Lab 4: Mini-Final Project

Introduction

At the start of this lab I had the freedom to choose my own research question. I decided to determine the best location to build a cabin in Sawyer County, Wisconsin. This question appealed to me because after a long hectic week of finals, a nice secluded cabin would be ideal. With that being said, this research can help hardworking families or individuals create a home-away-from-home that provides a quiet environment to relax and enjoy the outdoors. Criteria for this cabin includes…

            Location on a lake

            Near a forest or national park

            An isolated location

                        At least 5 miles from nearest city

                        About 2 miles away from any major roads

            Located near a hospital in case of an emergency

These parameters would create the best area for a nice-get away cabin.

Data Sources

Luckily, the data needed to complete this project was found relatively easily in the Wisconsin and ESRI geodatabase. The data necessary to complete this project included Wisconsin county boundaries, lakes, county forests, U.S. parks, hospitals, cities, and major roads. Sources of the data are as follows

            County Boundaries: Wisconsin DNR 2014 Data

Lakes: ESRI 2013 USA Data

County Forests: Wisconsin Forest Inventory & Reporting System (WisFIRS)

U.S. Parks: ESRI 2013 USA Data

            Hospitals: Geographic Names Information System (GNIS) Hospitals

            Cities: Wisconsin DNR 2014 Data

Major Roads: Census 2000 TIGER/Line files

The data I retrieved concerned me in a few ways. For starters, I am concerned about the cities data. I am curious as to what the data determines a city to be. Is it based on area or population? I feel as though Sawyer County may have more cities than portrayed which may alter the findings throughout the project. Initially, I was concerned about the water bodies shown in the map. Originally, the layer had several rivers/streams, along with some rather small bodies of water. After looking through the attribute table, I realized the data had all types of water bodies. After conducting a simple query, I was able to select only the lakes to create a “lakes” layer. Because most of my data is rather static, I was not concerned with the accuracy or chance that the data has changed since collected.

Methods

Before starting the project, I first made a database connection to both the ESRI 2013 geodatabase and the Wisconsin DNR geodatabase. This allowed the process of selecting and adding the necessary layers to the map much easier. The process of my project is shown in Figure 1 as a data flow model.

To begin, in ArcMap, I created a blank document and added the Wisconsin county boundaries. From here, I selected Sawyer County and exported it to create a new layer in the map. Once Sawyer County was a new layer, I then added the needed data layers from the two geodatabases to the map. These data layers included the lakes, county forests, U.S. parks, hospitals, cities, and major roads layers. I then clipped the each data layer with the Sawyer County layer to have only the data within Sawyer was shown. This allows the software to run much faster because it narrows the data being geoprocessed to those only within Sawyer County’s boundaries.

To begin the narrowing process, I put a union between the county forests and U.S. parks layers. I then put a 1 mile buffer around them because I want the cabin to be within that distance from a forest. I then dissolved the buffer to generalize the data. I then put a buffer of 100 feet around all the lakes within Sawyer County. I did this to ensure a lake front property. I then intersected the lake buffer layer and U.S. Parks/county forests layer. This allowed me to narrow down the locations to lakes near a forest. To ensure a somewhat close proximity to a hospital, I put a 20 miles buffer on the hospital layer. I chose 20 miles because that is a reasonable driving distance in the case of an emergency. If the buffer had been smaller it would have put the location closer to cities or major roads. By taking the hospital buffer layer and the lakes and pakrs/forest intersect layer and applying another intersect, I could see all of the desirable locations for a new cabin thus far.

Next, I started to take out the areas I did not want. I put a 2 mile buffer around the major roads layer to ensure a quiet location without noise pollution from traffic but still close enough for easy accessibility. I then determined the distance from a city would be 5 miles. I assumed that any closer would bring more noise and chaos to the area. I then but a 5 mile buffer on the city layer. I then took the two layers containing the areas I do not want and put a union between them.

Now that I know the areas near roads and cities, along with the desirable traits of a cabin location, it is time to take out what I do not want. To do this, I performed an erase between the desirable layer and the undesirable layer. This left me with the lakes near forests and a hospital but away from major roads and cities.

