Population shift bias in tests of space-time interaction

Computers, Environment and Urban Systems

Volumn 36, Issue 6, 2012, Pages 500-512

  Mack, Elizabeth A ^a   Malizia, Nicholas ^a   Rey, Sergio J ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Jacquez test; Knox test; Mantel test; Population shift bias; Space time interaction

Indexed keywords

JACQUEZ TEST; KNOX TEST; MANTEL TESTS; POPULATION SHIFT BIAS; SPACE-TIME INTERACTIONS;

TESTING;

POPULATION STATISTICS;

COMPUTER SIMULATION; HETEROGENEITY; POPULATION DYNAMICS; POPULATION GROWTH;

EID: 84869875175     PISSN: 01989715     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compenvurbsys.2012.05.001     Document Type: Article

References (48)

1. Adam, N., Janeja, V., & Atluri, V. (2004). Neighborhood based detection of anomalies in high dimension spatio-temporal sensor datasets. In Proceedings of the 2004 ACM Symposium on Applied Computing (pp. 576-583).
2. Aldstadt J. An incremental Knox test for the determination of the serial interval between successive cases of an infectious disease. Stochastic Environmental Research and Risk Assessment 2007, 21(5):487-500.
3. Alexander F. Space-time clustering of childhood acute lymphoblastic leukaemia: Indirect evidence for a transmissible agent. British Journal of Cancer 1992, 65(4):589.
4. Alexander F., Boyle P., Carli P., Coebergh J., Draper G., Ekbom A., et al. Spatial temporal patterns in childhood leukaemia: Further evidence for an infectious origin. British Journal of Cancer 1998, 77(5):812-817.
5. Arribas-Bel, D., Koschinsky, J., & Amaral, P. (2011). Improving the multi-dimensional comparison of simulation results: A spatial visualization approach. GeoDa Center for Geospatial analysis and computation working paper 5. http://geodacenter.asu.edu/category/public/improving-multi.
6. Baker R. Testing for space-time clusters of unknown size. Journal of Applied Statistics 1996, 23(5):543-554.
7. Birant D., Kut A. Spatio-temporal outlier detection in large databases. Journal of Computing and Information Technology 2006, 14(4):291-297.
8. Birch J., Alexander F., Blair V., Eden O., Taylor G., McNally R. Space-time clustering patterns in childhood leukaemia support a role for infection. British Journal of Cancer 2000, 82(9):1571-1576.
9. Bowers K., Johnson S. Domestic burglary repeats and space-time clusters. European Journal of Criminology 2005, 2(1):67-92.
10. Buuren S., Zaadstra B., Zwanikken C., Buljevac D., Noort J. Space-time clustering of multiple sclerosis cases around birth. Acta Neurologica Scandinavica 1998, 97(6):351-358.
11. Chadee D., Williams F., Kitron U. Impact of vector control on a dengue fever outbreak in Trinidad, West Indies, in 1998. Tropical Medicine & International Health 2005, 10(8):748-754.
12. Chen D., Chang L., Kou Y., Chen F. On detecting spatial outliers. GeoInformatica 2008, 12:455-475.
13. Chen R., Mantel N., Klingberg M. A study of three techniques for time-space clustering in Hodgkin's disease. Statistics in Medicine 1984, 3(2):173-184.
14. David F., Barton D. Two space-time interaction tests for epidemicity. British Journal of Preventive & Social Medicine 1966, 20:44.
15. De Smith M., Goodchild M., Longley P. Geospatial analysis: A comprehensive guide to principles, techniques and software tools 2006, Troubador Publishing.
16. Diggle P., Chetwynd A., Haggkvist R., Morris S. Second-order analysis of space-time clustering. Statistical Methods in Medical Research 1995, 4(2):124.
17. Grekousis G., Fotis Y. A fuzzy index for detecting spatiotemporal outliers. GeoInformatica 2011, 16(3):597-619.
18. Grubesic T., Mack E. Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology 2008, 24(3):285-306.
19. Gustafsson B., Carstensen J. Space-time clustering of childhood lymphatic leukaemias and non-hodgkin's lymphomas in sweden. European Journal of Epidemiology 2000, 16(12):1111-1116.
20. Hoebe C., de Melker H., Spanjaard L., Dankert J., Nagelkerke N. Space-time cluster analysis of invasive meningococcal disease. Emerging Infectious Diseases 2004, 10(9):1621-1626.
21. Jacquez G. A k nearest neighbour test for space-time interaction. Statistics in Medicine 1996, 15(18):1935-1949.
