Publications
Journal Articles
2025
- Annals Impact score: 4.4Mapping the invisible: Decoding perceived urban smells through geosocial media in New York CityQingqing Chen, Ate Poorthuis, and Andrew CrooksAnnals of the American Association of Geographers, 2025
Impact score: 4.4
Smells can shape people’s perceptions of urban spaces, influencing how individuals relate themselves to the environment both physically and emotionally. Although the urban environment has long been conceived as a multisensory experience, research has mainly focused on the visual dimension, leaving smell largely understudied. This article aims to construct a flexible and efficient bottom-up framework for capturing and classifying perceived urban smells from individuals based on geosocial media data, thus, increasing our understanding of this relatively neglected sensory dimension in urban studies. We take New York City as a case study and decode perceived smells by teasing out specific smell-related indicator words through text mining techniques from a historical set of geosocial media data (i.e., Twitter/X). The data set consists of more than 56 million data points sent by more than 3.2 million users. The results demonstrate that this approach, which combines quantitative analysis with qualitative insights, can not only reveal “hidden” places with clear spatial smell patterns, but also capture elusive smells that might otherwise be overlooked. By making perceived smells measurable and visible, we can gain a more nuanced understanding of smellscapes and people’s sensory experiences within the urban environment. Overall, we hope our study opens up new possibilities for understanding urban spaces through an olfactory lens and, more broadly, multisensory urban experience research.
@article{chen_mapping_2025, title = {Mapping the invisible: {Decoding} perceived urban smells through geosocial media in {New} {York} {City}}, volume = {0}, url = {https://doi.org/10.1080/24694452.2025.2485233}, doi = {10.1080/24694452.2025.2485233}, number = {0}, journal = {Annals of the American Association of Geographers}, author = {Chen, Qingqing and Poorthuis, Ate and Crooks, Andrew}, year = {2025}, pages = {1--21}, } - iScience Impact score: 4.6New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AIiScience, 2025
Impact score: 4.6
As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in-situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in- situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative Artificial Intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI.
@article{see2025new, title = {New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI}, author = {See, Linda and Chen, Qingqing and Crooks, Andrew and Bayas, Juan Carlos Laso and Fraisl, Dilek and Fritz, Steffen and Georgieva, Ivelina and Hager, Gerid and Hofer, Martin and Lesiv, Myroslava and others}, journal = {iScience}, doi = {10.1016/j.isci.2025.111919}, year = {2025}, publisher = {Elsevier} } - From print to perspective: A mixed-method analysis of the convergence and divergence of COVID-19 topics in newspapers and interviewsQingqing Chen, Andrew Crooks, Adam J. Sullivan, and 2 more authorsPLOS Digit Health, 2025
In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and individual actions (e.g., lockdowns, social distancing, and masking) have resulted in diverse pandemic experiences. This study aims to explore these varied experiences to inform more proactive responses for future public health crises. Employing a novel “big- thick” data approach, we analyze and compare key pandemic-related topics that have been disseminated to the public through newspapers with those collected from the public via interviews. Specifically, we utilized 82,533 U.S. newspaper articles from January 2020 to December 2021 and supplemented this “big” dataset with “thick” data from interviews and focus groups for topic modeling. Identified key topics were contextualized, compared and visualized at different scales to reveal areas of convergence and divergence. We found seven key topics from the “big” newspaper dataset, providing a macro-level view that covers public health, policies and economics. Conversely, three divergent topics were derived from the “thick” interview data, offering a micro-level view that focuses more on individuals’ experiences, emotions and concerns. A notable finding is the public’s concern about the reliability of news information, suggesting the need for further investigation on the impacts of mass media in shaping the public’s perception and behavior. Overall, by exploring the convergence and divergence in identified topics, our study offers new insights into the complex impacts of the pandemic and enhances our understanding of key issues both disseminated to and resonating with the public, paving the way for further health communication and policy-making.
