Lab 5: Vector Analysis Question

Introduction:  The goal of this lab was to create a spatial question on our own and then answer this question using tools in ArcMap and ArcCatalog.   The question I developed for this lab is: Where is the best place in Crow Wing County, Minnesota to build a cabin?   The objectives for this lab include a answering our own spatial question by using at least four tools, and at least three of the tools have to be different.  Also the area of interest must be in the United States and a minimum of three different spatial data layers must be used.   Then end product of this lab will result in a report of the lab, a map representing the answer to the question, and a data flow model showing the tools we used and how the question was answered.   The intended audience of my question would probably be a middle-aged to older male looking to buy or build a cabin to spend the summers in.  This information could be used by people looking to build or buy a cabin and also a real estate agency seeking to find the best location to buy land to sell. 

Data Sources:  First to begin the process of answering my question data had to be brought in from an ESRI database, which included: hydropoly (lakes, bays, glaciers), highways, golf courses, states, and counties.  The ESRI database was downloaded in ArcCatalog and then added to ArcMap.  All of my data was selected from either the ESRI usacensus data or the ESRI usadata database.  This step allowed me to add all of my required data which is key to answering my question.  I had a few concerns with this data, the first being how old the data is.  This could make my map not very useful as new highways or golf courses could have been built.  The second data concern is does the data include every lake and golf course?  As I just mentioned new golf courses could have been built since the data was gathered and what is the minimum size ESRI used to describe a lake. 

Methods:  First a series of requirements of where I think would be the best location for a cabin to be built must be created.  I decided that the cabin must be built within five miles of a golf course and within five miles of a lake.  Then I decided that the cabin must be at least five miles away from a major highway.  These three things were decided because of easy access to the golf course and lake and also far enough away from a highway to not be near a lot of noise. 

-Next US Counties was added to the map and Crow Wing county in Minnesota was selected in exported.  Then golf courses, hydropoly, and highways were added the map and all three were clipped within Crow Wing County. 

-Then all three of my data criteria were buffered to correctly fulfill the requirements I desire.  Golf courses were buffered in a five mile radius and dissolved to delete any existing boundaries.  A query was performed on hydropoly to select all lakes in Crow Wing County.  These lakes were then buffered in a five mile radius and dissolved to delete any existing boundaries.   Highways was also buffered within a five mile radius and dissolved to delete any existing boundaries.   

-Finally to make a map, that answers my question, lakes and golf courses were intersected to find the locations where both exist.  After this intersection the erase tool was used, highways was erased from the lake and golf course intersection.   This step is key because it erases the area of highways with a radius of five miles, therefore excluding this area for a cabin.   Below is a data flow model that shows all of the steps and tools I used in creating my final product. 

Results:  After all of these steps and tools were completed the final map was created.  The area in orange on the map includes all suitable areas to buy or build a cabin.  The green dots represent golf courses, blue areas represent lakes, and the red lines represent highways.  I found it interesting that most of the golf courses in Crow Wing County were located near a lake making the best location to build a cabin easy to predict.   At first I tried to exclude areas within ten miles of a highway but that answer was not sufficient enough because only a very small portion of the county was a suitable area. 

Final Map

Evaluation:  I though the lab was a lot of fun by creating our question and then making a map with the best possible location answered.  My question changed throughout the lab because I had trouble finding the correct features I wanted to use for my question but, I am very happy on how my map turned out.  If I were to repeat the project I would probably expand my area of interest and maybe pick two or three counties.  I also would use more features to come up with a better answer, I would maybe use forest types and urban areas. 

Sources:  ESRI database

-usacensus database

-usadata database

Lab 4 Vector Analysis: Location of Bears

Finished map product with All suitable Habitat for bears, bear locations, and DNR bear management  zones.
Also including a map of the study location

Goal

The goal of this lab was to use new learned tools/skills on ArcGIS and ArcCatalog to come up with the best location of where bears would be in Marquette County, Michigan.  These tools include: buffer, intersect, dissolve, erase, and queries.  Also another goal was to create a data flow model to show what tools I used in finding the best location of bears. 

