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Increasingly urbanized populations and climate change have shifted the focus of decision makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: (1) the physical system, consisting of geological, ecological, and human-built systems; (2) the social system, consisting of informal and formal human collective behavior; and (3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just to cities, but to disaster studies and connecting them in the CAS framework provides us with a new lens to study disasters across disciplines. This paper explores the theories and models used in disaster studies, provides a framework to study and explain disasters, and discusses how complex adaptive systems can support theory building in disaster science for promoting more sustainable and resilient cities.
Annetta Burger; William G. Kennedy; Andrew Crooks. Organizing Theories for Disasters into a Complex Adaptive System Framework. Urban Science 2021, 5, 61 .
AMA StyleAnnetta Burger, William G. Kennedy, Andrew Crooks. Organizing Theories for Disasters into a Complex Adaptive System Framework. Urban Science. 2021; 5 (3):61.
Chicago/Turabian StyleAnnetta Burger; William G. Kennedy; Andrew Crooks. 2021. "Organizing Theories for Disasters into a Complex Adaptive System Framework." Urban Science 5, no. 3: 61.
Using geolocated tweets to achieve situational awareness is an often researched topic in disaster and emergency management. However, little has been done in the area of drug cartels, which, as transnational crime organizations, continue to pose great risk to the stability and safety of our communities. This paper made an initial effort in using geolocated social media (specifically Twitter) to achieve situational awareness of drug cartels through temporal and spatial analysis of derived named entity clusters. The results show that detecting peaks in the time series of frequently occurring entity clusters enabled the tracking of important events in public discourse surrounding drug cartels. Correlations between time series also provided valuable insights into the synchronicity between different events. Further examining the spatial distribution of key events for different countries, we identify thematic hotpots of public discourse on cartel activity. Our methodology also addresses issues of language ambiguity when working with noisy social media data in order to achieve situational awareness on drug cartels.
Xiaoyi Yuan; Ron Mahabir; Andrew Crooks; Arie Croitoru. Achieving situational awareness of drug cartels with geolocated social media. GeoJournal 2021, 1 -19.
AMA StyleXiaoyi Yuan, Ron Mahabir, Andrew Crooks, Arie Croitoru. Achieving situational awareness of drug cartels with geolocated social media. GeoJournal. 2021; ():1-19.
Chicago/Turabian StyleXiaoyi Yuan; Ron Mahabir; Andrew Crooks; Arie Croitoru. 2021. "Achieving situational awareness of drug cartels with geolocated social media." GeoJournal , no. : 1-19.
Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.
Andrew Crooks; Alison Heppenstall; Nick Malleson; Ed Manley. Agent-Based Modeling and the City: A Gallery of Applications. The Life and Afterlife of Gay Neighborhoods 2021, 885 -910.
AMA StyleAndrew Crooks, Alison Heppenstall, Nick Malleson, Ed Manley. Agent-Based Modeling and the City: A Gallery of Applications. The Life and Afterlife of Gay Neighborhoods. 2021; ():885-910.
Chicago/Turabian StyleAndrew Crooks; Alison Heppenstall; Nick Malleson; Ed Manley. 2021. "Agent-Based Modeling and the City: A Gallery of Applications." The Life and Afterlife of Gay Neighborhoods , no. : 885-910.
While the world’s total urban population continues to grow, not all cities are witnessing such growth—some are actually shrinking. This shrinkage has caused several problems to emerge, including population loss, economic depression, vacant properties and the contraction of housing markets. Such issues challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom-up using dynamic computational models. To fill this gap, this paper presents a spatially explicit agent-based model stylized on the Detroit Tri-County area, an area witnessing shrinkage. Specifically, the model demonstrates how the buying and selling of houses can lead to urban shrinkage through a bottom-up approach. The results of the model indicate that, along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also denoted (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation for exploring urban shrinkage and potentially offers a means to test policies to achieve urban sustainability.
Na Jiang; Andrew Crooks; Wenjing Wang; Yichun Xie. Simulating Urban Shrinkage in Detroit via Agent-Based Modeling. Sustainability 2021, 13, 2283 .
AMA StyleNa Jiang, Andrew Crooks, Wenjing Wang, Yichun Xie. Simulating Urban Shrinkage in Detroit via Agent-Based Modeling. Sustainability. 2021; 13 (4):2283.
Chicago/Turabian StyleNa Jiang; Andrew Crooks; Wenjing Wang; Yichun Xie. 2021. "Simulating Urban Shrinkage in Detroit via Agent-Based Modeling." Sustainability 13, no. 4: 2283.
Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent‐based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual‐level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state‐of‐the‐art, and the outlook for the field over the next decade. We argue that although agent‐based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems.
Alison Heppenstall; Andrew Crooks; Nick Malleson; Ed Manley; Jiaqi Ge; Michael Batty. Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities. Geographical Analysis 2020, 53, 76 -91.
AMA StyleAlison Heppenstall, Andrew Crooks, Nick Malleson, Ed Manley, Jiaqi Ge, Michael Batty. Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities. Geographical Analysis. 2020; 53 (1):76-91.
Chicago/Turabian StyleAlison Heppenstall; Andrew Crooks; Nick Malleson; Ed Manley; Jiaqi Ge; Michael Batty. 2020. "Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities." Geographical Analysis 53, no. 1: 76-91.
During the early months of the COVID-19 pandemic, millions of people had to work from home. We examine the ways in which COVID-19 affect organizational communication by analyzing five months of calendar and messaging metadata from a technology company. We found that: (i) cross-level communication increased more than that of same-level, (ii) while within-team messaging increased considerably, meetings stayed the same, (iii) off-hours messaging became much more frequent, and that this effect was stronger for women; (iv) employees respond to non-managers faster than managers; finally, (v) the number of short meetings increased while long meetings decreased. These findings contribute to theories on organizational communication, remote work, management, and flexibility stigma. Besides, this study exemplifies a strategy to measure organizational health using an objective (not self-report based) method. To the best of our knowledge, this is the first study using workplace communication metadata to examine the heterogeneous effects of mandatory remote work.
Talha Oz; Andrew Crooks. Exploring the Impact of Mandatory Remote Work during the COVID-19 Pandemic. 2020, 1 .
AMA StyleTalha Oz, Andrew Crooks. Exploring the Impact of Mandatory Remote Work during the COVID-19 Pandemic. . 2020; ():1.
Chicago/Turabian StyleTalha Oz; Andrew Crooks. 2020. "Exploring the Impact of Mandatory Remote Work during the COVID-19 Pandemic." , no. : 1.
Social media content analysis often focuses on just the words used in documents or by users and often overlooks the structural components of document composition and linguistic style. We propose that document structure and emoji use are also important to consider as they are impacted by individual communication style preferences and social norms associated with user role and intent, topic domain, and dissemination platform. In this paper we introduce and demonstrate a novel methodology to conduct structural content analysis and measure user consistency of document structures and emoji use. Document structure is represented as the order of content types and number of features per document and emoji use is characterized by the attributes, position, order, and repetition of emojis within a document. With these structures we identified user signatures of behavior, clustered users based on consistency of structures utilized, and identified users with similar document structures and emoji use such as those associated with bots, news organizations, and other user types. This research compliments existing text mining and behavior modeling approaches by offering a language agnostic methodology with lower dimensionality than topic modeling, and focuses on three features often overlooked: document structure, emoji use, and consistency of behavior.
Melanie Swartz; Andrew Crooks; Arie Croitoru. Beyond Words: Comparing Structure, Emoji Use, and Consistency Across Social Media Posts. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 1 -11.
AMA StyleMelanie Swartz, Andrew Crooks, Arie Croitoru. Beyond Words: Comparing Structure, Emoji Use, and Consistency Across Social Media Posts. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():1-11.
Chicago/Turabian StyleMelanie Swartz; Andrew Crooks; Arie Croitoru. 2020. "Beyond Words: Comparing Structure, Emoji Use, and Consistency Across Social Media Posts." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 1-11.
Mesa is an agent-based modeling framework written in Python. Originally started in 2013, it was created to be the go-to tool in for researchers wishing to build agent-based models with Python. Within this paper we present Mesa’s design goals, along with its underlying architecture. This includes its core components: 1) the model (Model, Agent, Schedule, and Space), 2) analysis (Data Collector and Batch Runner) and the visualization (Visualization Server and Visualization Browser Page). We then discuss how agent-based models can be created in Mesa. This is followed by a discussion of applications and extensions by other researchers to demonstrate how Mesa design is decoupled and extensible and thus creating the opportunity for a larger decentralized ecosystem of packages that people can share and reuse for their own needs. Finally, the paper concludes with a summary and discussion of future development areas for Mesa.
Jackie Kazil; David Masad; Andrew Crooks. Utilizing Python for Agent-Based Modeling: The Mesa Framework. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 308 -317.
