Final Project

For our final project assignment we were given two basic choices: either apply what we've learned to a pre-fab project, or do the same to one we've created on our own.  I went with the latter option mainly because I was curious as to how the 2013 Rim Fire has affected the overall forest health in the area.  Given that the state of California is currently in a severe drought I suspected that these effects would also be highly visible on Landsat imagery.

Unsupervised classification of my study area.  MMU was 1:100,000 so take this with a grain of salt.

To start, I'd like to mention that I'm not a botanist, so a lot of the plant terminology and spectral analysis discussion mentioned throughout the course was something I've only recently picked up.  Before this class I had no idea what a NDVI was, or even how to tell one tree from another when viewing various multi-spectral data (I still can't, unless I've been given pointers on what to look for first!).  So in the spirit of applying all that I'd learned over this course I focused on a project that required the classification of healthy vs. non-healthy vegetation.

The first image shows a very rough classification of healthy vs. non-healthy vegetation.  The MMU was at 1:100,000, which at first I had done for time saving purposes (mostly to not go crazy during the analysis portion).  I now feel that I made the right call, since the study area is rather large, and I was trying to quantify general trends.  For something more in-depth, perhaps a 1:50,000 or larger scale should be used.

Comparison view of three years shown across two different band combinations.

The comparison of the multi-spectral imagery was perhaps the most exciting part of this project, and also the most enlightening.  For example, on the bottom left of the image above you'll see a map inset with a lot of bright green - that's healthy (fast-growing) vegetation.  The image in the middle was taken during the Rim Fire, and one can see how dark the remaining vegetation had become.  Part of this could be due to the change of Landsat satellites used - 2010 marked the wane of the Landsat TM 5, and since 2012 Landsat 8 has been generating imagery for the United States.  With the change of satellites marked a change in the number of bands, which is why I've listed two band combinations on the map.

The 2015 imagery had smoke cover... but the website said no cloud cover!  Which I suppose is technically true.  The smoke made interpretation of the image very difficult, although if nothing else the image shows how pollutants in the air can be visualized.


*Originally published December 9, 2015.  Updated on 2/27/2017 to repair image links.

Week 15 - Dasymetric Mapping

This lab marks our final lab of the semester.  In it we covered dasymetric mapping, which is a mapping style that has been around since the 1800s.  This technique involves using ancillary data such as land cover information to map where a given population is more likely to reside, based on various attributes of the ancillary data.  Dasymetric mapping is used when one wishes to extrapolate population information from an enumeration unit to another category, such as land cover cells for urban areas.  Since this type of mapping is an estimate, quite a bit of error can be introduced into the estimated results - particularly since one is estimating values from two different areas that do not cover the same spatial area.  Error checking is key to staying on track with dasymetric mapping.

For our lab we compared an areal weighting technique and a dasymetric mapping technique.  For the areal weighting we estimated the amount of the school aged population per a given high school zone, but that was located outside of areas covered in water. The theory here is that the population could reside anywhere within the census tract as it intersected with the high school zone, as long as that area was outside of a water polygon. 

View of impervious areas (in red) as they relate to census tract and high school zones.

The view above shows the dasymetric mapping result of our lab.  Instead of using land cover information for our ancillary data we instead used a measure of imperviousness.  The impervious areas are shown above in red; gray areas indicate zero imperviousness.  FYI, imperviousness indicates a built-up area (an impermeable area), and is a better measure of determining where people are more likely to reside than using land cover data alone.

The goal was to determine how many school aged children reside in the impervious areas per high school zone.  Each high school area (shown above bounded in dark gray) contains census tract data, but as the view shows the two are not spatially congruent.  This also holds true with the impervious areas, which are depicted above in raster format.

To make this all work I ended up using the Zonal Statistics to Table to determine the amount of impervious areas per census tracts.  This operation was completed first with my census tract and impervious data, and then again using an intersect of the census tract and high school zone information.  New fields were added to each result to account for a 'before' impervious area and an 'after' impervious area.  This was important because to ultimately determine the population of school age children per high school zone I used the following calculation: school aged children per high school = total school aged children * ('after' impervious area / 'before' impervious area).

The final result, after error checking against a reference population, showed that only 10% of the population was allocated incorrectly.  While not perfect it indicates a slight improvement over the areal weighting technique, which resulted in 11% of the population having been allocated incorrectly. 

*Originally published on December 17, 2015.  Updated on 2/27/2017 to repair image links.

GIS Student Portfolio

For our final internship seminar assignment we created a portfolio of our GIS related work.  My portfolio contains project examples that were created throughout the course of the entire GIS Certificate program, and can be viewed here.  An audio discussion about my portfolio content can be heard here. 

The portfolio also includes some examples from my GIS internship, which I'm happy about since many of these same map products were also quite time consuming to create!  The assignment helped show me how far I've actually come within this program.  It was kind of fun (and sometimes also cringe-worthy) to review old projects, and see how my overall map style has evolved. 

Overall I found this assignment to be very useful, and I will be incorporating this into my set of job search materials.  This includes adding a revised version of this portfolio to my LinkedIn page.  I realize I could have done that for this assignment - but then I wouldn't have had a handy paper copy to take with me to interviews!

Lab 14 - Spatial Data Aggregation

This week's lab focused on the modifiable areal unit problem (MAUP) and ways to identify it.  One very well known MAUP issue involves political districts and the practice known as gerrymandering.  Gerrymandering essentially represents a zonal effect of MAUP in that polygons which represent voting districts are drawn in such a way as to favor one political party or another... the result rarely very much to do with population pressures or the like.  Usually a gerrymandered district will appear very compact, yet also elongated and irregular in shape.  It's a polygon that looks like it was drawn as if the cherry-pick the residents who will fall within that particular district.

Example of a congressional district that is very compact - a possible sign of gerrymandering.

Another gerrymandering test involved viewing how well a congressional district represented its community.  Ideally the district would not break up a county - for example, ideally a county would have the same district, not multiple congressional districts.

*Originally published on December 1, 2015.  Updated on 2/27/2017 to repair image links.