Background

The purpose of this lab is to make a map that represents the best locations of where bears would be located in Marquette County.  It was found that bears are most likely located near streams because of the important resources that are found in them like water and fish.  The scenario also included habitat types that bears are most likely to be found.  Part of our researched also included making recommendation to the Michigan DNR for where to manage bears.  Areas of bears where located in DNR management zones had to be found excluding urban areas.  All of these factors where used in creating the most accurate map of where bears are most likely to be located.

Methods

-First a geodatabase was created in order to store any data created in the lab.  The next step involved going into ArcCatalog and adding the excel file containing the coordinates of bear locations to ArcMap.  This process was done by adding the coordinates as an event theme.  In ArcMap the File button was clicked followed but "Add Data" and "Add XY Data".   After these steps were done a new box appeared, inputting the correct data and saving it to the geodatabase were done to complete the step.  These points now contained the correct XY coordinates to be placed onto a map.

-The next steps involved using many tools to come up with the best location of bears.  First all of the bear management feature classes were added to the map.  Next to find out what type of land covers bears are located in I performed a spatial join of Bear locations and Land Cover.  Then to summarizing the Minor Type Field and selecting the top 3 types of bear locations.  They included: Mixed Forest Lands, Forested Wetlands, and Evergreen Forest Land.   These 3 types were selected and a new feature class was created for future use.  

-With biologist indicating that bears are most often found near streams, the streams in the area were buffered to capture an area of 500 meters within the streams.  Then the streams were dissolved to eliminate the existing boundaries to make a cleaner map.  Next, the streams and top 3 land covers were intersected and dissolved to find the best areas of where bears could be located (Bear_Hab).

-The next step of the lab involved creating a recommendations for the Michigan DNR for a bear management plan.  The DNR area shape file was added to the map and was intersected with Bear_Hab and then dissolved to get rid of any internal boundaries.  This intersecting produced a map of the best bear locations with the DNR study zone areas.  

-It was then decided that bear management areas should stay away from urban areas.  In order to create a new map excluding urban areas three steps were performed.  First, urban areas were selected and a new shape file was created.  From there the urban areas were buffered and dissolved to capture areas within 5 kilometers.  The new buffered urban areas were then erased with the DNR areas to get rid of the urban areas.  

-After all of these steps were finished the map was complete only adding a legend, title, and other amenities were needed to be completed.  After the finished product a Data Flow model, which can be seen below, was created to show all of the tools and steps I used to come up with my map.  

Results 

 You can view the results of my competed map at the top of this page.  The DNR zones are very limited to which areas they can manage bears, but this map gives the best possible locations to mange them.   The results from all suitable bear habitat in comparison to actual bear locations are very accurate.  Most of the bears lie inside the area of suitable habitat making it easy to track, study, or manage the bears

Sources

 All of the data were downloaded from the Michigan Center for Geographic Information

-Land cover 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 2: Downloading GIS Data

Introduction

The goal for Lab 3 was to learn how to download and map data from the U.S. Census Bureau.  Our tasks including going to the US Census Bureau website and download total population data from Wisconsin counties, a table of our choice, and a shapefile of Wisconsin boundaries from 2010.  Next after downloading the data our task was to join the tables and shapefile and map the data and cartographically pleasing map.   

Methods

First I downloaded data from the U.S. Census Bureau for total population per county in Wisconsin.  I then downloaded the data and placed it in my folder.  After doing this a zip file appeared in my folder, and in order to use the data you must unzip the data.  To unzip the data I extracted it to my folder where the two CSV tables appeared.  In order to use these CSV files you must save them as excel files so they can be added to ArcMap.   

Next I went back to U.S. Census Bureau page and went into the Geography page to download the shape file of Wisconsin Counties.  The same process of unzipping was done with this data so it will be ready to use in ArcMap. 

