Skip to main content

Applications of Spatial Scan Statistics: A Review

  • Chapter
  • First Online:
Book cover Scan Statistics

Part of the book series: Statistics for Industry and Technology ((SIT))

Abstract

In 1965, Joseph Naus published his now classical paper on spatial scan statistics, entitled ‘Clustering of random points in two dimensions’. This paper set in motion an important statistical theory of spatial scan statistics and an avalanche of spatial scan statistics applications in a wide variety of fields, including archaeology, astronomy, brain imaging, criminology, demography, early detection of disease outbreaks, ecology, epidemiology, forestry, geology, history, psychology and veterinary medicine. In this chapter, we survey this wide variety of applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abrial, D., Calavas, D., Lauvergne, N., Morignat, E. and Ducrot, C. (2003). Descriptive spatial analysis of BSE in western France, Veterinary Research, 34, 749–760.

    Article  Google Scholar 

  2. Ala, A., Stanca, C.M., Bu-Ghanim, M., Ahmado, I., Branch, A.D., Schiano, T.D., Odin, J.A. and Bach, N. (2006). Increased prevalence of primary biliary cirrhosis near superfund toxic waste sites, Hepatology, 43, 525–531.

    Article  Google Scholar 

  3. Ali, M., Asefaw, T., Byass, P., Beyene, H. and Karup Pedersen, F. (2005). Helping northern Ethiopian communities reduce childhood mortality: population-based intervention trial, Bulletin of the World Health Organization, 83, 27–33.

    Google Scholar 

  4. Allepuz, A., López-Quílez, A., Forte, A., Fernández, G. and Casal, J. (2007). Spatial analysis of bovine spongiform encephalopathy in Galicia, Spain (2000-2005), Preventive Veterinary Medicine, 79, 174–185.

    Article  Google Scholar 

  5. Alm, S.E. (1997). On the distributions of scan statistics of a two dimensional Poisson process, Adv. in Appl. Probab., 29, 1–18.

    Article  MATH  MathSciNet  Google Scholar 

  6. Alm, S.E. (1998). Approximation and simulation of the distributions of scan statistics for Poisson processes in higher dimensions, Extremes, 1, 111–126.

    Article  MATH  MathSciNet  Google Scholar 

  7. Andrade, A.L., Silva, S.A., Martelli, C.M., Oliveira, R.M., Morais Neto, O.L., Siqueira Junior, J.B., Melo, L.K. and Di Fabio, J.L. (2004). Population-based surveillance of pediatric pneumonia: use of spatial analysis in an urban area of central Brazil, Cadernos de Saúde Pública, 20, 411–421.

    Article  Google Scholar 

  8. Assunção, R., Costa, M.A., Tavares, A. and Ferreira, S. (2006). Fast detection of arbitrarily shaped disease clusters, Statistics in Medicine, 25:5, 723–742.

    Article  MathSciNet  Google Scholar 

  9. Beato Filho, C.C., Assunção, R.M., Silva, B.F., Marinho, F.C., Reis, I.A. and Almeida, M.C. (2001). Homicide clusters and drug traffic in Belo Horizonte, Minas Gerais, Brazil from 1995 to 1999, Cadernos de Saúde Pública, 17, 1163–1171.

    Article  Google Scholar 

  10. Berke, O. and Grosse Beilage, E. (2003). Spatial relative risk mapping of pseudorabies-seropositive pig herds in an animal-dense region, Journal of Veterinary Medicine, B50:4, 322–325.

    Google Scholar 

  11. Berke, O., von Keyserlingk, M., Broll, S. and Kreienbrock, L. (2002). On the distribution of Echinococcus multilocularis in red foxes in Lower Saxony: identification of a high risk area by spatial epidemiological cluster analysis. Berliner und Munchener Tierarztliche Wochenschrift, 115, 428–434.

