Dipak Gupta (Political Science) Brian Spitzberg (Communication) Ming-Hsiang Tsou (Geography) Li An...
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Dipak Gupta (Political Science) Brian Spitzberg (Communication) Ming-Hsiang Tsou (Geography) Li An (Geography) Jean Mark Gawron (Linguistics) San Diego
Dipak Gupta (Political Science) Brian Spitzberg (Communication)
Ming-Hsiang Tsou (Geography) Li An (Geography) Jean Mark Gawron
(Linguistics) San Diego State University
The spread of ideas in the age of the Internet is a
double-edged sword; it can enhance our collective welfare as well
as produce forces that can destabilize the world. This project aims
at understanding the process by which the impact of a single event
or idea diffuses throughout the world over time and space.
Slide 4
The world has seen four waves of violent actions, energized by
a core idea 1880 1920 The Anarchist movement 1920 1960Anti-colonial
movements 1960 1990New Left movements 1990 ????Religious
fundamentalism
Slide 5
Identify exemplars potentially significant event episodes
(e.g., Jihadi terrorism, hate group/militia activities, natural
disasters, disease outbreaks, etc.) Develop a semantic map identify
words and phrases that characterize relevant sites. Computational
linguistics becomes critical at this point. Collect web data on how
these phrases spread over time and space. Data are converted to
Excel file with their relevant web sites, geolocation, and
time.
Slide 6
Spatio-temporal analyses Statistical analyses and
interpretation seeks reasons for particular trajectories along
which an idea spreads (i.e., identify factors that to account for
diffusion susceptibility to and immunity from particular concepts).
Pattern analysis By plotting chronological geographic paths, we
test the hypothesis that the spread of ideas is not random. That
is, there are places, which are more prone to host these sites (and
accept and spread an idea) than others over time.
Slide 7
Slide 8
GEOSPATIAL MAP VISUALIZATION Spatial Web Automatic Reasoning
and Mapping System (SWARMS ) flowchart
Slide 9
Microsoft SQL server with Web- based GeoLocating services.
Access Bing and Yahoo search engines (search for 1000 results)
Slide 10
WHOIS databases host registrant street address
latitude/longitude
Slide 11
White Power keyword search in Yahoo (Nov. 5, 2010)
Slide 12
Kernel point density function was performed in the ArcGIS.
using 3 map unit threshold (radius) and 0.5 map unit output scale.
1 map unit =~ 50 miles. Search results ranking serve as the
"popularity" and the "population" in the kernel density algorithm.
Population = (1001 - rank#). A website ranked #1 will be assigned
to "1000" (1001 - 1) for its population parameter. Compare two
keywords: e.g. Jerry Sanders (San Diego Mayor) Antonio Villaraigosa
(L.A. Mayor)
Slide 13
Map Algebra (Raster-based): Differential Value = (Keyword-
A/Maximum-Kernel-Value-of-Keyword-A) - (Keyword-B/Maximum-
Kernel-Value-of-Keyword-B) Red hotspots indicate that "Jerry
Sanders" is more popular than "Antonio Villaraigosa" whereas and
the blue color areas indicate that "Antonio Villaraigosa" is more
popular than "Jerry Sanders The differential information landscape
map illustrates geospatial fingerprints hidden in the text-based
web search results depending on the context of selected keywords.
RED: comparatively higher web page density for Jerry BLUE:
comparatively higher web page density for Antonio
Slide 14
The following settings of kernel density thresholds for
detecting spatial fingerprints at different map scales were used. 6
- 8 map units for detecting the State level spatial fingerprints.
2-3 map units for detecting the County level spatial fingerprints.
1-0.5 map units for detecting the City level spatial fingerprints.
0.2 - 0.1 map units for detecting the Zipcode level spatial
fingerprints.
Slide 15
Global web page density map for Osama bin Laden (English
version).
Slide 16
Different language search top 1000 hits for Osama bin Laden
English Osama bin Laden Chinese (S) Arabic "
Slide 17
Osama bin Laden (Geronimo) (minus) Background Constant Note 1:
Hotspots in San Francisco and New York. RED: high density of web
pages related to Osama bin Laden (comparing to the average web page
density in U.S.) BLUE: low density of web pages related to Osama
bin Laden (comparing to the average web page density in U.S.)
Slide 18
Ayman al-Zawahiri (Al -Quaeda 2nd) (minus) Background Constant
Note 1: Hotspots in New York & Washington DC RED: High density
of web pages related to Zawahiri (compared to the average web page
density in U.S). Blue: Lower density of web pages related to
Zawahiri (compared to the average web page density in U.S).
Different pattern: only New York & D.C. are interested. Most
other areas are not interested in this keyword (person).
Slide 19
Burn Koran Yahoo search (1.30.11): The kernel density of burn
Koran keyword search results and 1000 associated websites (red
dots) with weighted ranks (radius: 3.0 map units, output grid: 0.5
map units). Standardize information landscapes: Compare two similar
keyword maps. Standardized by the population density (U.S.
maps).
Slide 20
The U.S population density map was used to standardize the
popularity density. After standardization, the red color hot spots
indicate San Jose, Houston, and the middle of Kansas are the
popular areas of "burn Koran" keywords. The blue color hot spots
indicate the negative value (less popular). WHY the hotspot in
Kansas? Near the City of Topeka, after the original event happen in
the church located in Gainesville, FL (green symbol), another
church in the city of Topeka, KS claimed that they will continue
the action of burn Koran. ) Burn Koran (1.30.11) (1.30.11)
Slide 21
Burn Koran Time Comparison: Compared burn Koran (1.30.11) map
to (4.3.11) immediately after Florida Koran burning incident. Hot
spots: Saint Louis, Pittsburgh, Philadelphia NEW trends? RED:
Increased density of web pages on April 03, 2011 (compared to
1.30.11) BLUE: Decreased density of web pages on April 03, 2011
(compared 1.30.11)
Slide 22
Background Constant (300 random keywords) Note 1: Hot spots for
Shahzad: New York & Chicago. Note 2: Why Chicago? (link to
David Headley?) Faisal Shahzad (Time Square bomber)
Slide 23
Querying the link between Chicago & Faisal Shahzad Faisal
Shahzad (Time Square bomber)
Slide 24
GLOBAL VIEW: Keyword search on 5.6.11 for Faisal Shahzad
Background Constant (300 random keywords) Faisal Shahzad (Time
Square bomber)
Slide 25
Project Website: http://mappingideas.sdsu.edu This innovative,
multidisciplinary project has wide application in many fields from
security studies to the spread of epidemics. It can also be used to
track marketing of a new product.