Skip to main content

Advertisement

SpringerLink
Log in

H.E.L.P: A GIS-based Health Exploratory AnaLysis Tool for Practitioners

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

The last two decades have been characterized by a growing number of Geographical Information System (GIS) applications to the field of health science. From a decision-making and policy perspective, undeniable benefits of GIS include the assessment of health needs and delivery of services, and also the appropriate allocation of workforce and prevention resources. Despite these attractive attributes, the literature suggests that there has been limited GIS uptake among health care decision makers. This paper presents a GIS-based Health Exploratory and anaLysis tool for Practitioners (H.E.L.P.) for the analysis and visualization of space-time point events, applied to hospital patients. H.E.L.P. is viewed as a spatial decision support system which provides a set of powerful analytical tools integrating the computational capabilities of Matlab with the visualization and database functionalities of GIS. The system outputs improve the understanding of disease dynamics and provide resources for decision-makers in allocating appropriate staffing. As an example, H.E.L.P. is applied to a dataset of hospital patients in Cali, Colombia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Albert, D. P., Gesler, W. M., & Levergood, B. (Eds.) (2000). Spatial analysis, GIS and remote sensing applications in the health sciences. CRC Press, 212p.

  • Anselin, L. (2006). How (not) to lie with spatial statistics. American Journal of Preventive Medicine, 30, 3–6.

    Article  Google Scholar 

  • AvRuskin, G., Jacquez, G., Meliker, J., Slotnick, M., Kaufmann, A., & Nriagu, J. (2004). Visualization and exploratory analysis of epidemiologic data using a novel space time information system. International Journal of Health Geographics, 3, 1–10.

    Article  Google Scholar 

  • Bailey, T. C., & Gatrell, A. C. (1995). Interactive spatial data analysis (2nd ed.). Harlow: Longman.

    Google Scholar 

  • Begur, S., Miller, D., & Weaver, J. (1997). An integrated spatial DSS for scheduling and routing home-health care nurses. Interfaces, 27, 35–48.

    Article  Google Scholar 

  • Bonner, M., Han, D., Nie, J., Rogerson, P., Vena, J., & Freudenheim, J. (2003). Positional accuracy of geocoded addresses in epidemiologic research. Epidemiology, 14, 408–412.

    Google Scholar 

  • Boots, B., & Getis, A. (1988). Point pattern analysis. Sage Scientific Geography Series. London: Sage.

    Google Scholar 

  • Boulos, M. N. K. (2004). Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom. International Journal of Health Geographics, 3, 1–50.

    Article  Google Scholar 

  • Boulos, M. N. K. (2005). Web GIS in practice III: creating a simple, interactive map of England’s strategic health authorities using Google Maps API, Google Earth KML, and MSN virtual earth map control. International Journal of Health Geographics, 4, 22.

    Article  Google Scholar 

  • Campbell, J. L., & Parker, E. B. (1998). Measuring access to primary medical care: some examples of the use of geographical information systems. Health and Place, 4, 183–193.

    Article  Google Scholar 

  • Carver, S. (1991). Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information Systems, 5, 321–339.

    Article  Google Scholar 

  • Casas, I., Delmelle, E., & Varela, A. (2009). A space-time approach to diffusion of health service provision information. International Regional Science Review. doi:10.1177/0160017609354760.

    Google Scholar 

  • Chung, K., Yang, D.-H., & Bell, R. (2004). Health and GIS: toward spatial statistical analysis. Journal of Medical Systems, 28, 349–360.

    Article  Google Scholar 

  • Clarke, K., McLafferty, S., & Templaski, B. (1996). On epidemiology and geographic information systems: a review and discussion for future directions. Emerging Infectious Diseases, 2, 85–92.

    Article  Google Scholar 

  • Cockings, S., Dunn, C. E., Bhopal, R. S., & Walker, D. R. (2004). Users’ perspectives on epidemiological, GIS and point pattern approaches to analyzing environment and health data. Health and Place, 10, 169–182.

    Article  Google Scholar 

  • Courtney, K. L. (2005). Visualizing nursing workforce distribution: policy evaluation using geographic information systems. International Journal of Medical Informatics, 74, 980–988.

    Article  Google Scholar 

  • Cromley, E., & McLafferty, S. (2002). GIS and public health. New York: Guilford. 340p.

    Google Scholar 

  • Croner, C., Sperling, J., & Broome, F. (1996). Geographic Information Systems (GIS): new perspectives in understanding human health and environmental relationships. Statistics in Medicine, 15, 1961–1977.

