Peer Reviewed Articles
Loring J Thomas, Peng Huang, Fan Yin, Junlan Xu, Zack W Almquist, John R Hipp, Carter T Butts (2022).
"Geographical patterns of social cohesion drive disparities in early COVID infection hazard".
Proceedings of the National Academies of Sciences 119 (12).
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Abstract:
he uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.
Bibtex:
@article{almquist_pnas,
title={Geographical patterns of social cohesion drive disparities in early COVID infection hazard},
author={Thomas, Loring J and Huang, Peng and Yin, Fan and Xu, Junlan and Almquist, Zack W and Hipp, John R and Butts, Carter T},
journal={Proceedings of the National Academy of Sciences},
volume={119},
number={12},
pages={e2121675119},
year={2022},
publisher={National Acad Sciences}
}
Mary-Catherine Anderson, Ashley Hazel, Jessica M. Perkins, and Zack W. Almquist (2021).
"The Ecology of Unsheltered Homelessness: Environmental and Social-Network Predictors of Well-Being among an Unsheltered Homeless Population".
International Journal of Environmental Research and Public Health 18(14), 1-15.
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Abstract:
People experiencing homelessness (PEH) face extreme weather exposure and limited social support. However, few studies have empirically assessed biophysical and social drivers of health outcomes among unsheltered PEH. Social network, health, and outdoor exposure data were collected from a convenience sample of unsheltered PEH (n = 246) in Nashville, TN, from August 2018–June 2019. Using multivariate fixed-effects linear regression models, we examined associations between biophysical and social environments and self-reported general health and emotional well-being. We found that study participants reported the lowest general health scores during winter months—Nashville’s coldest season. We also found a positive association between the number of nights participants spent indoors during the previous week and general health. Participants who spent even one night indoors during the past week had 1.8-point higher general health scores than participants who spent zero nights indoors (p < 0.01). Additionally, participants who experienced a conflict with a social contact in the past 30 days had lower emotional well-being scores than participants who experienced no conflict. Finally, women had worse general health and emotional well-being than men. Ecologically framed research about health and well-being among PEH is critically needed, especially as climate change threatens to increase the danger of many homeless environments.
Bibtex:
@article{almquist_ijerph,
title={The Ecology of Unsheltered Homelessness: Environmental and Social-Network Predictors of Well-Being among an Unsheltered Homeless Population},
author={Anderson, Mary-Catherine and Hazel, Ashley and Perkins, Jessica M and Almquist, Zack W},
journal={International Journal of Environmental Research and Public Health},
volume={18},
number={14},
pages={7328},
year={2021},
publisher={Multidisciplinary Digital Publishing Institute}
}
}
Guy J. Abel, Jack DeWaard, Jasmine Trang Ha, and Zack W. Almquist (2021).
"The form and evolution of international migration networks, 1990--2015".
Population, Space and Place 27(3), 1-15.
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Abstract:
Presently, there is no agreed upon data-driven approach for identifying the geographic boundaries of migration networks that international migration systems are ultimately manifested in. Drawing from research on community detection methods, we introduce and apply the Information Theoretic Community Detection Algorithm for identifying and studying the geographic boundaries of migration networks. Using a new set of estimates of country-to-country migration flows every 5 years from 1990 to 1995 to 2010–2015, we trace the form and evolution of inter- national migration networks over the past 25 years. Consistent with the concept of dynamic stability, we show that the number, size and internal country compositions of international migration networks have been remarkably stable over time; however, we also document many short-term fluctuations. We conclude by reflecting on the spirit of our work in this paper, which is to promote consensus around tools and best practices for identifying and studying international migration networks.
Bibtex:
@article{almquist_psp,
title={The form and evolution of international migration networks, 1990--2015},
author={Abel, Guy J and DeWaard, Jack and Ha, Jasmine Trang and Almquist, Zack W},
journal={Population, Space and Place},
volume={27},
number={3},
pages={1--15},
year={2021},
publisher={Wiley Online Library},
doi = {10.1002/psp.2432}
}
Loring J Thomas, Peng Huang, Fan Yin, Xiaoshuang Iris Luo, Zack W Almquist, John R. Hipp and Carter T Butts (2020).
"Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity".
