Student athletes increase the diversity of schools. Which universities draw students from distant and diverse locales? We put together “pull power statistics” for 160,000 student-athletes from more than 1,600 university team rosters at 128 schools over various years. Stats include mean distance traveled, count of unique hometowns, percentage of international student-athletes, and a new distance decay “apex” method to rank schools by their pull power. Western U.S. (like U. Idaho and U. Arizona) and private schools (like Harvard) lead the ranks. Schools like Rutgers, Catholic University, UMBC and U. Illinois-Chicago have a lot of local student athletes. 

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Andris C (2018) Measuring Geographic Pull Power: A Case Study of College Athletics. The Professional Geographer.

The social flow is a linear geographic feature that evidences an individual’s decision to connect places through travel, telecommunications and/or declaring personal relationships. These flows differ from traditional spatial networks (roads, etc.) because they are often non-planar, and provide evidence of personal intentionality to interact with the built environment and/or to perpetuate relationships with others.

We use tons of social flow data in today’s research, and so, we create new typologies, address new problems, and redefine social distance as the manifestation of social flows. We describe challenges for leveraging these data with commercial GISystems in terms of representing, visualizing, manipulating, statistically analyzing and ascribing meaning to social flows. 

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Andris C, Liu X and Ferreira Jr. J (2018) Challenges for Social Flows. Computers, Environment and Urban Systems.

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.

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Liu X, Hollister R and Andris C (2018) Wealthy Hubs and Poor Chains: Constellations in the U.S. Urban Migration System. In: Perez L, Kim EK, Sengupta R (eds) Agent-Based Models and Complexity Science in the Age of Geospatial Big Data. Advances in Geographic Information Science. Springer. Pp 73-86.

Findings: There’s a certifiable difference in urban hot spots and demand depending on couples’ relationship stage. Key factors for selecting a romantic date spot include ambiance (cozy & classy atmosphere), high prices, and downtown locations. Couples with families (children) tend to prefer less expensive restaurants, with great service and (probably) accessible parking. Special occasion locations are concentrated in downtown areas, especially for Las Vegas and Pittsburgh, but are more distributed for sprawling cities like Phoenix and Charlotte.

Method: Natural Language Processing of Yelp reviews to scan for correlations of certain keywords, cross-referenced with locations of restaurants.

Good For: If you are opening a bar or restaurant and would like to influence the potential clientele, or if you would like to glean insight into the romantic life cycle.

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Rahimi S, Liu X and Andris C (2017) Using Yelp to Find Romance in the City: A Case of Restaurants in Four Cities. Proceedings of the ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (UrbanGIS ’16). Nov 7, Los Angeles, CA.

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.

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Pistner M and Andris C (2017) Measuring Attraction and Redistribution of Institution-Based Movements. Geocomputation 2017. Sept 6-8, Leeds, UK.

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.

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Rahimi S, Martin M J R, Obeysekere E, Hellmann D, Liu X and Andris C (2017) A Geographic Information System (GIS)-Based Analysis of Social Capital Data: Landscape Factors That Correlate with Trust. Sustainability 9(3): 365.

Findings: Modeling Cities and Regions as Complex Systems effectively challenges long-standing principles of urban planning, and uses mathematical models to quantify land use types and and neighborhood effects, particularly pertaining to CA. They could expand by incorporating GIS and giving more context to maps to make them more easily understood.

Method: Reading the work and critically examining strengths and areas of improvements.

Good For: If you are considering reading Modeling Cities and would like a review from an expert in the field.

Andris C (2017) Book Review: Modeling Cities and Regions as Complex Systems: From Theory to Planning Applications. Environment and Planning B. 44(2): 385-386.

Findings: Considering where people are when we examine social networks can help us decide anything from where to advertise to where to build a hospital.

Methods: Describe why modeling socialization in geographic space is essential for understanding human behavior. Outline best practices and techniques for embedding SN in GISystems. Explore case studies in Bolivia, China, Côte d’Ivoire, Singapore, the United Kingdom, and the United States.

Good For: If you want to understand time importance of adding GIS to social network analysis. Some examples: considering how diseases spread (useful for CEID), how likely a person is to have a place to go in the event of a natural disaster, or If you’re deciding where to open a brick and mortar business that appeals to certain social networks.

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Andris C (2016) Integrating Social Network Data into GISystems. International Journal of Geographical Information Science. 31(1): 2009-2031.

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.

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Andris C (2016) A Computational Model for Dyadic Relationships. Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI ’16), Invited Paper, Jul 28, Pittsburgh, PA.  565 – 573.

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.

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Andris C, Alder L, Atwater C, Van Cleve J and O’Dwyer J  (2016) Assessing an Educational Mentorship Program in an Urban Context. Social Science Research Network (SSRN).

Findings: A city is a cityfriend with a city that sends the most migrants. Cities like Dallas and Atlanta have many cityfriends since they are central hubs for local migration. Wealthier people will move to certain places like Phoenix and Naples more often than places like LA and Austin.

