Mean SAT Scores & Participation for 2013 by State

This project involved displaying two datasets on one map, the mean SAT scores by state for 2013 as well as the rates of participation. The scores are displayed as a choropleth map with the participation rate overlaid as bar graphs that show the ratio of test-takers to non test-takers for each state. Care was taken that the bar graphs for each state were clearly assigned and readable.

There seems to be a clear inverse correlation between rates of participation and mean scores for each state. So much so that for the highest two classes for SAT scores (i.e. 1601 and above), the highest participation rate reaches only 13% (Ohio). Meanwhile, the states that have 100% participation or close to it (Idaho, Maine, Delaware) have mean scores among the lowest in the country. All three are in the bottom five along with another high participation region - Washington DC at 81% - and a middle of the road state when it comes to participation, Hawaii.

The low participation states seem to have a population of motivated test takers who have a vested interest in the outcome of the test, while the high participation states have made taking the SAT mandatory for all graduating high school students, thus diluting their results with a large volume of students with no stake in their test results.

The primary takeaway from this map, then, is that mean SAT scores by state is not a useful metric for comparing educational outcomes across states.

Mean SAT score totals by state for 2013

Georeferencing & ArcScene

This week's primary assignment involved taking aerial photos that had absolutely no geographical reference metadata, importing them into ArcGIS and georeferencing them with existing map data. This was familiar territory for me, as this has been the main activity I've done with GIS software as an archaeologist. I was provided with two rasters that showed portions of the UWF campus as well as projected vector files for campus buildings and roads. By associating points on the rasters with points in the vector layers, I rectified the raster layers in short order, getting them aligned with their vector counterparts. The rasters were in fairly good order, so there was not a lot of error in their placement, even though one of the rasters had been deliberately warped to make georeferencing it somewhat more complicated.

A secondary task this week was to produce an exaggerated 3D style map based on the georeferenced map I created using ArcScene.

Georeferenced UWF Campus Map

UWF 3D Map

Population Density Dot Map

This mapping project involved displaying population density by dot density. Provided with a base layer map of the counties of South Florida and a .xls file with population information, I joined the excel table to the map layer and then had ArcGIS display the population of the area by overlaying the basemap with dots representing a quantity of population.

In order to better represent the actual lived population density of the area, the size of the dots and the quantum of population they represented was adjusted manually until an appropriate relation was found (where in the densest portions of the map, the dots would begin to overlap each other). Also, a mask layer was employed that represented the urban areas of South Florida and all dots were constrained such that they had to fall within the limits of that mask, providing the result below.

Further work was done adding further geographical context, with large cities and some roads being identified on the map. However, ArcGIS' dot density feature proved to be problematic once masking was employed. Not only did redrawing the dot layer consume a lot of time every time a change was made to the base layer (scale, location, etc.), but when there were other layers in the .mxd file - cities and roads - turning on the masking feature caused ArcGIS to crash. Several workarounds were attempted, but there was nothing for it, so I simply redid the map base layer with the dot density information and produced a much sparer map than anticipated.

Population Density in South Florida

Case Study for GIS in the Utility and Communications Industry

Over the past 3 weeks, I've been part of a working group that gathered and summarized several case studies in GIS implementation for the utility and communications industries.

I looked specifically at the case of the DuPage Water Commission

When the commission started looking to GIS an alternative to paper mapping and institutional memory when it came to keeping track of its assets and resources, the established solutions for water utilities were found to be inappropriate. Ultimately, the DWC found a useful model that scaled well to their needs from another industry, the ArcGIS Pipeline Data Model.

Geocoding & Route Mapping

This week I was given a set of addresses and was required to geocode them, i.e. associate them with their proper locations on a map. In order to do so, I downloaded the TIGER/Line file from the U.S. Census Bureau for Lake County, FL, which is where the addresses were located. The TIGER/Line file is a topologically correct vector line dataset for streets and addresses in the U.S., which means that the dataset is made up of edges (usually road segments) and junctions (crossroads) such that a path or paths can be calculated from one junction to another. The TIGER/Line file also associates house numbers with street segments, as well as zip codes and a wealth of other addressing data.

To geocode my set of addresses, I ran a geocoding tool in ArcGIS, which correctly placed 68% of the addresses. A 32% miss rate is not acceptable, so I had to look at the addresses that went unmatched and manually locate them (with the help of google maps in this case). I was able to do this successfully with all but one address that seemed to have been misspelled, leaving me with a 95% success rate, which was good enough for the purposes of this exercise.

With my geocoded data, I was then able to use ArcGIS' Network Analyst tool to do some simple routing analysis, creating a route based on three arbitrarily selected road stops. A route was then created that minimized travel time and hit all three stops.

Optimal Routing Example for Lake County, FL