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 Some of the concerns that I have include outdated data, the population information I used is a few years old.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

Lab 3: Downloading GIS Data

Introduction
    The main objective of this lab was download data form a source and make two maps from the downloaded data.

Goals
1.) Download 2010 Census data (Total Population) from the US Censusu Bureau.
2.) Download a shapefile of the 2010 Census boundaries from the US Census Bureau.
3.) Join the downloaded data to the Census shapefile.
4.) Map the data.
5.) Download and map a variable of your choice.
6.) Build a layout with both maps.
7.) Post to blogger.

Process
  The first thing I had to do was download the data from the US census bureau's website. For my first map I choose to use total population by county of Wisconsin. After I downloaded the population data I had to get the Wisconsin shape file, which is also located on the US census bureau's website. After I downloaded both the shape file and the table data of the population I had to combine the data with the shape file. I did this by using the table join feature, the common attribute was the GEO_ID. After I combined the data with the shape I went to symbology and created a choropleth map.

Results
  The results

The map in blue is counties based of off Total male population by percentage and the yellow on the right is total population per county.

Source: United States Census Bureau 

ESRI vs MAG Labs

ESRI vs Mag Labs
  For this lab we had to complete the ESRI course Getting Started with the Geodatabase. It covered the basics of adding, loading, and importing data to a geodatabase that I created. This lesson was very specific in its instructions and easy to follow. After majority of the steps I had to complete there were images for me to look at to make sure I was following along properly, something I found to be very useful. Prior to this current ESRI course, I completed one other, it is called The 15-Minute Map: Creating a Basic Map in ArcMap. This course was very similar, it was precise, easy to follow, and interesting to follow along too. Also after you finish the project exercises, ESRI offers a quick review that goes over everything that had been learned. Based off of my experiences with the online ESRI courses I would highly recommend them to others.

  Besides the two ESRI labs, the rest of this semester I have been doing MAG Labs. MAG Labs are labs after each chapter that help develop the skills described in that chapter of the text book. MAG Labs are very informative and really help with understanding how ArcMap works and how to use it. The down side to the Mag Labs is that after awhile things either get repetitive or a little complicated to follow. So if I had to choose one over the other I would go with ESRI. Overall it seems that ESRI is easier to follow, helps you understand what you are doing and what you have learned better.

Lab 1: Base Data

Introduction
  The goal of this lab was to become familiar with various spatial data sets used in public land management, administration, land use and to prepare base maps for the Confluence Project. The Confluence Project is public-private local development where the Eau Claire and Chippewa Rivers meet. The Project will construct a new art center for the community, university student housing and commercial retail complex in downtown Eau Claire.

Goals
1.) Explore various data sets for the City and County of Eau Claire.
2.) Digitize the site for the proposed Confluence Project.
3.) Learn about the Public Land Survey System (PLSS).
4.) Create a brief legal description of the site.
5.) Create relevant base maps for the site.

Objective 1 
  My first objective for this lab was to familiarize myself with the geodatabase provided. I achieved this by exploring the feature datasets, previewing the feature classes, checking the properties of the feature classes, and reading up on some of the rules that restricted certain feature classes.

Objective 2
  For the second objective I had to digitize the site of the Confluence Project. To do this I created a new file geodatabase and added a new polygon feature class.I then added the coordinate system from the census feature class of the given geodatabase. The subsequent step was to add a base map to my feature dataset, I added the ESRI world image map. Next I zoomed in to the Project area and added the City of Eau Claire parcel area data. This added all of the buildings, bridges, parks, etc, to my map. The final part was digitizing the sites of the Project. Below is an image of the digitized areas for the Confluence Project.

Objective 3

  For the third objective I learned about the Public Land Survey System (PLSS). The PLSS is a method of surveying and spatially identifying parcels of land before they are bought. The PLSS is made up of townships, sections, and quarter-quarter sections. More information on the PLSS can be found here. So for the lab I simply added the PLSS feature class to an aerial base map of Eau Claire, I then compared the patterns.

Objective 4

     For the fourth objective I had to create a legal description of the proposed site. I accomplished this by using the Identify Tool and obtaining the parcel ID number for both parcels and putting the ID numbers into the City of Eau Claire's Property and Assessment Search Website. I then created a report with the digitized pictures of each parcel.

 (Eau Claire's Property Website)

Objective 5

  For the fifth and final objective of this lab I was asked to create six different maps. The first map was for civil divisions, the second was about the Census Boundaries, the third was the PLSS Features, fourth was the Eau Claire Parcel Data, the fifth was Zoning, and finally the last one was Voting Districts. 

  To begin all six of the maps have a few similar qualities. They all contain either the outlines of the proposed site that I created in objective two or a call out box that shows where the confluence project would be. They also all contain the same base map, ESRI World Imagery Map. Furthermore I added the scales and legends onto the maps from the layout view in ArcMap, ArcMap is the software I used to create the maps. 

  The Civil Divisions map I added the civil divisions dataset to the aerial view of Eau Claire. From there I gave the properties of the city of Eau Claire a different color from the rest of the county.

  The Census Boundaries map I added the population data from the census feature class. I grouped the ranges of population to realistic numbers, meaning I got rid of all of the decimals. Assigned each range of population it's own shade of green. I also added the Tracts Group, this added the gray lines that go around the different shades of green.

  The PLSS Feature map shows all of the PLSS areas where the Confluence is located, they are represented by the black lines. Then the red proposed site shows where in the PLSS area it is located. All I did was add the PLSS feature class data. 

  The EC City Parcel Data map I added the feature classes parcel area, centerlines, and water. I changed the symbol of the parcel area to a hollow color and gave it bold outline, doing this allows the background under the data set to be seen with an outline of the data still visible. The centerlines show all of the roads and the water data is shown over the rivers.

  The Zoning map is very similar to the Census Boundaries map. I added the Zoning feature class, which contained data for commercial, residential, industrial and transportation. I assigned each of these categories a unique color. Finally I added the centerlines.

  For the Voting Districts map I added the voting districts feature class for the city and labeled each districts by their ward numbers.

Below is the 6 maps that I created         

Source: City of Eau Claire and Eau Claire County 2013
Click on image to enlarge