AMA StyleJackie Kazil, David Masad, Andrew Crooks. Utilizing Python for Agent-Based Modeling: The Mesa Framework. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():308-317.
Chicago/Turabian StyleJackie Kazil; David Masad; Andrew Crooks. 2020. "Utilizing Python for Agent-Based Modeling: The Mesa Framework." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 308-317.
The use of agent-based models (ABMs) has become more widespread over the last two decades allowing resear chers to explore complex systems composed of heterogeneous and locally interacting entities. However, there are several challenges that the agent-based modeling community face. These relate to developing accurate measurements, minimizing a large complex parameter space and developing parsimonious yet accurate models. Machine Learning (ML), specifically deep reinforcement learning has the potential to generate new ways to explore complex models, which can enhance traditional computational paradigms such as agent-based modeling. Recently, ML algorithms have proved an important contribution to the determination of semi-optimal agent behavior strategies in complex environments. What is less clear is how these advances can be used to enhance existing ABMs. This paper presents Learning-based Actor-Interpreter State Representation (LAISR), a research effort that is designed to bridge ML agents with more traditional ABMs in order to generate semi-optimal multi-agent learning strategies. The resultant model, explored within a tactical game scenario, lies at the intersection of human and automated model design. The model can be decomposed into a format that automates aspects of the agent creation process, producing a resultant agent that creates its own optimal strategy and is interpretable to the designer. Our paper, therefore, acts as a bridge between traditional agent-based modeling and machine learning practices, designed purposefully to enhance the inclusion of ML-based agents in the agent-based modeling community.
Paul Cummings; Andrew Crooks. Development of a Hybrid Machine Learning Agent Based Model for Optimization and Interpretability. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 151 -160.
AMA StylePaul Cummings, Andrew Crooks. Development of a Hybrid Machine Learning Agent Based Model for Optimization and Interpretability. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():151-160.
Chicago/Turabian StylePaul Cummings; Andrew Crooks. 2020. "Development of a Hybrid Machine Learning Agent Based Model for Optimization and Interpretability." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 151-160.
Human resource management (HRM) draws on the field of organizational theory (OT) to identify, quantify, and manage people-based phenomena that impact organizational operations and outcomes. OT research has long used computational methods and agent-based modeling to understand complex adaptive systems. Agent-based modeling methodologies within HRM, however, are still rare. Within the HRM and management science literature, Herzberg’s et al. (1959) Two-Factor Theory (TFT) is a framework that has been tested and used for decades. Its ability to capture the interaction between a work force’s motivation and their environment’s hygiene lends itself well to agent-based modeling as a method of study. Here, we present the development of the Human Resources Management-Parameter Experimentation Tool (HRM-PET) as the first explicit ABM instantiation of TFT, filling the gap between the study of HRM and computational OT tools like agent-based modeling.
Carmen Iasiello; Andrew Crooks; Sarah Wittman. The Human Resource Management Parameter Experimentation Tool. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 298 -307.
AMA StyleCarmen Iasiello, Andrew Crooks, Sarah Wittman. The Human Resource Management Parameter Experimentation Tool. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():298-307.
Chicago/Turabian StyleCarmen Iasiello; Andrew Crooks; Sarah Wittman. 2020. "The Human Resource Management Parameter Experimentation Tool." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 298-307.
Social media is a popular source for political communication and user engagement around social and political issues. While the diversity of the population participating in social and political events in person are often considered for social science research, measuring the diversity representation within online communities is not a common part of social media analysis. This paper attempts to fill that gap and presents a methodology for labeling and analyzing diversity in a social media sample based on emojis and keywords associated with gender, skin tone, sexual orientation, religion, and political ideology. We analyze the trends of diversity related themes and the diversity of users engaging in the online political community during the leadup to the 2018 U.S. midterm elections. Our results reveal patterns along diversity themes that otherwise would have been lost in the volume of content. Further, the diversity composition of our sample of online users rallying around political campaigns was similar to those measured in exit polls on election day. The diversity language model and methodology for diversity analysis presented in this paper can be adapted to other languages and applied to other research domains to provide social media researchers a valuable lens to identify the diversity of voices and topics of interest for the less-represented populations participating in an online social community.
Melanie Swartz; Andrew Crooks; William Kennedy. Diversity from Emojis and Keywords in Social Media. International Conference on Social Media and Society 2020, 1 .
