Spatial Statistics and Public Health Events

  • Gouri Sankar BhuniaEmail author
  • Pravat Kumar Shit


Statistical investigation which covenants with spatial or spatio-temporal datasets is called as the science of spatial statistics. Spatial statistical study was first established in the 1950’s as an outcome of interest in a real or block averages for ore reserves in the mining industry. Coming to public health, the spatial statistics techniques provide imperative information on how a disease is extend; which are the regions affected by the disease and forecast the next regions which have higher prospect to be affected in order to control it. This chapter also describe the different aspect of spatial statistical method in relation to public health data analysis. Various methods of spatial clustering pattern of disease has been analyzed. A case study was described to examine the spatial-temporal patterns and distribution of vector borne disease using GIS tool and geo-statistical analysis. Such applications stated that the spatial statistical tool suitable to definite problems in spatial epidemiology to plan a strategy to control disease.


  1. Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115CrossRefGoogle Scholar
  2. Auchincloss AH, Gebreab SY, Mair C, Roux AVD (2012) A review of spatial methods in epidemiology, 2000–2010. Annu Rev Public Health 33:107–122. Scholar
  3. Bailey TC (2001) Spatial statistical methods in health. Cad. Saúde Pública, Rio de Janeiro, 17(5):1083–1098CrossRefGoogle Scholar
  4. Balakrishnan P, Saleem A, Mallikarjun ND (2011) Groundwater quality mapping using geographic information system (GIS): a case study of Gulbarga City. Karnataka, India. 5(12):1069–1084Google Scholar
  5. Bhat S, Motz LH, Pathak C, Kuebler L (2015) Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA. Environ Monit Assess 187:4183CrossRefGoogle Scholar
  6. Bhunia GS, Kesari K, Chatterjee N, Kumar V, Das P (2013) Spatial and temporal variation and hotspot detection of kala-azar disease in Vaishali district (Bihar), India. BMC Infect Dis 13:64. Scholar
  7. Bhunia GS, Shit PK, Maiti R (2016) Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). J Saudi Soc Agric Sci.
  8. Bindiya MV, Unnikrishnan A, Poulose JK (2013) Spatial clustering algorithms—an overview. Asian J Comput Sci Informat Technol 3:1–8Google Scholar
  9. Bonet M, Spiegel JM, Ibarra AM, Kouri G, Pintre A, Yassi A (2007) An integrated ecosystem approach for sustainable prevention and control of dengue in Central Havana. Int J Occup Environ Health 13:188–194CrossRefGoogle Scholar
  10. Boots BN, Getis A (1998) Point pattern analysis Newbury Park. Sage Publications, Newbury Park, CA, USAGoogle Scholar
  11. Caers J (2005) Petroleum geostatistics. An SPE Primer, Society of Petroleum Engineers, Richardson, TX, USAGoogle Scholar
  12. Central Ground Water Board (CGWB) (2008) Ground water information booklet—Muzaffarpur district, Bihar state. Ministry of Water Resources, (Govt. of India), Mid-Eastern Region, Patna.
  13. Chappuis F, Sundar S, Hailu A, Ghalib H, Rijal S, Peeling RW, Alvar J, Boelaert M (2007) Visceral leishmaniasis: what are the needs for diagnosis, treatment and control? Nat Rev (Microbiology) 5:873–882CrossRefGoogle Scholar
  14. Cliff AD, Haggett P (1988) Atlas of disease distributions. Oxford, United Kingdom: BlackwellGoogle Scholar
  15. Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion, LondonGoogle Scholar
  16. Cliff AD, Ord JK (1969) The Problem of Spatial Autocorrelation. In: Scott AJ (ed) Studies in regional science London papers in regional science, pp 25–55. Pion, LondonGoogle Scholar
  17. Cressie N (1993) Statistics for spatial data, rev.edn. Wiley, New York, NY (pp 900). (Original edition, 1991. Paperback edition in the Wiley Classics Library: Wiley, Hoboken, NJ, 2015)Google Scholar
  18. de la Cruz ML, Perez A, Bezos J, Pages E, Casal C, Carpintero J et al (2014) Spatial dynamics of bovine tuberculosis in the autonomous community of Madrid, Spain (2010–2012). PLoS One 9(12):e115632CrossRefGoogle Scholar
  19. Deutsch CV, Journel AG (1992) Geostatistical software library and user’s guide. Oxford University Press, p 340Google Scholar
  20. Diaz-Lacava AN, Walier M, Holler D, Steffens M, Gieger C, Furlanello C, Lamina C, Wichmann HE, Becker T (2015) Genetic geostatistical framework for spatial analysis of fine-scale genetic heterogeneity in modern populations: results from the KORA study. Int J Genom, Article ID 693193, p 15. Scholar
  21. Dionissios H (2015) Local geostatistical models and big data in hydrological and ecological applications. EGU General Assembly held 12–17 April, 2015 in Vienna, Austria. ID 2179Google Scholar
  22. Ebdon D (1985) Statistics in geography. BlackwellGoogle Scholar
  23. Elliott P, Wakefield J, Best N, Briggs D (2000) Spatial epidemiology: methods and applications, Oxford University PressGoogle Scholar