@article{qing_newspaper_2025, title = {From print to perspective: A mixed-method analysis of the convergence and divergence of COVID-19 topics in newspapers and interviews}, url = {https://doi.org/10.1371/journal.pdig.0000736}, doi = {10.1371/journal.pdig.0000736}, journal = {PLOS Digit Health}, author = {Chen, Qingqing and Crooks, Andrew and Sullivan, Adam J. and Surtees, Jennifer A. and Tumiel-Berhalter, Laurene}, year = {2025}, }
2024
- Urban Studies Impact score: 5.1Urban street dynamics: Assessing the relationship of sidewalk width and pedestrian activity in Auckland, New Zealand, based on mobile phone dataI-Ting Chuang, and Qingqing ChenUrban Studies, 2024
Impact score: 5.1
This study empirically examines the adequacy of sidewalk widths in Auckland’s Central Business District in light of increasing active mobility and sustainable urban planning trends. Recognising the need to retrofit street spaces to prioritise pedestrians, we aim to determine whether current sidewalk dimensions meet the diverse requirements of users. We analysed average sidewalk widths and developed four mobility metrics – inflow and outflow travel distance, and density of visitors and locals – using a large-scale mobile location dataset comprising 113 million data points from 1.4 million users. These metrics, reflecting urban vibrancy and sidewalk use, were correlated with sidewalk widths to assess their adequacy. Furthermore, we applied cluster analysis to these mobility metrics, along with the diversity of Points of Interest, to categorise sidewalk segments, uncovering intricate usage patterns. Our findings indicate that sidewalks typically range from 2 to 5 m, catering to varied urban needs. Notably, we observed no direct correlation between sidewalk width and mobility patterns, but significant differences in inflow and outflow travel distances were evident, especially between key urban hubs and quiet residential neighbourhoods. Moreover, we identified seven distinct sidewalk categories, each reflecting unique qualities, suggesting that uniform widths do not define sidewalk utility or character. This highlights the need to rethink current capacity-focused sidewalk design, advocating for a nuanced approach that addresses the intricate demands of urban spaces. Our methodology offers flexibility and can be tailored to suit different urban contexts, providing a versatile tool for urban analysis and planning.
@article{chuang_urban_2024, title = {Urban street dynamics: {Assessing} the relationship of sidewalk width and pedestrian activity in {Auckland}, {New} {Zealand}, based on mobile phone data}, issn = {0042-0980}, url = {https://doi.org/10.1177/00420980241293659}, doi = {10.1177/00420980241293659}, journal = {Urban Studies}, author = {Chuang, I-Ting and Chen, Qingqing}, year = {2024}, pages = {00420980241293659}, } - CEUS Impact score: 7.1Community resilience to wildfires: A network analysis approach by utilizing human mobility dataQingqing Chen, Boyu Wang, and Andrew CrooksComputers, Environment and Urban Systems, 2024
Impact score: 7.1
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.
@article{10.1016/j.compenvurbsys.2024.102110, title = {Community resilience to wildfires: {A} network analysis approach by utilizing human mobility data}, volume = {110}, issn = {0198-9715}, url = {https://www.sciencedirect.com/science/article/pii/S0198971524000395}, doi = {10.1016/j.compenvurbsys.2024.102110}, urldate = {2024-03-31}, journal = {Computers, Environment and Urban Systems}, author = {Chen, Qingqing and Wang, Boyu and Crooks, Andrew}, year = {2024}, keywords = {Community resilience, Human mobility data, Network analysis, Resilience triangle, Wildfire}, pages = {102110}, } - EPB Impact score: 3.3A nationwide dataset of de-identified activity spaces derived from geotagged social media dataAte Poorthuis, Qingqing Chen, and Matthew ZookEnvironment and Planning B: Urban Analytics and City Science, 2024
Impact score: 3.3
In this article, we present a historical dataset of activity spaces, originally based on publicly posted and geotagged social media sent within the United States from 2012 to 2019. The dataset, which contains approximately 2 million users and 1.2 billion data points, is de-identified and spatially aggregated to enable ethical and broad sharing across the research community. By publishing the dataset, we hope to help researchers to quickly access and filter data to study people’s activity spaces across a range of places. In this article, we first describe the construction and characteristics of this dataset and then highlight certain limitations of the data through an illustrative analysis of potential bias—an important consideration when using data not collected through representative sampling. Our goal is to empower researchers to create novel, insightful research projects of their own design based on this dataset.