Proceedings of the National Academies of Sciences 117(39), 24180-24187.
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Abstract:
Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S. cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly non-uniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform healthcare planning, predict community outcomes, or identify potential disparities.
Bibtex:
@article{almquist_pnas,
author = {Loring J Thomas, Peng Huang, Fan Yin, Xiaoshuang Iris Luo, Zack W Almquist, John R Hipp, Carter T Butts},
journal = {Proceedings of the National Academies of Sciences},
title = {Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity},
year = {2020},
volume = {117},
number = {39},
pages = {24180-24187},
doi = {10.1073/pnas.2011656117}
}
James H. Jones, Ashley Hazel, Zack W. Almquist (2020).
"Transmission-Dynamics Models for the SARS Coronavirus-2".
American Journal of Human Biology 32(5), 1-14.
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Bibtex:
@article{almquist_ajhb,
author = {James~H. Jones and Ashley Hazel and Zack~W. Almquist},
journal = {American Journal of Human Biology},
title = {Transmission-Dynamics Models for the SARS Coronavirus-2},
year = {forthcoming},
volume = {32},
number = {5},
pages = {1-14}
}
Benjamin E. Bagozzi, Daniel Berliner and Zack W. Almquist (2020).
"When Does Open Government Shut? Predicting Government Responses to Citizen Information Requests".
Regulation & Governance 7(2), 1–8.
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Abstract:
Methods for the analysis of “big data” on citizen-government interactions are necessary for theoretical assessments of bureaucratic responsiveness. Such big data methods also stand to benefit practitioners’ abilities to monitor and improve these emerging transparency mechanisms. We consider supervised latent Dirichlet allocation (sLDA) as a potential method for these purposes. To this end, we use sLDA to examine the Mexican government’s (non)responsiveness to all federal information requests filed with the federal Mexican government during the 2003-2015 period, and to identify the request topics most associated with (non)responsiveness. Substantively, our comparisons of the topics that are most highly predictive of responsiveness and nonresponsivess indicate that political sensitivity plays a large and important role in shaping official behavior in this arena. We thus conclude that sLDA provides unique advantages for, and in-sights into, the analysis of (1) textual records of citizen-government interactions and (2) bureaucratic (non)responsiveness to these interactions.
Bibtex:
@article{almquist_rg,
author = {Benjamin E. Bagozzi and Daniel Berliner and Zack W. Almquist},
journal = {Regulation \& Governance},
title = {When Does Open Government Shut? Predicting Government Responses to Citizen Information Request},
year = {in press},
month = {},
volume = {7},
number = {2},
url = {},
pages = {1-8},
doi = {doi.org/10.1111/rego.12282}
}
Zack W. Almquist and Benjamin E. Bagozzi (2020).
"Automated Text Analysis for Understanding Radical Activism: The Topical Agenda of the North American Animal Liberation Movement".
Research and Politics 7(2), 1-8.
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Abstract:
It is difficult to study radical social movements due to their often covert, fluid, and fleeting qualities. As a consequence, data limitations and/or theoretical disagreements abound within research on such movements. We contend that the texts produced by radical movements and their supporters provide a window into group features, and that recent advances in automated text analysis methods afford a means for unlocking these texts in a systematic fashion. We evaluate the contentions through an automated analysis of the radical animal liberation movement's primary North American publication. Our application provides novel insights into the topical agenda of animal liberationists, and the relative attention paid towards networking, (non)violence, radicalization, and direct actions. Examination of these topics over time further reveals number of ideological and tactical shifts, which are predictive of future direct-action events. This demonstrates the benefits of automated text analysis for the study of radical movements and their texts.
Bibtex:
@article{almquist20rap,
author = {Zack W. Almquist and Benjamin E. Bagozzi},
journal = {Research and Politics},
title = {Automated Text Analysis for Understanding Radical Activism: The Topical Agenda of the North American Animal Liberation Movement},
year = {2020},
month = {},
volume = {7},
number = {2},
pages = {1-8},
doi = {doi/10.1177/2053168020921742}
}
Zack W. Almquist (2020).
"Large-scale Spatial Network Models: An application to modeling information diffusion through the homeless population of San Francisco." Environment and Planning B: Urban Analytics and City Science, 47(3), 523–540.
