The U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S&T) announced today the selection of George Mason University in Fairfax, Va. to lead a consortium of U.S. academic institutions and other partners for a new Center of Excellence (COE) in Criminal Investigations and Network Analysis (CINA). S&T will provide CINA with a $3.85 million grant for its first operating year in a 10-year grant period.
The leadership team will consist of GGS professor Anthony Stefanidis, who serves as director of the new Center of Excellence; University Professor Louise Shelley, director of Mason’s Terrorism, Transnational Crime and Corruption Center; Paulo Costa, associate professor of systems engineering and operations research; professor David Weisburd, executive director of Mason’s Center for Evidence-Based Crime Policy; Allison Redlich, professor in the Department of Criminology, Law and Society; Jim Jones, associate professor of electrical and computer engineering; and professor Mary Ellen O’Toole, director of Mason’s Forensic Science Program.
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Originally from Zixi, China, Liming received his Master’s degree in Urban Planning from Tongji University in Shanghai. He came to the U.S. in 2012 to attend Carnegie Mellon University before transferring to George Mason.
Liming is currently working on his doctoral degree in Earth Systems and Geoinformation Science. He studies science because “[it] offers us a way to explain what we see in our world”.
In addition to his rigorous academic schedule, Liming is a member of Mason’s Dragon Dance Team. He loves to raise awareness for Chinese culture at Mason and hopes to establish a club here that allows his fellow students to learn about “Authentic Chinese culture”.
Term Assistant Professor/Instructor, Geography
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Candidate: Scott Heneghan
Master of Science in Geoinformatics and Geospatial Intelligence
Department of Geography and Geoinformation Science
Date: Tuesday, January 17, 2017
Time: 10:00 AM
Place: Exploratory Hall 2304
GRASSROOTS TO VOTING BOOTHS: A STUDY OF THE SPATIOTEMPORAL DYNAMICS OF TRADITIONAL MEDIA COVERAGE AND SOCIAL MEDIA IMPACT IN 2016 UNITED STATES PRESIDENTIAL CANDIDATES
Director: Dr. Anthony Stefanidis
Committee Members: Dr. Arie Croitoru, Dr. Andrew Crooks
The subject of social media effect on elections has been studied by multiple peer reviewed journals, however none take a space time statistical approach to social media’s effect on traditional media in the context of a presidential election. This article attempts to review the space time patterns of social media and how they can potentially drive news coverage from local papers across different parts of the United States. Specifically, the number of tweets around the newspaper locations as compared against the number of news reports about either Bernie Sanders or Donald Trump were reviewed in order to determine if social media is in fact a driver of change. The trends of the results over time were analyzed using the Pearson product moment correlation.
Candidate: Jason Shapiro
Doctor of Philosophy in Earth Systems and Geoinformation Sciences
Department of Geography and Geoinformation Science
Date: Friday, December 2, 2016
Time: 1:00 PM
Place: Exploratory Hall 2304
MODELING THE IMPACTS OF AIRPORTS ON URBAN DENSITY: CASE STUDY OF WASHINGTON DULLES INTERNATIONAL AIRPORT
Director: Dr. Donglian Sun
Committee Members: Dr. Matthew Rice, Dr. Terry Slonecker, Dr. Stephen Fuller
Airports are often seen as powerful anchors for urban development causing domino effects of induced impacts. Business activity caused by airports creates employment and population growth, resulting in increased urban development.
A case study of Washington Dulles International Airport is investigated to better understand the relationship between airports and urban development. The research is divided into four parts:
1. Models of urban development clustering;
2. Analysis of variables influencing urban density;
3. Temporal analysis of airport urban development dynamics; and
4. Tests of the causality relationship between airports and urban development.
The models of urban development clustering developed use a diverse set of economic and environmental urban density proxy measures including the intensity of night time lights, surface temperature, the existence of impervious surfaces, as well as property values and rents. The proxy measures are applied in an Anselin Local Moran I Cluster Analysis and in a Geographic Weighted Regression (GWR) to investigate and model the airport-urban development relationship. The Anselin Local Moran I Analysis detects, delineates, and investigates urban development clustering near an airport. A GWR investigates the impact of explanatory variables including distance to the airport, primary roads, and the central business district on urban development.
The analysis also considers the statistical significance and spatial diffusion of explanatory variables in anchoring urban development.
The results can be summarized in four key findings suggesting that there is an independent urban cluster near Dulles Airport and the airport has had a significant influence in the area’s urban dynamics:
1. Dulles Airport has anchored urban development;
2. Airport operations have affected the magnitude of development;
3. An independent urban cluster exists near Dulles Airport; and
4. Several factors in addition to the existence of the airport explain the urban development.
This research advances the science relating to the airport–urbanization relationship in several ways.
1. An empirical model for induced airport impacts on urbanization is developed. Previous efforts have focused on direct and indirect impacts such as the relationship between airports and employment and property values without addressing the induced impacts on urbanization. Other studies have conceptually identified this relationship without testing it empirically.
2. It models urban development at the local level rather than at the regional or state/nationwide level. It examines the influence of the airport and the local spatial distribution of urban development using 1200 meter cells. Previous studies examined urban development impacts from airports at a regional scale covering a metropolitan area or a larger geographic extent.
3. Remote sensing and GIS are used to model the relationship between the airport and urbanization. Previous studies have used an economic and conceptual analysis of airport-anchored development, but did not apply remote sensing data and GIS to examine the impacts.
4. It develops a case study for Washington Dulles International Airport that models airport-induced urbanization. Previous studies have conceptually identified Dulles Airport as anchoring urbanization, but have not empirically modelled the airport’s impacts on urbanization.
5. Urban clusters of development proximate to Dulles Airport are detected and delineated. The development of the Dulles Airport-anchored urban clusters is examined using the relationship of airport proximity with other explanatory variables, including distance to primary roads and the central business district. In addition, the spatial diffusion and land use of the urban clusters are analyzed. Previous studies have focused on conceptual and direct and indirect models, but have not empirically analyzed airport-anchored clusters.
George Mason University researchers and administrators briefed staff from the U.S. House Subcommittee on Research and Technology on scientific projects that will safeguard computer networks, protect drivers in automobile crashes, help airplanes fly safe, and more.
Elected officials and their staff visit George Mason as they seek to understand the latest research trends and how Mason research directly helps people.
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Dieter Pfoser and Andreas Züfle were awarded $507,852.00 by the National Science Foundation for their research project NSF/AitF: Collaborative Research: Modeling movement on transportation networks using uncertain data. The objective of the project is to create a unified framework for aggregating and analyzing diverse and uncertain movement data on transportation networks, with the aim to provide tools for querying and predicting traffic volume and movement in urban environments.