A. Lawson, A. Biggeri, D. Böhning, E. Lesaffre, J. Viel et al., Disease mapping and risk assessment for public health, 1999.

A. Lawson and D. Denison, Spatial Cluster Modelling, 2010.

S. Walter, Visual and statistical assessment of spatial clustering in mapped data, Statistics in Medicine, vol.84, issue.14, pp.1275-91, 1993.
DOI : 10.1002/sim.4780121402

A. Lawson, Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Second Edition, 2013.
DOI : 10.1201/9781584888413

M. Colonna and E. Sauleau, How to interpret and choose a Bayesian spatial model and a Poisson regression model in the context of describing small area cancer risks variations, Revue d'??pid??miologie et de Sant?? Publique, vol.61, issue.6, pp.559-67, 2013.
DOI : 10.1016/j.respe.2013.07.686

M. Kulldorff and N. Nagarwalla, Spatial disease clusters: Detection and inference, Statistics in Medicine, vol.132, issue.8, pp.799-810, 1995.
DOI : 10.1002/sim.4780140809

L. Huang, L. Pickle, and B. Das, Evaluating spatial methods for investigating global clustering and cluster detection of cancer cases, Statistics in Medicine, vol.42, issue.4, pp.5111-5153, 2008.
DOI : 10.1002/sim.3342

R. Potthoff and M. Whittinghill, Testing for homogeneity: II. The Poisson distribution, Biometrika, vol.53, issue.1-2, pp.183-193, 1966.
DOI : 10.1093/biomet/53.1-2.183

A. Cliff and J. Ord, Spatial autocorrelation Pion London, 1973.

M. Kulldorff, A spatial scan statistic, Communications in Statistics - Theory and Methods, vol.75, issue.6, pp.1481-96, 1997.
DOI : 10.1093/biomet/74.3.631

J. Gaudart, B. Poudiougou, S. Ranque, and O. Doumbo, Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk, BMC Medical Research Methodology, vol.71, issue.S2, pp.22-32, 2005.
DOI : 10.1186/1476-072X-4-11

URL : https://hal.archives-ouvertes.fr/inserm-00090279

J. Landier, J. Gaudart, C. K. , L. Seen, D. Guégan et al., Spatio-temporal Patterns and Landscape-Associated Risk of Buruli Ulcer in Akonolinga, Cameroon, PLoS Neglected Tropical Diseases, vol.7, issue.9, 2014.
DOI : 10.1371/journal.pntd.0003123.s002

URL : https://hal.archives-ouvertes.fr/hal-01307475

N. Anderson and D. Titterington, Some Methods for Investigating Spatial Clustering, with Epidemiological Applications, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.160, issue.1, pp.87-105, 1997.
DOI : 10.1111/1467-985X.00047

S. Liang, B. Carlin, and A. Gelfand, Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information, The Annals of Applied Statistics, vol.3, issue.3
DOI : 10.1214/09-AOAS240SUPP

G. Jacquez and D. Greiling, Local clustering in breast, lung and colorectal cancer in Long Island, Int J Health Geogr, vol.2, pp.10-1186, 2003.

G. Johnson, Small area mapping of prostate cancer incidence in New York State (USA) using fully Bayesian hierarchical modelling, International Journal of Health Geographics, vol.3, issue.1, pp.29-39, 2004.
DOI : 10.1186/1476-072X-3-29

T. Cassetti, L. Rosa, F. Rossi, L. , D. Alò et al., Cancer incidence in men: a cluster analysis of spatial patterns, BMC Cancer, vol.8, issue.7, pp.344-354, 2008.
DOI : 10.1037/1082-989X.8.3.294

Y. Mao, J. Hu, A. Ugnat, R. Semenciw, S. Fincham et al., Socioeconomic status and lung cancer risk in Canada, International Journal of Epidemiology, vol.30, issue.4, pp.809-826, 2001.
DOI : 10.1093/ije/30.4.809

P. Botella-rocamora, M. Martinez-beneito, and S. Banerjee, A unifying modeling framework for highly multivariate disease mapping, Statistics in Medicine, vol.32, issue.4, pp.1548-59, 2015.
DOI : 10.1002/sim.6423

D. Levy and V. Roux, Recensement de la population de 2006, Provence-Alpes- Côte d'Azur: une région très urbaine, une croissance équilibrée. Sud INSEE l'essentiel, 2009.

