Visualizing the impact of space-time uncertainties on dengue fever patterns

References

• Abellan, J.J., Richardson, S., and Best, N., 2008. Use of space-time models to investigate the stability of patterns of disease. Environmental Health Perspectives, 116 (8), 1111–1119.
   PubMed Web of Science ®Google Scholar
• Aldstadt, J. and Getis, A., 2006. Using AMOEBA to create a spatial weights matrix and identify spatial clusters. Geographical Analysis, 38 (4), 327–343.
   Web of Science ®Google Scholar
• Andrienko, G., et al., 2010. Space, time and visual analytics. International Journal of Geographical Information Science, 24 (10), 1577–1600.
   Web of Science ®Google Scholar
• Anselin, L., 2012. From SpaceStat to CyberGIS twenty years of spatial data analysis software. International Regional Science Review, 35 (2), 131–157.
   Web of Science ®Google Scholar
• Armstrong, M.P., 2000. Geography and computational science. Annals of the Association of American Geographers, 90 (1), 146–156.
   Web of Science ®Google Scholar
• Armstrong, M.P. and Densham, P.J., 1992. Domain decomposition for parallel processing of spatial problems. Computers, Environment and Urban Systems, 16 (6), 497–513.
   Web of Science ®Google Scholar
• Atkins, D.E., et al., 2003. Revolutionizing science and engineering through cyberinfrastructure. Report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure. Ann Arbor, MI: National Science Foundation.
   Google Scholar
• Bailey, T. and Gatrell, Q., 1995. Interactive spatial data analysis. Harlow: Pearson Education Limited.
   Google Scholar
• Bilcher, G., and Balchak, S., 2007. Address matching bias: ignorance is not bliss. Policing, 30, 32–60.
   Google Scholar
• Brunsdon, C., Corcoran, J., and Higgs, G., 2007. Visualising space and time in crime patterns: a comparison of methods. Computers, Environment and Urban Systems, 31 (1), 52–75.
   Web of Science ®Google Scholar
• Burra, T., et al., 2002. Conceptual and practical issues in the detection of local disease clusters: a study of mortality in Hamilton, Ontario. Canadian Geographer/Le Géographe Canadien, 46 (2), 160–171.
   Web of Science ®Google Scholar
• Cali, S.d.S.P.M.d., 2010. Historia del dengue en Cali. Endemia o una continua epidemia. Cali: Secretaria de Salud Publica Municipal de Cali.
   Google Scholar
• Casas, I., Delmelle, E., and Varela, A., 2010. A space-time approach to diffusion of health service provision information. International Regional Science Review, 33 (2), 134–156.
   Web of Science ®Google Scholar
• Cromley, E. and McLafferty, S., 2011. GIS and public health. New York: Guilford Press.
   Google Scholar
• DeGroote, J.P., et al., 2012. Application of geospatial technologies for understanding and predicting vector populations and vector‐borne disease incidence. Geography Compass, 6 (11), 645–659.
   Google Scholar
• Delmelle, E. 2009. Point pattern analysis. In: R. Kitchin and N. Thrift, eds. International Encyclopedia of Human Geography. Oxford: Elsevier, 204–211.
   Google Scholar
• Delmelle, E., et al., 2011. H.E.L.P: a GIS-based health exploratory analysis tool for practitioners. Applied Spatial Analysis and Policy, 4 (2), 113–137.
   Web of Science ®Google Scholar
• Delmelle, E., et al., 2013. Spatio-temporal patterns of Dengue Fever in Cali, Colombia. International Journal of Applied Geospatial Research, 4 (4), 58–75.
   Google Scholar
• DeLuca, P. and Kanaroglou, P., 2008. Effects of alternative point pattern geocoding procedures of health data on first and second order statistical measures. Journal of Spatial Science, 53, 131–141.
   Web of Science ®Google Scholar
• Demšar, U. and Virrantaus, K., 2010. Space–time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24 (10), 1527–1542.
   Web of Science ®Google Scholar
• Ding, Y. and Densham, P.J., 1996. Spatial strategies for parallel spatial modelling. International Journal of Geographical Information Systems, 10 (6), 669–698.
