Talks

Upcoming

1. GU

   The Impact of COVID-19 on Urban Hotspots Vitality: An illustrative study on human footfall in Auckland through mobile location data

   I-Ting Chuang, and Qingqing Chen

   The 8th International Conference on Green Urbanisum, Upcoming

   Abs

   The COVID-19 pandemic has affected people’s daily lives since its outbreak, taking the discussion around urban space recovery to the forefront. Locational data streaming provides opportunities to explore how individuals utilise urban spaces while their daily routines are disrupted. In this study, we build on the transformation of social behaviours imposed by the government’s emergency policies to explore the impact and recovery of visiting patterns in urban hotspots in Auckland, New Zealand. The “urban hotspots” here refer to vital urban places that attract both dense (high frequency) and diverse (visitors from different places) visitors. To quantify the impacts, we utilised mobile location data, which consists of 85.53 million data points collected from around 323k users in 2020. We then analysed and compared the changing visiting patterns in urban hotspots before and after two stringent COVID-19 lockdowns bi-weekly. Our findings suggest that, in addition to essential services (supermarkets and medical institutions), urban parks are a well-recovered urban infrastructure that offers crucial support for people. This result provides empirical evidence of the added value of urban parks to inform the planning and rethinking of urban structure strategies as part of the city’s post-COVID-19 recovery.

2024

1. AAG

   Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City

   Qingqing Chen, Ate Poorthuis, and Andrew Crooks

   AAG Annual Meeting, Apr 2024
2. AI & Health

   A Tale of Vaccination Debates & Public Responses: Data-driven Insights from a Multi-medium Exploration during the COVID-19 Era

   Qingqing Chen, Andrew Crooks, Adam Sullivan, and 2 more authors

   The first UB AI & Health Symposium, Feb 2024

   Poster

2023

1. ECTQG

   A Nationwide Dataset of De-identified Activity Spaces Derived from Geotagged Social Media Data

   Ate Poorthuis, Qingqing Chen, and Matthew Zook

   European Colloquium on Theoretical and Quantitative Geography, Sep 2023
2. SDSS

   Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data

   Qingqing Chen, Boyu Wang, and Andrew Crooks

   The Fourth Spatial Data Science Symposium, Sep 2023

   Slides
3. I-GUIDE

   International Supply Chain Shocks in a Metacoupled World

   Kate Brandt, Qingqing Chen, Keiko Nomura, and 5 more authors

   I-GUIDE Summer School, Aug 2023

   Slides
4. PIPP

   A Tale of Vaccination Debates & Public Responses: Data-driven Insights from a Multi-medium Exploration

   Qingqing Chen, and Andrew Crooks

   UB PIPP Modeling Workshop, Jul 2023

   Slides
5. AAG

   Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data

   Qingqing Chen, Boyu Wang, and Andrew Crooks

   AAG Annual Meeting, Mar 2023

   Abs Poster Slides

   Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society.

6. Spatial@UCSB

   Categorizing Urban Space based on Visitor Density and Diversity: A view through social media data

   I-Ting Chuang, Qingqing Chen, and Ate Poorthuis

   UCSB Spatial Lightning Talks, Mar 2023

   Abs Slides

   Analyses of urban spaces have often stressed the importance of both the density and diversity of the people they attract. However, the diversity of people is a challenging concept to operationalize within the context of urban spaces, which is why many evaluations of urban space have relied primarily on density-based measures. We argue that a focus on only one of the two aspects misses important aspects of the variety of urban spaces in our cities. To address this, we design a methodology that evaluates both the density and diversity of human behavior in urban spaces based on geosocial media data. We operationalize density as the frequency of tweets from visitors to a particular location and diversity as the variety of the home neighborhoods of those visitors. Taking Singapore as a test case, we identify networks between the home neighborhoods of 28k Twitter users based on 2.2 million geolocated tweets collected between 2012 and 2016. Based on these data, we categorize the urban landscape of Singapore into four ?performance? categories, namely High-Density/High-Diversity, High-Density/Low-Diversity, Low-Density/High-Diversity, and Low-Density/Low-Diversity. Our findings illustrate that this combined indicator provides useful nuance compared to differentiation between well and less performing spaces based on density alone. By enabling a categorization of urban spaces that fits closer to the diversity of human behavior in these spaces, human mobility data sets, such as the social media data we use, open the door to a practical evaluation of the design and planning of our heterogeneous urban environment.