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Abstract:
To address the effects of increasing homeless populations, planners must understand the size and distribution of their homeless populations, as well as how information and resources are diffused throughout homeless communities. Currently, there is limited publicly available information on the homeless population, e.g, the estimates of the homeless, gathered annually by the US Housing & Urban Development point in time (PiT) survey. While it is theorized in the literature that the networks of homeless individuals provide access to important information for planners in areas such as health (e.g. needle exchanges) or access (e.g. information diffusion about the location of new shelters), it is almost never measured, and if measured, only at a very small scale. This research addresses the question of how planners can leverage publicly available data on the homeless to better understand their own homeless networks (e.g. relations among the homeless themselves) in a cost-effective and reliable way. To this end we provide a method for simulating realistic networks of a social relation among the homeless population and perform a diffusion analysis over the resultant homeless-to-homeless networks, and also over a simulated homeless youth Facebook network. We validate the former through novel use of historical data, while the latter is based on recent work that demonstrated that the homeless youth have similar size Facebook networks and usage. We see much stronger spatial hopping and quicker diffusion over the youth network, i.e., we expect information to pass among the youth network much faster than the homeless-to-homeless network. This finding implies that non-government organizations and public health efforts that seek to provide information, goods or services to the homeless should start with the homeless youth, given the potential for faster diffusion when homeless youth are the initial transmitters. Overall, these methods and analysis provide a unique opportunity for visualizing, characterizing and inferring information for large-scale and hard to measure social networks.
Bibtex:
@article{almquist18epb,
author = {Zack W. Almquist},
journal = {Environment and Planning B: Urban Analytics and City Science},
title = {Large-scale Spatial Network Models: An application to modeling information diffusion through the homeless population of San Francisco},
year = {2020},
month = {},
volume = {47},
number = {3}
url = {https://doi.org/10.1177%2F2399808318785375},
pages = {523–540},
doi = {10.1177/2399808318785375}
}
Zack W. Almquist, Nathaniel E. Helwig and Yun You (2020).
"Connecting Continuum of Care point-in-time homeless counts to United States Census areal units".
Mathematical Population Studies, 20(1) 46-58.
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Abstract:
In 2007, the Department of Housing and Urban Development initiated a point-in-time count of the homeless across the United States. The counts are administered by the Continuum of Care Program, which provides spatial and temporal data for the homeless population over the last decade. Unfortunately, this administrative spatial unit does not align with the more common areal units defined by the United States Census Bureau, which limits usability of these data. To unify these two areal units, spatial disaggregation, matching, and imputa- tion allow for aligning Continuum of Care data with county data. The resulting county-level homeless counts for the years 2005 to 2017 are provided as an R package. The county-level data display more spatial precision and more temporal varia- tion than the Continuum of Care-level data. Nonparametric regression analyses reveal that the spatiotemporal variation in the data can be well approximated by additive spatial and temporal effects at both the county and Continuum of Care level.
Bibtex:
@article{almquist19mps,
author = {Zack W. Almquist and Nathaniel E. Helwig and Yun You},
journal = {Mathematical Population Studies},
title = {Connecting Continuum of Care point-in-time homeless counts to United States Census areal units},
year = {2020},
month = {},
volume = {20},
number = {1},
url = {https://doi.org/10.1080/08898480.2019.1636574},
pages = {46-58},
doi = {10.1080/08898480.2019.1636574}
}
Abhirup Mallik and Zack W. Almquist (2019).
"Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models".
Journal of Computational and Graphical Statistics, 28 (4) 967-979.
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Abstract:
Recent developments in computers and automated data collection strategies have greatly
increased the interest in statistical modeling of dynamic networks. Many of the statistical
models employed for inference on large-scale dynamic networks suffer from limited forward
simulation/prediction ability. A major problem with many of the forward simulation procedures
is the tendency for the model to become degenerate in only a few time steps, i.e., the
simulation/prediction procedure results in either null graphs or complete graphs. Here, we
describe an algorithm for simulating a sequence of networks generated from lagged dynamic
network regression models DNR(V), a sub-family of TERGMs. We introduce a smoothed
estimator for forward prediction based on smoothing of the change statistics obtained for a dynamic network regression model. We focus on the implementation of the algorithm, providing
a series of motivating examples with comparisons to dynamic network models from the literature. We find that our algorithm significantly improves multi-step prediction/simulation over standard DNR(V) forecasting. Furthermore, we show that our method performs comparably
to existing more complex dynamic network analysis frameworks (SAOM and STERGMs) for
small networks over short time periods, and significantly outperforms these approaches over
long time time intervals and/or large networks.
