Others have blogged on the directions for Esri technology, the emerging role of GIS-in-the-cloud, crowd-sourcing data, and the potentials for social networking in geohealth.  I attended the recent Esri International User Conference (UC) and realized that, while enormous strides are being made in technology development, geohealth still seems to be an emerging market segment.  Why? Ready accessibility of health-related geospatial data of all types is a key need.  Another key need is a clear vision of what “health analysis” means within a geospatial framework. 

Data – first key need in geohealth: Todd Park, Chief Technology Officer of Health and Human Services,  is advancing the precepts of open health data, and is making an increasing amount of geospatial health-related data accessible across government agencies. (Here’s a description of MedMap 2.0.) Nonetheless, geohealth analysis is often constrained by the 85/15 rule; spend 85% of your time finding and massaging the data, and 15% undertaking the analysis.  Esri’s development of Community Analyst holds promise as a source for ready-to-use geospatial data, making available hundreds of thousands of data layers.  Released in June, 2011, the utility of Community Analyst has yet to be demonstrated in applied geohealth studies, but it holds enormous promise.

Health analysis vision – second key need in geohealth:  Geohealth is a sprawling topic area.  At the Esri UC, I met with Bill Davenhall and we discussed the need for a well articulated vision of what comprises a geohealth analysis.  What are the common themes?  What questions are addressed, and what activities are undertaken in a geohealth analysis workflow?  These issues have driven our development of SpaceStat software, and I would like to present a vision for geohealth analysis as I see it. 

Geohealth – Definition and questions addressed:  Geohealth assesses relationships between dynamic local environments and human health outcomes from individual- to population-level scales.  It seeks to address questions such as these:

  • Is a given health intervention appropriately targeted and effective?
  • What neighborhoods have high cancer disparities?
  • Where are the foci of spread of infectious diseases?
  • Are vaccines being distributed in an equitable and efficient fashion?
  • Where are “at risk” markets underserved by needed prescription products?

Geohealth activities in SpaceStat:  What analysis activities may be undertaken in SpaceStat to address these and other questions?  My list of favorite activities follows, each of which can be undertaken completely within SpaceStat with time-dynamic data.

  1. Describe data using descriptive statistics and statistical graphics
  2. Visualize data through time using time plots, synchronized windows and statistical graphics animation
  3. Visualize data geographically using maps, cartographic brushing and map animation
  4. Identify statistical, spatial and temporal outliers using boxplots, histograms, variogram clouds and LISA statistics 
  5. Transform variables using the normal score and z-score transformations with time-slice and time-weighted means
  6. Evaluate rate stability to determine whether adjustment for the “small numbers” problem is needed
  7. Stabilize rates using empirical Bayes and Poisson kriging
  8. Interpolate data using nearest neighbor, distance and kriging methodsIdentify sub-populations with significant health disparities
  9. Identify clusters and undertake disease surveillance accounting for human mobility, known risk factors and covariates
  10. Quantify and model spatial dependencies using global and local spatial autocorrelation analysis
  11. Quantify and model spatial dependencies using variogram analysis
  12. Make predictions using aspatial regression through time (linear, Poisson, logistic)
  13. Make predictions using spatial lag, spatial error, geographically weighted, and multi-level regression
  14. Make predictions using geostatistics
  15. Analyze model residuals through time
  16. Compare model results to identify parsimonious models with the greatest explanatory power.
  17. Reduce dimensionality using PCA and other multivariate approaches

SpaceStat’s methods provide a comprehensive suite of tools that address a host of salient questions in geohealth.  Send me a message with your vision for geohealth analysis and let me know what you think!