Skip to main content

Spatial Statistics and Public Health Events

  • Chapter
  • First Online:
Geospatial Analysis of Public Health

Abstract

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.

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

  • Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115

    Article  Google Scholar 

  • 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. https://doi.org/10.1146/annurev-publhealth-031811-124655

    Article  Google Scholar 

  • Bailey TC (2001) Spatial statistical methods in health. Cad. Saúde Pública, Rio de Janeiro, 17(5):1083–1098

    Article  Google Scholar 

  • 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):1069–1084

    Google Scholar 

  • 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 187:4183

    Article  Google Scholar 

  • 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. https://doi.org/10.1186/1471-2334-13-64

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1016/j.jssas.2016.02.001

  • Bindiya MV, Unnikrishnan A, Poulose JK (2013) Spatial clustering algorithms—an overview. Asian J Comput Sci Informat Technol 3:1–8

    Google Scholar 

  • 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 13:188–194

    Article  Google Scholar 

  • Boots BN, Getis A (1998) Point pattern analysis Newbury Park. Sage Publications, Newbury Park, CA, USA

    Google Scholar 

  • Caers J (2005) Petroleum geostatistics. An SPE Primer, Society of Petroleum Engineers, Richardson, TX, USA

    Google Scholar 

  • 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. http://cgwb.gov.in/District_Profile/Bihar/Muzaffarpur.pdf

  • 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) 5:873–882

    Article  Google Scholar 

  • Cliff AD, Haggett P (1988) Atlas of disease distributions. Oxford, United Kingdom: Blackwell

    Google Scholar 

  • Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion, London

    Google Scholar 

  • 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, London

    Google Scholar 

  • 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 

  • 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):e115632

    Article  Google Scholar 

  • Deutsch CV, Journel AG (1992) Geostatistical software library and user’s guide. Oxford University Press, p 340

    Google Scholar 

  • 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. https://doi.org/10.1155/2015/693193

    Article  Google Scholar 

  • 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 2179

    Google Scholar 

  • Ebdon D (1985) Statistics in geography. Blackwell

    Google Scholar 

  • Elliott P, Wakefield J, Best N, Briggs D (2000) Spatial epidemiology: methods and applications, Oxford University Press

    Google Scholar 

  • Enkhtur B (2013) Geostatistical modelling and mapping of air pollution. https://www.itc.nl/library/papers_2013/msc/gfm/enkhtur.pdf

  • Environmental Systems Research Institute (ESRI), Inc (2009) How directional distribution: standard deviational ellipse (spatial statistics) works. http://webhelp.esri.com/arcgiSDEsktop/9.3/index.cfm? TopicName5—How directional distribution: standard deviational ellipse (Spatial Statistics) works

  • ESRI (2001) Using ArcGIS geostatistical analyst. ESRI Press, Redlands, CA

    Google Scholar 

  • 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 6:77

    Article  Google Scholar 

  • Feser E, Sweeney S, Renski H (2005) A descriptive analysis of discrete U.S. industrial complexes. J Regional Sci 45:395–419

    Article  Google Scholar 

  • Few S (2009) Introduction to geographical data visualization. Perceptual Edge Visual Business Intelligence Newsletter. https://www.perceptualedge.com/articles/-visual_business_intelligence/geographical_data_visualization.pdf

  • Fisher NI, Lewis T, Embleton BJJ (1987) Statistical analysis of spherical data. Cambridge Unviersity Press, Cambridge

    Book  Google Scholar 

  • 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. https://doi.org/10.1007/s11053-015-9287-7

    Article  Google Scholar 

  • Friendly M (2008) Milestones in the history of thematic cartography, statisticalgraphics, and data visualization. http://www.math.usu.edu/~symanzik/teaching/2009_stat6560-/Downloads/Friendly_milestone.pdf

  • Gandin LS (1963) Objective analysis of meteorological fields, Leningrad, Gidrometeorologicheskoe Izdatel’stvo (GIMIZ)

