Research Statement

My research builds on concepts and methods developed for the study of social networks. Networks offer a powerful and compelling framework for understanding fundamental social relations, whether these are the relations driving an individual’s life and success (e.g., advice, friendship) or the relations governing large organizations (e.g., informations passing between FEMA and local governments) – a central theme of sociological inquiry. While these tools are powerful in their combination of generalizability and precision in measuring our social world, too often they are limited in the contextual information they include. For example, network researchers have historically modeled network data using only formal features of the network, such as the size of an individual’s friend group, while ignoring other important characteristics such as how often the friends interact (time), and how closely they live to one another (space). This limited use of contextual information suggests a problem with much of the social network literature in that this area of the field often seems to overlook the enormous impact of our environment on our behavior (e.g., how space often limits who we meet which can then have an effect on whom we might end up spending our lives together with, etc.). By addressing contextual mechanisms through the powerful social network lens, we can improve both our understanding of social processes (e.g., information passing) and social action.

Thematic Overview

Environmental Action and Governance

Funding

Articles

  • Bagozzi, B. E., D. Berliner, and Z.W. Almquist (forthcoming). When Does Open Government Shut? Predicting Government Responses to Citizen Information Requests. Regulation & Governance.
  • Almquist, Z.W. and B. 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.
  • Almquist, Z.W. and B. E. Bagozzi (2019). Using Radical Environmental Texts to Uncover Network Structure and Network Features. Sociological Methods & Research 48(4), 905–960.
  • Almquist, Z.W. and B. E. Bagozzi (2016). The Spatial Properties of Radical Environmental Organizations in the UK: Do or Die! PloS ONE 11(11), 1–19.

Epidemology and Public Health

COVID-19

Funding

Articles

  • Thomas, L.J., P. Huang, F. Yin, X.I. Luo, Z.W. Almquist, J.R. Hipp, and C.T. Butts (2020). Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity. Proceedings of the National Academy of Sciences 117(39), 24180–24187.
  • Jones, J. H., A. Hazel, and Z.W. Almquist (2020). Transmission-Dynamics Models for the SARS Coronavirus-2. American Journal of Human Biology 32(5), 1-14.

Spatial Demography & Social Networks

Homelessness

Funding

Articles

  • Almquist, Z.W. (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.
  • Almquist, Z.W., N. E. Helwig, and Y. You (2020). Connecting Continuum of Care Point-in-Time Homeless Counts to United States Census Areal Units. Mathematical Population Studies 27(1), 46–58.

Spatial Demography

Funding

  • 2014-2015     Jack DeWaard (Co-PI) and Zack W. Almquist (Co-PI). “Internal Migration and Recovery from the Great Recession in Urban Minnesota Counties and Neighborhoods.” CURA Faculty Interactive Research Program, University of Minnesota. $45,000.

Articles

  • Maas, P, Almquist, Z.W., Giraudy, E. and Schneider, J. W. (2020). Using social media to measure demographic responses to natural disaster: Insights from a large-scale Facebook survey following the 2019 Australia Bushfires. arXiv preprint arXiv:2008.03665.
  • Thomas, L.J., P. Huang, F. Yin, X.I. Luo, Z.W. Almquist, J.R. Hipp, and C.T. Butts (2020). Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity. Proceedings of the National Academy of Sciences 117(39), 24180–24187.
  • Almquist, Z.W. (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.
  • Almquist, Z.W., N. E. Helwig, and Y. You (2020). Connecting Continuum of Care Point-in-Time Homeless Counts to United States Census Areal Units. Mathematical Population Studies 27(1), 46–58.
  • Almquist, Z.W. and C. T. Butts (2012). Point process models for household distributions within small areal units. Demographic Research 26 (22), 593–632.
  • Almquist, Z.W. (2010). US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages. Journal of Statistical Software 37(6), 1–31.

