Abstract

Large networks of sensors are used to detect intrusions and provide security at the borders of the United States. Sensor signals are used to detect possible intrusions such as illegal immigration traffic in drugs, weapons, and smuggled goods at specific targeted geographic locations. GIS systems can be used to capture, store and analyze this location based intervention data. Using a GIS system, a spatial database can be generated from the sensor intervention data which can take into account relevant geographic information in the vicinity of the sensed interventions. Important geographic features that are close to the intervention locations such as: plateaus, hills, valleys or roadways can be extracted and added to the analysis using ArcGIS. GIS techniques alone cannot reveal meaningful hidden information within geographic data. We have developed an integrated approach involving data mining and GIS techniques to extract patterns and trends in geographic data that can aid and inform analysis. Our approach uses both spatial and association data mining techniques. Spatial data mining is the process of discovering previously unknown, interesting and potentially useful patterns from spatial datasets. Applying association rule mining to the spatial data can reveal additional important spatial relationships and help determine the relevance and importance of the sensor data. Spatial association rule mining was used to discover patterns in the intervention data, such as linking a sensed intrusion with a potentially hidden location such as a canyon, to infer a high probability of illegal traffic or immigration.

Original languageEnglish (US)
Title of host publicationICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services
Pages127-130
Number of pages4
DOIs
StatePublished - Sep 1 2011
Event2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011 - In Conjunction with 8th Beijing International Workshop on Geographical Information Science, BJ-IWGIS 2011 - Fuzhou, China
Duration: Jun 29 2011Jul 1 2011

Publication series

NameICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Other

Other2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011 - In Conjunction with 8th Beijing International Workshop on Geographical Information Science, BJ-IWGIS 2011
CountryChina
CityFuzhou
Period6/29/117/1/11

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Fingerprint Dive into the research topics of 'Extracting geographic knowledge from sensor intervention data using spatial association rules'. Together they form a unique fingerprint.

  • Cite this

    Kondaveeti, A., Liu, H., Runger, G., & Rowe, J. (2011). Extracting geographic knowledge from sensor intervention data using spatial association rules. In ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (pp. 127-130). [5969018] (ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services). https://doi.org/10.1109/ICSDM.2011.5969018