Special Topics, Forestry, Project 5

Lynne Johnson

December 8, 2013

Special Topics – GIS 4930

Maple Sap Production in the Acadian National Forest Abstract

Clear-cutting is a traditional practice in the timber industry that generally has negative connotations.  This study focuses on an area in the Acadian National Forest and attempts to evaluate the clear-cut areas for maple sap production.  A variety of Geographic Information Systems (GIS) techniques and analyses were performed.  If enough trees prove to be sufficient in sap production, currently and in the future, then this will provide a productive alternative to clear cut areas.  A cross tabulation was created of the distances it would take to haul the sap to the nearby roads.  Results were then created and presented.

Power Point Presentation Link.

Special Topics, Week 13: Forestry - Report Week

This week finalized our analysis of clear cutting.  This topic was interesting and definitely had me divided about whether it's a good thing or a bad thing.  My final opinion and side was that I feel that it's not great but it's necessary.  If it's done correctly and follows the regulations that are set in place, then it can be a sustainable and profitable practice.  We had to create a poster, claiming our position and backing it up with analysis and additional scholarly articles.  

This poster was created to share my opinion of clear cutting practices via 

GIS analysis and research from scholarly articles.  I think it's an 

unfortunate necessity.  Regulations are in place for a reason

and as long as they're followed to point then it'll

be a profitable and sustainable practice.

Special Topics, Week 11: Forestry - Prepare Week

Clear cutting is a very controversial topic yet a necessity in order to produce many of the every day products we use.  There are, however, different ways to approach this practice. This week we used our analyzing techniques to establish the amount of clear cuts that are visible to the main roads in an area in New Brunswick, Canada.  We determined which areas were clear cut, determined the age of the clear cuts and also took into consideration, the elevation and terrain.  This will factor into the ability to see from the roads.  We used a variety of search queries and the viewshed tool for this.

Basemap of Fredricton, New Brunswick and study area

Map of clearcut which are visible and non-visible from the main roads

Special Topics, Week 10: Web Applications - Report Week - Final Story Map

This week we had to add one more customization to our story map and fine tune the map as a whole.  For my customization, I decided to add a zoom level to each point feature.  Initially, you're zoomed out, able to see from Chicago all the way to Hawaii.  As you go through the slide show, you're zoomed into that city.  Most of the time you're able to see all points located in the city at once.  I felt like this zoom level was optimal because it gives viewers some choice about the zoom level.  I also decided to change my map summary and edit a few of the site descriptions.  Here is the final link to my story map featuring my move to Hawaii!

Special Topics, Week 9: Web Applications - Analyze Week - Online Story Map

This week we put our story board plan into action.  Initially, we needed to find images for each of our locations (a full size image and thumbnail for each).  We then needed to edit the CSV template to contain our information, lat and long coordinates, URL links to images and thumbnails, a title and description.

From here, we uploaded the CSV into arcgis.com into a web map.  This automatically placed pin points at each of our locations.  We chose a base map that was appropriate and added an additional layer that pertained to the theme and feel of our story board.

Finally, we needed to edit scripts in the index html and the map configuration.  We needed to make sure the code knew which map we wanted to work with and where it could be found.  The last item was to add the UWF logo to the top right of the map.

I ran into a few issues that were pretty frustrating, but the major roadblock turned out to be an extra "s" in the hyperlink.  With endless troubleshooting and help from the professor, TA and classmates I finally have a rough draft to present.  Here it is.

Special Topics, Week 8: GIS Web Applications - Prepare Week - Storyboard Development

This week we started a new module and we're focusing on GIS web applications.  We're working on story maps.  Story maps are interactive maps that are generally less technical and more descriptive.  They are designed to tell a story but they also include pictures or images, descriptive texts, attached documents and even helpful hyperlinks.  For prepare week, I put together a storyboard for what I want my story map to show and how I want my story told.  The basis of my map is going to be my move from Wisconsin to Hawaii.  I'll be flying out of Chicago, have an extended layover in San Diego to see some sites and friends and I'll land in Honolulu.  I'll see some classic Honolulu sites before my final flight to the island of Maui.  I want my map to show my trip and the sites that I am going to visit along my way.

I plan on using various images and configured pop-ups to convey information about the individual sites.  Below is an example of one of the images I might use for my stop in Honolulu.

Diamond Head Crater State Park, Honolulu, HI

Special Topics, Week 7: Network Analysis - Report Week - Hurricane Evac. & Supply Route Map Distribution

This week wraps up our venture into Network Analysis.  This week we created more specific maps for different aspects of a hurricane evacuation.  The first maps we created were evacuation routes from Tampa General Hospital to nearby St. Joseph's and Memorial hospitals.  These were inserted into an informational pamphlet to be distributed to patients and their families.  