    Google Scholar 

  • Geary R (1954) The contiguity ratio and statistical mapping. Inc Stat 5(3):115–145

    Article  Google Scholar 

  • Georges Matheron (1963) Principles of geostatistics. Econ Geol 58(8):1246–1266

    Google Scholar 

  • Getis A (2008) A history of the concept of spatial autocorrelation: a geographer’s perspective. Geogr Anal 40:297–309

    Article  Google Scholar 

  • Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206

    Article  Google Scholar 

  • 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 5:7

    Article  Google Scholar 

  • Griffith D (1987) Spatial autocorrelation: a primer. Resource Publications in Geography, Association of American Geographers

    Google Scholar 

  • Griffith DA (2009) Spatial autocorrelation. http://booksite.elsevier.com/-brochures/hugy/SampleContent/Spatial-Autocorrelation.pdf

  • Gunarathna MHJP, Kumari MKN, Nirmanee KGS (2016) Evaluation of interpolation methods for mapping pH of groundwater. IJLTEMAS V(III):1–5

    Google Scholar 

  • Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edition, Morgan Kaufmann, San Francisco, CA

    Google Scholar 

  • 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):22–25

    Google Scholar 

  • Hart JF (1954) Central tendency in areal distributions. Econ Geogr 30:48–59

    Article  Google Scholar 

  • Hay SI, Randolph SE, Rogers DJ (2002) Advances in parasitology. Academic Press, London, Remote Sensing and Geographical Information system in Epidemiology. ISBN 0-12-333560-4

    Google Scholar 

  • Hay SI, Snow RW (2006) The malaria atlas project: developing global maps of malaria risk. PLoS Med 3:e473

    Article  Google Scholar 

  • 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 5:13

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/978-1-4020-6985-7_5

  • 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 76:814–819

    Article  Google Scholar 

  • Hubert LJ, Golledge RG, Costanza CM (1981) Generalized procedures for evaluating spatial autocorrelation. Geogr Anal 13:224–232

    Article  Google Scholar 

  • Isaaks EH, Srivastava M (1989) An introduction to applied geostatistics. Oxford University Press, New York

    Google Scholar 

  • 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):605–621

    Article  Google Scholar 

  • Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N (2001) Using ArcGIS geostatistical analyst. ESRI Press, Redlands, CA

    Google Scholar 

  • 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 45:105–111

    Google Scholar 

  • Journel AG (1986) Geostatistics: models and tools for the earth sciences. Math Geol 18:119–140. https://doi.org/10.1007/BF00897658

    Article  Google Scholar 

  • Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, London, p 600

    Google Scholar 

  • 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. https://doi.org/10.1111/j.1538-4632.2010.00782.x

    Article  Google Scholar 

  • Krivoruchko K, Butler K (2013) Unequal probability-based spatial mapping. Esri, Redlands, CA, USA. http://www.esri.com/esrinews/arcuser/spring2013/~/-media/Files/Pdfs/news/arcuser/0313/unequal.pdf

  • Kulldorff M, Feuer EJ, Freedman LS (1997) Breast cancer clusters in the Northeast United States: a geographic analysis. Am J Epidemiol 146(2):161–170

    Article  Google Scholar 

  • 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 7:1–9

    Article  Google Scholar 

  • Lawson AB (1989) Score tests for detection of spatial trend in morbidity data. Dundee, Dundee Institute of Technology

    Google Scholar 

  • Lee J, Wong DWS (2001) Statistical analysis with ArcView GIS. Wiley, New York

    Google Scholar 

  • 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 10:14–19

    Article  Google Scholar 

  • Maingi JK, Mukeka JM, Kyale DM, Muasya RM (2012) Spatio-temporal patterns of elephant poaching in south-eastern Kenya. Wildlife Res 39(3):234–249