AMA StyleMelanie Swartz, Andrew Crooks, William Kennedy. Diversity from Emojis and Keywords in Social Media. International Conference on Social Media and Society. 2020; ():1.
Chicago/Turabian StyleMelanie Swartz; Andrew Crooks; William Kennedy. 2020. "Diversity from Emojis and Keywords in Social Media." International Conference on Social Media and Society , no. : 1.
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources—TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied.
Xiaoyi Yuan; Andrew Crooks; Andreas Züfle. A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information. ISPRS International Journal of Geo-Information 2020, 9, 385 .
AMA StyleXiaoyi Yuan, Andrew Crooks, Andreas Züfle. A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information. ISPRS International Journal of Geo-Information. 2020; 9 (6):385.
Chicago/Turabian StyleXiaoyi Yuan; Andrew Crooks; Andreas Züfle. 2020. "A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information." ISPRS International Journal of Geo-Information 9, no. 6: 385.
Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets yields several weaknesses: sparse and small data sets, privacy concerns, and a lack of authoritative ground-truth. To overcome these weaknesses, we leverage a large-scale LBSN simulation to create a framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns of life. Such data not only captures the location of users over time but also their interactions via social networks. Patterns of life are simulated by giving agents (i.e., people) an array of “needs” that they aim to satisfy, e.g., agents go home when they are tired, to restaurants when they are hungry, to work to cover their financial needs, and to recreational sites to meet friends and satisfy their social needs. While existing real-world LBSN data sets are trivially small, the proposed framework provides a source for massive LBSN benchmark data that closely mimics the real-world. As such, it allows us to capture 100% of the (simulated) population without any data uncertainty, privacy-related concerns, or incompleteness. It allows researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. Our framework is made available to the community. In addition, we provide a series of simulated benchmark LBSN data sets using different synthetic towns and real-world urban environments obtained from OpenStreetMap. The simulation software and data sets, which comprise gigabytes of spatio-temporal and temporal social network data, are made available to the research community.
Joon-Seok Kim; Hyunjee Jin; Hamdi Kavak; Ovi Chris Rouly; Andrew Crooks; Dieter Pfoser; Carola Wenk; Andreas Zufle. Location-Based Social Network Data Generation Based on Patterns of Life. 2020 21st IEEE International Conference on Mobile Data Management (MDM) 2020, 158 -167.
AMA StyleJoon-Seok Kim, Hyunjee Jin, Hamdi Kavak, Ovi Chris Rouly, Andrew Crooks, Dieter Pfoser, Carola Wenk, Andreas Zufle. Location-Based Social Network Data Generation Based on Patterns of Life. 2020 21st IEEE International Conference on Mobile Data Management (MDM). 2020; ():158-167.
Chicago/Turabian StyleJoon-Seok Kim; Hyunjee Jin; Hamdi Kavak; Ovi Chris Rouly; Andrew Crooks; Dieter Pfoser; Carola Wenk; Andreas Zufle. 2020. "Location-Based Social Network Data Generation Based on Patterns of Life." 2020 21st IEEE International Conference on Mobile Data Management (MDM) , no. : 158-167.
Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.
Ron Mahabir; Ross Schuchard; Andrew Crooks; Arie Croitoru; Anthony Stefanidis. Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam. ISPRS International Journal of Geo-Information 2020, 9, 341 .
AMA StyleRon Mahabir, Ross Schuchard, Andrew Crooks, Arie Croitoru, Anthony Stefanidis. Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam. ISPRS International Journal of Geo-Information. 2020; 9 (6):341.
Chicago/Turabian StyleRon Mahabir; Ross Schuchard; Andrew Crooks; Arie Croitoru; Anthony Stefanidis. 2020. "Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam." ISPRS International Journal of Geo-Information 9, no. 6: 341.
Online social networking applications are popular venues for self-expression, communication, and building connections between users. One method of expression is that of emojis, which is becoming more prevalent in online social networking platforms. As emoji use has grown over the last decade, differences in emoji usage by individuals and the way they are used in communication is still relatively unknown. This paper fills this gap by comparing emoji use across users and collectively in user names, profiles, and in original and re-shared content. We present a methodology that enables comparison of semantically similar emojis based on Unicode emoji categories and subcategories. We apply this methodology to a corpus of over 44 million tweets and associated user names and profiles to establish a baseline which reveals differences in emoji use in user names, profile descriptions, non-retweets, and retweets. In addition, our analysis reveals emoji super users who have a significantly higher proportion and diversity of emoji use. Our methodology offers a novel approach for summarizing emoji use and enables systematic comparison of emojis across individual user profiles and communication patterns, thus expanding methods for semantic analysis of social media data beyond just text.