The second maps we created were for the emergency workers and supply delivery drivers.  Three shelters were established throughout Tampa Bay and supplies needed to be delivered from the Armory located downtown.  These maps were crude maps in gray scale to be only distributed to workers and drivers.  There was a view of the overall route and close-ups or zoomed versions at intersections.  Step-by-step directions were also provided.  One example is provided below.

Route map for supply drivers and emergency workers leaving the Armory and heading to the Tampa Bay Blvd Elementary School shelter.

The final scenario that was provided was to create a map that would be distributed to the media.  This provided a very basic and simple map of a polygonal area and which shelter was located the closest.  Very little detail was added to this map, as simplicity is key when conveying information to the public.  This map was initially created in ArcMap and then exported to Adobe Illustrator for the addition of final details.  The title is printed in red as that color is often associated with emergency and "something to which to pay attention."  The shelter names and addresses are then printed in the correlating color of that area polygon.  

Map to be distributed to the media.  Attention grabbing, simple and easy to understand.  Which shelter is nearest to you?

Special Topics, Week 6: Network Analysis - Analyze Week - Hurricane Evac. & Supply Routes

During the second week of our Network Analysis project, we created driving routes.  There were three different scenarios that needed to be planned out.  Tampa Bay is about to be hit by a hurricane and there is one hospital (Tampa General) that falls within the flood zone.  The patients at this hospital will need to be evacuated and moved to hospitals on higher ground - St. Joseph's and Memorial.  Using the Network Analysis toolbar, routes were established from Tampa General to St. Joseph's and Memorial using only roads that will most likely be open and won't be flooded.  

The second scenario is that the U.S. Army National Guard needs to distribute supplies to three local shelters.  They will be departing from the armory and traveling to Tampa Bay Blvd Elementary, Middleton High School and Oak Park Elementary to deliver supplies.  Routes needed to be created from the armory to these locations.  The same methods were used as the patient evacuation routes.  

The third and final scenario was informing the public.  Areas needed to be created that were within a certain distance of a shelter.  The color coded sections on this map, allow those people to see which shelter is closest to them.  People living within the blue shaded region should head to the Tampa Bay Blvd Elementary shelter.  People within the pink area should head to the Middleton High School and people located within the green area should head to the Oak Park Elementary School.

It was an interesting week of work and I hit a snag when trying to create the color-coded polygon's.  I figured out that last week, during prepare week, I used incorrect equations when calculating driving times and distances of the streets.  I had to go back and fix that and re-create a new network dataset.  Once I realized my mistake, figured out how to fix it and created a deliverable that I needed, I was happy.  I actually enjoyed the troubleshooting (after the fact).

This is a map of patient evacuation routes, supply routes and location of nearest shelter.

Special Topics, Week 3: Stats Analyze Week - Meth Labs

This week in lab we preformed an Ordinary Least Squares Regression Analysis.  This was done to identify and remove certain socio-economic variables that don't have a strong connection to the meth lab density.  The area of focus was Charleston, West Virginia.  Using specific measures, variables were removed to increase the Adjusted R-Squared value, or the accuracy of the model.  

This OLS Table displays variables that affect where a meth lab might occur.
This combination of variables resulted in an Adjusted R-Squared value of .727.

After running the OLS tool various times (about 22 times), I settled on these variables.  The above table displays the list of socio-economic factors which I thought were relevant to where a meth lab might be located.  Based on the Jarque-Bera statistic, my model does not appear to be biased.  

Next, we displayed this data in a map.  Below is a map of the study area with the results of my OLS.  The areas in yellow have a standard deviation of between -0.5 and 0.5 which means that it is an accurate prediction.  As you can see, other areas were over predicted while some were under predicted.

This map shows the resulting standard deviations.  The census tracts with a std value of
between -0.5 and 0.5 are accurate predictions of the number of meth labs in those areas.

Special Topics, Week 1 & 2: Stats Prepare Week - Meth Labs

This week was our first week of work in my Special Topics course.  First we had to manipulate some data within an attribute table.  This data contained information from two counties in West Virginia and were related to several Methamphetamine labs that were busted in the area between 2004 and 2008.  

We also had to create a base map of the study area.  It was kind of up to us as far as what's displayed in it.  I decided to display a basic outline of the study area and show the meth lab concentration.  I also provided an inset map to give reference to where in WV these two counties were located.

Base map of study area for meth lab distribution study in Charleston, West Virginia.

Finally, we had to start a report that will accompany this study.  We had to write an introduction about the drug, Methamphetamine and provide some background information about the study area and the data we'll be looking at.  I'm intrigued to see how this report will turn out in the coming weeks.