Abstract

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

Highlights

  • Cities are complex systems (Castells, 1996; Hall, 1966; Theodore, 2006), consisting of two main elements: the people as residents or visitors, and the infrastructure to fulfill their needs ranging from housing to recreation or even self-realization (Costanza et al, 2007; Maslow, 1943)

  • We developed a two-step filtering procedure to prepare the raw data for the subsequent analysis: Temporal binning: First, we created temporal bins from the raw data representing time periods before, during and after the Olympic Games (OG)

  • We defined three temporal subsets for our analysis: the time period of the OG and the same number of comparison days before and after the Olympics to test the effect of the OG on spatiotemporal tweeting behavior and on the tweets’ semantic content relating to RQ1 and RQ2

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Summary

Introduction

Cities are complex systems (Castells, 1996; Hall, 1966; Theodore, 2006), consisting of two main elements: the people as residents or visitors, and the infrastructure to fulfill their needs ranging from housing to recreation or even self-realization (Costanza et al, 2007; Maslow, 1943). Urban Planning, 2018, Volume 3, Issue 1, Pages 75–99 static and mostly physical, such as buildings or the road and electricity networks, whereas others are more dynamic, like social, transportation, or financial networks. From an urban analysis viewpoint, the dynamic nature of these systems is challenging, especially in the case of large cities with millions of people constantly on the move and having different needs and preferences. These challenges do result from the sheer amount of people, and from the intense spatiotemporal variability originating from urban dynamism and from the constantly changing subjective needs of each person. Effective planning practice requires analysis at high spatial and temporal scales to understand this dynamism of urban life and processes

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