    Article  Google Scholar 

  • Delmelle, E. (2009). Point pattern analysis. In N. Thrift & R. Kitchin (Eds.), International encyclopedia of human geography (Vol. 8, pp. 204–211). Oxford: Elsevier.

    Chapter  Google Scholar 

  • Delmelle, E. (2010). H.E.L.P. Matlab GIS Interface. Available at http://webpages.uncc.edu/∼edelmell/HELP, last accessed February 21 2010.

  • Gatrell, A., & Loytonen, M. (Eds.) (1998). GIS and health. Houghton Mifflin Harcourt, 212p.

  • Gatrell, A., Bailey, T., Diggle, P., & Rowlingson, B. (1996). Spatial point pattern analysis and its application in geographical epidemiology. Transactions of the Institute of British Geographers, 21, 256–274.

    Article  Google Scholar 

  • Ghosh, D. (2008). A loose coupling technique for integrating GIS and multi-criteria decision making. Transaction in GIS, 12, 365–375.

    Article  Google Scholar 

  • Glass, G., Schwartz, B., Morgan, J., Johnson, D., Noy, P., & Israel, E. (1995). Environmental risk factors for lyme disease identified with geographic information systems. American Journal of Public Health, 85, 944–948.

    Article  Google Scholar 

  • Grubesic, T., & Mack, E. (2008). Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology, 24, 285–306.

    Article  Google Scholar 

  • Guagliardo, M. (2004). Spatial accessibility of primary care: concepts, methods and challenges. International Journal of Health Geographics, 3, 1–13.

    Article  Google Scholar 

  • Han, D., & Rogerson, P. (2002). Application of a GIS-based statistical method to assess spatio-temporal changes in breast cancer clustering in the Northeastern United States. In O. Khan & R. Skinner (Eds.), Geographic information systems and health applications. Hershey: Idea Group.

    Google Scholar 

  • Heikkinen, T., & Järvinen, A. (2003). The common cold. Lancet, 361, 51–59.

    Article  Google Scholar 

  • Higgs, G., & Gould, M. (2001). Is there a role for GIS in the ‘new NHS’? Health and Place, 7, 247–559.

    Article  Google Scholar 

  • Higgs, G., Smith, D., & Gould, M. (2003). Realising joined-up geography in the National Health Service: the role of geographical information systems? Environment and Planning C, 21, 241–258.

    Article  Google Scholar 

  • Jacquez, G. M. (1996). A k-nearest neighbor test for space-time interaction. Statistics in Medicine, 15, 1935–1949.

    Article  Google Scholar 

  • Jacquez, G. M., Greiling, D. A., Durbeck, H., Estberg, L., Do, E., Long, A., et al. (2002). ClusterSeer User Guide 2: software for identifying disease clusters. Ann Arbor: TerraSeer.

    Google Scholar 

  • Jacquez, G. M., Greiling, D., & Kaufmann, A. (2005). Design and implementation of a Space-Time Intelligence System for disease surveillance. Journal of Geographical Systems, 7, 7–23.

    Article  Google Scholar 

  • Jacquez, G. M., Goovaerts, P., & Rogerson, P. (2005). Space-time intelligent systems: technology, applications and methods. Journal of Geographical Systems, 7, 1–5.

    Article  Google Scholar 

  • Jankowski, P., & Richard, L. (1994). Integration of GIS-based suitability analysis and multi-criteria evaluation: a spatial decision support system for route selection. Environment and Planning B, 21, 323–340.

    Article  Google Scholar 

  • Jankowski, P., Andrienko, N., & Andrienko, G. (2001). Map-centred exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15, 101–127.

    Article  Google Scholar 

  • Joyce, K. (2009). “To me it’s just another tool to help understand the evidence”: public health decision makers perceptions of the value of GIS. Health and Place, 15, 831–840.

    Article  Google Scholar 

  • Knox, E. G. (1964). The detection of space-time interactions. Applied Statistics, 13, 25–29.

    Article  Google Scholar 

  • Krieger, N., Waterman, P., Lemieux, K., Zierler, Z., & Hogan, J. W. (2001). On the wrong side of the tracts? Evaluating the accuracy of geocoding in public health research. American Journal of Public Health, 91, 1114–1116.

    Article  Google Scholar 

  • Kulldorff, M. (2006). SaTScan User Guide for Version 7.0: www.satscan.org (last accessed: January 15 2010).

  • Kwan, M.-P., Casas, I., & Ben, C. (2004). Protection of geo-privacy and accuracy of spatial information: how effective are geographical masks? Cartographica, 39, 15–38.

