Brian Altonen, MPH, MS

There are two main topics often seen in global health mapping projects or programs, and one method that I created and present here. They are as follows:

• Regional Mapping of Global Health
• Disease Mapping and Global Health
• Grid Mapping Global Health

This page focuses on the historical mapping of disease mapping globally, and is kept separately due to the nature of the maps and disease geographies that I plan to discuss and include in this section.

Regional Mapping

Any Regional Health related cartographic impressions not seen elsewhere are included here. Since most of the subjects I cover are historical, this is mostly a historical medical geography review. These results are not as impressive as those “firsts” I tend to cover on my 18th and 19th century disease mapping and medical geography pages.

Disease Mapping

The World Health Organization report is found in this section, with some of my maps of the same ICDs generated for U.S. cases a few years back–for comparisons. WHO also produced a number of very complete, detailed historical maps of foreign born disease migration. This maps were published during the 1950s and have historical disease migration data that is very hard to find in a single place. These will be included in this section as well. (There are about 15 of them, measuring 3′ x 4′ approximately).

Grid Mapping

Grid Mapping Global Health is perhaps the most exciting form of mapping population health and disease for the future.

I already developed several planar methods for grid mapping disease at the national level. These are explained in detail on other pages.

The next goal is to apply this method of grid mapping to the spheroid 3D rendering of earth. A method much like this is found on the GEcon page produced by the Economics Department at Yale University, although there are some important differences between my method and the GEcon method. The GEcon staff and Economics Department at Yale have already developed a standard for basemapping and displaying global health using GIS.

My plans are to produce a similar method for mapping data without the need for GIS, based solely upon true 3D spatial modeling equations. In the least, this method (algorithm) is not so much an invention as it is a new use for and old formula. However, its application is creative and quite impressive, and its potential applications to researching a large area or global marketplace are what stand in its favor.

In the current marketplace and those settings where consumer demands and corporate costs drive most of the activities taking place, there are several advantages to knowing how to apply non-GIS SAS algorithms to the work setting.

The major advantage is no need to make use of GIS for your reporting and generating your reports. This eliminates the need to proceed with a massive GIS program for producing your desired outcome. This doesn’t mean GIS is not necessary to the industry for analyzing overall business success. It simply provides a method to more rapidly produce a report and illustrate the results of this report at a national or large area level.

A second benefit for this method pertains mostly to the limits of time that are built into any mapping project. The richer the base map for your work, the more time is needed to reconstruct that based map each time a print out is produced. With time, speed will improve in GIS, but for now this is the time-limiting event in any large scale data mapping program. Producing raster or grid maps is always faster that line-arc mapping techniques when small area details are required for the final project. When we first began learning GIS back in the 1990s, and deciding between raster and vector methods was the hot topic, this was true due to the size differences in base data for each of these two methods. This base map data size issue is no longer impacting such processes as storage, analysis and production due to the design of larger storage methods and development of faster data pull and analytical techniques including parallel processing techniques at the Big Data level.

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Notes and Commentary

Over the next decade I suspect national population health mapping will become a norm. The major question that remains is how will these maps be presented?

The current trend is to display these generic 2D maps of the United States with choroplethic representation of regional health characteristics. Some of the better examples out there right now for these maps are the census related maps produced by ESRI due to their incredible accuracy, reliability and validity. Generally (subjectively) speaking, these are very flat, 2D representations of the health of the country. They have the ability to show regions where high numbers and frequency of illness, incidents, etc. take place in this country, and give us a nice overall impression of this country in terms of its blocks or segments of places where certain problems exist. But within these high risk blocks or segments, we have no idea where these problems are focused, may have problems speculating about why such a high prevalence exists in such a region. We are left to ponder, how are they developed? in which direction might they be heading?

If a medical problem is linked to population and therefore a product of population migration or flow across the land surface, it helps to know where the absolute center of that medical problem exists. Such a center can be where the most, or highest density of cases exist, or where the best physical environment for resulting in a disease exists, or where the right combination of physical environment and human features converge so as to result in such local concentrations.

Unfortunately, there are no peaks in two-dimensional choropleth maps of disease. There are just high, medium and low risk regions. This is where the role of 3D mapping comes into play. Even if a region is statistically equivalent to another in terms of overall or average risks, such as when a series of neighboring regions are all in that 5 out of 7 range category in terms of risk, we are still able to define a focus or nidus for a region using 3D mapping. This can make producing such maps controversial. Still, it is kind of frustrating trying to make good use of a 2D map for developing public health intervention activities. You dont don’t know where to start, or open you office or clinic. With the 3D grid or small areal map methodology for analyzing disease/medical measure over space, the results that are produced do allow for better planning to be made. You know where to focus and concentrate your preventive health activities.

But there are other less humanistic, less phenomenological reasons to select 3D mapping over 2D mapping, and in particular my 3D grid mapping technique over zip code focused tecniques. The advantages to 3D modeling over the use of the classic 2D flat model is twofold.

First, it provides us with the same data as the 2D model if programmed properly, but due to this programming nature also has the ability to provide still more data about seemingly identical regions displayed by the standard 2D model.

Second, depending upon the G* stats used to produce the base values for the grid of this map, it can also be used to tell you where the most demand or need for change exists. Whereas with the 2D map we know the area (county, census area) where we need to initiate a program might exist, with grid or small area modeling we know down to the neighborhood or community how this intervention has to begin and then proceed. For example, a census tract or zip code derived map used to propose an intervention plan does not tell us which cities to start our program in. So naturally, we go to one or more of the big cities in that region, or decide to focus on lower income regions identified using census block or block group data, or decide to intervene first where the most children are and so turn to school district data to identify these supposedly high risk regions. Unless we have good, hard data on the important features of these types of polygon defined areas, much of these intervention activities are planned for and decided upon based on certain assumptions being made, about where the poorest, most needy families live, or where the most underfed and malnourished children ago to school, where the most rug-related activity seems to be taking place, where the most spouse abuse and child sexual abuse are happening. The way to get around making such assumptions is to grid map the same data defining, smaller areas along the way, and seeing where the true concentrations exist.

So, the 3D modeling technique has its advantages over 2D modeling of data. But one is not meant necessarily to replace the other. We can still benefit from that basic 2D mapping that is so common to the public health academic community. But 3D modeling is the only way to go statistically if we are concerned about such things as corporate costs, institutional requirements, future projections regarding assisted living needs, overall low income family health coverage projections.

The Yale EGRID method of mapping national and global economic data is one way to approach this curiosity many of us are going to have about world health statistics.

The 3D National Population Grid Modeling technique is the second way to go.

The history of global health mapping is presented in this section for the most part. This section is designed to concentrate on the series of global health maps I have on infectious and vectored diseases, buy will probably go into other global health disease and medical related issues if and when such data is found to exist