To gain insight into the mixed findings surrounding Consensual Nonmonogamy (CNM), this study developed the Triple-C model of commitment, conceptualizing relationship structures with three key dimensions: mutual consent, communication, and comfort. Latent profile analyses in an online sample (N = 1,658) identified five classes of relationship structures: two monogamous groups (68%; representing earlier-and later-stage relationships), CNM relationships (7.7%, marked by low interest in monogamy and high levels of mutual consent, comfort, and communication around commitment and EDSA), partially-open relationships (13%, with more mixed attitudes toward monogamy and lower consent, comfort, and communication), and one-sided EDSA relationships (11%, in which one partner desires monogamy while the other partner engages in EDSA with low levels of mutual consent, comfort, and communication). The monogamous and CNM groups demonstrated high levels of relationship and individual functioning, whereas the partially-open and one-sided nonmonogamous groups demonstrated lower functioning. These findings highlight the diversity of nonmonogamy that likely exists within selfreport classifications like "swingers" and "open relationships," providing a possible explanation for the mixed findings in previous work. Decision tree analyses identified a 4-item algorithm (COMMIT4) that classifies individuals into these groups with 93% accuracy, offering a tool for incorporating relationship structure diversity in future work.

A pandemic, like other disasters, changes how systems work. In order to support research on how the COVID-19 pandemic impacted the dynamics of a single metropolitan area and the communities therein, we developed and made publicly available a “data-support system” for the city of Boston. We actively gathered data from multiple administrative (e.g., 911 and 311 dispatches, building permits) and internet sources (e.g., Yelp, Craigslist), capturing aspects of housing and land use, crime and disorder, and commercial activity and institutions. All the data were linked spatially through BARI’s Geographical Infrastructure, enabling conjoint analysis. We curated the base records and aggregated them to construct ecometric measures (i.e., descriptors of a place) at various geographic scales, all of which were also published as part of the database. The datasets were published in an open repository, each accompanied by a detailed documentation of methods and variables. We anticipate updating the database annually to maintain the tracking of the records and associated measures.

Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone.