Participation Assignment 1: GIS-based Red-cockaded Woodpecker Foraging Matrix Application

               

Red-cockaded woodpecker
(Photo Credit: USFWS)

 The red-cockaded woodpecker (RCW) is a federally endangered bird endemic to the United States.  It has been at the forefront of conservation efforts due to its dramatic decline in recent decades.  RCWs are obligate residents of old growth pine forests of the Southeast United States, an ecosystem that is highly fire-dependent.  The United States Fish and Wildlife Service (USFWS), the government agency responsible for harboring and protecting endangered species, has created a GIS-based habitat index matrix which scores suitable foraging habitat in the vicinity of know RCW recruitment locations.

                RCWs roost and nest in excavated cavities of living pine trees which are arranged in small clusters, with one bird per cavity.  Thus they have a “home” cluster to which they return each night after spending the day foraging and that they fiercely defend from adjacent RCW families.  Their foraging efforts are most efficient if they utilize habitat within a specific radius of their home cluster.  The RCW foraging matrix is used to quantitatively score the habitat within such a radius of a cluster.  The analysis is based on the assumption that RCW families will primarily forage within this area.

Array of cluster locations and circular buffer with
overlaid land use data
(Photo Credit: USFWS)

Concentric rings of varying radii from an RCW cluster are analyzed for suitability.  Forestry and land use data (dominant tree type, stand density, extent of undergrowth, etc.) collected in the field can be overlaid to these circles and the total area of “good-quality foraging habitat” can be determined.  The matrix computes a score of suitable foraging habitat within each concentric ring, giving higher priority to those circles with smaller radii (i.e. closer to the cluster core) so that land managers may alter silviculture or fire practices to produce more and better foraging habitat.  Thus an adaptive management strategy is applied using the output scores of the matrix.  The parameters as to what qualifies as “good-quality foraging habitat” is constantly changing with new and better information from applied ecological research.

Currently, the foraging matrix is an application published and updated by USFWS for intra- and extra-agency usage.  GIS specialists at wildlife refuges or the like may use this application by simply providing the given input data.

The article linked here is a master’s thesis that evaluates several clusters using the RCW foraging matrix. http://search.proquest.com/docview/89111636/previewPDF?accountid=14787

Module 2: Geoprocessing

This weeks assignment involved becoming familiar with model builder in ArcMap and scripting in PythonWin.  We were required to build a model which automatically ran a series of sequential steps and produced a desired output.  The input features were a polygon feature class of different soils (soils.shp) in a particular region and a polygon feature delineating a specific area of interest (basin.shp).  The steps were to first clip the soils feature to the basins feature, then to select those soils not suitable for farming, and finally to delete those soils not suitable for farming, thus producing a feature class of areas within the basin that had soil suitable for farming.

Above is the output of my model, and below the steps I followed for model completion:

1.       Right-clicked on Mod2_PCoppola.tbx, then from the drop down menu selected New > Model.

2.       By dragging-and-dropping from the ArcCatalog window in ArcMap, I added basin.shp and soils.shp to the model builder window.  I added the Clip tool the same way, but from the ArcToolbox > Analysis > Extract window.

3.       Doubled-clicked on the Clip tool within model builder window and added soils (blue recycle symbol) as the input feature and basin (blue recycle symbol) at the clipping feature.  I named the output feature class soils_basin.shp.  Clicked Auto layout and zoom extent to better visualize the model.

4.      Dragged and dropped the Select tool into the model builder window.  Double-clicked it to set parameters: Input feature = soils_basin; SQL was set to FARMLNDCL = 'Not prime farmland'; the output feature class was soils_notprime.shp.

5.      Dragged and dropped the Erase tool into the model builder window.  Double-clicked it to set the parameters: Input feature = soils_basin; Erase features = soils_notprime; output feature class = soils_prime.shp.

6.      Clicked auto layout and zoom extent, then saved the model as name = SoilErase; label = Prime Farmland Locator.

7.      I set each input parameter to a “model parameters” by right-clicking them and checking Model Parameter.

Week 2: Watershed Analysis

This week we focused on flow and watershed analysis.  While I was familiar the concepts behind these analyses (e.g. physics and geology), the process of running the appropriate procedures in ArcGIS was new.  We were given a Digital Elevation Model of Kuauai, HI and lead through the process of creating a stream network.  This involved: processing the raw DEM to fill any sinks, generating a Flow Direction Raster, Flow Accumulation Raster, and ultimately analyzing the output against the actual streams and watersheds as designated by the USGS.

Above is my comparison of a single watershed on Kuauai, the Lumahai River Watershed, to the USGS designated watershed boundaries and stream network.  This is a large watershed on the northern portion of the island, which at its terminus is a single river emptying into the Pacific Ocean, just west of the Hanalei Bay proper.  The two large maps compare the modeled Lumahai watershed and streams to the actual features, using a 200 cell threshold for the stream delineation.  The modeled watershed is significantly different in appearance near the northeast coast and the modeled stream system is relatively truncated compared to the actual stream system.  This is likely an affect of the threshold cell size, which could be decreased to better match the true stream network.

Module 1: Introducing Python

This is the first week on GIS 5103, GIS Programming.  We began the course with a general overview of the Python scripting language and programs, as well as the course syllabus.  Above is an image of the module folders that will be utilized throughout the course.  These folders were created by running a Python script that automatically generates the folders in the correct filepath - just one of the many uses for Python scripting language.

The script used to create these folders was provided by the instructor and we were responsible to "Run" it.  This was done in PythonWin, a scripting program that uses Python scripting language.  I was able to run the script using the following steps:

1.       With my cursor highlighting the CreateModuleFolders.py window, I selected File > Run… [an alternative would have been to press ctrl+R]

2.       In the Run Script window, I left all fields as default and pressed OK.

3.       To check that the script ran properly I browsed through my S:\ drive and found the GISProgramming folder, checking that all the modules and embedded folders were present.

As a graduate student enrolled in the course, I was also required to read and summarized the article:

Schauble, Holger, Oswald Marinoni, Matthias Hinderer. 2008. A GIS-based method to calculate flow accumulation by considering dams and their specific operation time. Computers & Geosciences 34: 635-646. 

This article describes and encourages the use of a new method for calculating flow accumulation of water and sediment, specifically for use in scenarios involving bulk flow and multiple, sequential dams.  The method uses an expansions of a pre-existing algorithm (D8), but incorporates two important, but thus far neglected variables: trapping efficiency (TE) and specific operation time (t_op) of dams.  Trapping efficiency is the percentage of water or sediment that is bunged at a dam and specific operation time is the ratio of dam construction to dam observation time (or the time over which trap efficiency has been recorded).  These two variables are important because several large rivers have been dammed in multiple places over several years; thus current D8-based models are unable to adequately predict flow accumulation.

Final Project: Bobwhite-Manatee Transmission Line Project

For our final project in 'Intro to GIS' we were put in the seat of GIS analyst asked to assess the completed Bobwhite-Manatee Transmission Line Project.  This was an actual project in which a new energy substation was being built, but needed a transmission line to connect with an existing energy center to the north.  The path chosen was based on meeting four objectives: 1) avoiding environmentally sensitive lands, 2) avoiding homes, 3) avoiding schools, 4) being cost efficient (i.e. not too long).  I assessed the proposed corridor based on how well it met those objectives.  Overall it sufficiently met them.  To see a detailed explanation on how I reached this conclusion, see my presentation at the bottom of this post.

Above is a map of the proposed corridor of the transmission line.  Below is a zoomed map of two environmentally sensitive areas.

Links to my Bobwhite-Manatee Project Presentation:

Power Point

Power Point Commentary

Have an awesome summer!

-PMC

Final Project: United States SAT Map

Introduction

At present, in the United States there are two major college preparedness exams: the Scholastic Aptitude Test (SAT) and the American College Testing (ACT) exam.  Both of these standardized exams are traditionally taken by graduating high school students and are utilized by colleges to assess these prospective students in terms of mathematics, reading comprehension, and critical thinking.  The SAT, for which scores range from 600-2400, is published by the non-profit organization College Board and administered by the Educational Testing Service (College Board, 2014).  The ACT, ranging from 1-36, is developed by American College Testing, Inc. and was created in 1959 to be a competitor to the then-dominant SAT (erikthered.com, 2014).  In 2005, the ACT for the first time administered more tests than the SAT (ACT, 2014).  The SAT, however, is still widely used in the United States today and there may be state-by-state patterns of implementation of and performance on this exam by graduating high school students.