    Google Scholar 

  12. Buntinx, F., Geys, H., Lousbergh, D., Broeders, G., Cloes, E., Dhollander, D., Op De Beeck, L., Vanden Brande, J., Van Waes, A. and Molenberghs, G. (2003). Geographical differences in cancer incidence in the Belgian province of Limburg, European Journal of Cancer, 39, 2058–2072.

    Article  Google Scholar 

  13. Callado Chavez, A. (2003). Fecundidad adolescente en el gran área metropolitana de Costa Rica, Población y Salud en Mesoamérica, 1, 4.

    Google Scholar 

  14. Ceccato, V. and Haining, R. (2004). Crime in border regions: The Scandinavian case of Öresund, 1998-2001, Annals of the Association of American Geographers, 94, 807–826.

    Google Scholar 

  15. Chaput, E.K., Meek, J.I. and Heimer, R. (2002). Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut, Emerging Infectious Diseases, 8, 943–948.

    Google Scholar 

  16. Chen, J. and Glaz, J (1996). Two-dimensional discrete scan statistics, Statist. Probab. Lett., 23, 751–771.

    MathSciNet  Google Scholar 

  17. Conover, W.J., Bement, T.R. and Iman, R.L. (1979). On a method for detecting clusters of possible uranium deposits, Technometrics, 21, 277–282.

    Article  Google Scholar 

  18. Cook, A.J., Gold, D.R. and Li, Y. (2007). Spatial cluster detection for censored outcome data, Biometrics, 63, 540–549.

    Article  MATH  MathSciNet  Google Scholar 

  19. Coulston, J.W. and Riitters, K.H. (2003). Geographic analysis of forest health indicators using spatial scan statistics, Environmental Management, 31, 764–773.

    Article  Google Scholar 

  20. Cousens, S., Smith, P.G., Ward, H., Everington, D., Knight, R.S.G., Zeidler, M., Stewart, G., Smith-Bathgate, E.A.B., Macleod, M.A., Mackenzie, J. and Will, R.G. (2001). Geographical distribution of variant Creutzfeldt-Jakob disease in Great Britain, The Lancet, 357, 1002–1007.

    Article  Google Scholar 

  21. Donnan, P.T., Parratt, J.D.E., Wilson, S.V., Forbes, R.B., O’Riordan, J.I. and Swingler, R.J. (2005). Multiple sclerosis in Tayside, Scotland: detection of clusters using a spatial scan statistic, Multiple Sclerosis, 11, 403–408.

    Article  Google Scholar 

  22. Duczmal, L. and Assunção, R.A. (2004). Simulated annealing strategy for the detection of arbitrarily shaped spatial clusters, Computational Statistics and Data Analysis, 45, 269–286.

    Article  MATH  MathSciNet  Google Scholar 

  23. Enemark, H.L., Ahrens, P., Juel, C.D., Petersen, E., Petersen, R.F., Andersen, J.S., Lind, P. and Thamsborg, S.M. (2002). Molecular characterization of Danish Cryptosporidium parvum isolates, Parasitology, 125, 331–341.

    Article  Google Scholar 

  24. Falconi, F., Ochs, H. and Deplazes, P. (2002). Serological cross-sectional survey of psoroptic sheep scab in Switzerland, Veterinary Parasitology, 109, 119–127.

    Article  Google Scholar 

  25. Fevre, E.M., Coleman, P.G., Odiit, M., Magona, J.W., Welburn, S.C. and Woolhouse, M.E.J. (2001). The origins of a new Trypanosoma brucei rhodesiense sleeping sickness outbreak in eastern Uganda, The Lancet, 358, 625–628.

    Article  Google Scholar 

  26. Forand, S.P., Talbot, T.O., Druschel, C. and Cross, P.K. (2002). Data quality and the spatial analysis of disease rates: congenital malformations in New York State, Health and Place, 8, 191–199.

    Article  Google Scholar 

  27. Fukuda, Y., Umezaki, M., Nakamura, K. and Takano, T. (2005). Variations in societal characteristics of spatial disease clusters: examples of colon, lung and breast cancer in Japan, International Journal of Health Geographics, 4, 16.

