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From gridlock in lawmaking to shortened holiday family dinners, partisan polarization pervades social and political life in the United States. We study the degree to which the dynamics of partisan polarization can be observed in patterns of county-to-county migration in the U.S. Specifically, we ask whether migration follows patterns that would lead individuals to homogeneous or heterogeneous partisan exposure, using annual county-to-county migration networks from 2002 to 2015. Adjusting for a host of factors, including geographic distance, population, and economic variables, we test the degree to which migration flows connect counties with similar political preferences. Our central finding is that over the period studied, county-to-county migration flows connect counties with similar partisan voting profiles. Moreover, partisan sorting is most pronounced among the most politically extreme counties. The implication of this finding in the context of partisanship is that U.S. migration patterns reinforce partisan sorting, limiting the degree to which individuals will experience cross-the-aisle local social contacts through spatial interaction. This finding builds on existing research that has documented (1) that individuals prefer to move to and live in locations inhabited by co-partisans, and (2) that local geographic areas have become more polarized in recent decades. Our results indicate that large scale patterns of polarized migration flows serve as a potential mechanism that contributes to geographic partisan polarization.

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.

It is widely reported that partisanship in the United States Congress is at an historic high. Given that individuals are persuaded to follow party lines while having the opportunity and incentives to collaborate with members of the opposite party, our goal is to measure the extent to which legislators tend to form ideological relationships with members of the opposite party. We quantify the level of cooperation, or lack thereof, between Democrat and Republican Party members in the U.S. House of Representatives from 1949-2012. We define a network of over 5 million pairs of representatives, and compare the mutual agreement rates on legislative decisions between two distinct types of pairs: those from the same party and those formed of members from different parties. We find that despite short-term fluctuations, partisanship or non-cooperation in the U.S. Congress has been increasing exponentially for over 60 years with no sign of abating or reversing. Yet, a group of representatives continue to cooperate across party lines despite growing partisanship.

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

Large events and gatherings, particularly those taking place indoors, have been linked to multitransmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geolocalized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real-time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy makers make prudent decisions (for example, increasing mask-wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions. The COVID-19 Real-Time Event Risk Assessment Planning Tool allows individuals to assess risk associated with attending events of different sizes via a real-time, interactive website and helps individuals assess whether this risk is worth taking.

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals’ risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool ( https://covid19risk.biosci.gatech.edu/ ) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.

Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.

The global community decorates their homes based on personal decisions and contextual influences of their larger cultural and economic surroundings. The extent to which spatial patterns emerge in residential decoration practices has been traditionally difficult to ascertain due to the private nature of interior home spaces. Yet, measuring these patterns can reveal the presence of geographic culture hearths and/or globalization trends. In this work, we collected over one million geolocated images of interior living spaces from a popular home rental website, Airbnb ( http://airbnb.com ), and used transfer learning techniques to automatically detect the presence of key stylistic objects: plants, books, decor, wall art and predominance of vibrant colors. We investigated patterns of home decor practices for 107 cities on six continents, and performed a deep dive into six major U.S. cities. We found that world regions show statistically significant variation in decorative element prevalence, indicating differences in geographic cultural trends. At the U.S. neighborhood level, elements were only weakly spatially clustered and found to not correlate with socio-economic neighborhood variables such as income, unemployment rates, education attainment, residential property value, and racial diversity. These results may suggest that American residents in different socio-economic environments put similar effort into personalizing and caring for their homes. More broadly, our results represent a new view of worldwide human behavior and a new application of machine learning techniques to the exploration of cultural phenomena.

Individuals connect to sets of places through travel, migration, telecommunications, and social interactions. This set of multiplex network connections comprises an individual’s “extensibility,” a human geography term that qualifies one’s geographic reach as locally‐focused or globally extensible. Here we ask: Are there clear signals of global vs. local extensibility? If so, what demographic and social life factors correlate with each type of pattern? To answer these questions, we use data from the Neighborhood Connectivity Survey conducted in Akron, Ohio, State College, Pennsylvania, and Philadelphia, Pennsylvania (global sample N = 950; in model n = 903). Based on the location of a variety of connections (travel, phone call patterns, locations of family, migration, etc.), we found that individuals fell into one of four different typologies: (a) hyperlocal, (b) metropolitan, (c) mixed‐many, and (d) regional‐few. We tested whether individuals in each typology had different levels of local social support and different sociodemographic characteristics. We found that respondents who are white, married, and have higher educational attainment are significantly associated with more connections to a wider variety of places (more global connections), while respondents who are Black/African American, single, and with a high school level educational attainment (or lower) have more local social and spatial ties. Accordingly, the “urban poor” may be limited in their ability to interact with a variety of places (yielding a wide set of geographic experiences and influences), suggesting that wide extensibility may be a mark of privileged circumstances and heightened agency.

This paper investigates the challenges and opportunities arising from incommensurate spatial partitions (ISPs) in regional science and spatial econometrics, focusing on how processes with overlapping yet distinct boundaries, interact and influence each other. ISPs are prevalent in various domains, including housing markets, employment centers, voting districts, and educational institutions, often complicating spatial econometric modeling and analysis. Using the intersection of school catchment areas and neighborhoods as a primary case study, the paper introduces a novel methodological framework utilizing bipartite graphs. This approach reframes the relationship between different spatial units, allowing for the analysis of multi-process spatial contexts without needing harmonization of spatial supports. The paper also develops new spatial weights derived from the bipartite graph, facilitating both exploratory spatial data analysis and confirmatory spatial econometric modeling. These methods are illustrated through a case study of San Diego, California analyzing 198 neighborhoods and 370 public elementary school catchments.