Melanie Swartz; Andrew Crooks. Comparison of Emoji Use in Names, Profiles, and Tweets. 2020 IEEE 14th International Conference on Semantic Computing (ICSC) 2020, 375 -380.
AMA StyleMelanie Swartz, Andrew Crooks. Comparison of Emoji Use in Names, Profiles, and Tweets. 2020 IEEE 14th International Conference on Semantic Computing (ICSC). 2020; ():375-380.
Chicago/Turabian StyleMelanie Swartz; Andrew Crooks. 2020. "Comparison of Emoji Use in Names, Profiles, and Tweets." 2020 IEEE 14th International Conference on Semantic Computing (ICSC) , no. : 375-380.
Interactions with nature can improve the wellbeing of urban residents and increase their interest in biodiversity. Many places within cities offer opportunities for people to interact with wildlife, including open space and residential yards and gardens, but little is known about which places within a city people use to observe wildlife. In this study, we used publicly available spatial data on people’s observations of birds from three online platforms—eBird, iNaturalist, and Flickr—to determine where people observe birds within the city of Chicago, Illinois (USA). Specifically, we investigated whether land use or neighborhood demographics explained where people observe birds. We expected that more observations would occur in open spaces, and especially conservation areas, than land uses where people tend to spend more time, but biodiversity is often lower (e.g., residential land). We also expected that more populated neighborhoods and those with higher median age and income of residents would have more bird observations recorded online. We found that bird observations occurred more often in open spaces than in residential areas, with high proportions of observations in recreation areas. In addition, a linear regression model showed that neighborhoods with higher median incomes, those with larger populations, and those located closer to Lake Michigan had more bird observations recorded online. These results have implications for conservation and environmental education efforts in Chicago and demonstrate the potential for social media and citizen science data to provide insight into urban human-wildlife interactions.
Bianca Lopez; Emily Minor; Andrew Crooks. Insights into human-wildlife interactions in cities from bird sightings recorded online. Landscape and Urban Planning 2020, 196, 103742 .
AMA StyleBianca Lopez, Emily Minor, Andrew Crooks. Insights into human-wildlife interactions in cities from bird sightings recorded online. Landscape and Urban Planning. 2020; 196 ():103742.
Chicago/Turabian StyleBianca Lopez; Emily Minor; Andrew Crooks. 2020. "Insights into human-wildlife interactions in cities from bird sightings recorded online." Landscape and Urban Planning 196, no. : 103742.
Research Summary Public mass shootings tend to capture the public's attention and receive substantial coverage in both traditional media and online social networks (OSNs) and have become a salient topic in them. Motivated by this, the overarching objective of this paper is to advance our understanding of how the public responds to mass shooting events in such media outlets. Specifically, it aims to examine whether distinct information seeking patterns emerge over time and space, and whether associations between public mass shooting events emerge in online activities and discourse. Towards this objective, we study a sequence of five public mass shooting events that have occurred in the United States between October 2017 and May 2018 across three major dimensions: the public's online information seeking activities, the media coverage, and the discourse that emerges in a prominent OSN. To capture these dimensions, respectively, data was collected and analyzed from Google Trends, LexisNexis, Wikipedia Page views, and Twitter. The results of our analysis suggest that distinct temporal patterns emerge in the public's information seeking activities across different platforms, and that associations between an event and its preceding events emerge both in the media coverage and in OSNs. Policy Implication Studying the evolution of discourse in OSNs provides a valuable lens to observe how society's views on public mass shooting events are formed and evolved over time and space. The ability to analyze such data allows tapping into the dynamics of reshaping and reframing public mass shooting events in the public sphere and enable it to be closely studied and modeled. A deeper understanding of this process, along with the emerging associations drawn between such events, can then provide policy and decision‐makers with opportunities to better design policies and communicate the significance of their goals and objectives to the public.
Arie Croitoru; Sara Kien; Ron Mahabir; Jacek Radzikowski; Andrew Crooks; Ross Schuchard; Tatyanna Begay; Ashley Lee; Alex Bettios; Anthony Stefanidis. Responses to mass shooting events. Criminology & Public Policy 2019, 19, 335 -360.