    Article  Google Scholar 

  28. George, M., Wiklund, L., Aastrup, M., Pousette, J., Thunholm, B., Saldeen, T., Wernroth, L., Zaren, B. and Holmberg, L. (2001). Incidence and geographical distribution of sudden infant death syndrome in relation to content of nitrate in drinking water and groundwater levels, European Journal of Clinical Investigation, 31, 1083–1094.

    Article  Google Scholar 

  29. Green, C., Hoppa, R.D., Young, T.K. and Blanchard, J.F. (2003). Geographic analysis of diabetes prevalence in an urban area, Social Science and Medicine, 57, 551–560.

    Article  Google Scholar 

  30. Gregorio, D.I., Kulldorff, M., Barry, L., Samociuk, H. and Zarfos, K. (2001). Geographic differences in primary therapy for early stage breast cancer. Annals of Surgical Oncology, 8, 844–849.

    Article  Google Scholar 

  31. Gregorio, D.I., Kulldorff, M., Barry, L. and Samociuk, H. (2002). Geographic differences in invasive and in situ breast cancer incidence according to precise geographic coordinates, Connecticut, 1991–1995. International Journal of Cancer, 100, 194–198.

    Article  Google Scholar 

  32. Guerin, M.T., Martin, S.W., Darlington, G.A. and Rajic, A. (2005). A temporal study of Salmonella serovars in animals in Alberta between 1990 and 2001, Canadian Journal of Veterinary Research, 69, 88–89.

    Google Scholar 

  33. Han, D.W., Rogerson, P.A., Nie, J., Bonner, M.R., Vena, J.E., Vito, D., Muti, P., Trevisan, M., Edge, S.B. and Freudenheim, J.L. (2004). Geographic clustering of residence in early life and subsequent risk of breast cancer (United States), Cancer Causes and Control, 15, 921–929.

    Article  Google Scholar 

  34. Hanson, C.E. and Wieczorek, W.F. (2002). Alcohol mortality: a comparison of spatial clustering methods, Social Science and Medicine, 55, 791–802.

    Article  Google Scholar 

  35. Heres, L., Brus, D.J. and Hagenaars, T.J. (2008). Spatial analysis of BSE cases in the Netherlands, BMC Veterinary Research, 4:21.

    Article  Google Scholar 

  36. Hjalmars, U., Kulldorff, M., Gustafsson, G. and Nagarwalla, N. (1996). Childhood leukemia in Sweden: using GIS and a spatial scan statistic for cluster detection, Statistics in Medicine, 15, 707–715.

    Article  Google Scholar 

  37. Hoar, B.R., Chomel, B.B., Rolfe, D.L., Chang, C.C., Fritz, C.L., Sacks, B.N. and Carpenter, T.E. (2003). Spatial analysis of Yersinia pestis and Bartonella vinsonii subsp. berkhoffii seroprevalence in California coyotes (Canis latrans), Preventive Veterinary Medicine, 56, 299–311.

    Google Scholar 

  38. Hsu, C.E., Jacobson, H.E. and Soto Mas, F. (2004). Evaluating the disparity of female breast cancer mortality among racial groups - a spatiotemporal analysis, International Journal of Health Geographics, 3:4.

    Article  Google Scholar 

  39. Huang, L., Kulldorff, M. and Gregorio, D. (2007). A spatial scan statistic for survival data, Biometrics, 63, 109–118.

    Article  MATH  MathSciNet  Google Scholar 

  40. Huang, L., Tiwari, R., Zuo, J., Kulldorff, M. and Feuer, E. (2009). Weighted normal spatial scan statistic for heterogenous population data, Journal of the American Statistical Association, in press.

    Google Scholar 

  41. Huillard d’Aignaux, J., Cousens, S.N., Delasnerie-Laupretre, N., Brandel, J.P., Salomon, D., Laplanche, J.L., Hauw, J.J. and Alperovitch, A. (2002). Analysis of the geographical distribution of sporadic Creutzfeldt-Jakob disease in France between 1992 and 1998, International Journal of Epidemiology, 31, 490–495.