22. Jacquez G., Meliker J., Kaufmann A. In search of induction and latency periods: Space-time interaction accounting for residential mobility, risk factors and covariates. International Journal of Health Geographics 2007, 6:35.
23. Johnson S. A brief history of the analysis of crime concentration. European Journal of Applied Mathematics 2010, 21(4-5):349-370.
24. Johnson S., Bernasco W., Bowers K., Elffers H., Ratcliffe J., Rengert G., et al. Space-time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology 2007, 23(3):201-219.
25. Johnson S., Bowers K. The stability of space-time clusters of burglary. British Journal of Criminology 2004, 44:55-65.
26. Klauber M., Mustacchi P. Space-time clustering of childhood leukemia in San Francisco. Cancer Research 1970, 30(7):1969.
27. Knox E. The detection of space-time interactions. Journal of the Royal Statistical Society: Series C (Applied Statistics) 1964, 13(1):25-30.
28. Knox E. An epidemic pattern of murder. Journal of Public Health 2002, 24(1):34-37.
29. Kulldorff M. Statistical methods for spatial epidemiology: Tests for randomness. GIS and health 1998, 49-62. Taylor & Francis, London.
30. Kulldorff M., Athas W., Feurer E., Miller B., Key C. Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health 1998, 88(9):1377.
31. Kulldorff M., Hjalmars U. The Knox method and other tests for space-time interaction. Biometrics 1999, 55(2):544-552.
32. Lu Y., Thill J. Assessing the cluster correspondence between paired point locations. Geographical Analysis 2003, 35(4):290-309.
33. Lu Y., Thill J. Cross-scale analysis of cluster correspondence using different operational neighborhoods. Journal of Geographic Systems 2008, 10:241-261.
34. Malizia, N. & Mack, E. A. (2012). Enhancing the Jacquez k nearest neighbor test for space-time interaction. Statistics in Medicine, http://dx.doi.org/10.1002/sim.5348.
35. Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Research 1967, 27(2):209-220.
36. Marshall R. A review of methods for the statistical analysis of spatial patterns of disease. Journal of the Royal Statistical Society: Series A (Statistics in Society) 1991, 154(3):421-441.
37. McNally R., Alexander F., Birch J. Space-time clustering analyses of childhood acute lymphoblastic leukaemia by immunophenotype. British Journal of Cancer 2002, 87(5):513-515.
38. McNally R., Colver A. Space-time clustering analyses of occurrence of cerebral palsy in Northern England for births 1991 to 2003. Annals of Epidemiology 2008, 18:108-112.
39. McNally R., Eden T., Alexander F., Kelsey A., Birch J. Is there a common aetiology for certain childhood malignancies? Results of cross-space-time clustering analyses. European Journal of Cancer 2005, 41:2911-2916.
40. Norström M., Pfeiffer D., Jarp J. A space-time cluster investigation of an outbreak of acute respiratory disease in Norwegian cattle herds. Preventive Veterinary Medicine 2000, 47(1-2):107-119.
41. Petridou E., Revinthi K., Alexander F., Haidas S., Koliouskas D., Kosmidis H., et al. Space-time clustering of childhood leukaemia in Greece: Evidence supporting a viral aetiology. British Journal of Cancer 1996, 73(10):1278-1283.
42. Rey S., Anselin L. PySAL: A Python library of spatial analytical methods. Handbook of applied spatial analysis 2010, 175-193. Springer, New York.
43. Rogerson P. Monitoring point patterns for the development of space-time clusters. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2001, 164(1):87-96.
44. Rogerson P., Yamada I. Statistical detection and surveillance of geographic clusters 2009, Chapman & Hall/CRC, New York.
45. Tango T. Statistical methods for disease clustering 2010, Springer, New York.
46. Townsley M., Homel R., Chaseling J. Infectious burglaries: A test of the near repeat hypothesis. British Journal of Criminology 2003, 43(3):615.
47. Ward M., Carpenter T. Analysis of time-space clustering in veterinary epidemiology. Preventive Veterinary Medicine 2000, 43(4):225-237.
48. Williams G. Time-space clustering of disease. Statistical methods for cancer studies 1984, 167-227. Marcel Dekker, New York. R.G. Cornell (Ed.).