A. Auchincloss, S. Gebreab, C. Mair, D. Roux, and A. , A Review of Spatial Methods in Epidemiology, 2000???2010, Annual Review of Public Health, vol.33, issue.1, pp.107-129, 2000.
DOI : 10.1146/annurev-publhealth-031811-124655

C. Muirhead, Methods for detecting disease clustering, with consideration of childhood leukaemia, Statistical Methods in Medical Research, vol.35, issue.4, pp.363-83, 2006.
DOI : 10.1191/0962280206sm457oa

I. Jung and M. Kulldorff, Theoretical properties of tests for spatial clustering of count data, Canadian Journal of Statistics, vol.1, issue.3, pp.433-479, 2007.
DOI : 10.1002/cjs.5550350307

J. Besag, J. York, and A. Mollié, Bayesian image restoration, with two applications in spatial statistics, Annals of the Institute of Statistical Mathematics, vol.74, issue.1, pp.1-20, 1991.
DOI : 10.1007/BF00116466

J. Gaudart, N. Graffeo, D. Coulibaly, G. Barbet, S. Rebaudet et al., SPODT: An R Package to Perform Spatial Partitioning, J Stat Softw, vol.63, pp.1-23, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01208245

L. Waller and C. Gotway, Applied spatial statistics for public health data, 2004.
DOI : 10.1002/0471662682

M. Kulldorff, L. Huang, L. Pickle, and L. Duczmal, An elliptic spatial scan statistic, Statistics in Medicine, vol.4, issue.22, pp.3929-3972, 2006.
DOI : 10.1002/sim.2490

A. Gelman, Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper), Bayesian Anal, vol.1, pp.515-549, 2006.

F. Gerber and R. Furrer, Pitfalls in the Implementation of Bayesian Hierarchical Modeling of Areal Count Data: An Illustration Using BYM and Leroux Models, Journal of Statistical Software, vol.63, issue.Code Snippet 1, 2015.
DOI : 10.18637/jss.v063.c01

D. Lemke, V. Mattauch, O. Heidinger, E. Pebesma, and H. Hense, Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study, International Journal of Health Geographics, vol.12, issue.1, pp.54-64, 2013.
DOI : 10.1186/1476-072X-5-52

D. Spiegelhalter, N. Best, B. Carlin, and A. Van-der-linde, Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.93, issue.4, pp.583-639, 2002.
DOI : 10.1002/1097-0258(20000915/30)19:17/18<2265::AID-SIM568>3.0.CO;2-6

M. Mmartinez-beneito, A general modelling framework for multivariate disease mapping, Biometrika, vol.100, issue.3, pp.539-53, 2013.
DOI : 10.1093/biomet/ast023

Y. Macnab, On Gaussian Markov random fields and Bayesian disease mapping, Statistical Methods in Medical Research, vol.24, issue.23, pp.49-68, 2011.
DOI : 10.1177/0962280210371561

X. Jin, S. Banerjee, and B. Carlin, Order-free co-regionalized areal data models with application to multiple-disease mapping, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.121, issue.5, pp.817-855, 2007.
DOI : 10.1111/j.1467-9868.2007.00612.x

A. Gelfand and P. Vounatsou, Proper multivariate conditional autoregressive models for spatial data analysis, Biostatistics, vol.4, issue.1, pp.11-16, 2003.
DOI : 10.1093/biostatistics/4.1.11

R. Bivand and . Package, spdep " : Spatial Dependence: Weighting Schemes, Statistics and Models. R package version 0, pp.5-56

V. Gómez-rubio, J. Ferrándiz-ferragud, and A. López-quílez, Detecting clusters of disease with R, Journal of Geographical Systems, vol.7, issue.2, pp.189-206, 2005.
DOI : 10.1007/s10109-005-0156-5

V. Gómez-rubio and A. López-quílez, Statistical Methods for the Geographical Analysis of Rare Diseases, Rare Dis. Epidemiol, pp.151-71, 2010.
DOI : 10.1007/978-90-481-9485-8_10

S. Sturtz, U. Ligges, and A. Gelman, R2WinBUGS : A Package for Running WinBUGS from R, J Stat Softw, vol.12, 2005.

A. Guttmann, X. Li, J. Gaudart, Y. Gérard, J. Demongeot et al., Spatial heterogeneity of type I error for local cluster detection tests, International Journal of Health Geographics, vol.13, issue.1, pp.15-25, 2014.
DOI : 10.1016/j.sste.2012.04.004

URL : https://hal.archives-ouvertes.fr/inserm-00998280

R. Assunção and E. Reis, A new proposal to adjust Moran's I for population density, 16<2147::AID-SIM179>3.0.CO;2-I, pp.2147-62, 1999.