  24. Enkhtur B (2013) Geostatistical modelling and mapping of air pollution.
  25. Environmental Systems Research Institute (ESRI), Inc (2009) How directional distribution: standard deviational ellipse (spatial statistics) works. TopicName5—How directional distribution: standard deviational ellipse (Spatial Statistics) works
  26. ESRI (2001) Using ArcGIS geostatistical analyst. ESRI Press, Redlands, CAGoogle Scholar
  27. Fang L, Yan L, Liang S, Vlas SJD, Feng D, Han X, Zhao W, Xu B, Bian L, Yang H, Gong P, Richardus JH, Cao W (2006) Spatial analysis of hemorrhagic fever with renal syndrome in China. BMC Infect Dis 6:77CrossRefGoogle Scholar
  28. Feser E, Sweeney S, Renski H (2005) A descriptive analysis of discrete U.S. industrial complexes. J Regional Sci 45:395–419CrossRefGoogle Scholar
  29. Few S (2009) Introduction to geographical data visualization. Perceptual Edge Visual Business Intelligence Newsletter.
  30. Fisher NI, Lewis T, Embleton BJJ (1987) Statistical analysis of spherical data. Cambridge Unviersity Press, CambridgeCrossRefGoogle Scholar
  31. Fouedjio F (2016) Space Deformation Non-stationary geostatistical approach for prediction of geological objects: Case Study at El Teniente Mine (Chile). Nat Resour Res 25:283. Scholar
  32. Friendly M (2008) Milestones in the history of thematic cartography, statisticalgraphics, and data visualization.
  33. Gandin LS (1963) Objective analysis of meteorological fields, Leningrad, Gidrometeorologicheskoe Izdatel’stvo (GIMIZ)Google Scholar
  34. Geary R (1954) The contiguity ratio and statistical mapping. Inc Stat 5(3):115–145CrossRefGoogle Scholar
  35. Georges Matheron (1963) Principles of geostatistics. Econ Geol 58(8):1246–1266Google Scholar
  36. Getis A (2008) A history of the concept of spatial autocorrelation: a geographer’s perspective. Geogr Anal 40:297–309CrossRefGoogle Scholar
  37. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206CrossRefGoogle Scholar
  38. Goovaerts P (2006) Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using poisson Kriging and P-Field simulation. Int J Health Geogr 5:7CrossRefGoogle Scholar
  39. Griffith D (1987) Spatial autocorrelation: a primer. Resource Publications in Geography, Association of American GeographersGoogle Scholar
  40. Gunarathna MHJP, Kumari MKN, Nirmanee KGS (2016) Evaluation of interpolation methods for mapping pH of groundwater. IJLTEMAS V(III):1–5Google Scholar
  41. Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edition, Morgan Kaufmann, San Francisco, CAGoogle Scholar
  42. Hani A, Abari SAH (2011) Determination of Cd, Zn, K, pH, TNV, organic material and electrical conductivity (EC) distribution inagricultural soils using geostatistics and GIS (case study: SouthWestern of Natanz—Iran). World Acad Sci Eng Technol 5(12):22–25Google Scholar
  43. Hart JF (1954) Central tendency in areal distributions. Econ Geogr 30:48–59CrossRefGoogle Scholar
  44. Hay SI, Randolph SE, Rogers DJ (2002) Advances in parasitology. Academic Press, London, Remote Sensing and Geographical Information system in Epidemiology. ISBN 0-12-333560-4Google Scholar
  45. Hay SI, Snow RW (2006) The malaria atlas project: developing global maps of malaria risk. PLoS Med 3:e473CrossRefGoogle Scholar
  46. Hinman S, Blackburn JK, Curtis A (2006) Spatial and temporal structure of typhoid outbreaks in Washington DC., 1906–1909: evaluating local clustering with the *i G statistic. Int J Health Geogr 5:13CrossRefGoogle Scholar
  47. Holliger K, Tronicke J, Paasche H, Dafflon B (2008) Quantitative integration of hydrogeophysical and hydrological data: geostatistical approaches. Overexploitation and contamination of shared groundwater resources. Springer Netherlands, ISBN 978-1-4020-6985-7.
