Lab 15 - Dasymetric Mapping

The objective of this lab was to learn to use ArcGIS to perform dasymetric mapping, which is a technique for allocating data from one set of boundaries to another. In this lab we learn to use a common example in which data is allocated from census units to another set of boundaries, in this case school zones. We start by calculating an estimate of high school students population based on the population of children aged 5-14 by using areal weighting, and then do the same analysis using dasymetric mapping.

First, we are provided with county and census block data, as well as data for the Manatee river basin. We quickly see that some of the census blocks are partially within and outside the river basin, but we want the population just of the river basin. To do this, we use areal weighting. An assumption made is that population is distributed evenly within each block group. Areal weighting is basically taking the population of a census block and, using the percentage of that block within the river basin, calculating the population of that block that is within the basin. For example, a block 30% within the basin with a population of 200 will have a new population of 60 within the basin.

In the second part of the lab, I started working with the data to be used the rest of the lab. The objective was to used the population aged 5-14 to predict the upcoming high school attendance. Our data layers are census tracts, water polygons, and high school boundaries. We can assume that the population is equally distributed in areas outside the water polygons. The trick here was determining the area before and after the polygons are joined. Before any overlay is done, I added a field for area and calculated the area of each census block. Then I clipped both the high schools layer and the hydro layer to the extent of the census blocks. I also clipped the hydro clip and the original census layers to the extent of the high school clip in an attempt to avoid "slivers", which could affect my population estimate. I used the Union tool to combine all 3 layers, created a new area field, and calculated the area. Next I created 4 new population fields (one for each original population field) and used the equation provided to calculate the new population. The equation is:

new population = original population * (area after / area before).

So this is simply an adjustment in the population based on the ratio of the area within the school zones. To calculate the error, I used provided reference population values and compared them to values determined by my areal weighted estimate. To calculate the overall accuracy, I used the equation:

Sum of Abs(Error) / Sum (reference population) * 100.

Based on this, the total % of people aged 5-14 allocated incorrectly was 11.6%.

In the next portion of the lab, we were to use dasymetric mapping to see if we could decrease the % allocated incorrectly. To me, this was a very confusing concept, but I think I have the concept (if not the correct values) at the end. In addition to the data from the previous section, we are provided with a 30 m x 30 m raster of imperviousness values. Impervious objects are objects that are covered by impervious features (i.e. concrete, asphalt, etc). Generally as the % imperviousness increases, so does the population, so we can use this as ancillary data to try to better predict the high school populations. Based both on what is said in this lab and on reading papers about it, we can safely assume a linear relationship between imperviousness and population. I knew that I wanted to do the same type of calculation as with the areal weighting, but was not sure for the longest time how to determine the new imperviousness. Using the Zonal Statistics as Table tool, I was able to obtain the information I needed from the imperviousness raster, specifically the Count, the Mean, and the Sum. After joining this with the census data, the count value is the number of pixels in that tract, the mean is the average imperviousness per census tract, and the sum is the mean * count. I performed an intersect using this layer and the high schools layer, which is where I was stuck for a while. I finally realized that since the census tracts are getting broken up into school zones, wherever they don't "fit" perfectly (where portions of a census block are in multiple school zones), I have multiple values for that census tract. To determine the new imperviousness, I selected each census block and calculated the new imperviousness by dividing the original imperviousness by the number of school zones each is broken up into. Based on that, I was able to calculate the imperviousness weighted estimate using the equation:

new population = original population (new imperviousness / original imperviousness).

After calculating my overall accuracy, using imperviousness leads to a misallocation of students of 11.9%. I am questioning these results however, because I believe that imperviouness should reduce the % misallocation of population. It is possible my methodology or math is incorrect as this was a complicated topic, and I'm still not sure I fully understand its application. It took a lot of trying different kinds of overlay before I figured out the correct methodology. Based on my results, it appeared that the imperviousness definitely reduces the % of incorrectly allocated students in school zones that have larger census blocks in them, especially if the tracts are broken up less by the school zones.

GIS Portfolio

As one of our final assignments for the GIS certification program, we were tasked with creating a GIS Portfolio, which is uploaded here. Additionally, I have created an audio portfolio, also uploaded here. I feel that throughout the coursework, I have a better understanding of how to create a map, considering what I am trying to convey to the map reader. I also learned how to use various color schemes and symbologies effectively and how to use different types of input (shapefiles, aerial imagery, satellite data) to create effective maps. I learned a lot during the course of this certification program and feel prepared to use these techniques throughout my career.

GIS Portfolio (PDF)

https://drive.google.com/file/d/0B6JDrUuVfdOhSGhZUVFnZ0dGSjA/view?usp=sharing

GIS Portfolio (Audio)

https://drive.google.com/file/d/0B6JDrUuVfdOhTmZOYjZhOVJhLUk/view?usp=sharing