Bibtex:
@article{almquist18jcgs,
author = {Abhirup Mallik and Zack W. Almquist},
journal = {Journal of Computational and Graphical Statistics},
title = {Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models},
year = {2019},
month = {},
volume = {28},
number = {4},
url = {https://doi.org/10.1080/10618600.2019.1594834},
pages = {967-979},
doi = {10.1080/10618600.2019.1594834}
}
Zack W. Almquist, Sakshi Arya, Li Zeng and Emma S. Spiro
(2019).
"Unbiased Sampling of Users from (Online) Activity Data." Field Methods, 31(1) 23-38.
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Online platforms offer new opportunities to study human behavior. However, while social scientists are often interested in using behavioral trace data – data created by a user over the course of their every-day life – to draw inferences about users, many online platforms only allow data to be sampled based on user activities (leading to datasets that are biased towards highly active users). Here, we introduce a simple method for reweighting activity-based sample statistics in order to provide descriptive (and potentially model-based) estimates of the user population. We illustrate these techniques by applying them to a case study of an online fitness community (STRAVA), and use it to explore basic network properties. Last, we explore the weights effect on model based estimates for count data.
Bibtex:
@article{almquist17fieldmethods,
author = {Zack W. Almquist and Sakshi Arya and Li Zeng and Emma S. Spiro},
journal = {Field Methods},
title = {Unbiased Sampling of Users from (Online) Activity Data},
year = {2019},
month = {},
volume = {31},
number = {1},
url = {},
pages = {23--38},
doi = {10.1177/1525822X18799426}
}
Zack W. Almquist and Benjamin E. Bagozzi (2019).
"Using Radical Environmental Texts to Uncover Network Structure and Network Features." Sociological Methods & Research, 48(4) 905-960.
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Radical social movements are broadly engaged in, and dedicated to, promoting change in their social environment. In their corresponding efforts to call attention to various causes, communicate with like-minded groups, and mobilize support for their activities, radical social movements also produce an enormous amount of text. These texts, like radical social movements themselves, are often (i) densely connected and (ii) highly variable in advocated protest activities. Given a corpus of radical social movement texts, can one uncover the underlying network structure of the radical activist groups involved in this movement? If so, can one then also identify which groups (and which sub-networks) are more prone to radical versus mainstream protest activities? Using a large corpus of British radical environmentalist texts (1992-2003), we seek to answer these questions through a novel integration of network discovery and unsupervised topic modeling. In doing so, we apply classic network descriptives (e.g. centrality measures) and more modern statistical models (e.g. Exponential Random Graph Models) to carefully parse apart these questions. Our findings provide a number of revealing insights into the networks and nature of radical environmentalists and their texts.
Bibtex:
@article{almquist17smr,
author = {Zack W. Almquist and Benjamin E. Bagozzi},
journal = {Sociological Methods \& Research},
title = {Using Radical Environmental Texts to Uncover Network Structure and Network Features},
year = {forthcoming},
month = {},
volume = {48},
number = {4},
url = {},
pages = {905–960},
doi = {10.1177/0049124117729696}
}
Zack W. Almquist and Carter T. Butts (2018).
"Dynamic Network Analysis with Missing Data: Theory and Methods." Statistica Sinica, 28(3) 1245-1264.
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Abstract: Statistical methods for dynamic network analysis have advanced greatly in the past decade. This article extends current estimation methods for dynamic network logistic regression (DNR) models, a subfamily of the Temporal Exponential-family Random Graph Models, to network panel data which contain missing data in the edge and/or vertex sets. We begin by reviewing DNR inference in the complete data case. We then provide a missing data framework for DNR families akin to that of Little and Rubin (2002) or Gile and Handcock (2010). We discuss several methods for dealing with missing data, including multiple imputation (MI). We consider the computational complexity of the MI methods in the DNR case and propose a scalable, design-based approach that exploits the simplifying assumptions of DNR. We dub this technique the "complete-case" method. Finally, we examine the performance of this method via a simulation study of induced missingness in two classic network data sets.