Spatial Networks

Funding

Articles

  • Thomas, L.J., P. Huang, F. Yin, X.I. Luo, Z.W. Almquist, J.R. Hipp, and C.T. Butts (2020). Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity. Proceedings of the National Academy of Sciences 117(39), 24180–24187.
  • Almquist, Z.W. (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.
  • Almquist, Z.W. and B. E. Bagozzi (2016). The Spatial Properties of Radical Environmental Organizations in the UK: Do or Die! PloS ONE 11(11), 1–19.
  • Spiro, E.S., Z.W. Almquist, and C.T. Butts (2016). The Persistence of Division: Geography, Institutions, and Online Friendship Ties. Socius: Sociological Research for a Dynamic World 2(1), 1–15.
  • Almquist, Z.W. and C. T. Butts (2015). Predicting Regional Self-Identification from Spatial Network Models. Geographical Analysis 47(1), 50–72.
  • Boessen, A., J.R. Hipp, E.J. Smith, C. T. Butts, N N. Nagle, and Z.W. Almquist (2014). Networks, Space, and Residents’ Perception of Cohesion. American Journal of Community Psychology 53(3), 447–461.
  • Smith, E. J., C. S. Marcum, A. Boessen, Z.W. Almquist, J. R. Hipp, N. N. Nagle, and C. 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.

Health and Networks

Activity-Based Networks

Funding

Articles

  • Almquist, Z.W., S. Arya, L. Zeng, and E. S. Spiro (2019). Unbiased Sampling of Users from (Online) Ac- tivity Data. Field Methods 31(1), 23–38.
  • Zeng, L., Z.W. Almquist, and E. S. Spiro (2019). “Friending” in Online Fitness Communities: Exploring Activity-Based Online Network Structure. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, pp.2822–2831.
  • Zeng, L., Z.W. Almquist, and E. S. Spiro (2018). Stay Connected and Keep Motivated: Modeling Activity Level of Exercise in an Online Fitness Community. In: Social Computing and Social Media. Technologies and Analytics. Ed. by G. Meiselwitz. Vol. 10914. Lecture Notes in Computer Science. Springer International Publishing, pp.137–147.
  • Zeng, L., Z.W. Almquist, and E. S. Spiro (2017). Let’s Workout! Exploring Social Exercise in an Online Fitness Community. In: The iConference 2017 Proceedings, Wuhan, China. Vol. 2, pp.87–98.

Mental Health and Network Models

Funding

Articles

  • Anker, J.J., M. Forbes, Z.W. Almquist, J. Menk, P. Thuras, and M. G. Kushner (2017). A Network Ap- proach to Conceptualizing Comorbid Internalizing and Alcohol Use Disorders. Journal of Abnormal Psychology 126(3), 325–339.
  • Boessen, A., J.R. Hipp, E. J. Smith, C. T. Butts, N. N. Nagle, and Z.W. Almquist (2014). Networks, Space, and Residents’ Perception of Cohesion. American Journal of Community Psychology 53(3), 447–461.

Organizational Networks

Funding

Articles

  • Almquist, Z.W., E. S. Spiro, and C. T. Butts (2016). “Shifting Attention: Modeling Follower Relationship Dynamics among US Emergency Management-related Organizations During a Colorado Wildfire”. In: Social Network Analysis of Disaster Response, Recovery, and Adaptation. Ed. by A. Faas and E. Jones. Philadelphia, PA: Elsevier.
  • Almquist, Z.W. and B. E. Bagozzi (2016). The Spatial Properties of Radical Environmental Organizations in the UK: Do or Die! PloS ONE 11(11), 1–19
  • Almquist, Z.W. and C. 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.

Methodology

Statistics

Funding

Articles

  • Mallik, A. and Z.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.
  • Almquist, Z.W. and C. T. Butts (2018). Dynamic Network Analysis with Missing Data: Theory and Methods. Statistica Sinica 28(3), 1245–1264.
  • Meeden, G., Z. Almquist, and C. Geyer (2016). Better adjusted weights for respondents in skewed popula- tions. In: Proceedings of Statistics Canada Symposium 2016.
  • Almquist, Z.W. and C. T. Butts (2014). “Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics”. In: Bayesian Inference in the Social Sciences. Ed. by I. Jeliazkov and X.-S. Yang. Hoboken, New Jersey: John Wiley & Sons.
  • Almquist, Z.W. (2010). US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages. Journal of Statistical Software 37(6), 1–31.

