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Abstract
Large-scale demographic datasets with spatial information provide a rich platform for human development research. Much emphasis is often placed on understanding deviations from dataset-level behavior across demographic attributes within spatially coherent regions, since those could point to a local condition worth addressing through regional policies, or at the other extreme, a less known success story that offers new learnings. Inspired by such scenarios, we build upon domain knowledge from HDR to devise an interestingness scoring for spatial regions and formulate the computational task of interesting spatial region identification. Accordingly, we develop a taxonomic organization of spatial regions and formulate bounds on interestingness scores, which are then leveraged to develop an efficient technique to address the task. Our search method is empirically evaluated over two real-world datasets, and is seen to record orders of magnitude of response time improvements over region enumeration. The absolute response times and the memory overheads of our approach are seen to be within highly desirable ranges, establishing the effectiveness of our solution for the task.
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Authors and Affiliations
Sungkyunkwan University, Seoul, Republic of Korea
Carl Duffy
Queen’s University Belfast, Belfast, UK
Deepak P., Cheng Long & M. Satish Kumar
Jawaharlal Nehru University, New Delhi, India
Amit Thorat & Amaresh Dubey
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Clausthal University of Technology, Clausthal-Zellerfeld, Germany
Sven Hartmann
Victoria University of Wellington, Wellington, New Zealand
Hui Ma
Paul Sabatier University, Toulouse, France
Abdelkader Hameurlain
University of Regensburg, Regensburg, Germany
Günther Pernul
Johannes Kepler University, Linz, Austria
Roland R. Wagner
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Duffy, C., P., D., Long, C., Satish Kumar, M., Thorat, A., Dubey, A. (2018). Fast Identification of Interesting Spatial Regions with Applications in Human Development Research. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_37
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