Lab 5: Mini-Final Project

Introduction

After downloading forest fire data from the Wisconsin DNR I noticed that there were a number of fires which occurred outside of the protected regions of the state. Based on this I decided to ask the following question: In what areas outside of the already protected area of the state should there be increased protection from forest fires based on fire occurrences from 2007 and 2008? 

My objectives were to first select only the fire data from 2007 and 2008 and create a buffer of 5 miles surrounding each fire to better determine areas where there needs to be increased fire protection. The results from this project would benefit the Wisconsin DNR to develop more extensive fire protection areas within the state. 

Data Sources

In order to answer my study question I needed a variety of data. This included: fire occurrences, fire protection regions, national forest and county forest areas as well as state outline data. I gathered my data from the Wisconsin DNR website where they have all data files they offer to the public. This website I used was: ftp://dnrftp01.wi.gov/geodata/. This data was collected from a reputable source and therefore I was not concerned with the accuracy of the data. However, there was some concerns about the age of the data because it was difficult to determine the age of fire protection region.

Methods


For this lab, I used a variety of tools including: buffer, intersect, erase, union, dissolve and clip. I first started by intersecting the county and national forest datasets to produce one region of fire protection. I then created a new feature layer which selected just the intensive and extensive protection types of fire protection region. I then used the union tool to combine the intensive and extensive protection types to the county and national forest protection areas. Next, I created a new feature layer which selected the 2007 and 2008 fire occurrences from the total occurrence dataset. Once I had the new feature class, I applied a buffer of 5 miles around each of the new data points. After this was done, I erased the fire occurance data points from 2007 and 2008 which were included in the regions of fire protection. Once this was done I was left with only data values which existed outside of fire protected regions in the state. With these regions being buffered they needed to be dissolved so the points appeared continuous. To finish I clipped the data values to make sure that the new regions were all within the state of Wisconsin.

Results


My results from this lab can be seen in the map above. After I ran the model shown in the methods section I produced figure 3 as shown in my final map. This shows the regions in bright green where there should be an increase in fire protection in the state besides those which are already protected.

Evaluation

Overall I enjoyed this project. It provided me with the challenge of not only creating a project of my own but also applying the skills I have learned throughout this course to complete said project. If I were asked to repeat this project I would change the amount of fire occurance data points used. I would increase them from only 2007 and 2008 to maybe 2000 to 2008 for a more complete result. By increasing the number of points taken into consideration I would be able to produce a more complete map of where there should be an increase in the areas which are protected from fires by the Wisconsin DNR. I faced a number of challenges when creating my model because I originally used a spatial join rather than unions. 

Lab 4: Vector Analysis with ArcGIS

Goals

The goal of this lab was to use various geoprocessing tools in order to utilize to perform vector analysis in ArcGIS. For this particular lab I was put in the scenario that I was to help the DNR develop a map showing suitable habitat for bears in Marquette County, Michigan.

Background

To start this lab I was given a number of GPS data points which showed recent bear locations within the study area of Marquette County, MI. I also was given a number of parameters which I needed to take into consideration when developing a map of suitable habitat. These included: stream proximity, land cover type and distance from urban/built-up areas. Another big issue to consider was to make sure that my proposed area was within the DNR management zones which was data also given to me.

Methods

The first step in this process was to develop the land cover types which most typically contain bears. Using the bear location data and the land cover data I was given, I spatially joined the feature classes. Once they were joined together I summarized the land cover feature in order to determine how many bears (based on the location data) were found in each habitat/ land cover type. From the summary I learned that most bears were found in either mixed forest land, forested wetland or evergreen forest land. I then exported these three particular land cover types as the bear_land_cover feature class. 

Next, I needed to determine how many of the bears were found near streams to see if they typically were or were not located near these features. To do this I used the select by location tool to select the bears from the location data which are within 500 meters of a stream. After I did this I found that 49 of the 68 bear locations  (72.06%) met these parameters. Based on this value of 72%, I was able to conclude that streams are of high importance when determining bear habit since biologists would consider a feature such as this important if the value was greater than 30%.