@article{poorthuis2024nationwide, title = {A nationwide dataset of de-identified activity spaces derived from geotagged social media data}, author = {Poorthuis, Ate and Chen, Qingqing and Zook, Matthew}, journal = {Environment and Planning B: Urban Analytics and City Science}, url = {https://journals.sagepub.com/doi/full/10.1177/23998083241264051}, pages = {23998083241264051}, year = {2024}, publisher = {SAGE Publications Sage UK: London, England}, doi = {10.1177/23998083241264051}, }
2023
- DIGITAL HEALTH Impact score: 3.7A comparison between online social media discussions and vaccination rates: A tale of four vaccinesQingqing Chen, Arie Croitoru, and Andrew CrooksDIGITAL HEALTH, 2023
Impact score: 3.7
The recent COVID-19 pandemic has brought the debate around vaccinations to the forefront of public discussion. In this discussion, various social media platforms have a key role. While this has long been recognized, the way by which the public assigns attention to such topics remains largely unknown. Furthermore, the question of whether there is a discrepancy between people’s opinions as expressed online and their actual decision to vaccinate remains open. To shed light on this issue, in this paper we examine the dynamics of online debates among four prominent vaccines (i.e., COVID-19, Influenza, MMR, and HPV) through the lens of public attention as captured on Twitter in the United States from 2015 to 2021. We then compare this to actual vaccination rates from governmental reports, which we argue serve as a proxy for real-world vaccination behaviors. Our results demonstrate that since the outbreak of COVID-19, it has come to dominate the vaccination discussion, which has led to a redistribution of attention from the other three vaccination themes. The results also show an apparent discrepancy between the online debates and the actual vaccination rates. These findings are in line with existing theories, that of agenda-setting and zero-sum theory. Furthermore, our approach could be extended to assess the public’s attention toward other health-related issues, and provide a basis for quantifying the effectiveness of health promotion policies.
@article{10.1177/20552076231155682, title = {A comparison between online social media discussions and vaccination rates: {A} tale of four vaccines}, volume = {9}, copyright = {All rights reserved}, issn = {2055-2076}, doi = {10.1177/20552076231155682}, journal = {DIGITAL HEALTH}, author = {Chen, Qingqing and Croitoru, Arie and Crooks, Andrew}, year = {2023}, pages = {20552076231155682}, file = {SAGE PDF Full Text:/Users/qing/Zotero/storage/8WSL6KBM/Chen et al. - 2023 - A comparison between online social media discussio.pdf:application/pdf}, } - EPB Impact score: 3.3Categorizing urban space based on visitor density and diversity: A view through social media dataI-Ting Chuang, Qingqing Chen, and Ate PoorthuisEnvironment and Planning B: Urban Analytics and City Science, 2023
Impact score: 3.3
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.
@article{10.1177/23998083221139848, title = {Categorizing urban space based on visitor density and diversity: {A} view through social media data}, volume = {50}, copyright = {All rights reserved}, issn = {2399-8083}, doi = {10.1177/23998083221139848}, language = {en}, number = {6}, urldate = {2024-03-31}, journal = {Environment and Planning B: Urban Analytics and City Science}, author = {Chuang, I-Ting and Chen, Qingqing and Poorthuis, Ate}, year = {2023}, pages = {1471--1485}, }
2022
- JAG Impact score: 7.6Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemicQingqing Chen, and Andrew CrooksInternational Journal of Applied Earth Observation and Geoinformation, 2022
Impact score: 7.6
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.
@article{10.1016/j.jag.2022.102783, title = {Analyzing the vaccination debate in social media data {Pre}- and {Post}-{COVID}-19 pandemic}, volume = {110}, copyright = {All rights reserved}, issn = {1569-8432}, doi = {10.1016/j.jag.2022.102783}, language = {en}, journal = {International Journal of Applied Earth Observation and Geoinformation}, author = {Chen, Qingqing and Crooks, Andrew}, year = {2022}, keywords = {COVID-19 Pandemic, Sentiment Analysis, Social Media, Time \& Space, United States, Vaccination}, pages = {102783}, file = {ScienceDirect Snapshot:/Users/qing/Zotero/storage/3MMEA22R/S030324342200109X.html:text/html}, } - SSRNAre Urban Hotspots to Avoid or to Embrace? Determining the Resilience of Auckland City’s Urban Hotspots Under Lockdown ConstraintsI-Ting Chuang, and Qingqing ChenSSRN, 2022
The COVID-19 pandemic has unprecedentedly affected the daily routines of people everywhere. Mass media channels and data-streaming techniques have assisted in tracking the spread and the trajectory of infection, providing many rich datasets that record human mobility patterns. Such sources can provide valuable insights about how people cope when their daily lives are heavily disrupted and social activities are prohibited. In this article, we take Auckland City, the largest city in New Zealand, as a case study and utilize mobile location data collected in 2020 to explore the impacts on urban hotspots due to the radical transformation of social behaviours imposed by the government’s emergency policy. ‘Urban hotspots’ in this article are defined as vibrant urban spaces that attract both dense (high frequency) and diverse (visitors from various places) visitors. We first construct human-geographical networks of individual users and then analyse human mobility patterns with regard to visitor density and diversity before and after two COVID-19 lockdowns in 2020. Our findings suggest that urban parks could be perceived as resilient urban infrastructures that offer crucial support for people during a pandemic. The findings could inform a rethinking of urban space planning strategies as part of the city’s post-COVID-19 recovery.