However, after looking at the final product I realized my data and answer did not make any sense. If I had left it the way it was, it would have shown that a desirable location for a new cabin would be in the middle of a lake. To solve this problem I erased the original lakes layer from the output. This left me with just the areas along the lake that were near forests and hospitals and a desired distance from any cities or major roads.

Figure 1: Data Flow Model

 

Results

As a final product, I decided to create two separate maps. The first map shows the desired areas in Sawyer County that meet the specified parameters (Figure 2). The second map, though very similar, shows the buffers created in the process to show the reasoning behind the highlighted desirable areas in both maps. Also, in both maps, I provided a small locator map of Wisconsin along with a large-scale map of the top locations. Large-scale Map #1 shows the locations on Nelson Lake in the Northwest corner of Sawyer County. Nelson Lake has a lot of possible locations for a cabin which makes it a marketable location. Large-scale Map#2 shows the locations on Spider-Clear Lake in the Northern region of Sawyer County. This lake is smaller compared to Nelson Lake which can come with both benefits and disadvantages. This lakes also has a substantial less amount of land to choose to from when deciding where to build a cabin.  

Figure 2: Top Cabin Locations in Sawyer County, Wisconsin

 

Figure 3: Top Cabin Locations in Sawyer County, Wisconsin showing criteria

 

 

Evaluation

 I felt this project did a good job in showing the new skills we have learned throughout the semester in GIS I. If I had to repeat the project, I would add some criteria to it. I am interested in finding out whether or not the areas found desirable are DNR managed land or already owned by others. I could do this by adding the DNR managed land layer in found in the WiDNR geodatabase. Some challenges I faced involved the buffers throughout the project. Some of the preferred distances would have landed me with no locations to choose from. I had to change the buffer distances to ensure a desirable location would result. Something I would like to do in the future is expand the area of interest to the entire state of Wisconsin.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Lab 3: Vector Analysis with ArcGIS

Background

The purpose of this lab was to determine the most suitable land to be used as a bear habitat within the study area in Marquette County, Michigan. While keeping in mind previous locations of bears, presence of streams, proximity to urban areas, and most suitable land type I was able to use data provided by the state of Michigan to select and remove land based on the given criteria.

·         Within at least 500 meters of a stream

·         Favorable land cover type

·         Areas of DNR managed land

·         At least 5 kilometers from an Urban or Built up land cover type

Goal

The goal for this lab is to become familiar with the different types of geoprocessing tools and be able to determine which ones to use to figure out what land is best suitable for a new bear habitat.

Methodology

Throughout this lab’s objectives, I used several different methods to narrow down the suitable land of a new bear habitat. You can see a simplified model of my methods in Figure 1.

Objective one:

During objective one, I was able to explore the data and the file types that were to be used in this lab.

                marquette_bear_study

                landcover

                bear_management

Within these data files, was an excel file specifying the XY coordinates of previous bear locations. Because the excel file is a non-spatial database, I first had to add the coordinates as an “event theme”. An “event theme” allows you to plot these XY coordinates spatially within ArcMap. Once added, I exported the locations to my lab 3 geodatabase.

Objective two:

After adding all of the data from the bear_management_area dataset, I created a unique value map of the land cover by “Minor Type”. I then wanted to determine which land cover type had the most bear locations. By intersecting the land cover and bear_location feature classes, I was able to generate a table that had both the id of the bears and the various types of land cover. For this objective, I focused on the “minor type” field and summarized the field to determine the count of bears in each type. I concluded that the top 3 habitat types were…

                  1. Mixed Forest Land (964)

                  2. Forest Wetland (644)

                  3. Evergreen Forest Land (576)

I then created a separate layer for these 3 land types and named it suitable_land_cover

Objective three:

After receiving information from biologists, I wanted to determine how man of the bears were found in close proximity of streams. To do so, I conducted a query by location and determined that nearly 72% of the bears were found within 500 meters of a stream. Because of this large percentage, I believed it to be important criteria to keep in mind when determining suitable bear habitat locations.  This importance led me to create a 500 meter buffer around all streams within the study area (stream_buff). I then dissolved the buffer to generalize the feature class and clean it up.

