Human Scale GIS
The drop in the number of Microsoft developers has received a bit of attention lately.
What attracts programmers to a platform? Certainly robust APIs, good documentation, and market demand. But there’s something else too, at least for GIS programmers. While programmers in general are migrating to Linux, I think GIS programmers often choose a platform where they feel they are part of a community that is doing something.
In spite of the Bill & Melinda Gates foundation, Microsoft doesn’t instill programmers with a feeling they are working on a platform that’s making a difference. Google (do no evil) does. And of course, so does ESRI.
Google’s management understands Human Scale GIS. Instead of focusing on tools for sophisticated statistical analysis, they focus on tools that allow one to quickly drill down to individual people. Writing in Google’s Geospatial Organizing Principle, Michael Jones (Google CTO) says:
“Zooming to the human level of Figure 9, we move
from an abstract awareness of 300,000 deaths to the
remembrance of just one, M, as told by her brother,
a Zaghawa man from Miski. Does news of her death
feel less, more, or exactly 1/300,000 as powerful as
Jonathan Gurwitz’ article piqued my interest about Paul Slovik’s paper, “If I Look at the Mass, I will never act: Psychic Numbing and Genocide“, published by the Society for Judgement and Decision Making. Slovik offers an experiential (and yes, even statistical) basis for human scale information.
“Numerical representations of human lives do not necessarily convey the importance of those lives. All too often the numbers represent dry statistics, ‘human beings with the tears dried off.'”
ESRI has a long history in humanitarian aid. In the realm of Human Scale GIS, ESRI offers ArcExplorer. Unlike Google Earth, it has the ability to do very sophisticated geooprocessing.
How about GIS for Microlending?
It would be interesting to spatially enable Kiva to support coordinated lending. Kiva already manages information about microlending at the human scale. What’s missing are the more traditional GIS scales. These other scales of information would allow lending to be more coordinated. For example, suppose one lender is considering loans for dairy processing, other lenders could offer loans for cows, by browsing through maps of recently funded dairy processing centers. This would allow lenders to collectively focus where they can have the greatest regional impact.