    Article  Google Scholar 

  42. Jacquez, G.M. (1996). A k-nearest neighbour test for space-time interaction, Statistics in Medicine, 15:18, 1935–1949.

    Article  Google Scholar 

  43. Joly, D.O., Ribic, C.A., Langenberg, J.A., Beheler, K., Batha, C.A., Dhuey, B.J., Rolley, R.E., Bartelt, G., Van Deelen, T.R. and Samual, M.D. (2003). Chronic wasting disease in free-ranging Wisconsin white-tailed deer, Emerging Infectious Disease, 9, 599–601.

    Google Scholar 

  44. Jung, I., Kulldorff, M. and Klassen, A. (2007). A spatial scan statistic for ordinal data, Statistics in Medicine, 26, 1594–1607.

    Article  MathSciNet  Google Scholar 

  45. Klassen, A., Kulldorff, M. and Curriero, F. (2005). Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors, International Journal of Health Geographics, 4, 1.

    Article  Google Scholar 

  46. Kleinman K., Abrams A., Kulldorff M. and Platt R. (2005). A model-adjusted space-time scan statistic with an application to syndromic surveillance, Epidemiology and Infection, 133, 409–419.

    Article  Google Scholar 

  47. Knox, E.G. (1964). The detection of spacetime interactions, J. Appl. Stat., 13, 24–30.

    Google Scholar 

  48. Knuesel, R., Segner, H. and Wahli, T. (2003). A survey of viral diseases in farmed and feral salmonids in Switzerland., Journal of Fish Diseases, 26:4, 167–182.

    Google Scholar 

  49. Kuehl, K.S. and Loffredo, C.A. (2006). A cluster of hypoplastic left heart malformation in Baltimore, Maryland, Pediatric Cardiology, 27, 25–31.

    Article  Google Scholar 

  50. Kulldorff M. (1997). A spatial scan statistic, Communications in Statistics: Theory and Methods, 26, 1481–1496.

    Article  MATH  MathSciNet  Google Scholar 

  51. Kulldorff, M., Feuer, E.J., Miller, B.A. and Freedman, L.S. (1997). Breast cancer in northeastern United States: a geographical analysis, American Journal of Epidemiology, 146, 161–170.

    Google Scholar 

  52. Kulldorff M., Athas W., Feuer E., Miller B. and Key C. (1998). Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, American Journal of Public Health, 88, 1377–1380.

    Article  Google Scholar 

  53. Kulldorff M. (2001). Prospective time-periodic geographical disease surveillance using a scan statistic, Journal of the Royal Statistical Society, A164, 61–72.

    MathSciNet  Google Scholar 

  54. Kulldorff M., Heffernan R., Hartman J., Assunção R.M. and Mostashari F. (2005). A space-time permutation scan statistic for the early detection of disease outbreaks, PLoS Medicine, 2, 216–224.

    Article  Google Scholar 

  55. Kulldorff. M., Huang, L. and Konty, K. (2008). A spatial scan statistic for normally distributed data, Manuscript.

    Google Scholar 

  56. Loader, C.R. (1991). Large-deviation approximations to the distribution of scan statistics, Adv. in Appl. Probab., 23, 751–771.

    Article  MATH  MathSciNet  Google Scholar 

  57. Marcos, R.D.L.F. and Marcos, C.D.L.F. (2008). From star complexes to the field: open cluster families, Astrophysical Journal, 672, 342–351.

    Article  Google Scholar 

  58. Margai, F. and Henry, N. (2003). A community-based assessment of learning disabilities using environmental and contextual risk factors, Social Science and Medicine, 56, 1073–1085.

    Article  Google Scholar 

  59. Miller, M.A., Gardner, I.A., Kreuder, C., Paradies, D.M., Worcester, K.R., Jessup, D.A., Dodd, E., Harris, M.D., Ames, J.A., Packham, A.E. and Conrad, P.A. (2002). Coastal freshwater runoff is a risk factor for Toxoplasma gondii infection of southern sea otters (Enhydra lutris nereis), International Journal for Parasitology, 32, 997–1006.

