A domain expert can process heterogeneous data to make meaningful interpretations or predictions from the data. For example, by looking at research papers and patent records, an expert can determine the maturity of an emerging technology and predict the geographic location(s) and time (e.g., in a certain year) where and when the technology will be a success. However, this is an expert- and manual-intensive task. In this project, we are building an end-to-end system that leverages data collected from public sources to predict the (geographic) center(s) of a technology and when the center(s) will emerge. In our pilot study, we built a system to predict the future (geographic) center(s) for fuel cell technologies. The system extracts and cleanses data from public sources including research papers and patent records. After data extraction and cleansing, the system uses an ontology-based data integration method to generate knowledge graphs in the RDF (Resource Description Framework) format and enables users to switch quickly between machine learning models for predictive analytic tasks.
* Modeling, Integrating, and Visualizing Geospatial Data
1. Take a scanned map...(here shows an USGS historical topographic map)
and a symbol example
2. Automatically identify map symbols that look like the symbol example (the blue boxes)
Linking Maps to Other Spatiotemporal Datasets
* Linking Map Symbols to DBPedia:
1. Take a scanned map...
2. Automatically identify hotel symbols and link the symbol locations to DBpedia
3. Linked locations in a GIS
Map Journals and Data Visualization
* Linking Historical Maps to USC Shoah Foundation Visual History Archive
The Visual History Archive (VHA) in the USC Shoah Foundation contains a large digital life story collection of survivors before, during, and after the Holocaust and other genocides. Currently, location information (e.g., place names) mentioned in the VHA is indexed by keywords. For example, using “Poland” as the keyword for place search on the VHA Online returns 5,325 indexing terms in which the indexing terms (place names) with verified locations are displayed in a Google Maps web interface. Since place names and administration boundaries can change significantly over time, displaying search results on a current map would not provide the best visualization tool for navigating the VHA digital collection through space and time. In addition, a number places mentioned (indexed) in the testimony could not be located due to the lack of historical sources for verifying the location information of these places. This limits the opportunity for researchers, educators, and the general public to access valuable VHA materials and prevents the VHA collection from being indexed and searched by advanced spatial queries (e.g., finding the testimonies mentioned cities or towns in Poland between 1930 and 1945).
Historical maps are a great source of detailed place information in the past. For example, during the World War II (WWII), the US Army Map Service (AMS) created around 40,000 maps covering a significant amount of the earth. Other map sources provide detailed historical pre- and post-WWII maps, such as the Polish mapping company, Centrum Kartografii, which offers pre-WWII maps of Poland with a comprehensive list of place names including towns, manufacturing plants, monuments, etc. These historical maps can be found in either paper or scanned (digital) format in map archives such as the David Rumsey Map Collection or libraries including the USC Libraries, UCLA Map Libraries, Western Michigan Libraries, and the Library of Congress. The problem we are addressing here is how to systematically and effectively link places mentioned in the VHA collection to relevant historical maps and other historical materials.
Created by Andrew Hsu Created by Robin Franke Created by Alex Chen
* Visualzing Climate Change with Maps:
Climate Change, Created by Hariprabha Mallyah Ocean Acidification, Created by Nandan Nayak Climate Refugee, Created by Joanna Wang
We are always looking for students and summer interns to work on interesting problems in spatial science, data science,
computer science. Please feel free to send us an email if you are interested to join
USC Graduate Students
Credit or non-credit (Computer Science, Data Informatics, or GIST studetns), we simply ask you to put down at least 10 hours a week so that you will have enough
time to finish a cool project. You can also take the geospatial data integration course from CS department
to learn more about our research.
USC Undergraduate Students
We love to work with undergraduate students. Join us and gain experience in research and build some awesome
Visitors and Students from Other Schools (including international scholars and students)
We welcome visitors and students from other schools. We had great experience with international summer
interns in the past. Come work with us in Los Angeles and enjoy the nice weather!