Lab 5: A Spatial Question

Introduction
The objective of this lab was to come up with a spatial question that could be answered using the tools in ArcMap that I have learned over the past semester. I decided to investigate the question, which cities would be a good place for the next NFL football team? My intended audience includes football fans and members of the corporate offices of the NFL. The people who may be able to use this information would be mayors of the cities in which have been selected as potential candidates for a new football team and NFL Corporate.

Data Sources
I used three different data sources for this lab, the first source was the city data set provided by ESRI in the MagLabs. It contained population data as well as the locations of the cities. The locations of the NFL teams I received from the NFL website, specifically www.nfl.com/teams. The final set of data I used was for the location and amount of passengers that each airport in the U.S.A. I got this data from the online ESRI data sources. Some of the concerns that I have include outdated data, the population information I used is a few years old. The population data was based off of the years 2000 and 2007. As for the NFL data some of the stadiums were located in cities that were not included in the cities data that I acquired, so I used the closet city to it that was included in my data sources.

Methods
To answer this question I set up a list of criteria that I thought would be pertinent in determining which cities should host the new NFL Team.
- Can't already contain a NFL Team
- Be in a state with less then 2 NFL Teams
- Population greater then 250,000
- Population Growth of 1.04% or higher
- 150 miles from a current team
- Most be in a city with an airport that deals with 1.5 passengers a day

  To begin I went to the NFL website and found all of the locations of every NFL team and put that data into a mircosoft excel sheet, along with the state abbreviations of each city, a field that contained a 1 and the object ID numbers that ESRI used for the cities in the ESRI data. The field that contains the one is the field that will allow me to select the NFL cities from the rest. The rest of the cities will have "null" in the fields with the 1's. After I got the data table with the NFL teams locations I joined it to the ESRI cities data. After the join was complete I opened the new data table and selected by attributes all cities that contain an NFL team. Next I switched the selection, meaning that instead of having all of the NFL cities I now had all cities without an NFL team. I then created a new layer from all the cities without a NFL team.
  For the proceeding step I went back to the NFL cities data and ran a summarize on based on the state abbreviations to find the amount of teams per state. I found that California, Florida, New York, Ohio, Pennsylvania, and Texas all have 2 or more teams. Going back to the data with all cities without a NFL team, I selected by attributes all cities that are located in one of the cities listed above, switched selection again so I had all cities not located in the above states, and created a new layer from selection.
  The next two steps were pretty easy. First, from the new layer, I selected by attributes all cities that have a population greater then 250,000 and created a new layer from the selection. I then added a new field labeled "Population Growth". I opened the field calculator and divided the 2007 population from the 2000 population and add the results to the population field. I then selected from attributes all cities that have a population growth of 1.04% or higher.
  The next step I had to use a new tool select by location. I opened the select by location tool and made the target layer potential cities for a NFL team and made the source layer current NFL teams. I then set the distance to 150 miles and ran the tool. The results were all of the cities are within 150 miles of a current NFL team, I switched selection and created a layer from selection. I now had all cities that were 150 miles or more from every current city that host a NFL team.
  The final step was to add US airports from the online ESRI data sources. I created a buffer around the five remaining cities of 50 miles and saw that 4 of the 5 remaining cities contained a large enough airport. I selected the 4 cities and created a layer from the selection.
The 4 different tools that I used are,
-Join
-Select by Location
-Field Calculator
-Buffer

This is the flow chart that outlines all of the steps that I took to come to my conclusion,

(Flow Chart of the Steps took during my research)

Results

  The results that I obtained were that Anchorage Alaska, Oklahoma City Oklahoma, Albuquerque New Mexico, and Las Vegas Nevada were the best candidates for a new NFL Franchise based off of the criteria that I selected.

(The Map of Current and Potential NFL Cities)

Evaluation

  I really liked this project, it helped my apply the knowledge that I had learned over the semester without having a step by step list to follow. The biggest challenge that I faced was creating my own data table and have it added to ArcMap to be manipulated to help me answer my question. When you create an excel file and add it to ArcMap only recognizes it as words, which is inconvenient when you need the data to be locations. To over come this problem I joined the data table that I created to a table that ArcMap recognized as locations. If I could redo this assignment I would probably email the NFL corporate to get the criteria that they use instead of basing my study off of areas that I believe will play a factor in the decision.     

            

Lab 4: Bear Habitats

Introduction
The main objective of this lab was to use a variety of geoprocessing tools in ArcMap in order to figure out the ideal areas for bear habitats that can be monitored by the DNR in the study area in Marquette County, Michigan.

Goals
1.) To map a GPS MS Excel file of black bears locations in Michigan.
2.) To determine the forest types where black bears are found in central Marquette County, Michigan based on GPS locations of black bears.
3.) To determine if bears are found near streams.
4.) To find suitable bear habitats based on two criteria.
5.) To find all areas of suitable bear habitat within areas managed by the Michigan DNR.
6.) To eliminate areas near urban or built up lands.
7.) Generate cartographic output.
8.) Generate a digital data flow model of the procedures used to determine suitable bear habitat in Marquette County, Michigan.

Process
  First step that I took was to add the excel spread sheet of bear locations to ArcMap. This was a little tricky because the only way to add no spatial data into ArcMap and be able to use it is to create an "event theme". An event theme is a temporary display of X,Y coordinates in ArcMap. Once the event theme was in ArcMap I exported the data so it would be brought into ArcMap as a feature class. Now that I had all of the bear locations I needed to find the land types in the study area that most of the bears lived in. I added the landcover data of the study area to ArcMap and used a spatial join with the bear locations to find all the areas that housed black bears. From here I summarized the new joined data by the minor types of landcover and found that Mixed Forest Land, Forrested Wetlands, and Evergreen Forrest Land housed the most black bears. I next created a new layer that contained all areas that were Mixed Forest Land, Forrested Wetlands, and Evergreen Forrest Land.
  Now that I knew the best land types for black bears I had to find out if bears lived near streams. I made a 500 meter buffer around all the streams in the study area and found that 72% of all the bears in the region lived withing 500 meters of a stream. I intersected the three land types with the 500 meter buffer around the streams and found all the preferred bear land types with in 500 meters of all the streams. The DNR was not interested in each different type of land type just the entire preferred bear habitats as a whole. So I used the dissolved tool to get rid of the inner boarders and created one large bear habitat.
  The next dilemma that I faced was incorporating the areas that DNR are were legally able to monitor. So I added the DNR manageable areas for the entire county of Marquette and intersected it with my study area to find all of the DNR manageable areas inside my study area. With this new data I was able to intersect it with bear habitats within 500 meters of a stream to find all the bear habitats that the DNR is able to maintain.
  The final step was to eliminate all the areas of the bear habitats that were within 5 kilometers of an urban or built up area. To do this I selected all of the area that are urban or built up from the landcover dataset. Once they were selected I created a new layer and then added a 5 kilometer buffer around these urban areas. After I had the buffers I erased the areas that they covered from the map to eliminate the bear habitats too close to urban sprawls. So after I erased the habitats too close to urban areas I was left with bear habitats that were within 500 meters of a stream, able to be maintained by the Michigan DNR, and over 5 kilometers from urban areas.

Results
Here are the results of my study.

Here is a flow chart outlining all of the steps that I took to complete this lab.

Sources       

Landcover is from USGS NLCD

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

DNR management units

http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

Streams from

 http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html