@article{10.2139/ssrn.4015368, title = {Are Urban Hotspots to Avoid or to Embrace? Determining the Resilience of Auckland City's Urban Hotspots Under Lockdown Constraints}, author = {Chuang, I-Ting and Chen, Qingqing}, journal = {SSRN}, year = {2022}, }
2021
- CEUS Impact score: 7.1Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in SingaporeQingqing Chen, I-Ting Chuang, and Ate PoorthuisComputers, Environment and Urban Systems, 2021
Impact score: 7.1
Traditional approaches to human mobility analysis in Geography often rely on census or survey data that is resource-intensive to collect and often has a limited spatio-temporal scope. The advent of new technologies (e.g. geosocial media platforms) provides opportunities to overcome these limitations and, if properly leveraged, can yield more granular insights about human mobility. In this paper, we use an anonymized Twitter dataset collected in Singapore from 2012 to 2016 to investigate this potential to help understand the footprints of urban neighbourhoods from both a spatial and a relational perspective. We construct home-to-destination networks of individual users based on their inferred home locations. In aggregated form, these networks allow us to analyze three specific mobility indicators at the neighbourhood level, namely the distance, diversity, and direction of urban interactions. By mapping these three indicators of the spatial footprint of each neighbourhood, we can capture the nuances in the position of individual neighbourhoods within the larger urban network. An exploratory spatial regression reveals that socio-economic characteristics (e.g. share of rental housing) and the built environment (i.e. land use) only partially explain these three indicators and a residual analysis points to the need to explicitly include each neighbourhood’s position within the transportation network in future work.
@article{10.1016/j.compenvurbsys.2021.101708, title = {Entangled footprints: {Understanding} urban neighbourhoods by measuring distance, diversity, and direction of flows in {Singapore}}, volume = {90}, copyright = {All rights reserved}, issn = {0198-9715}, doi = {10.1016/j.compenvurbsys.2021.101708}, journal = {Computers, Environment and Urban Systems}, author = {Chen, Qingqing and Chuang, I-Ting and Poorthuis, Ate}, year = {2021}, keywords = {Activity space twitter, Human mobility, Location-based-services (LBS), Singapore, Spatial networks, Urban Neighbourhoods}, pages = {101708}, file = {Chen et al. - 2021 - Entangled footprints Understanding urban neighbou.html:/Users/qing/Zotero/storage/XLVA3EZN/Chen et al. - 2021 - Entangled footprints Understanding urban neighbou.html:text/html;ScienceDirect Snapshot:/Users/qing/Zotero/storage/JPWBIWU6/S0198971521001150.html:text/html;Submitted Version:/Users/qing/Zotero/storage/DI23UKPX/Chen et al. - 2021 - Entangled footprints Understanding urban neighbou.pdf:application/pdf}, } - IJGIS Impact score: 5.2Identifying home locations in human mobility data: An open-source R package for comparison and reproducibilityQingqing Chen, and Ate PoorthuisInternational Journal of Geographical Information Science, 2021
Impact score: 5.2
Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which – compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research.