  48. Hu W, Tong S, Mengersen K, Oldenburg B (2007) Exploratory analysis of social and environmental factors associated with the incidence of Ross River viruses in Brisben, Australia. Am J Trop Med Hyg 76:814–819CrossRefGoogle Scholar
  49. Hubert LJ, Golledge RG, Costanza CM (1981) Generalized procedures for evaluating spatial autocorrelation. Geogr Anal 13:224–232CrossRefGoogle Scholar
  50. Isaaks EH, Srivastava M (1989) An introduction to applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  51. Jensen OP, Christman MC, Miller TJ, Jensen AF, Olaf P, Christman MC, Miller TJ (2006) Landscape-based geostatistics: a case study of the distribution of blue crab in Chesapeake Bay. Environmetrics 17(6):605–621CrossRefGoogle Scholar
  52. Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N (2001) Using ArcGIS geostatistical analyst. ESRI Press, Redlands, CAGoogle Scholar
  53. Joshi A, Narain JP, Prasittisuk C, Bhatia R, Hashim G, Jorge A, Banjara M, Kroeger A (2008) Can visceral leishmaniasis be eliminated from Asia? J Vector Borne Dis 45:105–111Google Scholar
  54. Journel AG (1986) Geostatistics: models and tools for the earth sciences. Math Geol 18:119–140. Scholar
  55. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, London, p 600Google Scholar
  56. Kerry R, Goovaerts P, Haining RP, Ceccato V (2010) Applying geostatistical analysis to crime data: car-related thefts in the Baltic States. Geogr Anal 42(1):53–77. Scholar
  57. Krivoruchko K, Butler K (2013) Unequal probability-based spatial mapping. Esri, Redlands, CA, USA.
  58. Kulldorff M, Feuer EJ, Freedman LS (1997) Breast cancer clusters in the Northeast United States: a geographic analysis. Am J Epidemiol 146(2):161–170CrossRefGoogle Scholar
  59. Kumar A, Kadiyala A, Sarmah D (2014) Evaluation of geographic information systems-based spatial interpolation methods using Ohio Indoor Radon data. Open Environ Eng J 7:1–9CrossRefGoogle Scholar
  60. Lawson AB (1989) Score tests for detection of spatial trend in morbidity data. Dundee, Dundee Institute of TechnologyGoogle Scholar
  61. Lee J, Wong DWS (2001) Statistical analysis with ArcView GIS. Wiley, New YorkGoogle Scholar
  62. Li XF, Chen ZB, Chen HB, Chen ZQ (2011) Spatial distribution of soil nutrients and their response to land use in eroded area of South China. Proc Environ Sci 10:14–19CrossRefGoogle Scholar
  63. Maingi JK, Mukeka JM, Kyale DM, Muasya RM (2012) Spatio-temporal patterns of elephant poaching in south-eastern Kenya. Wildlife Res 39(3):234–249CrossRefGoogle Scholar
  64. Malaviya P, Picado A, Singh SP, Hasker E, Singh RP, Boelaert M, Sundar S (2011) Visceral leishmaniasis in Muzaffarpur district, Bihar, India from 1990 to 2008. PLoS One 46(3):e14751CrossRefGoogle Scholar
  65. Mitchell A (2005) The ESRI Guide to GIS Analysis: Volume 2 Spatial Measurements and Statistics. ESRI Press, Redlands, CaliforniaGoogle Scholar
  66. Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23CrossRefGoogle Scholar
  67. Moran PAP (1948) The interpretation of statistical maps. J Roy Stat Soc B 10:243–251Google Scholar
  68. Nakhapakorn K, Jirakajohnkool S (2006) Temporal and spatial autocorrelation statistics of dengue fever. Dengue Bulletin 30:177–183Google Scholar
  69. Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92:211–225CrossRefGoogle Scholar
  70. Olea RA (1991) Geostatistical glossary and multilingual dictionary. Oxford University Press, New YorkGoogle Scholar
  71. Openshaw S, Charlton M, Wymer C, Craft A (1987) A mark 1 geographical snalysis machine for the automated analysis of point data sets. Int J Geogr Inf Syst 1(4):335–358CrossRefGoogle Scholar
  72. Ord JK, Getis A (1995) Local spatial autocorrelations statistics: distributional issues and application. Geogr Anal 27:286–306CrossRefGoogle Scholar