Method: Use network schematics of migration movement dynamics to illustrate differentiation between the roles of cities in their regional systems.

Good For: If you would like to learn where people will migrate to or to predict economic gains and losses of cities based on migration flows. If you work with an American at-risk industry in a given city and want to predict labor availability.

Liu X and Andris C (2016) Migrant Routing the U.S. Urban System. GIScience, Sept 27, Montreal.

Findings: Photos in international AirBNB listings are starting to have more in common as globalization homogenizes the wold. However, variation between listings in neighborhoods is rising.

Method: 500,000 images downloaded from AirBNB were rated by Mechanical Turk participants.

Good For: If you want to learn how citizens of different locales decorate their homes differently, or to identify favorable interiors to global customers. Useful for AirBNB hosts or hotel marketers seeking a competitive advantage.

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Rahimi S,  Liu X and Andris C (2016) Hidden Style in the City: An Analysis of Geolocated Airbnb Rental Images in Ten Major Cities.  Proceedings of the ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (UrbanGIS ’16). Oct 31, Burlingame, CA.

Findings: People do not move out of Boston for the same reasons they move out of Detroit. When we look at migration in and out of cities, we can use smarter metrics to classify the economic health of a city.

Methods: Regresses a network of US CBSA to CBSA migration flows for more than 200 million people in Pennsylvania from 1990-2011.

Good for: If you are attempting to clarify and rehabilitate the image of a city, or plan how to increase population inflows. Housing developers may find value in predicting inflows/outflows of certain demographics.

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Andris C and Cook C (2015) Using Migration Degree to Distinguish Post-Industrial U.S. Cities. Computing in Urban Planning and Urban Management (CUPUM) Jul 7-10, Cambridge, MA.

Findings: The Butterfly Effect is measurable. There is a 14-point scale of how much impact a Location Based Social Network event has on the world. The lightest is one person speaking to another and the most significant is the migration of 7,000 people.

Methods: Presentation of a 14-tier scale for navigating spatial patterns, along with pectoral representations.

Good for: If you are interested in classifying the root causes of why people go places, ranging from the selection of a retirement community to travel of emigrants after an infestation.

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Andris C (2015) LBSN and the Social Butterfly Effect. Proceedings of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN ’15), Nov 6, Bellevue, WA.

Findings: Phone call friends use the city similarly, and you have a higher chance of running into a friend if you live in the suburbs.

Method: mapping ellipse-shaped polygons of where people make/receive calls in Jiamusi China, and those of his or her phone call friends.

Good for: if you have a geolocated social network and you want to learn more about who uses which parts of the city, and whether the city is set up for friendships. Hospitality focused companies could choose locations based on where interactions are most likely to happen.

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Wang Y, Kang C, Bettencourt LMA, Yu L and Andris C (2015) Linked Activity Spaces: A New Approach for Spatializing Social Networks. In: Helbich M, Arsanjani J, and Leitner M (eds). Computational Approaches for Urban Environments. Springer. Pp. 313-335.

Findings: Analysis of voting records show proof that US congresspeople are more partisan than ever. Thankfully, Texan Democrats and Northeastern Republicans  are ‘supercooperators’ who have stepped across party lines.

Methods: Charting frequency distributions of roll call vote data from the U.S. House of Representatives from 1949 to 2012 to display cross-party voting patterns.

Good for: If you are interested in the gradual polarization of American congress people, or would like to learn which congress people cooperate outside of their party.

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Andris C, Lee D, Hamilton MJ, Martino M, Gunning CE and Selden JA (2015) The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives. PLOS ONE [interactive web]  10(4):e0123507.

Findings: People who participated in a mentoring program in Santa Fe formed strong relationships regardless of the wide-ranging barriers (from distance to lack of social ties) between the pair of participants.

Methods: Gravity models show results of a two-tailed Kolmogorov-Smirnoff test.

Good for: If you are looking to start a mentoring program or participate in one.

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Andris C, Frey N, Van Cleve J and O’Dwyer J (2015) Geocomputation Methods for Dyadic Relationships. Geocomputation 2015. May 20-23, Dallas, TX.

Findings: Certain colleges like West Virginia and Providence have much less ‘pull power’ to attract far-away athletes than schools like Stanford or Kenyon College.

Methods: Data analysis from over 1000 U.S. university athletics webpages from 78 universities, 20 different types of sports, to produce a 90,000+ record dataset of U.S. and international student athletes and their hometowns.

Good for: If you hope to improve a collegiate athletic team by diagnosing and expanding its geographic reach. If you are interested in selling more collegiate apparel by targeting feeder areas.

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Andris C and Andris Z (2015) Exploring Institution-Driven Mobility. Proceedings of the IEEE International Conference on Mobile Data Management 3rd Annual Workshop on Human Mobility Computing (HuMoComP ’15), Jun 15, Pittsburgh, PA.

Findings: Singapore has good public transportation and other mobility options making it easy to get around the city.

Method: a visualization of the morning commute in Singapore.

