Serendipitously Approaching GIS from Both Ends

"Serendipity" is the word I think most fitting to describe how I ended in a Geographic Information Sciences Graduate Certificate program at George Mason University.  In brief, after getting involved with Code for NOVA with the aim of finding real world learning opportunities for my students and hearing the folks there talking enthusiastically about publicly available data sets, I decided to enroll in GGS 550: Geospatial Science Fundamentals during the Fall 2014 semester.  This course got me totally hooked on GIS!  It provides me with a way to apply my mathematics and computer science degrees to real problems.

It seems like my timing has been wonderfully serendipitous as well.  A few years ago it appears I would have been limited to proprietary software and military industrial applications. Now it's free software and citizen science.  I mentioned last post how my current GIS course is a python programming course.  I'm beginning work on my final project for that course:

Mapping Housing Affordability in Arlington Virginia

Generate maps of Arlington Virginia which show the location of affordable housing units, both committed affordable (CAFs) and market rate affordable (MARKs).  If time permits, I'd like to be able to produce a map that shows changes in availablility over time (availability is rapidly declining), but that may be beyond what I can do in our available time.

My goal is to do this using Python with the needed libraries.  Our Python Geospatial Development book addresses everything I would need for this project, but it will require finishing all of the book, since working with shape files isn't discussed until chapters 11 and 12.

Since the project is due May 3rd, I will only be able to get started with the full map by that time, but if I begin to generate custom maps of Arlington and to place geospatial data on them (housing locations) together with relevant meta-data, I'll be confident from there that with time and can map whatever maps I want.

We are using Python Geospatial Development as our text, and I'm working my way through all the examples, converting them from Python 2 to Python 3. I'll be documenting what I'm learning in posts over the next several weeks.

With python libraries and things like GeoDjango to take care of the back end, I'm looking at Firefox OS for front end mobile application development.  With built-in GPS and other sensors, mobile devices are great tools for citizen scientists, and Firefox OS's mission of expanding access to information is just what I was looking for.  I've got several students working on Firefox OS projects this Spring.  I've started reading Firefox OS in Action with an eye toward both learning to program on the platform myself and to using this book as a resource with students.  I'll be documenting that as over the next several weeks as well.