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Findings: Geographically isolated, large cities (like Dallas and Phoenix) tend to receive the most migrants from other US cities. Destination cities turn over regularly over time. The overall diversity of destinations (bag-of-cities) for migrants is decreasing despite the rise of mobile technologies, i.e. migrants now have ‘hot spots’ that they go to, instead of a distributed set of options like in the early 1990s. We find this counter-intuitive, as the Internet makes it easier to search for new jobs and housing in distant places. Poorer migrants tend to follow chains of nearby cities, while richer migrants create a hub and spoke network, where the hubs are often retirement areas in Florida like Cape Coral.

Method: Mapping US migration flows in constellations, assigning nodes and edges to cities, and charting changes based on 20 years of IRS data at the MSA Level.

Good For: If you would like to learn more about where a population of a given city lived previously, or if you are interested in predicting where people will go. Any business marketing to transplant demographics. Guide to using graph theory motifs for urban networks.

Findings: Penn State draws more students that are closer to the university and come from higher income families. Alumni from large PA cities facing economic downturns return to those cities more often than those from Philadelphia or New Jersey.

Method: Applying clustering and socio-economic modelling statistical methods to measure changes in the Pennsylvania State University’s population draw and redistribution power from 1995-2015, focusing on the U.S. Mid-Atlantic Region.

Good For: If you want to know the geographic composition of Penn State students, which can be useful for marketing to under-represented areas or planning alumni events.

Findings: We trust our neighbors less when there are more people, lower housing value, weaker amenities (libraries and schools), and lower home ownership rates.

Method: Used Harvard University’s Saguaro Seminar’s 2006 Social Capital Community Benchmark Survey to create a national-level case study and a specific city case study for Rochester, NY.

Good For: If you are interested in learning how to increase trust in a community. Targeted marketing for websites like Neighborly which rely on strong communities.

Findings: Pairs of people in all kinds of relationships tend to collocate, telecommunicate, and feel inclinations towards one and other. People will telecommunicate with and feel inclinations towards their mothers more than their significant others, and find themselves collocated with friends more often than with roommates.

Method: Collecting diaries from 54 dyads (pairs of people) containing times and frequencies of interaction.

Good For: If you are seeking to understand human relationships through a quantified set of interactions. Marketers like 1800Flowers catering to pair sets could adjust advertisements based on how we prioritize our relationships.

Findings: Participants in mentoring programs report increases in social capital with people with socio-economic differences, but most mentors and proteges are from similar socio-economic neighborhoods.

Methods: Surveying participants in mentoring programs in Santa Fe, New Mexico and cross-referencing US Census data on local demographics.

Good For: If you are considering participating in or starting a mentoring program and want to learn about perceived societal benefits.