Objective four:

Based on the findings up to this point, I decided to intersect the stream buffer and the suitable land cover because of the important role they play in the location of bears. I dissolved the result of the intersection to combine the internal boundaries of the layer to simplify the data. 

Objective five:

Because the bear habitat must be on DNR managed land, I then chose to intersect the suitable land near streams (objective 4 outcome) and the DNR managed land. I also dissolved this output to create a more appealing layer. 

Objective six:

For this task, I was asked to manipulate the data further and select areas that are not near any urban or built up land. To do this, I used a query on the land cover layer and created a new layer of the urban and built up land. I then put a 5 kilometer buffer on the layer (then dissolved it again). With this 5 kilometer buffer, I was able to erase the areas that were within the designated area from the suitable land managed by the DNR leaving us with only areas away from urban land. 

Figure 1: Data Flow Model to find suitable land for bear habitat

Practice with Python:

To gain some practice using python coding, I did some of the previous geoprocessing tools by typing commands within the python window (Figure 2). I proceeded to do a buffer analysis, an intersect analysis, and an erase.

Figure 2: Python Coding Practice

Results

Figure 3: Final Results

 The final results from this lab are shown above (figure 3).This map shows the study area within the Marquette County boundary. It also includes a location map on the right showing where Marquette county is located in Michigan. The map also shows the bear locations, the locations of streams, the suitable land types near streams (objective 4), and the suitable land near streams on DNR managed land (objective 6). Most of the urban and built up areas we wanted to avoid in this selection are found in the southern region of the study area. Overall, all areas shown in the pink/salmon color would be perfect habitat for bears following the criteria. In my opinion, I would select the area in the north east of the study area because of the large area allowing the bears to roam and its numerous streams for the bears. 

Sources

"Michigan 1992 NLCD Shapefile by County." Michigan 1992 NLCD Shapefile by County. Accessed December 6, 2015. http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html.

"Michigan Geographic Framework: Marquette County." Michigan Geographic Framework: Marquette County. Accessed December 6, 2015. http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html.

"Wildlife_mgmt_units." Wildlife_mgmt_units. Accessed December 6, 2015. http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm.

Lab 2: Downloading GIS Data

Introduction

The purpose of this lab was to practice downloading demographic data for the state of Wisconsin. After acquiring the data, our main objective was to create two aesthetically pleasing maps each showing its own demographic information. Another purpose in this lab was to learn how to use ArcOnline to create a webmap of our final product.

Methodology

The methods for each section of the lab vary depending on the goal of the objective. Before continuing onto objective one. I read through the definitions regarding census data that had been provided to us. This was done to gain a better understanding of the different types of census data and the information they provide.

Objective one: Download 2010 Census Data

Before downloading any information, I made a “Lab 2” folder in my personal Q drive. This was important because then I was able to have all of my downloaded information files in one folder making it easier to find and navigate.

Next I began downloading information. To do so, I went to the American Factfinder website provided by the U.S. Census Bureau. Navigating through the website, I chose to search for data of Wisconsin by county. Because of the reading I did prior to this objective, I knew to select the SF1 data because it is the most simplistic data with the sole purpose of providing an accurate count of people living in the U.S. for political reasons. It also provides other demographic information (household size, racial breakdowns, housing units, etc.) which will be of use later on in the lab. The information I downloaded showed the 2010 Total Population. The information downloaded was then saved to my “Lab 2” folder where I then unzipped/ extracted the information. This was so that I could get each individual file within the dataset. For this dataset there was a metadata folder and a file containing the tabular information of the data. The metadata file was quite simple with only two columns, but it contained very important id information to be used later. I then saved the tabular data as a MS excel file entitled “Excel Workbook”.

Objective two: Download the shapefile for the WI census data

For this objective, I returned to the American Factfinder website to download the shapefile for WI. This is needed to add the data to a map in ArcMap. After downloading the file to the “Lab 2” folder, I once again had to unzip the file to see all of the parts of the file. All of these individual files together make up the shapefile so they are all important.    

