Metrics and methods for social distance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-189).Distance measures are important for scientists because they illustrate the dynamics of geospatial topologies for physical and social processes. Two major types of distance are generally used for this purpose: Euclidean Distance measures the geodesic dispersion between fixed locations and Cost Distance characterizes the ease of travel between two places. This dissertation suggests that close inter-place ties may be an effect of human decisions and relationships and so embraces a third tier of distance, Social Distance, as the conceptual or physical connectivity between two places as measured by the relative or absolute frequency, volume or intensity of agent-based choices to travel, communicate or relate from one distinct place to another. In the spatial realm, Social Distance measures have not been widely developed, and since the concept is relatively new, Chapter 1 introduces and defines geo-contextual Social Distance, its operationalization, and its novelty. With similar intentions, Chapter 2 outlines the challenges facing the integration of social flow data into the Geographic Information community. The body of this dissertation consists of three separate case studies in Chapters 3, 4 and 5 whose common theme is the integration of Social Distance as models of social processes in geographic space. Each chapter addresses one aspect of this topic. Chapter 3 looks at a new visualization and classification method, called Weighted Radial Variation, for flow datasets. U.S. Migration data at the county level for 2008 is used for this case study. Chapter 4 discusses a new computational method for predicting geospatial interaction, based on social theory of trip chaining and communication. U.S. Flight, Trip and Migration data for the years 1995-2008 are used in this study. Chapter 5 presents the results of the tandem analysis for social networks and geographic clustering. Roll call vote data for the U.S. House of Representatives in the 111th Congress are used to create a social network, which is then analyzed with regards to the geographic districts of each congressperson.by Clio Andris.Ph.D

Visualizing commuting in Singapore

Singapore’s urban planning initiatives have garnered great interest from onlookers in the transportation and planning domains in the past twenty years (Vasoo and Lee, 2001). The government has implemented a number of schemes, such as congestion pricing (Santos, 2005) and high tariffs for automobiles (May, 2004), that have encouraged residents to use public transportation. According to the 2008 Household Interview Travel Survey (HITS), (LTA, 2008) 64% of peak AM trips (eg, travel to work, school, and morning errands) used public transit in 2008. In comparison, in the US, New York City ranks highest in public transportation rates (55% of commuters), followed by Washington, DC at 38% (US Census Bureau, 2008–12)

Characteristics of Jetters and Little Boxes: An Extensibility Study Using the Neighborhood Connectivity Survey

Individuals connect to sets of places through travel, migration, telecommunications, and social interactions. This set of multiplex network connections comprises an individual’s “extensibility,” a human geography term that qualifies one’s geographic reach as locally‐focused or globally extensible. Here we ask: Are there clear signals of global vs. local extensibility? If so, what demographic and social life factors correlate with each type of pattern? To answer these questions, we use data from the Neighborhood Connectivity Survey conducted in Akron, Ohio, State College, Pennsylvania, and Philadelphia, Pennsylvania (global sample N = 950; in model n = 903). Based on the location of a variety of connections (travel, phone call patterns, locations of family, migration, etc.), we found that individuals fell into one of four different typologies: (a) hyperlocal, (b) metropolitan, (c) mixed‐many, and (d) regional‐few. We tested whether individuals in each typology had different levels of local social support and different sociodemographic characteristics. We found that respondents who are white, married, and have higher educational attainment are significantly associated with more connections to a wider variety of places (more global connections), while respondents who are Black/African American, single, and with a high school level educational attainment (or lower) have more local social and spatial ties. Accordingly, the “urban poor” may be limited in their ability to interact with a variety of places (yielding a wide set of geographic experiences and influences), suggesting that wide extensibility may be a mark of privileged circumstances and heightened agency

Understanding Demographics and Experience of Tourists in Yellowstone National Park through Social Media

This study compared tourists’ demographic variables between survey data and Twitter data in Yellowstone National Park and explored tourists’ experience through Twitter data. First, there were significant differences in age groups of tourists between social media data and survey data. Compared to survey data, tourists who identified by Twitter data concentrated on middle age groups. Secondly, the spatial distribution of geotagged tweets reflected the road network and main attractions in Yellowstone National Park. The peak visitation season is from June to September in survey data, while, in social media data, the peak visitation season is slightly shorter. Finally, the sentiment analysis was conducted and only 6.7% of tweets were negative, indicating that most tourists in Yellowstone National Park had good experience. Therefore, analyzing Twitter data will be helpful for understanding tourists’ demographics, attitudes and experience in the national parks and improving customer service in the further

Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses

Analyzing Ideological Communities in Congressional Voting Networks

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties

Redrawing the Map of Great Britain from a Network of Human Interactions

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.National Science Foundation (U.S.)AT & TAudi AGUnited States. Dept. of Defense (National Defense Science and Engineering Fellowship Program