   Web of Science ®Google Scholar
• Duncombe, J., et al., 2012. Review: geographical information systems for dengue surveillance. American Journal of Tropical Medicine and Hygiene, 86 (5), 753.
   PubMed Web of Science ®Google Scholar
• Eisen, L. and Eisen, R., 2011. Using geographic information systems and decision support systems for the prediction, prevention, and control of vector-borne diseases. Annual Review of Entomology, 56 (1), 41–61.
   PubMed Web of Science ®Google Scholar
• Eisen, L. and Lozano-Fuentes, S., 2009. Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Neglected Tropical Diseases, 3 (4), e411.
   Google Scholar
• Epanechnikov, V.A., 1969. Non-parametric estimation of a multivariate probability density. Theory of Probability & Its Applications, 14 (1), 153–158.
   Web of Science ®Google Scholar
• Fischer, M.M. and Getis, A., eds., 2010. Handbook of applied sapatial analysis: software tools, methods and applications, Heidelberg: Springer.
   Google Scholar
• Gherman, G., et al., 1998. SaTScan v 2.1: software for the spatial and space-time scan statistics. Bethesda, MD: National Cancer Institute.
   Google Scholar
• Goodchild, M. and Zhang, J., 2002. Uncertainty in geographical information. New York: CRC Press.
   Google Scholar
• Griffith, D.A., et al., 2007. Impacts of positional error on spatial regression analysis: a case study of address locations in Syracuse, New York. Transactions in GIS, 11 (5), 655–679.
   Google Scholar
• Guan, Q., Kyriakidis, P.C., and Goodchild, M.F., 2011. A parallel computing approach to fast geostatistical areal interpolation. International Journal of Geographical Information Science, 25 (8), 1241–1267.
   Web of Science ®Google Scholar
• Gubler, D.J. and Clark, G.G., 1995. Dengue/dengue hemorrhagic fever: the emergence of a global health problem. Emerging Infectious Diseases, 1 (2), 55.
   PubMed Web of Science ®Google Scholar
• Harada, Y. and Shimada, T., 2006. Examining the impact of the precision of address geocoding on estimated density of crime locations. Computers & Geosciences, 32 (8), 1096–1107.
   Web of Science ®Google Scholar
• Heuvelink, G.B.M., 1998. Error propagation in environmental modelling with GIS. New York: CRC Press.
   Google Scholar
• Jacquez, G.M., 1999. Spatial statistics when locations are uncertain. Annals of GIS, 5 (2), 77–87.
   Google Scholar
• Jacquez, G., 2012. A research agenda: does geocoding positional error matter in health GIS studies? Spatial and Spatio-Temporal Epidemiology, 3 (1), 7–16.
   Google Scholar
• Jacquez, G., Greiling, D., and Kaufmann, A., 2005. Design and implementation of a space-time intelligence system for disease surveillance. Journal of Geographical Systems, 7 (1), 7–23.
   Google Scholar
• Khormi, H.M. and Kumar, L., 2012. The importance of appropriate temporal and spatial scales for dengue fever control and management. Science of the Total Environment, 430, 144–149.
   PubMed Web of Science ®Google Scholar
• Kitron, U., 1998. Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. Journal of Medical Entomology, 35 (4), 435–445.
   PubMed Web of Science ®Google Scholar
• Kitron, U., 2000. Risk maps: transmission and burden of vector-borne diseases. Parasitology Today, 16, 324–325.
   PubMedGoogle Scholar
• Knox, G.E., 1964. The detection of space-time iterations. Journal of the Royal Statistical Society, 13, 25–29.
   Google Scholar
• Kulldorff, M., 1997. A spatial scan statistic. Communications in Statistics – Theory and Methods, 26 (6), 1481–1496.
   Web of Science ®Google Scholar
• Kulldorff, M., et al., 1998. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health, 88 (9), 1377–1380.
   PubMed Web of Science ®Google Scholar
• Kulldorff, M., et al., 2005. A space–time permutation scan statistic for disease outbreak detection. PLoS Medicine, 2 (3), e59.