  73. Osei FB, Duker AA (2008) Spatial and demographic patterns of Cholera in Ashanti region—Ghana. Int J Health Geogr 7:44CrossRefGoogle Scholar
  74. Pilz J, Spöck G (2007) Why do we need and how should we implement Bayesian Kriging methods. Stoch Env Res Risk Assess 22(5):621–632CrossRefGoogle Scholar
  75. Relethford JH (2008) Geostatistics and spatial analysis in biological anthropology. Am J Phys Anthropol 136:1–10CrossRefGoogle Scholar
  76. Rushworth AM, Peterson EE, Ver Hoef JM, Bowman AW (2015) Validation and comparison of geostatistical and spline models for spatial stream networks. Environmetrics 26(5):327–338CrossRefGoogle Scholar
  77. Ruston R, Lolonis P (1996) Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 15:717–726CrossRefGoogle Scholar
  78. Sarah EB, Daniel S, Michael PF (2016) Shapes on a plane: evaluating the impact of projection distortion on spatial binning. Cartogr Geogr Informat Sci 1–12. Scholar
  79. Shit PK, Bhunia GS, Maiti R (2016) Spatial analysis of soil properties using GIS based geostatistics models. Model Earth Syst Environ 2:107CrossRefGoogle Scholar
  80. Shyti B, Fetahu E (2015) Spatial statistical methods in the analysis of public health data. Eur Scientif J 11(21):47–55Google Scholar
  81. Singh VP, Ranjan A, Topno RK, Verma RB, Siddique NA, Ravidas VN, Kumar N, Pandey K, Das P (2010) Estimation of under-reporting of visceral leishmaniasis cases in Bihar. India. Am J Trop Med Hyg 82(1):9–11CrossRefGoogle Scholar
  82. Skøien JO, Merz R, Bloschl G (2006) Top-kriging—geostatistics on stream networks. Hydrol Earth Syst Sci 10:277–287CrossRefGoogle Scholar
  83. Stein ML (1999) Predicting random fields with increasingly dense observations. Ann Appl Probab 9:242–273CrossRefGoogle Scholar
  84. Tobler WR (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr 46:234CrossRefGoogle Scholar
  85. Tola E, Al-Gaadia KA, Madugundua R, Zeyadaa AM, Kayad AG, Biradar CM (2016) Characterization of spatial variability of soil physicochemical properties and its impact on Rhodes grass productivity. Saudi J Biol Sci.
  86. Waller LA, Jacquez GM (1995) Disease models implicit in statistical tests of disease clustering. Epidemiology 6:584–590CrossRefGoogle Scholar
  87. Warburg A, Faiman R (2011) Research priorities for the control of phlebotomine sand flies. J Vector Ecol 36(1):S10–S16CrossRefGoogle Scholar
  88. White P (2005) Spatial data cluster analysis. Public Health Intelligen, Ministry of Health New ZealandGoogle Scholar
  89. Whittle P (1954) On stationary processes in the plane. Biometrika 41:434–449CrossRefGoogle Scholar
  90. Woodruff RE, Guest GS, Garner MG, Becker N, Lindsay M (2006) Early warning of Ross River virus epidemics: combining surveillance data on climate and mosquitoes. Epidemiology 17:569–575CrossRefGoogle Scholar
  91. Wu CF, Lai CH, Chu HJ, Lin WH (2011) Evaluating and mapping of spatial air ion quality patterns in a residential garden using a geostatistic method. Int J Environ Res Public Health 8(6):2304–2319CrossRefGoogle Scholar
  92. Wu PC, Lay JG, Guo HR, Lin CY, Lung SC, Su HJ (2009) Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. Sci Total Environ 407:2224–2233CrossRefGoogle Scholar
  93. Youden WJ (1951) Statistical methods for chemists. John Wiley & Sons, New YorkGoogle Scholar
  94. Zeqiri R, Kelmendi S, Zeqiri I (2012) Geostatistics in modern mining planning. J Int Environ Appl Sci 7(2):310–317Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Science and TechnologyBihar Remote Sensing Application CentrePatnaIndia
  2. 2.Department of GeographyRaja Narendra Lal Khan Women’s CollegeMidnaporeIndia

Personalised recommendations