Bibtex:
@article{almquist17_statsinica,
author = {Zack W. Almquist and Carter T. Butts},
journal = {Statistica Sinica},
title = {Dynamic Network Analysis with Missing Data: Theory and Methods},
year = {2018},
month = {},
volume = {28},
number = {3},
pages = {1245-1264},
doi = {}
}
Justin J. Anker, Miriam K. Forbes, Zack W. Almquist, Jeremiah S. Menk, Paul Thuras, Amanda S.Unruh and Matt G. Kushnera (2017).
"A Network Approach to Modeling Comorbid Internalizing and Alcohol Use Disorders." Journal of Abnormal Psychology, 126(3), 325-339.
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Internalizing disorders co-occur with alcohol use disorder (AUD) at a rate that exceeds chance and compromise conventional AUD treatment. The "vicious cycle" model of comorbidity specifies drinking to cope (DTC) as a link between these disorders that, when not directly addressed, undermines the effectiveness of conventional treatments. Interventions based on this model have proven successful but there is no direct evidence for how and to what extent DTC contributes to the maintenance of comorbidity. In the present study, we used network analysis to depict associations between syndrome-specific groupings of internalizing symptoms, alcohol craving, and drinking behavior, as well as DTC and other extra-diagnostic variables specified in the vicious cycle model (e.g., perceived stress, and coping self-efficacy). Network analyses of 363 individuals with comorbid anxiety and AUD assessed at the beginning of residential AUD treatment indicated that while internalizing conditions and drinking elements had only weak direct associations, they were strongly connected with DTC and perceived stress. Consistent with this, centrality indices showed that DTC ranked as the most central/important element in the network in terms of its "connectedness" to all other network elements. A series of model simulations—in which individual elements were statistically controlled for—demonstrated that DTC accounted for all the relationships between the drinking-related elements and internalizing elements in the network; no other variable had this effect. Taken together, our findings suggest that DTC may serve as a "keystone" process in maintaining comorbidity between internalizing disorders and AUD.
Bibtex:
@article{almquist16_jap,
author = {Justin J. Anker and Miriam K. Forbes and Zack W. Almquist
and Jeremiah S. Menk and Paul Thuras and Amanda S.Unruh and Matt G. Kushnera},
journal = {Journal of Abnormal Psychology},
title = {A Network Approach to Modeling Comorbid Internalizing and Alcohol Use Disorders},
year = {2017},
volume = {126},
number = {3},
pages = {325-339},
doi = {10.1037/abn0000257}
}
Zack W. Almquist and Benjamin E. Bagozzi (2016).
"The Spatial Properties of Radical Environmental Organizations in the UK: Do or Die!" PLoS ONE, 11(11), 1-19.
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Abstract:
Radical environmental groups and their members have a wide and varied agenda which often encompasses both local and global issues. In their efforts to call attention to environmental problems, communicate with like-minded groups, and mobilize support for their activities, radical environmental organizations also produce an enormous amount of text, which can be used to estimate the complex communications and task-based networks that underlie these organizations. Moreover, the tactics employed to garnish attention for these groups' agenda can range from peaceful activities such as information dissemination to violent activities such as fire-bombing buildings. To obtain these varied objectives, radical environmental organizations must harness their networks, which have an important spatial component that structures their ability to communicate, coordinate and act on any given agenda item. Here, we analyze a network built from communications and information provided by the semi-annual ``Do or Die'' (DoD) magazine published in the UK over a 10 year period in the late 1990s and early 2000s. We first employ structural topic model methods to discover violent and nonviolent actors within the larger environmental community. Using this designation, we then compare the spatial structure of these groups, finding that violent groups are especially likely to engage in coordination and/or communication if they are sufficiently close, but exhibit a quickly decreasing probability of interaction over even a few kilometers. Further, violent and nonviolent groups each have a higher probability of coordination with their own group than across groups over even short distances. In these respects, we see that violent groups are especially local in their organization and that their geographic reach is likely very limited. This suggests that nonviolent environmental groups seek each other out over both large and short distances for communication and coordination, but violent groups tend to be highly localized.