   PubMed Web of Science ®Google Scholar
• Kwan, M.-P., 2012. The uncertain geographic context problem. Annals of the Association of American Geographers, 102 (5), 958–968.
   Web of Science ®Google Scholar
• Kwan, M.-P., Casas, I., and Schmitz, B.C., 2004. Protection of geoprivacy and accuracy of spatial information: how effective are geographical masks? Cartographica, 39 (2), 15–28.
   Google Scholar
• Lam, N., 2012. Geospatial methods for reducing uncertainties in environmental health risk assessment: challenges and opportunities. Annals of the Association of American Geographers, 102 (5), 942–950.
   Web of Science ®Google Scholar
• Levine, N., 2006. Crime mapping and the crimestat program. Geographical Analysis, 38 (1), 41–56.
   Web of Science ®Google Scholar
• Levoy, M., 1988. Display of surfaces from volume data. Computer Graphics and Applications, IEEE, 8 (3), 29–37.
   Web of Science ®Google Scholar
• Malizia, N., 2012. The effect of data inaccuracy on tests of space-time interaction. Transactions in GIS, 17 (3), 426–451.
   Google Scholar
• Malizia, N., 2013. Inaccuracy, uncertainty and the space-time permutation scan statistic. PLoS One, 8 (2), e52034.
   PubMed Web of Science ®Google Scholar
• Mammen, M., et al., 2008. Spatial and temporal clustering of dengue virus transmission in Thai villages. PLoS Medicine, 5 (11), e205.
   PubMed Web of Science ®Google Scholar
• Mantel, N., 1967. The detection of disease clustering and a generalized regression approach. Cancer Research, 27 (2 Part 1), 209–220.
   PubMed Web of Science ®Google Scholar
• Mazumdar, S., et al., 2008. Geocoding accuracy and the recovery of relationships between environmental exposures and health. International Journal of Health Geographics, 7 (1), 13.
   PubMed Web of Science ®Google Scholar
• Méndez, F., et al., 2006. Human and mosquito infections by dengue viruses during and after epidemics in a dengue-endemic region of Colombia. The American Journal of Tropical Medicine and Hygiene, 74, 678–683.
   PubMed Web of Science ®Google Scholar
• Monath, T., ed., 1988. The arboviruses: epidemiology and ecology. Boca Raton, FL: CRC Press.
   Google Scholar
• Morrison, A., et al., 1998. Exploratory space-time analysis of reported dengue cases during an outbreak in Florida, Puerto Rico, 1991–1992. American Journal of Tropical Medicine and Hygiene, 58, 287–298.
   PubMed Web of Science ®Google Scholar
• Nakaya, T., and Yano, K., 2010. Visualising crime clusters in a space-time cube: an exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14 (3), 223–239.
   Web of Science ®Google Scholar
• Peterson, I., et al., 2009. A temporal-spatial analysis of malaria transmission in Adama, Ethiopia. The American Journal of Tropical Medicine and Hygiene, 81 (6), 944–949.
   PubMed Web of Science ®Google Scholar
• Robertson, C. and Nelson, T.A., 2010. Review of software for space-time disease surveillance. International Journal of Health Geographics, 9, 16.
   PubMed Web of Science ®Google Scholar
• Rogers, D.J. and Randolph, S.E., 2003. Studying the global distribution of infectious diseases using GIS and RS. Nature Reviews Microbiology, 1 (3), 231–237.
   PubMed Web of Science ®Google Scholar
• Rogerson, P. and Sun, Y., 2001. Spatial monitoring of geographic patterns: an application to crime analysis. Computers, Environment and Urban Systems, 25 (6), 539–556.
   Google Scholar
• Rogerson, P. and Yamada, I., 2008. Statistical detection and surveillance of geographic clusters. Boca Raton, FL: CRC Press.
   Google Scholar
• Silverman, B.W., 1986. Density estimation for statistics and data analysis. London: Chapman and Hall.
   Google Scholar
• Tang, W. and Bennett, D.A., 2011. Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units. Ecological Modelling, 222, 3605–3615.