Good for: Those interested in how people move around Singapore. Improving ride-share services.

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Andris C and Ferreira J (2014) Visualizing Commuting in Singapore. Environment and Planning A. 46(11): 2543-2545.

Findings: Despite a devastating N-S Civil War, phone calls still connect N-S cities, moreso than E-W cities. Coastal cities host most of the calling.

Method: mobile phone call flows from city to city.

Good for: understanding a nationwide economic system.

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Andris C and Bettencourt LMA (2014) Development, Information and Social Connectivity in Côte d’Ivoire. Infrastructure Complexity. 1(1): 1-18.

Findings: Some great publications show us that social interaction studies would be better if they included GIS. For example, an otherwise tranquil marriage may be suffering because traffic in the city is keeping the couple from spending time together.

Methods: Outlining various literatures across bodies of research.

Good for: If you are looking to learn why GIS is important across industries and need a place to start, or how your company can begin to think about incorporating GIS.

Andris C (2014) Relevant Literature for Examining Social Networks in Geographic Space. Santa Fe Institute Working Paper. SFI WORKING PAPER: 2014-04-010

Findings: Given it’s ability here to find geographic variances in students admitted to a university, the Support Vector Machine is a good tool for recognizing hidden patterns in a dataset.

Method: the paper tests Support Vector Machine against the more traditional method of Linear Discriminant Analysis.

Good for: Researchers looking for new or additional pattern recognition tools.

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Andris C, Wittenbach J and Cowen D (2013) Support Vector Machine for Spatial Variation. Transactions in GIS. 17(1): 41-61.

Findings: It’s hard to create a chart showing where a million people were picked up and dropped off. We can use clusters to simplify visualization.

Method: Spacial clustering of massive GPS points and mapping cluster-based flow measures to discover spacial and temporal patterns. Results are demonstrated through taxi cabs in Shenzhen, China.

Good for: If you are interested in traffic analysis or would like a user-friendly system to show where large groups of people are moving at different times.

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Guo D, Zhu X, Jin H, Gao P and Andris C (2012) Discovering Spatial Patterns in Origin-Destination Mobility Data. Transactions in GIS. (3): 411-429.

Findings: Understanding how people travel through Singapore and how successful the existing travel options are in connecting those people to important places like their homes, schools and workplaces will be important in planning for an increasingly urbanized future.

Method: Activity-based modeling.

Good for: Understanding mobility in Singapore. Improving ride-share services.

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Andris C (2012) Neighborhood Differentiation and Travel Patterns in Singapore. Technical Report, Singapore-MIT Alliance for Research and Technology (SMART) – Future Urban Mobility Internal Research Group, 1-39.

Findings: A technique called Weighted Radial Variation makes it easier to visualize migration connections created from a node-edge matrix.

Method: Extracting stars where the node is the center. Creating a signature vector comprised of an edge weight circling around the node from 0-360 degrees.

Good for: If you are looking to publish maps that are easier to understand.

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Andris C (2011) Weighted Radial Variation for Node Feature Classification [arXiv: 1102.4873v1 physics.data-an]

Findings: People’s social ties, not just cost and distance, play a role in deciding where people migrate.

Method: A Bayesian place-pair mode tested against other existing models used to predict migration like the traditional gravity model.

Good for: Understanding inter-city migration in the United States, which impacts the economic lives of cities, urban planning and more. If you are considering how to prepare for fluctuations in housing or employment based on inflows and outflows.

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Andris C, Halverson S and Hardisty F (2011) Predicting Migration Dynamics with Conditional and Posterior Probabilities. Proceedings of the IEEE International Conference on Spatial Data Mining and Geographic Knowledge Discovery (ICDSM ’11). Jun 29-Jul 1, Fuzhou, Fujian, P. R. China, 192-197.

Findings: The boundaries of regional governments correspond quite well with how people communicate via phone in the UK. Scotland is the least connected to the rest of the regions of the country.

Method: Telephone data for 12 billion calls over a one month period.

Good for: Discussion of regional cohesiveness and its impacts on independence movements. Independence advocates sometimes argue that traditional government boundaries are artificial to how people actually interact. According to this, that’s not entirely true.

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Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R and Strogatz S (2010) Redrawing the Map of Great Britain from a Network of Human Interactions. PLOS ONE 5(12).

Findings: A new tool called Ocean of Information helps track flows of people over time with 3D visualizations.

Method: Combining where people move as groups and as individuals and offering interactive tools for finding patterns, applied to Massachusetts during a Madonna concert and the 2006 World Cup Final.

Good for: If you are exploring how viruses spread in a city or if you are planning a large public event and want to predict how crowds will behave.

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Martino M, Calabrese F, DiLorenzo G, Andris C, Liu L and Ratti C (2010) An ocean of information: Fusing Aggregate & Individual Dynamics for Metropolitan Areas. Proceedings of the 14th International ACM Conference on Intelligent User Interfaces (IUI ’10). Feb 7-10, Hong Kong, China, 357-360.