AMA StyleArie Croitoru, Sara Kien, Ron Mahabir, Jacek Radzikowski, Andrew Crooks, Ross Schuchard, Tatyanna Begay, Ashley Lee, Alex Bettios, Anthony Stefanidis. Responses to mass shooting events. Criminology & Public Policy. 2019; 19 (1):335-360.
Chicago/Turabian StyleArie Croitoru; Sara Kien; Ron Mahabir; Jacek Radzikowski; Andrew Crooks; Ross Schuchard; Tatyanna Begay; Ashley Lee; Alex Bettios; Anthony Stefanidis. 2019. "Responses to mass shooting events." Criminology & Public Policy 19, no. 1: 335-360.
Mass shootings, like other extreme events, have long garnered public curiosity and, in turn, significant media coverage. The media framing, or topic focus, of mass shooting events typically evolves over time from details of the actual shooting to discussions of potential policy changes (e.g., gun control, mental health). Such media coverage has been historically provided through traditional media sources such as print, television, and radio, but the advent of online social networks (OSNs) has introduced a new platform for accessing, producing, and distributing information about such extreme events. The ease and convenience of OSN usage for information within society’s larger growing reliance upon digital technologies introduces potential unforeseen risks. Social bots, or automated software agents, are one such risk, as they can serve to amplify or distort potential narratives associated with extreme events such as mass shootings. In this paper, we seek to determine the prevalence and relative importance of social bots participating in OSN conversations following mass shooting events using an ensemble of quantitative techniques. Specifically, we examine a corpus of more than 46 million tweets produced by 11.7 million unique Twitter accounts within OSN conversations discussing four major mass shooting events: the 2017 Las Vegas concert shooting, the 2017 Sutherland Springs church chooting, the 2018 Parkland School Shooting and the 2018 Santa Fe school shooting. This study’s results show that social bots participate in and contribute to online mass shooting conversations in a manner that is distinguishable from human contributions. Furthermore, while social bots accounted for fewer than 1% of total corpus user contributors, social network analysis centrality measures identified many bots with significant prominence in the conversation networks, densely occupying many of the highest eigenvector and out-degree centrality measure rankings, to include 82% of the top-100 eigenvector values of the Las Vegas retweet network.
Ross Schuchard; Andrew Crooks; Anthony Stefanidis; Arie Croitoru. Bots fired: examining social bot evidence in online mass shooting conversations. Palgrave Communications 2019, 5, 1 -12.
AMA StyleRoss Schuchard, Andrew Crooks, Anthony Stefanidis, Arie Croitoru. Bots fired: examining social bot evidence in online mass shooting conversations. Palgrave Communications. 2019; 5 (1):1-12.
Chicago/Turabian StyleRoss Schuchard; Andrew Crooks; Anthony Stefanidis; Arie Croitoru. 2019. "Bots fired: examining social bot evidence in online mass shooting conversations." Palgrave Communications 5, no. 1: 1-12.
Coastal flooding is the most expensive type of natural disaster in the United States. Policy initiatives to mitigate the effects of these events are dependent upon understanding flood victim responses at an individual and municipal level. Agent-Based Modeling (ABM) is an effective tool for analyzing community-wide responses to natural disaster, but the quality of the ABM’s performance is often challenging to determine. This paper discusses the complexity of the Protective Action Decision Model (PADM) and Protection Motivation Theory (PMT) for human decision making regarding hazard mitigations. A combined (PADM/PMT) model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It is validated against damage assessments and post-storm surveys. The contribution of socio-economic factors and built environment on model performance is also addressed and suggests that mitigation for townhouse communities will be challenging.
Kim McEligot; Peggy Brouse; Andrew Crooks. Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy. 2019 Winter Simulation Conference (WSC) 2019, 251 -262.
AMA StyleKim McEligot, Peggy Brouse, Andrew Crooks. Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy. 2019 Winter Simulation Conference (WSC). 2019; ():251-262.
Chicago/Turabian StyleKim McEligot; Peggy Brouse; Andrew Crooks. 2019. "Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy." 2019 Winter Simulation Conference (WSC) , no. : 251-262.
Xiaoyi Yuan; Andrew Crooks. Assessing the placeness of locations through user-contributed content. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery 2019, 15 -23.
AMA StyleXiaoyi Yuan, Andrew Crooks. Assessing the placeness of locations through user-contributed content. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. 2019; ():15-23.
Chicago/Turabian StyleXiaoyi Yuan; Andrew Crooks. 2019. "Assessing the placeness of locations through user-contributed content." Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery , no. : 15-23.