When I first began producing these maps in 1996/7, I felt I was onto something that had potential if it were used correctly. At the time it was impossible to produce an animation with computer generated remote sensing imagery or GIS 3D maps using DEMs and the like. The availability of space on the hard drive was too limited to store all the images required for a good video, and the terabyte was just a theory back then, not common to the PC market like it is today.

During the 1990s, one usually had to resort to any of several methods to store data outside the UNIX environment. The old Magnetic Storage tape reels were finally replaced by a microcassette version, which required a couple of hours to update each night. But these typically were used for storing exceptionally large amounts of information, like the holdings of a university library, a large collection of satellite imagery, etc. Still, this method of producing and storing videos was one step further for us remote imaging and animation enthusiasts. Because GIS and computerized video productions did not go hand in hand that easily back then, most of this work still required skills mastered only by cinematographers and students in the art department or nearby art school.

So, for my first class on spatial imagery, I produced this rotating 3D floodplain model, but could only present it in segments, selecting just a few rotation angles, and then presenting these one at a time on an overhead. The point was to show how the flood plain would fill with water if the surface level went up 50 or 100 feet, along a local creek. This was done in order to demonstrate that water rising along a river bed follows a unique pattern dependent on flow patterns, topographic surfaces, soil surface slopes, vegetation features and land use activities.

Relating this to my thesis was my major goal for this session. I used this methodology to define the diffusion patterns of water as it assisted in the dispersal of vibrio cholerae, the organism responsible for Asiatic cholera. When this diffusion process is overlaid on such areal features as soil type and chemistry, one can see where alkaline soils manage to impact the viability of the organisms before they infect people, once the water settles. This method can also be used to demonstrate how specific land forms, vegetation cover features and utilization patterns can have an impact of where and when people are first exposed to the vibrio. My thesis was on the ecology of waterborne microbial diseases like Asiatic cholera, to determine why the cholera only occurred in certain parts of the Oregon Trail, whereas other causes for trail diarrhea and less frequent deaths tended to occur along other portions of the trail, such as the Salmonella intermedia associated with the non-vibrio induced diarrhea epidemics west of Nebraska along the Oregon Trail.

Twenty years ago, all of this work had to be stored for later use, and zip drives were the only choice for the time. CDs were just then becoming popular, but burners were expensive.

Zip drives were in their peak in 1997, and when they first came out every student spent 8 to 10 dollars a disk and usually had to buy two per quarter, more if you had more than one lab course. Buying these in bulk brought the price down to a few dollars per disk in the late 90s. Thes cost was around five dollars per disk by 2000.

These disks stored quite a bit, but still took up a lot of storage space as physical objects. (I still have two or three boxes of these sitting right next to me as I try to figure out what to do with them now. ) Back then, each disk could store just one Landsat satellite image, in its various parts (5 band to 7 band). With IDRISI, I could easily produce a dozen maps from the Landsat image in just one session, and up to one hundred related shapefile and raster images that also needed saving if I wasn’t careful.

Reviewing my study of 3D modeling of disease patterns recently, I realized I had some projects remaining from years ago that could now be accomplished. The merging of the images into a video was now possible. So with the new software tools out there, the larger hard drives, the availability of the terabyte storage device, I was finally able to return to these old projects, and produce a map presented in the form of a video. As was the case with the IDRISI software, the new software I used allowed me to employ some old tricks I learned regarding the development of this novel surveillance tool. One could use this methodology to produce maps in 3D and 4D formats, allowing for images that changed their z-axis, demonstrate a tilting effect, or result in a map series that demonstrated temporal sequencing of a disease flow pattern.

A number of different examples of producing these maps have been provided. They demonstrate the value of this use of a GIS in a standard surveillance and disease monitoring program. Because this methodology is yet another way to map and illustrate disease patterns, it serves as support for the more traditional methods old timer fall back to in the form of census data, demographic and disease ecology GIS studies. To me these maps serve multiple purposes. They are excellent teaching tools on how and what to map that effectively demonstrate a very active end product able to turn heads. They also provide epidemiologist with new insights into older findings, based upon newer methods of presentation and review. For non-epidemiologists, they tell us something about society as a whole–like where most of the people reside and where most of the money needed to cure diseases or improve the quality of life exist. They also tell us about where certain diseases demonstrate tendencies to act differently, out of the norm when it comes to defining predictability.

Sometimes you look at these maps and just know that the peaks that are illustrated will recur again and again. Other times, a peak appears just for that moment and then goes away, only to pop up in some other unpredictable location. Still other behaviors we can tell are very much regional, will recur spontaeously, and at times be unpreventible due to human nature and/or the natural ecology of a given disease patterns and type.

There are several maps I developed that can be used to illustrate different disease patterns.

I found most disease maps to be indicative of and based upon human population density. There are also maps that illustrate disease patterns developed independent of population density, due only to environmental features. Human populations can be broken down into specific age groups and gender groups, even more if we have the right data (ethnicity, religion, Hispanic or not, etc.) Some medical conditions, diseases or ICDs demonstrate behaviors of one type in one part of the country, with other age groups mainfesting the same in other very different parts of the country. Suicide for example manifests itself in younger populations quite heavily in the Pacific Northwest, whereas older, pre-retirement populations show a greater likelihood of engaging in suicide in areas famous for this event, such as Niagara Falls and western New York.

The following are examples of disease map videos.

The first is what led to this mapping idea for public health applications. This work was developed due to my remote sensing work. The formulas and sqls used demonstrate a new application for an old tool.

Example 1: Immunizable Diseases

Immunizable Diseases

‘Healthy People 2020 – Missed Opportunities in Current Childhood Immunization Programs.’ 2009/2010.

The rest that follow are in the works . . . 

Example 2: Nature’s Fungal Diseases — Soil, Climate, Topography and Disease

Example 3: an example of Regionalism

Example 4: Japanese Migration into the Midwest–Culturally-Bound Biological Syndromes and Conditions

Example 5: Hereditary Choroid Dystrophy on Long Island–A Possible Haven for Genetic Diseases (with a Rich and Very Well Localized Gene Pool)

Example 6: The Pacific Northwest–From Radicalism to Changes in Self-help Disease Prevention Activities

Example 7: Pyromancy–the Ubiquitous Nature of Bad Teen Behavior

Example 8: Rhinosporidiosis in Western Florida — the winds and humidity of the Gulf stream matter

Example 9: Agriculture Country and Farmer’s Liver Fluke

Example 10: Homeless in Seattle (16-20 yo)

Example 11: Shaken Baby Syndrome

Example 12: Infibulation and African Culture

Example 13: Pregnancy and the Heart of Tobacco Country

Example 14: Altitude Sickness — is it all in the head?

Example 15: Coccidiomycosis — the Best of the South, the Worst of the South

Example 16: Crack Babies

Example 17: Children Abused by Kids

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