Clio Andris

Integrating social network data into GISystems

ABSTRACT Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape. Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like ‘social flow’ and ‘anthrospace’, and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Côte d’Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, ego–alter relationships, node feature roles, modularity, and network transitivity.

The Rise of Partisanship and Super- Cooperators in the U.S. House of Representatives

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.

Linked Activity Spaces: Embedding Social Networks in Urban Space

We examine the likelihood that a pair of sustained telephone contacts (e.g. friends, family, professional contacts, called “friends”) uses the city similarly. Using call data records from Jiamusi, China, we estimate a proxy for the daily activity spaces of each individual subscriber by interpolating the points of geo-located cell towers he or she uses most frequently. We then calculate the overlap of the polygonal activity spaces of two established telephone contacts, what we call linked activity spaces.

Challenges for social flows

Abstract Social and interpersonal connections are attached to the built environment: people require physical infrastructure to meet and telecommunicate, and then populate these infrastructures with movement and information dynamics. In GIS analysis, actions are often represented as a unit of spatial information called the social flow–a linear geographic feature that evidences an individuals 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 unlike networks in operations systems (such as flight networks), provide evidence of personal intentionality to interact with the built environment and/or to perpetuate relationships with others. En masse, these flows sum to illustrate how humans, information and thoughts spread between and within places. Amid a growing abundance and usage of social flow data, we extend formal definitions of this data type, create new typologies, address new problems, and redefine social distance as the manifestation of social flows. Next, we outline challenges to fully leveraging these data with commercial GISystems by providing examples and potential solutions for representing, visualizing, manipulating, statistically analyzing and ascribing meaning to social flows. The goal of this discussion is to improve the dexterity of social flow data for geographic, environmental and social research questions.

Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities

In this article, we analyze geolocated Airbnb rental images in ten major cities. Airbnb is a hallmark institution in the sharing economy, allowing anyone with a bed and shelter to act like a micro-hotel, i.e. a bed-and-breakfast for other travelers. Travelers often spend less on Airbnb rentals than hotels and get a residential experience in a new place. Since hosts advertise their rentals on Airbnb, the site has a wealth of residential interior images from all over the world: from rural Africa to downtown Manhattan. As part of an ongoing project, we have downloaded over 200,000 images posted on Airbnb to ask: how do people decorate their homes in different locales? Do they use certain colors, or have a certain ornate or simple style? Here, we test ten major metropolitan areas using image rating responses from Mechanical Turk as well as automated image color predominance routines to investigate geographical differences in interior styles. We find overarching indicators of globalization and a lack of local culture in the case of color, but that different neighborhoods within cities have different levels or ornateness when decorating their properties. The results of this research can also help to identify the kinds of interiors that are more pleasant in the eyes of customers.

LBSN Data and the Social Butterfly Effect (Vision Paper)

LBSN data are well-suited for research questions and perspectives on social or spatial phenomena. Researchers often subset large LBSN datasets into different social networks (using snowball sampling), temporal or spatial granularities, to test for statistical patterns. Yet, researchers lack a way to examine how human interpersonal behavior results in digital traces of geolocated social events, although macro global flows of movement and communication are built from micro individual human intentions. To help navigate between the individual mind and the resultant big LBSN data that researchers use to understand society and space, I list a 14-tier scale of connectivity typologies. Each step can provide different a perspective of a single LBSN dataset. This scale can illustrate how perturbations at one level affect another level. E.g. How will reported escalating rates of autism affect the future network of connectivity between global cities? Will a change in migration policy strain emotional ties between an international family? The scale allows us to track changes at different levels between micro-, meso- and macro-scale social-spatial phenomena in a computationally-friendly way.

Measuring Geographic Pull Power: A Case Study of U.S. College Athletic Teams

Institutions such as firms, universities, and governments drive migration. Yet we know little about the different “pull powers” of institutions; that is, their ability to draw new members from distant and diverse locales. In this case study, we examine various pull power statistics of institutions of higher education as they draw student-athletes to their campuses. We collected data on nearly 160,000 student-athletes from more than 1,600 university team rosters at 128 schools over various years. Because roster records include hometowns, we use these data to quantify the distances and variety of hometowns from which universities attract students. We use descriptive statistics such as the 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. Results show that western U.S. and private schools tend to pull students from more distant and diverse locales, although many exceptions exist. Results also show that certain sports are likely to furnish more international than domestic students and that teams create pipelines that source multiple athletes from a single country. Finally, pull power is not found to correlate with endowment and athletic expenditure statistics. This analysis provides a fresh perspective on the movement of student-athletes, modern-day chain migration, and the value of institutions in catalyzing migration.

Wealthy Hubs and Poor Chains: Constellations in the U.S. Urban Migration System

Flows of people connect cities into complex systems. Urban systems research focuses primarily on creating economic models that explain movement between cities (whether people, telecommunications, goods or money), and more recently, finding strongly and weakly-connected regions. However, geometrically graphing the dependency between cities within a large network may reveal the roles of small and peripheral city agents in the system to show which cities switch regions from year to year, which medium-sized cities serve as collectors for large cities, and how the network is configured when connected by wealthy or deprived agents.

Assessing an Educational Mentorship Program in an Urban Context

Mentorships are important relationships that pair youth with more experienced members of the community. Mentorships facilitated by organizations often connect two community members who are unlikely to meet by happenstance, thus increasing social capital and aiding community development. Yet, difficulties in measuring how formal mentorships improve a city’s social capital and participant welfare might dissuade communities from investing in such programs. We describe a mixed-methods approach for measuring the impact of mentorship programs using a case study in Santa Fe, New Mexico. We survey participants and analyze differences between mentor and protege neighborhoods using U.S. Census data in a Geographic Information System.We find that participants report increases in social capital indicators including connections between individuals with socio-economic differences. However, Census data indicate that mentors and proteges have relatively few socio-economic differences in their neighborhoods. This divergence emphasizes the importance of measuring social capital in an urban and spatial context.