Objective three: Join the data together

To join the data together, I first opened up a blank map in ArcMap and saved it as Lab2. I renamed the data frame “Population” and added the shapefile I just downloaded to the map. From here, I add the MS excel file “Excel Workbook”. This file contained the data I want to map. I then open up both datasets’ attribute table to conduct a table join. A table join is needed to join the data itself and is done so by linking a common attribute. In this case, the common attribute was the GEO#id. To actually join the tables, you right click the shapefile and go to “joins and relates” from here you put in the common field and join the tables.

Objective four: Map the data

In order to map the data, we must change the data type to numeric so that it can be mapped quantitatively. To change it, we must create a new field and use the field calculator to use data from the original field. By doing this, we can create a graduated color map of the population data we acquired. We can then select our symbology and select the new field for the value to be mapped.

Objective five: Map a variable of your choice

For the next task, we were asked to select a variable to map and compare to the population data from the previous objectives. I returned to the census website, with my previous criteria of all counties within Wisconsin still present, I was able to look at other demographic variables that fit these parameters. Focusing on the 2010 SF1 100% data, I ended up choosing age groups by sex. However, I just wanted to map the percent women in each county. After downloading the data and saving it to the lab2 folder, I unzipped it and looked at the tabular data. Because I do not need all of the fields it gives me, I made note of the field that said the amount of total women in each county. I then had to delete the row that said the field names or else ArcMap would have thought it was data for the counties. After saving the data as an MS excel file, I added the file to ArcMap in a new data frame along with a shapefile of Wisconsin counties. I then joined the tables together and added a new field so that I could amp it as a quantity map. I then changed the symbology of the map and for this map, I had to normalize the data. I normalized the female population value with the total population data from map #1. I then changed the labeling to show percentage.

Objective six: Build a Layout

This task was all about making an aesthetically pleasing map. In the layout view, I changed the page orientation to landscape and made the two data frames equal size. I also set both data frames’ projection to NAD 1983 Wisconsin TM. I also set both the scales to be the same. I then went on to add a title, “ U.S. Census Data by County- Wisconsin 2010”. I added a scale bar, legend, and north arrow to both maps. I added a source, US census bureau 2010, and my name as the author. In the legend, I made sure that I used a good amount of significant digits and got rid of unnecessary zeros. I then added a light gray canvas basemap to each map. By doing all of this, I created a professional looking map document from which I could compare total population to the percent women in Wisconsin by county (Fig.1).

Fig 1. Wisconsin County Census Data

Objective seven: Create a Webmap

To create a webmap, I first logged into ArcGIS Online through Arcmap. I then made a copy of the second map which showed the percent women in each Wisconsin County. I then had to create a feature service for the map and published it to the cloud. Afterward, I signed into my ArcGIS online account through the internet. Here, I could see my feature service I had just created under the “My Content” tab. For this service, there was a feature layer and a service definition which just provided information about the service. The feature layer was what we wanted to use to create the webmap. I added this layer to map and the data showed up on a topographic basemap within ArcGIS. From here, I went into the pop-up properties of the map to create proper labels for the interactive map. Under Configure Attributes, I selected the attribute I wanted to be shown; name, Female_Pop, and d001new. I then changed the field alias so that it would show up more professional to the users. County, Female Population, and Total Population were my aliases for the attributes. I made sure that there were 0 decimal places because it made the most sense (you cant have part of a person). After saving, I tested the pop-ups to be sure it came up properly. I then saved the map and shared it with the UWEC Geography and Anthropology organization. 

Female Population by Wisconsin County:

http://uwec.maps.arcgis.com/home/item.html?id=76d3db357bc94dc0b621d1b513db8ebb

Sources

  Factfinder2.census.gov. (2014). American factfinder - search. [online] Retrieved                                    from:http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtmlrefresh=t

               [Accessed: 28 Oct 2015].

