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Exploiting online sources to accurately geocode addresses
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Source Geographic Information Systems archive
Proceedings of the 12th annual ACM international workshop on Geographic information systems table of contents
Washington DC, USA
SESSION: Distributed data sources table of contents
Pages: 194 - 203  
Year of Publication: 2004
ISBN:1-58113-979-9
Authors
Rahul Bakshi  University of Southern California, Marina del Rey, CA
Craig A. Knoblock  University of Southern California, Marina del Rey, CA
Snehal Thakkar  University of Southern California, Marina del Rey, CA
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Many Geographic Information System (GIS) applications require the conversion of an address to geographic coordinates. This process is called geocoding. The traditional geocoding method uses a street vector data source, such as, Tigerlines, to obtain address range and coordinates of the street segment on which the given address is located. Next, an approximation technique is used to estimate the location of the given address using the address range of the selected street segment. However, this provides inaccurate results since the approximation assumes that properties exist at all possible addresses and all properties are of equal size. To address the inaccuracy of the traditional geocoding approach, we propose two new methods for geocoding using additional online data sources. The first method, the uniform-lot-size method, uses the number of addresses/lots present on the street segment to approximate the location of an address. The second method, the actual-lot-size method, takes into consideration the lot sizes on the street segment and the orientation of the lots as well. Moreover, we describe an implementation of these methods using an information mediator to obtain information about actual number of lots and sizes of the lots on the streets from various property tax web sites. We geocoded an area covering 13 blocks (267 addresses) using all three methods. Our evaluation shows that the traditional method results in an average error of 36.85 meters, while the uniform-lot-size and the actual-lot-size methods result in the average error of 7.87 meters and 1.63 meters, respectively.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Bakshi, R. Exploiting online sources to accurately geocode addresses. Masters Thesis, University of Southern California, Los Angeles, CA, 2004.
 
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Chen, C.-C., Knoblock, C.A., Shahabi, C., and Thakkar, S. Automatically and Accurately Conflating Satellite Imagery and Maps, International Workshop on Next Generation Geospatial Information, Cambridge, MA, 2003.
 
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Chen, C.-C., Thakkar, S., Knoblock, C.A., and Shahabi, C. Automatically Annotating and Integrating Spatial Datasets, In the Proceedings of International Symposium on Spatial and Temporal Databases, Santorini Island, Greece, 2003.
 
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Thakkar, S., Ambite, J.L., and Knoblock, C.A. A Data Integration Approach to Automatically Composing and Optimizing Web Services, In Proceedings of the ICAPS Workshop on Planning and Scheduling for Web and Grid Services, 2004.
 
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Thakkar, S. and Knoblock, C.A. Efficient Execution of Recursive Integration Plans, In Proceeding of 2003 IJCAI Workshop on Information Integration on the Web, Acapulco, Mexico, 2003.


Collaborative Colleagues:
Rahul Bakshi: colleagues
Craig A. Knoblock: colleagues
Snehal Thakkar: colleagues