Temporal Networks

Funding

  • 2014-2017     Zack W. Almquist (PI). “Scalable Temporal Network Models with Population Dynamics: Estimation, Simulation, and Prediction.” Award #W911NF-14-1-0577, Young Investigator Program, Army Research Office. $146,079.
  • 2013-2014     Carter T. Butts (PI) and Zack W. Almquist (Co-PI). “Doctoral Dissertation Research: Dynamic Network Models for the Scalable Analysis of Networks with Missing or Sampled Joint Edge/Vertex Evolution.” Grant #SES-1260798, NSF, Social, Behavioral and Economic Sciences (SBE), Methodology, Measurement and Statistics. $15,140.

Articles

  • Mallik, A. and Z.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.
  • Almquist, Z.W. and C. T. Butts (2018). Dynamic Network Analysis with Missing Data: Theory and Methods. Statistica Sinica 28(3), 1245–1264.
  • Almquist, Z.W., E. S. Spiro, and C. T. Butts (2016). “Shifting Attention: Modeling Follower Relationship Dynamics among US Emergency Management-related Organizations During a Colorado Wildfire”. In: Social Network Analysis of Disaster Response, Recovery, and Adaptation. Ed. by A. Faas and E. Jones. Philadelphia, PA: Elsevier.
  • Butts, C. T. and Z.W. Almquist (2015). A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs. The Journal of Mathematical Sociology 39(3), 163–167.
  • Almquist, Z.W. and C. T. Butts (2014). “Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics”. In: Bayesian Inference in the Social Sciences. Ed. by I. Jeliazkov and X.-S. Yang. Hoboken, New Jersey: John Wiley & Sons.
  • Almquist, Z.W. and C. T. Butts (2014). Logistic Network Regression for Scalable Analysis of Networks with Joint Edge/Vertex Dynamics. Sociological Methodology 44(1), 273–321.
  • Almquist, Z.W. and C. 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.

Sampling and Measurement

Sampling

Funding

  • 07/2014         Zack W. Almquist (PI) and Glen Meeden (Co-PI). “A Bayesian Approach to Finite Population Sampling for the Social Sciences: Applications to Sample Weighting and Small Area Estimation.” Population Center Proposal Development Grant, University of Minnesota. $8,000.

Articles

  • Nilakanta, H., Z.W. Almquist, and G. L. Jones (2019). Ensuring Reliable Monte Carlo Estimates of Net- work Properties. arXiv preprint arXiv:1911.08682.
  • Almquist, Z.W., S. Arya, L. Zeng, and E. S. Spiro (2019). Unbiased Sampling of Users from (Online) Activity Data. Field Methods 31(1), 23–38.
  • Meeden, G., Z. Almquist, and C. Geyer (2016). Better adjusted weights for respondents in skewed popula- tions. In: Proceedings of Statistics Canada Symposium 2016.
  • Kurant, M., M. Gjoka, Y. Wang, Z.W. Almquist, C. T. Butts, and A. Markopoulou (2012). Coarse-Grained Topology Estimation via Graph Sampling. In: Proceedings of ACM SIGCOMM Workshop on Online Social Networks (WOSN) ’12. Helsinki, Finland.

Network Measurment

Funding

Articles

  • Almquist, Z.W. and B. E. Bagozzi (2019). Using Radical Environmental Texts to Uncover Network Structure and Network Features. Sociological Methods & Research 48(4), 905–960.
  • Almquist, Z.W. (2012). Random errors in egocentric networks. Social Networks 34(4), 493–505.

Text and Networks

Funding

Articles

  • Bagozzi, B. E., D. Berliner, and Z.W. Almquist (forthcoming). When Does Open Government Shut? Predicting Government Responses to Citizen Information Requests. Regulation & Governance.
  • Almquist, Z.W. and B. 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.
  • Almquist, Z.W. and B. E. Bagozzi (2019). Using Radical Environmental Texts to Uncover Network Structure and Network Features. Sociological Methods & Research 48(4), 905–960.