    Article  Google Scholar 

  60. Mostashari, F., Kulldorff, M., Hartman, J.J., Miller, J.R. and Kulasekera, V. (2003). Dead bird clustering: a potential early warning system for West Nile virus activity, Emerging Infectious Diseases, 9, 641–646.

    Google Scholar 

  61. Naiman, D.Q. and Priebe, C.E. (2001). Computing scan statistic p-values using importance sampling, with applications to genetics and medical image analysis, Journal of Computational & Graphical Statistics, 10, 296–328.

    Article  MathSciNet  Google Scholar 

  62. Naus, J. I. (1965). Clustering of random points in two dimensions, Biometrika, 52, 263–267.

    Article  MATH  MathSciNet  Google Scholar 

  63. Norström, M., Pfeiffer, D.U. and Jarp, J. (2000). A space-time cluster investigation of an outbreak of acute respiratory disease in Norwegian cattle herds, Preventive Veterinary Medicine, 47, 107–119.

    Article  Google Scholar 

  64. Nkhoma, E.T., Hsu, C.E., Hunt, V.I. and Harris A.M. (2004). Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 - 2001, International Journal of Health Geographics, 3:25.

    Google Scholar 

  65. Odoi, A., Martin, S.W., Michel, P., Middleton, D., Holt, J. and Wilson, J. (2004). Investigation of clusters of giardiasis using GIS and a spatial scan statistic, International Journal of Health Geographics, 3:11.

    Article  Google Scholar 

  66. Olea-Popelka, F.J., Griffin, J.M., Collins, J.D., McGrath, G. and Martin, S.W. (2003). Bovine tuberculosis in badgers in four areas in Ireland: does tuberculosis cluster? Preventive Veterinary Medicine, 59, 103–111.

    Article  Google Scholar 

  67. Ozdenerol, E., Williams, B.L., Kang, S.Y. and Magsumbol, M.S. (2005). Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters, International Journal of Health Geographics, 4:19.

    Article  Google Scholar 

  68. Patil, G.P. and Taillie, C. (2003). Geographic and network surveillance via scan statistics for critical area detection, Statistical Science, 18:4, 457–465.

    Article  MATH  MathSciNet  Google Scholar 

  69. Patil, G.P. and Taillie, C. (2004). Upper level set scan statistic for detecting arbitrarily shaped hotspots, Environmental and Ecological Statistics, 11, 183–197.

    Article  MathSciNet  Google Scholar 

  70. Pearl, D.L., Louie, M., Chui, L., Dore, K., Grimsrud, K.M., Leedell, D., Martin, S.W., Michel, P., Svenson, L.W. and McEwen, S.A. (2006). The use of outbreak information in the interpretation of clustering of reported cases of Escherichia coli O157 in space and time in Alberta, Canada, 2000-2002, Epidemiology and Infection, 134, 699–711.

    Article  Google Scholar 

  71. Popescu, L.M. and Lewitt, R.M. (2006a). Comparison between TOF and non-TOF PET using a scan statistic numerical observer, In 2006 IEEE Nuclear Science Symposium Conference Record, 3, 1774–1780.

    Google Scholar 

  72. Popescu, L.M. and Lewitt, R.M. (2006b). Small nodule detectability evaluation using a generalized scan statistic model, Physics in Medicine and Biology, 51, 6225–6244.

    Article  Google Scholar 

  73. Priebe, C. E., Olson, T. and Healy D.M. Jr (1997a). A spatial scan statistic for stochastic scan partitions, Journal of the American Statistical Association, 92, 1476–1484.

    Article  MATH  MathSciNet  Google Scholar 

  74. Priebe, C. E., Olson, T. and Healy D.M. Jr (1997b). Exploiting stochastic partitions for minefield detection. Proceedings of SPIE, the International Society for Optical Engineering, 3079, 508.

