Cartographic Skills: Final Project

SAT Mean Test Scores and Participation Rates in the United States - 2013

The SAT, or Scholastic Aptitude Test, is the standardized admission test most commonly used
by colleges and universities in the United States (College Board, 2014).  High school students may also choose to take the ACT test and both tests are equally accepted.

For the final project in Cartographic Skills class, we were given a web link to SAT score and participation rates for the fifty United States and District of Colombia.  We then created a single map to represent both datasets.  I used a chorpleth map to represent the mean scores for the states, with five classes and the Jenks Natural Breaks method.  For the percent participation, I used a proportional symbol scheme.  The participation rates range from 2% to 100% and I decided that it was not necessary to employ a square root or cube root factor to reduce the size range of the symbols.  The significant variation in SAT participation rate is well depicted with a direct proportional representation.  An inset map was necessary to detail the smaller northeastern states because their relatively large proportional symbols would otherwise obscure them on the map.

The most striking data characteristic that might be taken from the map by the user is the strong negative correlation between participation rate and mean SAT score.  The seven states in the central U.S. that make up the highest score class (dark blue) also have low participation rates.  The three states with lowest scores have nearly 100% participation as a group.  Without this dual data display, the map reader might be left with the impression that some states have much better-educated high school students.  In order to have a truly useful map display, however, it would be necessary to pair this map with a similar one covering mean score and participation rates for the ACT. Some states which have a very low SAT participation rate have a very high ACT participation rate; for example, only 3% of high school graduates in Wyoming take the SAT, but 100% take the ACT test.

Data Sources:

ACT.org (2014). 2013 ACT National and State Scores.
Retrieved from http://www.act.org/newsroom/data/2013/states.html.

College Board (via Connecticut Department of Education, 2014). Mean SAT® Critical Reading,
Mathematicsand Writing Scores by State, with Changes for Selected Years. Retrieved from
http://www.sde.ct.gov/ sde/lib/sde/pdf/ evalresearch/ ct_sat_public_schools_2013.pdf.

Module 12 - Cartography - Google Earth

In our final week of Cartographic Skills class, before we start our final projects, we learned more about Google Earth. 

Many of us look at Google Earth and Street View all the time, to find out how to reach a destination, or just for fun.  It has a lot of additional capabilities that we explored with this lab.  For example, it is easy to export an ArcMap layer or map as a KML file, which is Google Earth's format, and open it into the appropriate location in Google Earth, complete with out data from ArcGIS.

Creating a tour is also a very useful and fun aspect of Google Earth.  You can create as many place markers as you like (those little yellow push-pins), then zoom from one to the next, and even zoom in close to see 3D buildings and landscapes and street views.  Or, you can create a path, then have the tour follow the path.   It will be recorded as a video, to which you can add audio if you like.  You can then save the video and share it to be viewed in Google Earth.  

Here are two views of First United Methodist Church, St. Petersburg, Florida, taken from Google Earth.  

On the left is a Street View photo, and on the right is a 3D photorealistic model of the church.  St. Petersburg is part of a Google Earth Tour made by students in Cartographic Skills class at the University of West Florida, April 2014. 

Week 13 -GIS - Georeferencing and ArcScene

Georeferenced Aerial Imagery of UWF Campus

In this week's lesson, we learned how to georeference an image with no coordinate system to an existing vector file.  In order to do that, we matched points on the raster air photo to the same points on the vector file of UWF buildings.  By locating matching at least ten easily-identifiable points from the two files, such as the corners of buildings, the Georeferencing tools in ArcMap make it pretty straight-forward to stretch and adjust the raster image to fit over the vector polygon features.
The RMS Error is a measure of the accuracy: a number below about 15 is acceptable.  In this example, I managed to get the RMS errors down to between 3 and 5.  What's most important, of course, is that the features on the raster visibly line up with their counterparts in the vector shapefile.

In the second part of this exercise, we used ArcMap to digitize a building and a road that were not previously part of the polygon and linear vector shapefiles.

The upper unset map to the right shows the location of an eagle nest that is being protected by a buffer zone in which no new development can take place.  There is a .jpg photo of the nest located in the attribute table of the .mxd, for the point representing the eagles' nest (below).  This photo can be opened from within the ArcMap .mxd, using the Information button.

An Eagle Nest near the UWF campus

In the second part of this week's lab, we used the ArcScene prohgram to create an oblique, 3D image from the UWF campus map that we earlier georeferenced with the aerial imagery.

To do this, we "draped" the raster imagery and the vector files of buildings and roads over a Digital Elevation Model (DEM) of this area.  Because the height of each building is contained within the attributes of the Building vector dataset, we could instruct the ArcScene program to add the height of each building to the ground elevation (with 5x exaggeration to help visibility), and thus have a 3D picture of the buildings on the campus.

Module 11 - Cartography - Dot Mapping

Dot Map showing Urban Population Distribution
in South Florida

This type of map is a dot map of the population distribution of urban South Florida.  All of the dots are the same size and each represents a given value, in this case, 20,000 persons. The source of the population data is the US Census Bureau, and it has been added to ArcMap, by county, in the form of an Excel spreadsheet.

The dot symbols are more tightly packed in areas where population density is higher, providing a very intuitive presentation.  In this map, the dots representing the population of each county have also been restrained to areas defined as urban.  To do this, the ArcMap masking tool is used within the symbology controls of the population layer.  As a natural result of this masking operation, no dots fall within areas of water, because no urban areas are located there.

Introduction to GIS - Natural Resources Group Project: Map Gallery and Summary

Intro to GIS: Group 3 Natural Resources Map Gallery

ArcGIS Case Study
Assessing the Success of Surface Coal Mining Reclamation at Kayenta Coal Mine, Arizona

My map and summary of a GIS case study address Peabody Western Coal Company's Kayenta Coal Mine in Arizona.  All surface coal mines in the United States are mandated by federal law to reclaim the mined land as coal extraction is completed.  Ayers Associates engineering firm, contracted by PWCC, has created an automated ArcGIS/ArcObjects-based user interface which greatly improves the speed and efficiency at which erosion and sedimentation stability modeling is done as part of the reclamation process.

The map I included in our group Map Gallery show the Four Corners region of Arizona.  The Kayenta mine is indicated by the pin symbol, and its features can be seen in the imagery of the base map.

Week 12 -GIS - Geocoding and Network Analysis

Geocoded EMS locations and an Optimal Route

This week in Intro to GIS we learned about how addresses can be put into a code, or geocoded, in order to be used in GIS.  In this case, we had a spreadsheet listing the street addresses of some Emergency Medical Services facilities in Lake County, Florida.  We created an Address Locator coding tool and then placed their locations onto an edge map from the US Census TIGER site.

We then used the Network Analyst tools in ArcMap to plot out an ideal route from one of the EMS locations to two other locations in the mapped area.  This route is the optimal route, taking into account the characteristics of streets and junctions along the way, and computing the estimated time it will take.