It is the goal of this project to generate a map that depicts two phenomena: 1) the percentage of graduating high school students in each state to take the SAT in 2013, including the District of Columbia, and 2) the mean SAT score of these students for each state.  This map will be visually inspected for any spatial patterns existing therein.

Methods

Both ArcMap (version 10.2) and Adobe Illustrator (version CS5.1) will be used to generate a United States map depicting SAT participation and performance.  The map will clearly show all states and the District of Columbia with appropriate labels.  The map will have an orientation baring (north arrow) and scale.  The two phenomena depicted will be easily discerned by the audience using an imbedded legend.  ArcMap will be used to first compile all the data and create a rough map.  Adobe Illustrator will be used to further visually enhance the map and tidy up the layout.

ArcMap

In ArcMap, a United States layer (from “Module 7: Choropleth Mapping”) will be added to a blank template.  Using the editor tool, I will add a new column to the attribute table for ‘Percent Participation’.  This field, which is the percentage of graduating high school students for each state that completed the SAT in 2013, will be entered using data provided by College Board and the Western Interstate Commission for Higher Education (WICHE).  A graduated color scheme symbology with manual breaks will be used to display this data – light states will have low percentage participation, dark with high.  Then, in layout mode, a legend, north arrow, and scale will be added to this map.  This file will then be exported to Adobe Illustrator (.ai) for further manipulation.

Adobe Illustrator

This program will be used to add a proportional (graduate) symbol scheme depicting the 2013 average SAT score per state of graduating high school students.  An Excel spreadsheet of the data from College Board and WICHE will be created for easy re-sorting, as well as calculation of sizes for proportional symbols.  These symbols will be simple solid red circles with a 60% transparency setting placed over the state label.

Results

State participation ranged from 2% to 100%, and state mean SAT scores ranged from 1200 to 1838.  States with lower participation rates had higher mean SAT scores than stares with high participation.

Discussion

The (presumably significant) negative correlation between participation rate and state mean SAT score suggests that mean SAT score may be a misleading representation of the average graduate student’s aptitude in a particular state.  While it appears that the Midwest states outperform the northeast and northwest states, this may not be actually true.  For example, only the ‘best and brightest’ may take the SAT in these states, while in states like Maine and Idaho all students take it, thus lowering the average.  Therefore, comparisons between states may not be valid.

The between state differences in participation itself, however, is worth speculation.  The spatial pattern of SAT participation may be a result of tradition.  For example, the universities in coastal states may preferentially request SAT scores to ACT scores and thus there are more students who complete them.  Alternatively, these coastal states may have higher graduation rates and more students who wish to continue on to college.  Regardless of the reasons, there is a real discrepancy between geographic location (e.g. inland vs. coastal) and SAT participation rate that is visually obvious in the map generated.

Week 12: Google Earth

This weeks assignment was to generate a map using Google Earth.  This required first using the Map to KML tool in ArcMap, then loading this newly created file in Google Earth.  Google Earth is an open-source GIS in which users can create and easily share maps.  Here, Google Earth was used to re-create a map that was produced in a previous lab, then use the Tour function to highlight certain points of interest. The points of interest were major areas of high population density (i.e. Cities).  I chose to use my own Data Layer from the Dot Mapping Lab.

The Tour function in Google Earth may be useful during presentations in which the speaker is presenting spatial data, or explaining spatial patterns.  The audience would be able to easily visualize the landscape.  Another useful aspect of Google Earth is the ability to share maps over the internet.  Sharing ArcGIS (.mxd) files is often a difficult, or at least time-consuming, process.  Other users are required to have the layer or FGDB files for the map to be editable in a meaningful way.  In Google Earth, editable files can be shared or even edited simultaneously, thus providing ease of use between multiple users.  However, Google Earth is limited in its spatial analysis abilities.