@article{10.1080/13658816.2021.1887489, title = {Identifying home locations in human mobility data: {An} open-source {R} package for comparison and reproducibility}, volume = {0}, issn = {1365-8816}, doi = {10.1080/13658816.2021.1887489}, number = {0}, journal = {International Journal of Geographical Information Science}, author = {Chen, Qingqing and Poorthuis, Ate}, year = {2021}, pages = {1--24}, }
2017
- Sensors Impact score: 3.4A multi-platform optical sensor for in vivo and in vitro algae classificationChee-Loon Ng, Qingqing Chen, Jia-Jing Chua, and 1 more authorSensors, 2017
Impact score: 3.4
Differentiation among major algal groups is important for the ecological and biogeochemical characterization of water bodies, and for practical management of water resources. It helps to discern the taxonomic groups that are beneficial to aquatic life from the organisms causing harmful algal blooms. An LED-induced fluorescence (LEDIF) instrument capable of fluorescence, absorbance, and scattering measurements; is used for in vivo and in vitro identification and quantification of four algal groups found in freshwater and marine environments. Aqueous solutions of individual and mixed dissolved biological pigments relevant to different algal groups were measured to demonstrate the LEDIF’s capabilities in measuring extracted pigments. Different genera of algae were cultivated and the cell counts of the samples were quantified with a hemacytometer and/or cellometer. Dry weight of different algae cells was also measured to determine the cell counts-to-dry weight correlations. Finally, in vivo measurements of different genus of algae at different cell concentrations and mixed algal group in the presence of humic acid were performed with the LEDIF. A field sample from a local reservoir was measured with the LEDIF and the results were verified using hemacytometer, cellometer, and microscope. The results demonstrated the LEDIF’s capabilities in classifying and quantifying different groups of live algae.
@article{10.3390/s17040912, title = {A multi-platform optical sensor for in vivo and in vitro algae classification}, volume = {17}, url = {https://www.mdpi.com/1424-8220/17/4/912}, doi = {10.3390/s17040912}, number = {4}, journal = {Sensors}, author = {Ng, Chee-Loon and Chen, Qingqing and Chua, Jia-Jing and Hemond, Harold F.}, year = {2017}, keywords = {absorbance, algae sensor, fluorescence, in situ real-time algae classification, in vivo algae measurement, optical sensor}, pages = {912}, }
2016
- WPT Impact score: 1.6Corrections for matrix effects on fluorescence measurement of a multi-platform optical sensorChee-Loon Ng, Yan-Jie Ng, Qingqing Chen, and 1 more authorWater Practice and Technology, 2016
Impact score: 1.6
The LEDIF (LED-induced fluorescence) is an in situ optical instrument that utilizes fluorescence, absorbance, and scattering to identify and quantify substances in water bodies. In this study, matrix effects on fluorescence signals caused by inner filtering, temperature, intramolecular deactivation, turbidity, and pH were investigated, and compensation equations developed to correct measured values and improve accuracy. Multiple simultaneous matrix effect corrections were demonstrated with a laboratory sample subjected to known interferences and physical conditions. In general, compensation was found to be important to improve the accuracy of fluorescence measurements.
@article{10.2166/wpt.2016.069, title = {Corrections for matrix effects on fluorescence measurement of a multi-platform optical sensor}, volume = {11}, issn = {1751-231X}, doi = {10.2166/wpt.2016.069}, number = {3}, journal = {Water Practice and Technology}, author = {Ng, Chee-Loon and Ng, Yan-Jie and Chen, Qingqing and Hemond, Harold F.}, year = {2016}, pages = {644--660}, }
Editorials
2024
- EPB Impact score: 3.3Exploring the new frontier of information extraction through large language models in urban analyticsAndrew Crooks, and Qingqing ChenEnvironment and Planning B: Urban Analytics and City Science, 2024
Impact score: 3.3
@article{10.1177/23998083241235495, title = {Exploring the new frontier of information extraction through large language models in urban analytics}, volume = {51}, issn = {2399-8083}, url = {https://doi.org/10.1177/23998083241235495}, doi = {10.1177/23998083241235495}, number = {3}, journal = {Environment and Planning B: Urban Analytics and City Science}, author = {Crooks, Andrew and Chen, Qingqing}, year = {2024}, pages = {565--569}, }
Peer Reviewed Conference Papers
2021
- SIGSPATIAL Impact score: 4.6Delineating a ’15-minute city’: An agent-based modeling approach to estimate the size of local communitiesQingqing Chen, and Andrew CrooksIn Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, 2021
Impact score: 4.6
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.
@inproceedings{10.1145/3486184.3491080, address = {New York, NY, USA}, series = {{GeoSim} '21}, title = {Delineating a '15-minute city': {An} agent-based modeling approach to estimate the size of local communities}, isbn = {978-1-4503-9101-6}, doi = {10.1145/3486184.3491080}, booktitle = {Proceedings of the 4th {ACM} {SIGSPATIAL} {International} {Workshop} on {GeoSpatial} {Simulation}}, publisher = {Association for Computing Machinery}, author = {Chen, Qingqing and Crooks, Andrew}, year = {2021}, pages = {29--37}, }