   Web of Science ®Google Scholar
• Tang, W. and Wang, S., 2009. HPABM: a hierarchical parallel simulation framework for spatially-explicit agent-based models. Transactions in GIS, 13 (3), 315–333.
   Web of Science ®Google Scholar
• Tang, W., et al., 2011. Agent-based modeling within a cyberinfrastructure environment: a service-oriented computing approach. International Journal of Geographical Information Science, 25 (9), 1323–1346.
   Web of Science ®Google Scholar
• Tran, A., et al., 2004. Dengue spatial and temporal patterns, French Guiana, 2001. Emerging Infectious Diseases, 10 (4), 615–621.
   PubMed Web of Science ®Google Scholar
• Unwin, D.J., 1996. GE, spatial analysis and spatial statistics. Progress in Human Geography, 20 (4), 540–551.
   Web of Science ®Google Scholar
• Wang, S. and Armstrong, M.P., 2009. A theoretical approach to the use of cyberinfrastructure in geographical analysis. International Journal of Geographical Information Science, 23 (2), 169–193.
   Web of Science ®Google Scholar
• Widener, M., Crago, N., and Aldstadt, J., 2012. Developing a parallel computational implementation of AMOEBA. International Journal of Geographical Information Science, 26 (9), 1707–1723.
   Web of Science ®Google Scholar
• Wilkinson, B. and Allen, M., 2004. Parallel programming: techniques and applications using networked workstations and parallel computers. 2nd ed. Upper Saddle River, NJ: Pearson Prentice Hall.
   Google Scholar
• Wilkinson, L. and Friendly, M., 2009. The history of the cluster heat map. The American Statistician, 63 (2), 179–184.
   Web of Science ®Google Scholar
• WHO, 2011. Dengue and dengue haemorrhagic fever – Fact sheet no. 117. WHO 2009. Available from: http://www.who.int/mediacentre/factsheets/fs117/en/ [Accessed 17 January 2011].
   Google Scholar
• Wu, P.-C., et al., 2009. Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. The Science of the Total Environment, 407, 2224–2233.
   PubMed Web of Science ®Google Scholar
• Yang, C., et al., 2011. Using spatial principles to optimize distributed computing for enabling the physical science discoveries. Proceedings of the National Academy of Sciences, 108 (14), 5498–5503.
   PubMed Web of Science ®Google Scholar
• Yoon, I.-K., et al., 2012. Fine scale spatiotemporal clustering of dengue virus transmission in children and Aedes aegypti in rural Thai villages. PLoS Neglected Tropical Diseases, 6 (7), e1730.
   PubMed Web of Science ®Google Scholar
• Zandbergen, P., 2008. A comparison of address point, parcel and street geocoding techniques. Computers, Environment and Urban Systems, 32 (3), 214–232.
   Web of Science ®Google Scholar
• Zandbergen, P., 2009. Geocoding quality and implications for spatial analysis. Geography Compass, 3 (2), 647–680.
   Google Scholar
• Zandbergen, P. and Green, J., 2007. Error and bias in determining exposure potential of children at school locations using proximity-based GIS techniques. Environmental Health Perspectives, 115 (9), 1363–1370.
   PubMed Web of Science ®Google Scholar
• Zimmerman, D., Li, J., and Fang, X., 2010. Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering. Statistics in Medicine, 29 (9), 1025–1036.
   PubMed Web of Science ®Google Scholar
• Zimmerman, D.L. and Li, J., 2010. The effects of local street network characteristics on the positional accuracy of automated geocoding for geographic health studies. International Journal of Health Geographics, 9, 10.
   PubMed Web of Science ®Google Scholar
• Zimmerman, D.L., et al., 2007. Modeling the probability distribution of positional errors incurred by residential address geocoding. International Journal of Health Geographics [online], 6 (1). Available from: http://www.ij-healthgeographics.com/content/6/1/1 [Accessed 26 December 2013].
   Google Scholar
• Zinszer, K., et al., 2010. Residential address errors in public health surveillance data: a description and analysis of the impact on geocoding. Spatial and Spatio-temporal Epidemiology, 1 (2–3), 163–168.
   PubMedGoogle Scholar