    Google Scholar 

  75. Reperant, L.A. and Deplazes, P. (2005). Cluster of Capillaria hepatica infections in non-commensal rodents from the canton of Geneva, Switzerland, Parasitology Research, 96, 340–342.

    Article  Google Scholar 

  76. Riitters, K.H. and Coulston, J.W. (2005). Hot spots of perforated forest in the eastern United States, Environmental Management, 35, 483–492.

    Article  Google Scholar 

  77. Roche, L.M., Skinner, R. and Weinstein, R.B. (2002). Use of a geographic information system to identify and characterize areas with high proportions of distant stage breast cancer, Journal of Public Health Management and Practice, 8, 26–32.

    Google Scholar 

  78. Sabel, C.E., Boyle, P.J., Lytnen, M., Gatrell, A.C., Jokelainen, M., Flowerdew, R. and Maasilta P. (2003). Spatial clustering of amyotrophic lateral sclerosis in Finland at place of birth and place of death, American Journal of Epidemiology, 157, 898–905.

    Article  Google Scholar 

  79. Sankoh, O.A., Ye, Y., Sauerborn, R., Muller, O. and Becher, H. (2001). Clustering of childhood mortality in rural Burkina Faso, International Journal of Epidemiology, 30, 485–492.

    Article  Google Scholar 

  80. Sauders, B.D., Fortes, E.D., Morse, D.L., Dumas, N., Kiehlbauch, J.A., Schukken, Y., Hibbs, J.R. and Wiedmann, M. (2003). Molecular subtyping to detect human listeriosis clusters, Emerging Infectious Diseases, 9, 672–680.

    Google Scholar 

  81. Schwermer, H., Rufenacht, J., Doherr, M.G. and Heim, D. (2002). Geographic distribution of BSE in Switzerland, Schweizer Archiv fur Tierheilkunde, 144, 701–708.

    Article  Google Scholar 

  82. Sheehan, T.J., DeChello, L.M., Kulldorff, M., Gregorio, D.I., Gershman, S. and Mroszczyk, M. (2004). The geographic distribution of breast cancer incidence in Massachusetts 1988-1997, adjusted for covariates, International Journal of Health Geographics, 3, 17.

    Article  Google Scholar 

  83. Sheehan, T.J. and DeChello, L.M. (2005). A space-time analysis of the proportion of late stage breast cancer in Massachusetts, 1988 to 1997, International Journal of Health Geographics, 4, 15.

    Article  Google Scholar 

  84. Sheridan, H.A., McGrath, G., White, P., Fallon, R., Shoukri, M.M. and Martin, S.W. (2005). A temporal-spatial analysis of bovine spongiform encephalopathy in Irish cattle herds, from 1996 to 2000, Canadian Journal of Veterinary Research, 69, 19–25.

    Google Scholar 

  85. Smith, K.L., DeVos, V., Bryden, H., Price, L.B., Hugh-Jones, M.E. and Keim, P. (2000). Bacillus anthracis diversity in Kruger National Park, Journal of Clinical Microbiology, 38, 3780–3784.

    Google Scholar 

  86. Sudakin, D.L., Horowitz, Z. and Giffin, S. (2002). Regional variation in the incidence of symptomatic pesticide exposures: applications of geographic information systems, Journal of Toxicology - Clinical Toxicology, 40, 767–773.

    Google Scholar 

  87. Tango, T. and Takahashi, K. (2005). A flexible shaped spatial scan statistic for detecting clusters, International Journal of Health Geographics, 4, 11.

    Article  Google Scholar 

  88. Thomas, A.J. and Carlin, B.P. (2003). Late detection of breast and colorectal cancer in Minnesota counties: an application of spatial smoothing and clustering, Statistics in Medicine, 22, 113–127.

    Article  Google Scholar 

  89. Tuia, D., Ratle, F., Lasaponara, R., Telesca, L. and Kanevski, M. (2008). Scan statistics analysis of forest fire clusters. Communications in Nonlinear Science and Numerical Simulation, 13, 1689–1694.

