imia chiang spatial computing - 2016

1

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?

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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?