Judges’ Queries and Presenter’s Replies

  • Members may log in to read judges’ queries and presenters’ replies.

Presentation Discussion

  • May 22, 2012 | 10:41 a.m.

    Very cool research and presentation! What were the key factors that seemed to create the distinct boundary within Santo Domingo? Also, have you considered how community membership may change in the future as the social, economic and ecological context changes?

  • Icon for: Timothy Caughlin

    Timothy Caughlin

    Presenter
    May 22, 2012 | 01:13 p.m.

    Hi Julia,
    I think the main key factor in the distinct boundary with Santo Domingo was wealth: the two areas have very different levels of poverty. This was pretty apparent when I visited the country. It would be fascinating to consider how community membership might change in the future. We found that sugar cane production was important for communities, but the country is currently making an economic transition from agricultural activities to manufacturing and tourism, so I expect sugar cane to be less important in the future. Another major change is that people are migrating from rural to urban areas very rapidly. I would expect that a consequence of this migration is that connections between urban and rural communities might be strengthened, as people that moved to urban areas call relatives and friends who still live in rural areas. I’d love to collect this data again in 10 years to see if we can detect those changes!

  • Icon for: Annie Aigster

    Annie Aigster

    Coordinator
    May 22, 2012 | 03:05 p.m.

    Very interesting interdisciplinary research. I think it would be fascinating to look at social networks in various states in the U.S. Because the state of Florida is so culturally diverse, it would be interesting to see what attributes may help predict community membership. Are there any plans to look at the attributes in other locations? In other countries? or in the U.S?

  • Icon for: Timothy Caughlin

    Timothy Caughlin

    Presenter
    May 23, 2012 | 11:35 a.m.

    Hi Annie,
    Thanks for visiting. I agree, Florida would be particularly interesting. I would expect to see very distinct communities in the different parts of the state-Miami, Gulf Coast and Northern Florida. Tourism vs. agriculture as different economic activities would be some of the first place-based attributes I would try in Florida.
    We are interested in extending this approach to other countries-at the top of our list for the next country to try is Haiti. Haiti and the Dominican Republic share the island of Hispaniola but are totally different culturally, economically and historically, so I’m very curious to compare community structure between the two.

  • Icon for: Carrie Seltzer

    Carrie Seltzer

    Trainee
    May 25, 2012 | 11:06 a.m.

    How interesting! What inspired this research and why did you examine the Dominican Republic?

  • Further posting is closed as the competition has ended.

  1. Timothy Caughlin
  2. http://www.igert.org/profiles/2453
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Florida
  1. Miguel Acevedo
  2. http://www.igert.org/profiles/1557
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Florida
  1. Kenneth Lopiano
  2. http://www.igert.org/profiles/2254
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Florida
  1. Olivia Prosper
  2. http://www.igert.org/profiles/3904
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Florida
  1. Nick Ruktanonchai
  2. http://www.igert.org/profiles/2130
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Florida
Judges’
Choice

Place-based attributes predict community membership in a large-scale social network

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. Significant modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as communication between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, can correctly predict community membership for over 70% of mobile phone towers. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.