Abstract
Spatial information plays an important role in the identification of sources of outbreaks for many different health-related conditions. In the public health domain, as in many other domains, the available data is often aggregated into geographical regions, such as zip codes or municipalities.
In this paper we study the problem of finding clusters in spatially aggregated data. Given a subdivision of the plane into regions with two values per region, a case count and a population count, we look for a cluster with maximum density. We model the problem as finding a placement of a given shape R such that the ratio of cases contained in R to people living in R is maximized. We propose two models that differ on how to determine the cases in R, together with several variants and extensions, and give algorithms that solve the problems efficiently.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
R. Agrawal, J. Gehrke, D. Gunopulus, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. ACM-SIGMOD Intl. Conf. on Mgmt. of Data, pages 94–105, 1998.
H. Brody, M. R. Rip, P. Vinten-Johansen, N. Paneth, and S. Rachman. Map-making and myth-making in Broad Street: the London cholera epidemic, 1854. The Lancet, 356:64–68, 2000.
N. Cleave, P. Brown, and C. Payne. Methods for ecological inference: an evaluation. Journal of the Royal Statistical Society, Series A, 158:55–75, 1995.
L. H. Cox. Protecting confidentiality in small population health and environmental statistics. Stat. Med., 15:1895–1905, 1996.
E. Cromley and S. McLafferty. GIS and Public Health. The Guilford Press, New York, 2002.
J. W. Den Boer, L. Verhoef, M. A. Bencini, J. P. Bruin, R. Jansen, and E. P. Yzerman. Outbreak detection and secondary prevention of legionnaires disease: A national approach. International Journal of Hygiene and Environmental Health, 210:1–7, 2007.
M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, pages 226–231, 1996.
A. Gilsdorf, C. Kroh, S. Grimm, E. Jensen, C. Wagner-Wiening, and K. Alpers. Large Q fever outbreak due to sheep farming near residential areas. Accepted for publication to Epidemiol. Infect., 2007.
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Academic Press, San Diego, 2001.
J. Hartigan. Clustering Algorithms. John Wiley & Sons, New York, 1975.
A. Jain and R. Dubes. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, New Jersey, 1988.
G. King. A Solution to the Ecological Inference Problem. Princeton University Press, Princeton, New Jersey, 1997.
M. Kulldorff. A spatial scan statistic. Communications in Statistics: Theory and Methods, 26,:1481–1496, 1997.
M. Kulldorff and N. Nagarwalla. Spatial disease clusters: detection and inference. Stat. Med., 14:799–810, 1995.
S. Openshaw. The Modifiable Areal Problem. CATMOG No.38. Geo Books, Norwich, 1984.
S. Openshaw, M. Charlton, C. Wymer, and A. Craft. A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets. Int. J. Geographical Information Systems, 1:335–358, 1987.
P. Phillips and I. Lee. Areal aggregated crime reasoning through density tracing. In Proc. International Workshop on Spatial and Spatio-temporal Data Mining, 2007.
I. Reinbacher, M. van Kreveld, and M. Benkert. Scale dependent definitions of gradient and aspect and their computation. In A. Riedl, W. Kainz, and G. A. Elmes, editors, Proc. 12th Intern. Symp. Spatial Data Handling (SDH’06), pages 863–879, 2006.
W. Robinson. Ecological correlations and the behavior of individuals. American Sociological Reviews, 15:351–357, 1950.
M. Sharir. On k-sets in arrangements of curves and surfaces. Discrete Comput. Geom., 6:593–613, 1991.
J. Snow. On the Mode of Communication of Cholera. Churchill Livingstone, London, 2nd edition, 1854.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Buchin, K., Buchin, M., van Kreveld, M., Löffler, M., Luo, J., Silveira, R.I. (2008). Clusters in Aggregated Health Data. In: Ruas, A., Gold, C. (eds) Headway in Spatial Data Handling. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68566-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-68566-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68565-4
Online ISBN: 978-3-540-68566-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)