R. Sherman, K. Henry, S. Tannenbaum, D. Feaster, E. Kobetz et al., Applying Spatial Analysis Tools in Public Health: An Example Using SaTScan to Detect Geographic Targets for Colorectal Cancer Screening Interventions, Preventing Chronic Disease, vol.11, 2014.
DOI : 10.5888/pcd11.130264

J. Wakefield, N. Best, L. Waller, P. Elliott, J. Wakefield et al., Bayesian approaches to disease mapping Spatial epidemiology: methods and applications, pp.104-127, 2000.

G. Aamodt, S. Samuelsen, and A. Skrondal, A simulation study of three methods for detecting disease clusters, International Journal of Health Geographics, vol.5, issue.1, p.15, 2006.
DOI : 10.1186/1476-072X-5-15

A. Ozonoff, C. Jeffery, J. Manjourides, F. White, L. Pagano et al., Effect of spatial resolution on cluster detection: a simulation study, International Journal of Health Geographics, vol.6, issue.1, pp.52-62, 2007.
DOI : 10.1186/1476-072X-6-52

C. Jeffery, A. Ozonoff, L. White, M. Nuno, and M. Pagano, Power to Detect Spatial Disturbances under Different Levels of Geographic Aggregation, Journal of the American Medical Informatics Association, vol.16, issue.6, pp.847-54, 2009.
DOI : 10.1197/jamia.M2788

S. Goujon-bellec, C. Demoury, A. Guyot-goubin, D. Hémon, and J. Clavel, Detection of clusters of a rare disease over a large territory: performance of cluster detection methods, International Journal of Health Geographics, vol.10, issue.1, pp.53-63, 2011.
DOI : 10.1002/sim.2418

URL : https://hal.archives-ouvertes.fr/inserm-00637064

G. Alton, D. Pearl, K. Bateman, B. Mcnab, and O. Berke, Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance, BMC Veterinary Research, vol.9, issue.1, pp.231-241, 2013.
DOI : 10.1198/jasa.2009.ap07613

A. Tran, Mapping Disease Incidence in Suburban Areas using Remotely Sensed Data, American Journal of Epidemiology, vol.156, issue.7, pp.662-670, 2002.
DOI : 10.1093/aje/kwf091

G. Lyratzopoulos, J. Barbiere, B. Rachet, M. Baum, M. Thompson et al., Changes over time in socioeconomic inequalities in breast and rectal cancer survival in England and Wales during a 32-year period (1973-2004): the potential role of health care, Annals of Oncology, vol.22, issue.7, pp.1661-1667, 1973.
DOI : 10.1093/annonc/mdq647

M. Coleman, D. Forman, H. Bryant, J. Butler, B. Rachet et al., Cancer survival in Australia, Canada, Denmark, Norway, Sweden, and the UK, 1995???2007 (the International Cancer Benchmarking Partnership): an analysis of population-based cancer registry data, The Lancet, vol.377, issue.9760, pp.127-165, 1995.
DOI : 10.1016/S0140-6736(10)62231-3

D. Clayton, L. Bernardinelli, and C. Montomoli, Spatial Correlation in Ecological Analysis, International Journal of Epidemiology, vol.22, issue.6, pp.1193-202, 1993.
DOI : 10.1093/ije/22.6.1193

J. Hughes and M. Haran, Dimension reduction and alleviation of confounding for spatial generalized linear mixed models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.2, issue.1, pp.139-59, 2013.
DOI : 10.1111/j.1467-9868.2012.01041.x

B. Reich, J. Hodges, and V. Zadnik, Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease-Mapping Models, Biometrics, vol.53, issue.4, pp.1197-206, 2006.
DOI : 10.1111/j.1541-0420.2006.00617.x