    Article  Google Scholar 

  • 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):e14751

    Article  Google Scholar 

  • Mitchell A (2005) The ESRI Guide to GIS Analysis: Volume 2 Spatial Measurements and Statistics. ESRI Press, Redlands, California

    Google Scholar 

  • Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23

    Article  Google Scholar 

  • Moran PAP (1948) The interpretation of statistical maps. J Roy Stat Soc B 10:243–251

    Google Scholar 

  • Nakhapakorn K, Jirakajohnkool S (2006) Temporal and spatial autocorrelation statistics of dengue fever. Dengue Bulletin 30:177–183

    Google Scholar 

  • Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92:211–225

    Article  Google Scholar 

  • Olea RA (1991) Geostatistical glossary and multilingual dictionary. Oxford University Press, New York

    Google Scholar 

  • 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):335–358

    Article  Google Scholar 

  • Ord JK, Getis A (1995) Local spatial autocorrelations statistics: distributional issues and application. Geogr Anal 27:286–306

    Article  Google Scholar 

  • Osei FB, Duker AA (2008) Spatial and demographic patterns of Cholera in Ashanti region—Ghana. Int J Health Geogr 7:44

    Article  Google Scholar 

  • Pilz J, Spöck G (2007) Why do we need and how should we implement Bayesian Kriging methods. Stoch Env Res Risk Assess 22(5):621–632

    Article  Google Scholar 

  • Relethford JH (2008) Geostatistics and spatial analysis in biological anthropology. Am J Phys Anthropol 136:1–10

    Article  Google Scholar 

  • 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):327–338

    Article  Google Scholar 

  • Ruston R, Lolonis P (1996) Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 15:717–726

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/15230406.2016.1180263

    Article  Google Scholar 

  • Shit PK, Bhunia GS, Maiti R (2016) Spatial analysis of soil properties using GIS based geostatistics models. Model Earth Syst Environ 2:107

    Article  Google Scholar 

  • Shyti B, Fetahu E (2015) Spatial statistical methods in the analysis of public health data. Eur Scientif J 11(21):47–55

    Google Scholar 

  • 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):9–11

    Article  Google Scholar 

  • Skøien JO, Merz R, Bloschl G (2006) Top-kriging—geostatistics on stream networks. Hydrol Earth Syst Sci 10:277–287

    Article  Google Scholar 

  • Stein ML (1999) Predicting random fields with increasingly dense observations. Ann Appl Probab 9:242–273

    Article  Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr 46:234

    Article  Google Scholar 

  • 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. http://dx.doi.org/10.1016/j.sjbs.2016.04.013

  • Waller LA, Jacquez GM (1995) Disease models implicit in statistical tests of disease clustering. Epidemiology 6:584–590

    Article  Google Scholar 

  • Warburg A, Faiman R (2011) Research priorities for the control of phlebotomine sand flies. J Vector Ecol 36(1):S10–S16

    Article  Google Scholar 

  • White P (2005) Spatial data cluster analysis. Public Health Intelligen, Ministry of Health New Zealand

    Google Scholar 

  • Whittle P (1954) On stationary processes in the plane. Biometrika 41:434–449

    Article  Google Scholar 

  • 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 17:569–575

    Article  Google Scholar 

  • 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):2304–2319

    Article  Google Scholar 

  • 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 407:2224–2233

    Article  Google Scholar 

  • Youden WJ (1951) Statistical methods for chemists. John Wiley & Sons, New York

    Google Scholar 

  • Zeqiri R, Kelmendi S, Zeqiri I (2012) Geostatistics in modern mining planning. J Int Environ Appl Sci 7(2):310–317

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gouri Sankar Bhunia .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bhunia, G.S., Shit, P.K. (2019). Spatial Statistics and Public Health Events. In: Geospatial Analysis of Public Health. Springer, Cham. https://doi.org/10.1007/978-3-030-01680-7_4

Download citation

Publish with us

Policies and ethics