After these files were unzipped they are ready to be used in ArcMap to make a map.  First I added the the data and Excel file with the total population data.  Then the processing of joining the two tables must be done to map the data.  I joined the two tables by a common field, GEO#id, because it is both tables.  Then after joining the data I made a pleasing color map of the total population per county of Wisconsin.  I switched the classification from five to six fields to make the map more easy to read. 

My next step was going back in U.S. Census Bureau to add a table of my choice to along side my total population map.  I choose to map the number of people over the age of 62 per county in Wisconsin just by random selection.  The same process was done to make the table ready for ArcMap and it was added to the map.  I choose a different color scheme to use for this map and applied it to the map using the default, five classes for this map. 

Then using the layout view I made a frame where both maps can displayed evenly.  I added legends, titles, scales, north arrows, basemaps, and sources to the map to make it professional and my process was completed. 

Results

Below is picture of the maps I created sitting side my side to represent my data.  The one on the right is the first map I created and the map on the left is the second.  I find the Age 62 years and older more cartograpically pleasing to the eye but I do think the right color schemes were chosen for each map.  The basemaps in the background really add a nice piece to the map all the color schemes to pop out more because of the grey background. 

Sources

U.S. Census Bureau 2010

Cartographer: Tanner Borgen



GIS 1 Lab 3: GPS Mapping

Tanner Borgen

Introduction

-The goal for this lab is to become familiar with creating a geodatabase, how to collect feature data points with a GPS, and adding those features to our geodatabase map.  

-The objectives of this lab were to create a geodatabase and to prepare that geodatabase for the use of the Trimble Juno GPS unit.  Then to load the geodatabase onto our Trimble unit and become familar on how to use a Trimble Juno GPS by collecting features as points, lines, and polygons.  Then Collecting these points using Arc Pad on the GPS and placing the data back into ArcGIS to complete our map. 

Methods



-The first step in the lab was to create new geodatabase then followed by adding feature classes named lines, polygons, and points.  The coordinate system used for this map is NAD83_HARN_Wisconsin_TM.  Then a raster of the University of Wisconsin Eau-Claire was added to the geodatabase.  This campus image is out of date and doesn't properly reflect the features in the map.  All of these features including the raster is added to ArcMap to create a map.

-The next step was to add this map into our Trimble GPS unit.  First from the customize menu then extensions and checking ArcPad Data Manager so this tool can be used.  Adding the ArcPad Data Manager Toolbar to ArcMap is next.  The first button on the ArcPad toolbar was clicked then from the Action Menu choosing an AXF layer and enabling editing.  In the Action Menu again, I choose Checkout all Geodatabase layers and copy out all other layers.  Then a folder with this data was created and put into my folder, then in the deployment options window I clicked on create the ArcPad data and it was created so we can use this map on our GPS device. 

-Adding the data to the Juno GPS unit is the next step.  Connecting the unit to the computer and navigating our folder that we just created to our new Juno folder is how this task was completed. 

-Then I became familiar on how to map GPS features onto my correct map.  Practicing collecting polygons, lines, and points around UWEC's campus mall was very important.  Also practicing on how I want to collect my data withe either point averaging or point streaming.  You can use point averaging and point streaming when mapping a line or polygon feature.  Point averaging takes the average vertex that the satellites read and creating the average location.  Using this method you must click add vertex on your unit each time you change direction.  When using point streaming the satellites describe your location by using an interval.  Clicking a new vertex point will not be necessary only the green arrow when finishing the feature.  When collecting point features the GPS uses the average vertexes collected in one location, in this case I used the average of 10.

-Then using the GPS unit I collected data of point, line, and polygon features.  I collected one line feature of the campus footbridge using the point averaging technique.  I then collected 3 trees and 3 light poles on campus by using the point feature class.  Finally I collected 6 different polygons around sidewalks by using the polygon feature.  3 used the point averaging method and 3 using the point streaming method. 

This is a close up of my map.  You can see some major errors while
I was using the GPS device.  

-After collecting all of your data adding it ArcMap is next.  I reconnected the Juno the computer and used the ArcPad Data Manager toolbar. Clicking the 4th button on the toolbar will bring you to a screen where I clicked the green arrow to add data.  Checking each box with my feature classes and importing them to the map.  The data I collected then appeared neatly on my map making it ready for me to create a map with clean legends and text. 