    Google Scholar 

  • Levine, N. (2005). Space-time analysis. In N. Levine (Ed.), CrimeStat v.3.1: a spatial statistics program for the analysis of crime incident locations, chapter 9. Houston: Ned Levine & Associates/The National Institute of Justice, http://www.icpsr.umich.edu/crimestat (last accessed: January 15 2010).

  • Luo, W., & Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environment and Planning B: Planning and Design, 30, 865–884.

    Article  Google Scholar 

  • Malczewski, J. (1999). GIS and multicriteria decision analysis. New York: Wiley. 392p.

    Google Scholar 

  • Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209–220.

    Google Scholar 

  • Martin, P., LeBoeuf, E., Dobbins, J., Daniel, E., & Akbowitz, M. (2005). Interfacing GIS with water resources: a state-of-the art review. Journal of the American Water Resources Association, 41, 1471–1487.

    Article  Google Scholar 

  • Okabe, A., & Yamada, I. (2001). The K-function method on a network and its computational implementation. Geographical Analysis, 33, 271–290.

    Article  Google Scholar 

  • Okabe, A., Yomono, H., & Kitamura, M. (1995). Statistical analysis of the distribution of points on a network. Geographical Analysis, 27, 152–175.

    Article  Google Scholar 

  • Parchman, M. L., Ferrer, R. L., & Blanchard, K. S. (2002). Geography and geographic information systems in family medicine research. Family Medicine, 34, 132–137.

    Google Scholar 

  • Philips, R. L., Kinman, E. L., Schnitzer, P. G., Lindbloom, E. J., & Ewigman, B. (2000). Using geographical information systems to understand health care access. Archives of Family Medicine, 9, 971–978.

    Article  Google Scholar 

  • Plescia, M., Koontz, S., & Laurent, S. (2001). Community assessment in a vertically integreated health care system. American Journal of Public Health, 91, 811–814.

    Article  Google Scholar 

  • Rafalski, E., & Zun, L. (2004). Using GIS to monitor emergency room use in a large hospital in Chicago. Journal of Medical Systems, 28(3), 311–319.

    Article  Google Scholar 

  • Reissman, D., Staley, F., Curtis, G., & Kaufman, R. (2001). Use of geographic information system technology to aid health department decision making about childhood lead poisoning prevention activities. Environmental Health Perspectives, 109, 89–94.

    Article  Google Scholar 

  • Rogerson, P., & Sun, Y. (2001). Spatial monitoring of geographic patterns: an application to crime analysis. Computers, Environment and Urban Systems, 25, 539–556.

    Article  Google Scholar 

  • Rogerson, P., & Yamada, I. (2008). Statistical detection and surveillance of geographic clusters. Boca Raton: CRC. 360p.

    Book  Google Scholar 

  • Rushton, G. (2003). Public health, GIS and spatial analytical tools. Annual Review of Public Health, 24, 43–56.

    Article  Google Scholar 

  • Schuurman, N., Leight, M., & Berube, M. (2008). A web-based GUI for evidence-based decision making for health care allocations in rural areas. International Journal of Health Geographics, 7, 49.

    Article  Google Scholar 

  • Vine, M., Degnan, D., & Hanchette, C. (1997). Geographic Information Systems: their use in environmental epidemiologic research. Environmental Health Perspectives, 105, 598–605.

    Article  Google Scholar 

  • Yamada, I., Rogerson, P., & Lee, G. (2009). GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters. Journal of Geographical Systems. In press.

Download references

Acknowledgment

The authors would like to thank Alejandro Varela Secretary of Health of the Municipality of Cali - Colombia and his staff of doctors, engineers, administrators, and other key personnel without which this project would have not been possible, also Bradley Biggers from the Gaston County Health Department (North Carolina, USA) for his comments on an earlier version of the paper. The authors thank the reviewers and the editor of ASAP for their insightful and detailed comments which helped improve the paper.

Author information

Authors and Affiliations

  1. Department of Geography and Earth Sciences and Center for Applied GIS (CAGIS), University of North Carolina at Charlotte, 9201 University Blvd., Charlotte, NC, 28223, USA

    Eric Delmelle

  2. Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University Blvd., Charlotte, NC, 28223, USA

    Elizabeth Cahill Delmelle & Thomas Barto

  3. Louisiana Tech University, Ruston, LA, USA

    Irene Casas

Authors
  1. Eric Delmelle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth Cahill Delmelle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Irene Casas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Barto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric Delmelle.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Delmelle, E., Delmelle, E.C., Casas, I. et al. H.E.L.P: A GIS-based Health Exploratory AnaLysis Tool for Practitioners. Appl. Spatial Analysis 4, 113–137 (2011). https://doi.org/10.1007/s12061-010-9048-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-010-9048-2

Keywords

Search

Navigation