    Article  Google Scholar 

  90. Turnbull, B., Iwano, E.J., Burnett, W.S., Howe, H.L. and Clark, L.C. (1990). Monitoring for clusters of disease: application to leukemia incidence in upstate New York, Amer. J. Epidemiology, 132, 136–143.

    Google Scholar 

  91. United States Department of Agriculture. (2001). West Nile virus in equids in the Northeastern United States in 2000. USDA, APHIS, Veterinary Services.

    Google Scholar 

  92. Usher, B.M. and Allen, K.L. (2005). Identifying kinship clusters: SatScan for genetic spatial analysis, American Journal of Physical Anthropology, Supplement, 126, S40, 210.

    Google Scholar 

  93. Viel, J.F., Arveux, P., Baverel, J. and Cahn, J.Y. (2000). Soft-tissue sarcoma and non-Hodgkins lymphoma clusters around a municipal solid waste incinerator with high dioxin emission levels, American Journal of Epidemiology, 152, 13–19.

    Article  Google Scholar 

  94. Waller, L.A. (2006). Detection of Clustering in Spatial Data. Emory University, Department of Biostatistics, Technical Report 06-12.

    Google Scholar 

  95. Walsh, S.J. and Fenster, J.R. (1997). Geographical clustering of mortality from systemic sclerosis in the Southeastern United States, Journal of Rheumatology, 24, 2348–2352.

    Google Scholar 

  96. Walsh, S.J. and DeChello, L.M. (2001). Geographical variation in mortality from systemic lupus erythematosus in the United States, Lupus, 10, 637–646.

    Article  Google Scholar 

  97. Ward, M.P. (2001). Blowfly strike in sheep flocks as an example of the use of a time-space scan statistic to control confounding, Preventive Veterinary Medicine, 49, 61–69.

    Article  Google Scholar 

  98. Ward, M.P. (2002). Clustering of reported cases of leptospirosis among dogs in the United States and Canada, Preventive Veterinary Medicine, 56, 215–226.

    Article  Google Scholar 

  99. Washington, C.H., Radday, J., Streit, T.G., Boyd, H.A., Beach, M.J., Addiss, D.G., Lovince, R., Lovegrove, M.C., Lafontant, J.G., Lammie, P.J. and Hightower, A.W. (2004). Spatial clustering of filarial transmission before and after a Mass Drug Administration in a setting of low infection prevalence, Filaria Journal, 3, 3.

    Article  Google Scholar 

  100. Witham, C.S. and Oppenheimer, C. (2004). Mortality in England during the 1783-4 Laki Craters eruption, Bulletin of Volcanology, 67, 15–25.

    Article  Google Scholar 

  101. Wylie, J.L., Cabral T. and Jolly, A.M. (2005). Identification of networks of sexually transmitted infection: a molecular, geographic, and social network analysis, Journal of Infectious Diseases, 191, 899–906.

    Article  Google Scholar 

  102. Yiannakoulias, N., Rowe, B.H., Svenson, L.W., Schopflocher, D.P., Kelly, K. and Voaklander, D.C. (2003). Zones of prevention: the geography of fall injuries in the elderly, Social Science and Medicine, 57, 2065–2073.

    Article  Google Scholar 

  103. Yoshida, M., Naya, Y. and Miyashita, Y. (2003). Anatomical organization of forward fiber projections from area TE to perirhinal neurons representing visual long-term memory in monkeys, Proceedings of the National Academy of Sciences of the United States of America, 100, 4257–4262.

    Article  Google Scholar 

Download references

Acknowledgement

This research was funded by grant #RO1CA095979 from the National Cancer Institute.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Birkhäuser Boston, a part of Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Costa, M., Kulldorff, M. (2009). Applications of Spatial Scan Statistics: A Review. In: Glaz, J., Pozdnyakov, V., Wallenstein, S. (eds) Scan Statistics. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4749-0_6

Download citation

Publish with us

Policies and ethics