Results

-The results of my map can be seen below.  You can tell that the image we used is out of date and does not entirely match up with our new campus, as it went under major construction in 2012.  I choose to use point averaging when collecting points on my line feature class and found it to be successful.  If you look closely you can tell that some of my polygons are not accurate and jump out at some corners.  Next time when I GPS I will be more careful when collecting data so this doesn not happen.  It was tough to collect clean data with all the people on the paths I was trying to map.  





Sources: Data Collected by Tanner Borgen
Aerial photo: NAIP 201X

Lab 1: Base Data

Background: The purpose of this projects was to use our skills we have learned and apply them to a real world project.  The city of Eau Claire, Wisconsin is working on a project that will add a new Arts Center in downtown Eau Claire.  In 2012, Clear Vision Eau Claire announced this project, called the "Confluence Project",  to beginning in 2014.  This projects is focused on enhancing the cultural center of city and the University of Wisconsin Eau-Claire.

Goal:  To further our skills with ArcMap and ArcCatalog programs and apply them to maps surrounding the Confluence Project location. 

Methods: Maps going left to right in order 1,2,3.4,5,6.  Legends are added to the maps to represent different data.
Map 1: I first digitized the Confluence Project proposed site by using the editor and polygon tool and added the proposed site to each map. This is a map of the civil divisions of the county of Eau Claire.  I captured data from Eau Claire County and the City of Eau Claire and added them to the map.  I also added a basemap, World Imagery, to the background from the ArcMap basemaps.  All maps contain the same basemap in the background.  This map is zoomed around the city of Eau Claire and has labels of Civil Divisions and the Confluence Project proposed site. 

Map 2: This is a map of Census boundaries. I gathered data from the City of Eau Claire and Eau Claire County.  I then added block groups and tracts data to the map.  We were assigned to choose a random symbology category to represent the map.  I choose 2007 population per square mile.  I choose a good difference of warm colors and cool colors to represent the data.

Map 3  This map shows the Public Land Survey System, PLSS, districts of the proposed site area.  The data was gather again from the City of Eau Claire and Eau Claire County.  PLSS quarter quarter was added to map to and zoomed in to show the how the districts are formed. 

Map 4 The next step in the map creation was to to show Parcel Areas.  Parcel Areas, Centerlines, and Water were added to the map from the City of Eau Claire and Eau Claire County.  This map shows the many parcel areas in the region of the proposed site.  It helps understand what areas belong to the proposed site of the Confluence Project.  This map has a lot of color, getting the right colors to help read the map is tricky.  I am still a little unsatisfied with my selection. 

Map 5:  Map 5  is a map of the different types of buildings in the proposed site area.  It represents the zoning areas in different buildings.  This map will help in deciding where the best place it is to build something in relation to the other buildings around.  The proposed site is surrounded by a river and central business districts, the location seems to have a very cultural feel.  In order to create a clean legend some grouping was applied to all of the labels. 

Map 6   Voting districts from 2011 were gathered from the City of Eau Claire and Eau Claire County.  This map represents the voting districts that surround the proposed site.  This map was fairly easy to create and adding the numbers to each voting district is key. 

Results:  These six maps help understand the area where the proposed site of a new Cultural Center is located.  It is in a location of high population and close to the University.  With the site being right on the Chippewa River and Eau Claire River, I think it gives it a great cultural feel and beauty. 

Sources: City of Eau Claire and Eau Claire County 2013
Eau Claire Community Arts Center.  Retrieved from
http://www.eauclairearts.com/confluence/
Universtiy of Wisconsin Eau Claire News.  Retrieved from
http://www.uwec.edu/News/more/confluenceprojectFAQs.htm
Kupfer, T . (5/17/2013).  $85 million "Confluence Project" coming downtown.  Retrieved
http://volumeone.org/news/1/posts/2012/05/15/3134_arts_center