii
List of Tables xvii

1 Introduction 1
1.1 Overview 1
1.2 Big Data and Urban Studies 1
1.2.1 Features of Big Data 2
1.2.2 New Challenges for Data Visualization in Urban Studies 3
1.3 Methodology 5
1.4 Case Studies in Brief 5
1.4.1 Motivation and Contributions 5
1.4.2 Chronology 9
1.5 Definitions 10

2 BinSq 13
2.1 Introduction 15
2.1.1 BinSq 15
2.1.2 Visual Clutter 16
2.2 Related Work 17
2.2.1 Jittering 18
2.2.2 Refinement 18
2.2.3 Distortion 18
2.2.4 Aggregation 19
2.2.5 Histogram Equalization 20
2.2.6 Summary of the Solution Space 20
2.3 Design 20
2.3.1 Nested Binning 21
2.3.2 Dot Prioritization 23
2.3.3 Legend 23
2.4 Implementation 25
2.4.1 Nested Binning 25
2.4.2 Dot Prioritization 26
2.5 The Design Space of BinSq 27
2.5.1 Data 28
2.5.2 Metrics 28
2.5.3 Parameters 29
2.5.4 Scalability 37
2.5.5 Comparison to Existing Clutter Reduction Techniques 37
2.6 Discussion 44
2.7 Conclusion 46

3 FlowSampler 48
3.1 Introduction 50
3.2 Data 51
3.3 Related Work 52
3.3.1 Land Use Analysis 52
3.3.2 Crisis Management Systems 52
3.3.3 Mobility Analysis 53
3.3.4 Visual Analytics 53
3.4 Design 54
3.4.1 Data Transformation 55
3.4.2 Interface Components 55
3.5 Use Cases 59
3.5.1 Investigating Daily Routine in Trip Making Behavior 59
3.5.2 Investigating Exceptional Trip Making Behavior 59
3.6 Limitations, Uncertainty and Bias 61
3.7 Future Work 63

4 Mapping Cilento 64
4.1 Introduction 66
4.2 Case Study 67
4.2.1 Research Questions and Data Criteria 68
4.2.2 Limitations with Existing Data 69
4.3 Alternative Methods to Collect Tourist Flow Data 71
4.3.1 Non-Observational Methods 71
4.3.2 Observational Methods 74
4.4 Methodology 76
4.4.1 Data Collection 76
4.4.2 Data Processing 76
4.4.3 Data Visualization 78
4.5 Results 80
4.5.1 Demographic Breakdown of Tourist 81
4.5.2 Uncovering Temporal Characteristics of Tourism 82
4.5.3 Spatial Topology of Tourist Flows 83
4.5.4 Insights 88
4.5.5 Discussion 90
4.6 Conclusion 92

5 Who is Really Using FixMyStreet? 93
5.1 Introduction 95
5.2 RelatedWork 96
5.2.1 Crowdsourcing Civic Participation with Web-based Platforms 96
5.2.2 FixMyStreet and Relevant Research Findings 98
5.2.3 BrusselsOfficialStatisticsDataset 98
5.2.4 Geotagged Social Media Data as an Alternative Source ofInformation 99
5.3 Context 100
5.4 Method 102
5.4.1 Indicators for FixMyStreet Usage Data 103
5.4.2 IndicatorsforEthnicity 103
5.4.3 Alternate Indicators for Ethnicity 104
5.5 Results 105
5.5.1 Exploratory Analysis of FMS Usage Logs 105
5.5.2 EthnicityandIncome 112
5.5.3 Languages 112
5.5.4 HypothesisTesting 114
5.6 Discussion 117
5.7 Conclusion 118

6 What Public Transit API Logs Tell Us about Travel Flows 119
6.1 Introduction 121
6.2 Related Work 121
6.2.1 Studying Query Logs 122
6.2.2 Visualizing Travel Flows 122
6.3 The Query Logs 124
6.4 Method 126
6.4.1 Data Processing 127
6.4.2 Data Visualization 127
6.5 Results 129
6.5.1 Time Based Analysis 129
6.5.2 Structure of Flows in Belgium 131
6.5.3 Counter-Intuitive Results 135
6.6 Publishing Transport Data 135
6.7 Conclusion and Future Work 137

7 Comparative Framework and Analysis of Case Studies 138
7.1 Introduction 138
7.2 Analysis 139
7.2.1 Design 139
7.2.2 Development 144
7.2.3 Application 144
7.3 Discussion 146
7.3.1 The Role of Data Visualization in Urban Studies 146
7.3.2 When in Addition to Why, What and How 147
7.3.3 Limitations of this Framework 147

8 Conclusion 151
8.1 Conclusion 151
8.2 Closing Remarks 152
8.2.1 Are Novel Visualization Techniques Required? 153
8.2.2 Interdisciplinary Skill Set 154
8.3 Future Work 154

Appendix A 157
Bibliography 163
Publication status: published
KU Leuven publication type: TH
Appears in Collections:Architecture and Design (+)

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