Bibtex:
@article{almquist16plos,
author = {Almquist, Zack W. AND Bagozzi, Benjamin E.},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {The Spatial Properties of Radical Environmental Organizations in the UK: Do or Die!},
year = {2016},
month = {11},
volume = {11},
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0166609},
pages = {1-19},
doi = {10.1371/journal.pone.0166609}
}
Emma S. Spiro, Zack W. Almquist and Carter T. Butts (2016).
"The Persistence of Division: Geography, Institutions, and Online Friendship Ties." Socius: Sociological Research for a Dynamic World, 2, 1-15.
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As noted by theorists such as Blau, Durkheim, Mayhew, and others, interaction opportunity is a fundamental determinant of social structure. One of the most empirically well established factors influencing interaction opportunity is that of physical distance. The strength of this effect in modern societies, however, has been called into question because of technological advances (the so-called death-of-distance hypothesis). Here, the authors examine the effect of distance in an extreme case, considering weak friendship ties among university-affiliated persons in a large-scale online social network. Additionally, the authors explore institutional covariates, such as prestige and public or private status, as moderators of the relationship between distance and social tie probability. The findings demonstrate that geographical distance continues to affect social ties, despite the absence of physical barriers to tie formation and maintenance. The authors find moreover that institutional factors differentially affect the propensity for two university-affiliated individuals to be tied across large distances, illustrating that systematic differences in network structure along status lines persist even in ostensibly unconstrained settings.
Bibtex:
@article{almquistSocius,
Author = {Emma S Spiro and Zack W Almquist and Carter T Butts},
Journal = {Socius: Sociological Research for a Dynamic World},
Title = {The Persistence of Division: Geography, Institutions, and Online Friendship Ties},
Year = {2016},
volume={1},
number={},
pages={1--15}
}
Carter T. Butts and Zack W. Almquist. (2015).
"A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs." The Journal of Mathematical Sociology, 39(3), 163-167.
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The conventional exponential family random graph model (ERGM) parameterization leads to a baseline density that is constant in graph order (i.e., number of nodes); when modeling multiple networks of varying order, this is potentially problematic. Prior work has suggested a simple alternative that results in constant expected mean degree. Here, we extend this approach by suggesting another alternative parameterization that allows for flexible modeling of scenarios in which baseline expected degree scales as an arbitrary power of order. This parameterization is easily implemented by the inclusion of an edge count/log order statistic along with the traditional edge count statistic in the model specification.
Bibtex:
@article{almquistJSM,
Author = {Carter T Butts and Zack W Almquist},
Journal = {The Journal of Mathematical Sociology},
Title = {A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs},
Year = {2015},
volume={39},
number={3},
pages={163--167}
}
Zack W. Almquist and Carter T. Butts. (2015).
"Predicting Regional Self-identification from Spatial Network Models." Geographical Analysis, 47(1), 50-72.
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Social scientists characterize social life as a hierarchy of environments, from the micro level of an individual's knowledge and perceptions to the macro level of large-scale social networks. In accordance with this typology, individuals are typically thought to reside in micro- and macro-level structures, composed of multifaceted relations (e.g., acquaintanceship, friendship, and kinship). This article analyzes the effects of social structure on micro outcomes through the case of regional identification. Self identification occurs in many different domains, one of which is regional; i.e., the identification of oneself with a locationally-associated group (e.g., a "New Yorker" or "Parisian"). Here, regional self-identification is posited to result from an influence process based on the location of an individual's alters (e.g., friends, kin or coworkers), such that one tends to identify with regions in which many of his or her alters reside.The structure of this paper is laid out as follows: initially, we begin with a discussion of the relevant social science literature for both social networks and identification. This discussion is followed with one about competing mechanisms for regional identification that are motivated first from the social network literature, and second by the social psychological and cognitive literature of decision making and heuristics. Next, the paper covers the data and methods employed to test the proposed mechanisms. Finally, the paper concludes with a discussion of its findings and further implications for the larger social science literature.