2022

1. AAG

   Tracking the Dynamics of Vaccination Sentiment in Large-Scale Social Media Data

   Qingqing Chen, and Andrew Crooks

   AAG Annual Meeting, Feb 2022

   Abs Poster Slides

   The COVID-19 virus has caused and continues to cause unprecedented impacts on the life trajectories of millions of people globally. Recently, to combat the transmission of the virus, vaccination campaigns around the world have become prevalent. However, while many see such campaigns as positive (e.g., protecting lives), others see them as negative (e.g., the side effects that are not fully understood scientifically), resulting in diverse sentiments towards vaccination campaigns. In addition, the diverse sentiments have seldom been systematically quantified let alone their dynamic changes over space and time. To shed light on this issue, we propose an approach to analyze vaccine sentiments in space and time by using supervised machine learning combined with word embedding techniques. Taking the United States as a test case, we utilize a Twitter dataset (approximately 11.7 million tweets) from January 2015 to July 2021 and measure and map vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the nation. In doing so, we can capture the heterogeneous public opinions within social media discussions regarding vaccination among states. Results show how positive sentiment in social media has a strong correlation with the actual vaccinated population. Furthermore, we introduce a simple ratio between Anti and Pro-vaccine as a proxy to quantify vaccine hesitancy and show how our results align with other traditional survey approaches. The proposed approach illustrates the potential to monitor the dynamics of vaccine opinion distribution online, which we hope, can be helpful to explain vaccination rates for the ongoing COVID-19 pandemic.

2021

1. SIGSPATIAL

   Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities

   Qingqing Chen, and Andrew Crooks

   ACM SIGSPATIAL 2021 International Workshop on Geospatial Simulation, Nov 2021

   Abs Slides

   With progressively increased people living in cities, and lately the global COVID-19 outbreak, human mobility within cities has changed. Coinciding with this change, is the recent uptake of the ‘15-Minute City’ idea in urban planning around the world. One of the hallmarks of this idea is to create a high quality of life within a city via an acceptable travel distance (i.e., 15 minutes). However, a definitive benchmark for defining a ‘15- Minute City’ has yet to be agreed upon due to the heterogeneous character of urban morphologies worldwide. To shed light on this issue, we develop an agent-based model named ‘D-FMCities’ utilizing realistic street networks and points-of-interest, in this instance the borough of Queens in New York City as a test case. Through our modeling we grow diverse communities from the bottom up and estimate the size of such local communities to delineate 15-minute cities. Our findings suggest that the model could be helpful to detect the flexibility of defining the extent of a ‘15-minute city’ and consequently support uncovering the underlying factors that may affect its various definitions and diverse sizes throughout the world.

2. Urban E-Planing

   Identifying Meaningful Locations of Social Media Users

   I-Ting Chuang, and Qingqing Chen

   5th International Conference ‘Urban E-Planning’, Sep 2021

   Abs Slides

   The global COVID-19 outbreak has deeply affected everyone’s daily life and constrained human mobility behaviors by the preventive measures put in place to mitigate the transmission. Different mass media channels and live location data streaming techniques have contributed to tracking the spread and predicting the trajectory of the pandemic developments. Moreover, the human mobility patterns hidden in these digital datasets – if properly interpreted- can document and reflect how people cope with the ‘paradoxical’ daily activities when public life and social encounter are prohibited. In this research, we utilize the mobile location data collected from January 2019 to date within Auckland city, New Zealand, to explore the social impacts on urban ‘hotspots’ concerning the transformation of social behaviors. The urban ‘hotspots’ here refer to user-defined urban spaces with crowded human mobility and intensive social and economic activities regardless of their designated urban programs. We first construct the human-geographical networks and compare the pre- and post-COVID-19 pandemic human mobility patterns with respect to visitors’ density and diversity. By mapping the density and diversity patterns, we can capture and derive insights from urban ‘hotspots’ spatial distribution changes to identify the adoptable urban space typologies that are quintessential places during the disruptive crisis. The research findings provoke rethinking on our urban spaces design and planning strategies and contribute to better-informed decisions towards resilient urban life environment while countries worldwide are slowly resuming normalcy.

3. Dynamic Modeling

   Human Mobility Simulation: Investigating the impact of urban vitality inidcators on human movements

   Qingqing Chen

   Dynamic Modeling, Apr 2021

   Slides

2019

1. GSES & GeoAI-UC

   Identifying Meaningful Locations of Social Media Users

   Qingqing Chen, and Ate Poorthuis

   Geoinformatics Week 2019 GSES & GeoAI-UC, Nov 2019

   Abs Slides

   This conference presentation discusses an ensemble approach for meaningful location identification from geotagged social media content through a developed R package. Extracting such ‘meaningful’ locations from geotagged data traces is a cutting-edge research area. However, this field is currently using mostly bespoke, custom approaches that are difficult to verify and/or reproduce. To bring clarity and consistency to this field, we present an easy-to-use software package (written within the R ecosystem, which is one of the industry-standard software/languages for this type of analysis) that allows users to either use several built-in algorithms or easily define their own algorithms. This also enables users to choose an ensemble approach (i.e. using multiple algorithms) to increase the reliability of the extracted locations.Extracting such locations is an essential step in the analysis of geotagged data traces and is most often used to extract home and office locations based on the spatio-temporal behavior of users. Although we use social media data in this presentation, the same methods can be applied to other data sources such as cell phone data or mobile app data that have similar characteristics. Once home locations are extracted, these datasets can then be used to analyze and predict urban mobility patterns, for different groups of people and for different neighborhoods.