US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages

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

Book Chapter in Computational Demography and Health

Recent developments in computing, data entry and generation, and analytic tools have changed the landscape of modern demography and health research. These changes have come to be known as computational demography, big data, and precision health in the field. This emerging interdisciplinary research comprises social scientists, physical scientists, engineers, data scientists, and disease experts. This work has changed how we use administrative data, conduct surveys, and allow for complex behavioral studies via big data (electronic trace data from mobile phones, apps, etc.). This chapter reviews this emerging field's new data sources, methods, and applications

When does open government shut? Predicting government responses to citizen information requests

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 public 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 insights into, the analysis of (i) textual records of citizen–government interactions and (ii) bureaucratic (non)responsiveness to these interactions

Let's Workout! Exploring Social Exercise in an Online Fitness Community

Increasing attention has been paid to promoting certain healthy habits through social interaction in online communities. At the intersection of social media and activity tracking applications, these platforms capture information on physical activities as well as peer-to-peer interactions. Importantly, they also offer researchers a novel opportunity to understand health behaviors by utilizing the large-scale behavioral trace data they archive. In this study we explore the characteristics and dynamics of social exercise (i.e. fitness activities with at least one peer physically co-present) using data collected from an online fitness community popular with cyclists and runners. In particular, we ask if factors such as temporal seasonality, activity performance and social feedback vary by the number of people participating in an activity; we do so by comparing associations for both men and women. Our results indicate that when peers are physically co-present for fitness activities (i.e. group workouts), exercise tends to be more intense and receive more feedback from other users, across both genders. Findings also suggest gender differences in the observed tendency to complete activities with others. These results have important implications for health and wellness interventions

Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity

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