Bibtex:
@article{almquistGA,
Author = {Zack W Almquist and Carter T Butts},
Journal = {Geographical Analysis},
Title = {Predicting Regional Self-Identification from Spatial Network Models},
Year = {2015},
volume={47},
number={1},
pages={50--72}
}
Emily J. Smith, Christopher S. Marcum, Adam Boessen, Zack W. Almquist, John R. Hipp, Nicholas N. Nagle, and Carter T. Butts. (2014).
"The Relationship of Age to Personal Network Size, Relational Multiplexity, and Proximity to Alters in the Western United States." The Journal of Gerontology: Series B, 70(1), 91-99.
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Abstract:
Objectives: This study examines the association of age and other socio-demographic variables with properties of personal networks; using samples of individuals residing in the rural western United States and the City of Los Angeles, we evaluate the degree to which these associations vary with geographical context. For both samples, we test the hypothesis that age is negatively associated with network size (i.e. degree) and positively associated with network multiplexity (the extent of overlap) on six different relations: core discussion members, social activity participants, emergency contacts, neighborhood safety contacts, job informants, and kin. We also examine the relationship between age and spatial proximity to alters.
Methods: Our data consists of a large-scale, spatially stratified ego- centric network survey containing information about respondents and those to whom they are tied. We use Poisson regression to test our hypothesis regarding degree while adjusting for covariates, including education, gender, race, and self-reported sense of neighborhood belonging. We use multiple linear regression to test our hypotheses on multiplexity and distance to alters.
Results: For both rural and urban populations we find a non- monotone association between age and numbers of core discussants and emergency contacts, with rural populations also showing non-monotone as- sociations for social activity partners and kin. These non-monotone rela- tionships show a peak in expected degree at midlife, followed by an eventual decline. We find a decline in degree among the elderly for all relations in both populations. Age is positively associated with distance to non-household alters for the rural population, although residential tenure is associated with shorter ego-alter distances in both rural and urban settings. Additionally, age is negatively associated with network multiplexity for both populations.
Discussion: Although personal network size ultimately declines with age, we find that increases for some relations extend well into late-midlife and most elders still maintain numerous contacts across diverse relations. The evidence we present suggests that older people tap into an wider va- riety of different network members for different types of relations than do younger people. This is true even for populations in rural settings, for whom immediate access to potential alters is more limited.
Bibtex:
@article{almquistGer,
Author = {Emily J Smith and Christopher S Marcum and Adam Boessen and Zack W Almquist and John R Hipp and Nicholas N Nagle and Carter T Butts},
Journal = {The Journal of Gerontology: Series B},
Title = {The Relationship of Age to Personal Network Size, Relational Multiplexity, and Proximity to Alters in the Western United States},
volume={70},
number={1},
pages={91--99},
year={2014}}
Zack W. Almquist and Carter T. Butts. (2014).
"Logistic Network Regression for Scalable Analysis of Networks with Joint Edge/Vertex Dynamics." Sociological Methodology, 44(1), 273-321.
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Abstract:
Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.
Bibtex:
@article{almquistSM,
Author = {Zack W. Almquist and Carter T. Butts},
Journal = {Sociological Methodology},
Title = {Logistic Network Regression for Scalable Analysis of Networks with Joint Edge/Vertex Dynamics},
Volume = {44},
Pages = {273-321},
Year = {2014}}
Adam Boessen, John R. Hipp, Emily J. Smith, Carter T. Butts, Nicholas N. Nagle, and Zack W Almquist. (2014).
"Networks, Space, and Residents' Perception of Cohesion." American Journal of Community Psychology, 53(3) 447-461.
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Abstract: Community scholars increasingly focus on the linkage between residents’ sense of cohesion with the neighborhood and their own social networks in the neighborhood. A challenge is that whereas some research only focuses on residents’ social ties with fellow neighbors, such an approach misses out on the larger constellation of individuals’ relationships and the spatial distribution of those relationships. Using data from the Twin Communities Network Study, the current project is one of the first studies to examine the actual spatial distribution of respondents’ networks for a variety of relationships and the consequences of these for neighborhood and city cohesion. We also examine how a perceived structural measure of cohesion—triangle degree—impacts their perceptions of neighborhood and city cohesion. Our findings suggest that perceptions of cohesion within the neighborhood and the city depend on the number of neighborhood safety contacts as well as on the types of people with which they discuss important matters. On the other hand, kin and social friendship ties do not impact cohesion. A key finding is that residents who report more spatially dispersed networks for certain types of ties report lower levels of neighborhood and city cohesion. Residents with higher triangle degree within their neighborhood safety networks perceived more neighborhood cohesion.