The other important factor that the DNR requires consideration of when working to produce a potential habitat for these bears is whether the areas are within the DNR's management zone. The DNR only has certain zones in which they can set up management areas so it is important to take this into consideration when I develop a final proposal. In order to get the zones ready to analyze I clipped out any of the zones outside of the study area. 

To produce my final proposed bear habitat regions I had to take into consideration the following characteristics: must be within either mixed forest lands, forested wetlands or evergreen forest land, must be within 500 meters of a stream and must be within the DNR management area. The first step was to buffer the streams at 500 meters and intersect these three features to come up with a potential habitat for the bears. However, another factor I need to take into consideration is the proximity these areas have to urban/built-up areas. It is best to keep the bear management lands at least 5 kilometers from any urban areas. After exporting the urban/built-up land class from the overall land cover data I buffered it by 5 kilometers. Next I used the erase tool to eliminate all of the possible bear habitat within 5 kilometers of any urban areas. Once this was done I was left with a final proposed habitat (or in my data model, final_output).

Results

The final map that I produced as a result of the last model can be seen below. This map clearly shows both the potential bear habitat within this region of Marquette County, MI and the areas where I would propose there should be habitat. The proposed habitat is within 500 meters of a stream, within the DNR management zones, at least 5 kilometers from urban/built-up land and located in either mixed forest lands, forested wetlands or evergreen forest land. While there are still some bears which are located in the north-west region of the study area there is no proposed habitat there because overall there is very little DNR management zones in that region of the study area. It would be good based on the lack of DNR management in a part of the study area where so many bears have been found to possibly expand the management zones to better protect more bear habitat. The pink, "potential habitat" includes the proposed habitat as it is the habitat before taking into consideration the proximity to urban/built up land. Based on the results from this lab I would propose that the regions shaded in green are the best regions where bear management zones should be in this portion of Marquette County.

Sources

USGS NLCD:
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

Michigan DNR:
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

Lab 3: Downloading GIS Data

Introduction:

The goal of this lab exercise was to experience the process of producing a map from data collected from the US Census Bureau using the methods we have learned thus far. While the first portion of the overall map was created using directions provided by our professor the second portion was practice for us to create one ourselves. 

Methods:

First, we needed to download data via the US Census Bureau website. After searching through the multitude of data they offer, we needed to download it from the website and unzip the files into a separate folder. Within the zip file was a shape file of the Wisconsin counties as well as other data regarding the counties. Once the files were unzipped we could pull the data into excel and review it before bringing it into ArcGIS. In order to have the data represented in a way that could be used geographically, the downloaded data from the US Census Bureau was joined with the table which was imported from Excel. Once this was complete, we can use the quantities: graduated colors method of symbology to represent our data on the map of the state of Wisconsin and its counties. 

The next step was to complete this process again using a set of data that I chose individually. The process was the same, however instead of looking at population of the counties I looked at the percent of the population of African Americans in each 

Results:

As can be seen by analyzing the maps above, there is no distinct correlation between the population per county and the percent of that county's population which is African American. While some counties such as Milwaukee County have some similarities because there is a very high population in general and also a high percentage of African Americans. Both these maps could stand alone as they display different sets of data. The map on the left represents the populations for each county in the state of Wisconsin while the map on the right shows the percent of African Americans within the population of each county. 

Sources:

U.S. Census Bureau. (n.d.). American FactFinder.  Retrieved from http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t  

ESRI Virtual Campus v. MAG Labs

Throughout this course we have done a number of lab exercises in order to apply what we are learning in the classroom to the real world of GIS. Not only have we used the Mastering ArcGIS textbook labs to sharpen our knowledge and skills but also utilized the ESRI Virtual Campus. While both have their advantages they offer the same objective.

MAG labs (those which come from the Mastering ArcGIS textbook) are extremely helpful to understanding the information read in the textbook. The teaching tutorials provide step by step instructions on how to apply all the new things taught in that chapter to real data. For me, this is something that really helps me to understand the material. They are often quite long but make it so that no part of the process is confusing. 

We more recently started to use the ESRI Virtual Campus and the various topics they teach. After taking two, three hour courses (one on making a 15-Min Map and another on Geodatabases) it was clear just how helpful these courses are. They provide great detail when explaining the subject matter and do so in a concise manner that allows you to really get a grasp on the topic. They also offer practice procedures so that you can test your knowledge and apply it to really make sure you understand what is being discusses. After doing the reading and watching the very informative videos you are tested on what you have learned. These "exams" are nice because you really see what areas of the subject you understand and what areas you need to review and get some more practice in. 

Both of these different lab exercise methods are very helpful in gaining a better understanding of the key topics in GIS, however, the ESRI Virtual Campus courses are very straight forward and the videos have really helped me to grasp difficult topics. For this reason, I prefer the ESRI Virtual Campus labs but the MAG labs certainly have their strengths as well. The instructions for the teaching tutorials are amazing and extremely clear. This really teaches the methods well.

Lab 1: Base Data

Goal:

The goal of this laboratory exercise was to apply the knowledge learned in our Geography 335 course to a real world situation. Throughout this lab we will demonstrate the skills we have learned thus far including digitizing features, manipulation of layers, as well as applying cartographic techniques. Also, the lab shows real world application of GIS in order to produce the necessary materials required for the legal acquisition of a property.

Background:

The objective of this assignment was to produce the necessary maps used in order to present a legal proposal for the University of Wisconsin- Eau Claire confluence project. This project brings together UWEC and the Eau Claire community by creating an area downtown where art students and local businesses can come together. In order to acquire the necessary property there were many city-wide votes to approve or turn down this project. A part of this process includes the presentation of various materials which show the impact this new confluence project will have on the community. For our project we worked to create some of the maps which might be used to make such a decision. 

Methods:

Top Left to Right: Fig.1, Fig. 2, Fig. 3

Bottom Left to Right: Fig. 4, Fig. 5, Fig. 6

As seen in Fig. 1-6, the following six maps were produced in order to provide a thorough, real world proposal for the confluence project in Eau Claire, WI. For all of the individual maps, a base map was added in order to show an aerial image of the proposed site as well as the surrounding area. After this was applied to all the maps, more specific data was added to each of the six maps.

Fig. 1- First the civil division data, collected from Eau Claire county was added in order to give a more detailed view of the area surrounding the proposed site for the confluence project.

Fig. 2- In this map, the block groups were added and show the population (as collected in 2007) of each section per square mile. Tracts were also added into this map to better show the borders of the block groups.

Fig. 3- The map in Fig. 3 uses parcel data as well as water and centerline data from the city database. This shows the various parcels throughout the area, centerlines and water features.

Fig. 4- This particular map shows the Public Land Survey System (PLSS) quarter quarter areas. This data was added from the Eau Claire County database.

Fig. 5- In this map the zing data, collected from the Eau Claire city database, is added in order to show the different zoning areas in the surrounding area of the proposed site. Once this data was added it was grouped based on like zoning codes: C (commercial), I (industrial), R (residential), CBD (central business district), P (public properties) and T (transportation).

Fig. 6- The map in Fig. 6 contains the Eau Claire voting wards from the city of Eau Claire database which are also labeled by the voting ward number assigned to them. 

Results:

It is interesting how close the proposed site for the confluence project is to the central business district as shown in Fig. 5. This is something which was publicized when the proposal of the confluence project was being voted on because it achieves the major point of the University which is to incorporate more of the student body into the downtown area of Eau Claire. In doing so  not only does it benefit students living there because they gain entertainment and other opportunities to be more active in the community but the local businesses receive more attention as well. One downfall of this proposed site though can be seen in Fig. 3 through the centerlines. These centerlines show how accessible the site is and based on the lines shown in this particular map, there is a lack of “easy” routes from the UW-Eau Claire campus to the proposed site. Based on the lack of accessibility there is a greater likelihood that there will be traffic congestion in the area which could cause problems for locals as well as commuting students. 

Sources:

Eau Claire Regional Arts Center (n.d.).

Confluence Projected Site Plan. Retreived from

http://www.uwec.edu/alumni/confluence/

City of Eau Claire

Eau Claire County