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Spatial Computing Yao-Yi Chiang
Spatial Sciences Institute University of Southern California
Title
USC SSI Programs
• BS in GeoDesign
• MS and Certificate in GIST
• MS in Spatial Informatics
• PhD in Population, Health and Place
GeoScavenge
Spatial Computing: Who we are?
• We are a research team at Spatial Sciences Institute, University of Southern California
• We develop computer algorithms and build applications to solve real world problems in spatial sciences
Spatial Computing @ USC SSI
• Since 2013, we worked with 45 students and 6 postdoctoral researchers
• one local high school student, a number of visiting international students, and some USC undergraduate and graduate students
• GeoDisgn, electrical engineering, spatial informatics, computer science, and data informatics
• A third of the 45 research students are female students in engineering
What is the problem?
5
Large Volumes and Varieties of Heterogeneous
Geographic Data
Manual conversion of large volumes of maps to a usable format for data analysis is time
consuming and does not scale
Problem
What are we building?
6
Large Volumes and Varieties of Heterogeneous
Geographic Data
Problem
We build algorithms and tools to bridge the gaps
e.g., Strabo
Digital Map Processing
Motivation
• Existing data sources typically contain only contemporary datasets
• e.g., present place names
• Maps contain detailed geographic information at various times in the past
• spatiotemporal datasets that cover long periods of time and large areas
Land reclamation in Hong Kong (http://www.oldhkphoto.com/coast/)
Use Case: Identify Contamination Sites in the Past from Historical Ordinance Survey Maps
Potential Polluted Area
Use Case: Identify Pollution Sources in the Past from Historical USGS Maps
Circa 1956
Circa
1921
Railway transportation is a serious
source of pollution but many of the
railroad records no longer exists
Exploiting Context in Cartographic Evolutionary Documents to Extract and
Build Linked Spatial-Temporal Datasets
• Editions in map series not independent
• Change incrementally (updates)
• Overlap in content
• Can be used as training data for feature extraction!
2012
1964
1950
A Case Study and Outlook Map Processing: Impact & Challenges Geographic Context & Map Processing
US National Science Foundation award IIS 1564164
and 1563933 to the University of Southern California
and the University of Colorado at Boulder
“Exploiting Context in Cartographic Evolutionary
Documents to Extract and Build Linked Spatial-
temporal Datasets”
Information Extraction & Geographic Context
(1) Building contextual information • Create generic semantic models:
• Locations, Type & Attributes
• Geometry (e.g., line feature, width)
• Inferring semantic rules ((un)likely situations)
(2) Adaptive graphics sampling
• Collect spatially constrained graphics examples
• “LOCATION” to define sampling areas
• Overlap: map contents & contextual data
(3) Compute feature descriptors: Knowledge base creation
• Shape, color, texture descriptors to be used in matching process
Gazetteer Admin Records (x,y)
A Case Study and Outlook Map Processing: Impact & Challenges Geographic Context & Map Processing
Geographic Data Integration
Spatial Record Linkage
Mining the Web for Location Data
Building Knowledge Graphs from Public Data for Predictive Analysis
• A Case Study on Predicting Technology Future in Space and Time
Ontology-based integration
Annotate Other Historical Materials with Map Content
Murray Burger’s
testimony
The USC Shoah Foundation contains 53,000 audiovisual testimonies of
survivors and witnesses of the Holocaust and other genocides that have been
catalogued and indexed at the Institute
Use map content to enrich the
testimony metadata
Other Research Projects
Thank you
• Questions?