Bibtex:
@article{bossenajcp,
Author = {Adam Boessen and John R Hipp and Emily J Smith and Carter T Butts and Nicholas N Nagle and Zack W Almquist},
Journal = {American Journal of Community Psychology},
Number = {3},
Pages = {447--461},
Title = {Networks, Space, and Residents' Perception of Cohesion},
Volume = {53},
Year = {2014}}
Zack W. Almquist and Carter T. Butts. (2013).
"Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-group Blog Citation Dynamics in the 2004 US Presidential Election." Political Analysis, 21(4), 430-448.
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Abstract:
Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention–designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.
Bibtex:
@article{almquistpa,
title={Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter-and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election},
author={Almquist, Zack W and Butts, Carter T},
journal={Political Analysis},
volume={21},
number={4},
pages={430--448},
year={2013}
}
Zack W. Almquist and Carter T. Butts. (2012).
"Point process models for household distributions within small areal units." Demographic Research, 26(22), 593-632.
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Abstract:
Spatio-demographic data sets are increasingly available worldwide, permitting ever more realistic modeling and analysis of social processes ranging from mobility to disease transmission. The information provided by these data sets is typically aggregated by areal unit, for reasons of both privacy and administrative cost. Unfortunately, such aggregation does not permit fine-grained assessment of geography at the level of individual households. In this paper, we propose to partially address this problem via the development of point process models that can be used to effectively simulate the location of individual households within small areal units.
Bibtex:
@article{almquistdr,
title={Point process models for household distributions within small areal units},
author={Almquist, ZackW and Butts, Carter T},
journal={Demographic Research},
volume={26},
year={2012}
}
Zack W. Almquist. (2012).
"Random errors in egocentric networks." Social Networks, 34(4), 493-505.
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Abstract:
The systematic errors that are induced by a combination of human memory limitations and common survey design and implementation have long been studied in the context of egocentric networks. Despite this, little if any work exists in the area of random error analysis on these same networks; this paper offers a perspective on the effects of random errors on egonet analysis, as well as the effects of using egonet measures as independent predictors in linear models. We explore the effects of false-positive and false-negative error in egocentric networks on both standard network measures and on linear models through simulation analysis on a ground truth egocentric network sample based on facebook-friendships. Results show that 5–20% error rates, which are consistent with error rates known to occur in ego network data, can cause serious misestimation of network properties and regression parameters.
Bibtex:
@article{almquistsn,
title={Random errors in egocentric networks},
author={Almquist, Zack W},
journal={Social networks},
volume={34},
number={4},
pages={493--505},
year={2012}
}
Zack W. Almquist. (2010).
"US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages." Journal of Statistical Software, 37(6), 1-31.
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Abstract:
The US Decennial Census is arguably the most important data set for social science research in the United States. The UScensus2000 suite of packages allows for convenient handling of the 2000 US Census spatial and demographic data. The goal of this article is to showcase the UScensus2000 suite of packages for R, to describe the data contained within these packages, and to demonstrate the helper functions provided for handling this data. The UScensus2000 suite is comprised of spatial and demographic data for the 50 states and Washington DC at four different geographic levels (block, block group, tract, and census designated place). The UScensus2000 suite also contains a number of functions for selecting and aggregating specific geographies or demographic information such as metropolitan statistical areas, counties, etc. These packages rely heavily on the spatial tools developed by bivand08, i.e., the sp and maptools packages. This article will provide the necessary background for working with this data set, helper functions, and finish with an applied spatial statistics example.
Bibtex:
@article{almquistjss,
author = "Zack W. Almquist",
title = "US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages",
journal = "Journal of Statistical Software",
volume = "37",
number = "6",
pages = "1--31",
day = "17",
month = "11",
year = "2010",
CODEN = "JSSOBK",
ISSN = "1548-7660",
bibdate = "2010-10-04",
URL = "http://www.jstatsoft.org/v37/i06",
}