Lab 1: Base Data

GIS I Lab 1: Base Data

Background

I was told I was an intern for Clear Vision Eau Claire, a collaboration between citizens and the government to help improve the county of Eau Claire, working on the Confluence Project. The goal of this project was to build a new community arts center along with additional university student housing and commercial retail facilities. Two parcels have been purchased by UW- Eau Claire for this project and are located at 128 Graham Avenue, Hay Market parking lot and 202 Eau Claire Street. My first assignment as an intern is to develop a report which comprises of various significant information and maps regarding the Confluence Project.

Goal

The goal for this lab is to work with spatial data sets for the City and County of Eau Claire and integrate information about the Confluence Project to create 6 base maps.

Methodology

This lab had several objectives, each of which has a separate methodology.

Objective one: Explore various data sets for the City and County of Eau Claire

First, I began by creating a folder connection to the "lab1" folder in both ArcMap and ArcCatalog which contains two geodatabases for Eau Claire;
     2009_07_Eau Claire
     City of Eau Claire

By creating a folder connection, I was able to navigate the software more quickly. I was then asked to answer several questions about the City of Eau Claire geodatabase. I started by opening  "PARCEL_FEATURES"  feature dataset in the ArcCatalog. From here, I was able to determine how many feature classes and feature types were present in the dataset. By using tools learned in the previous MAG assignments, I was able to navigate/identify/preview various feature datasets and classes and their attributes/descriptions. Not all of the terms in this objective were clear. For example, I was unaware what each zoning codes meant and what their descriptions were. I looked at the "Zoning Districts and Maps" pdf provided to us to get a better understanding of Eau Claire's zoning codes and apply it to streets around town, such as water street and my own street.

Objective two: Digitize the site for the proposed Confluence Project

Once I created a blank geodatabase in ArcCatalog titled "EC_confluence" I began to digitize the proposed site for the project. I did this by first adding a blank feature class to the geodatabase with the name "pro_site" with a polygon feature type. I then added this feature class to a data frame a World Imagery base map in ArcMap. After locating the proposed site (128 Graham and 202 Eau Claire), I added the parcel area feature class and hollowed it to determine the perimeter of the two sites. To digitize the site I opened the editor toolbar and used the polygon construction tool. I used the end snapping and vertex snapping tool to create polygons around the two parcels creating a layer for the proposed site.

Objective Three: Learn about the Public Land Survey System

After reading and becoming familiar with the Public Land Survey System (PLSS) I inserted a new data frame in ArcMap with the world imagery as the base map. After finding the city of Eau Claire, I added two PLSS_townships feature classes from both the 2009-17-13 Eau Claire and the City of Eau Claire geodatabases. From here, I was able to determine what the difference was between a town and a township within the Civil Divisions feature class. During this objective, I learned that I could use the identify tool to describe locations within each township. Doing this allows for a more precise description of the location of the area at question whether it be a city, like Eau Claire, or a specific lot. There are specific ways to describe these locations in the township and range system. To help in this process, I turned to a website provided to me. This website helped in explaining the proper vocabulary and their meanings.

http://www.sco.wisc.edu/plss/legal-descriptions.html 

After dabbling with the identify tool, I made the townships hollow with a bright outline so that the underlying base map showed through. I added the PLSS_Sections feature class from both geodatabases and symbolized them by number and unique values based on Section Number. This allowed me to notice patterns within the map. Once again, I made this feature class hollow and added a new feature class, PLSS_Quarter_Quarter_sections from both geodatabases and moved them beneath the sections feature class. After making these feature classes hollow as well, I then used the identify tool again to create a description of the parcel through the given attributes.

Objective Four: Create a brief legal description of the proposed site

To begin this task, you need to find out the Parcel NO/ID for each of the two parcels of the proposed site for the Confluence Project. Determining the ID of each parcel is rather simple. Using the identify tool, I simply clicked on the parcel and the table of attributes has it right there for you. Parcel 1 (the 128 Graham) had a parcel number of 02-0365. Parcel 2 had a parcel number of 02-0363. I then went to the City of Eau Claire's Property and Assessment Search Website.

http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services

From here, I was able to input each parcel number and a full report of the property. From here, I was able to put together the legal description for both parcel 1 and parcel 2. These legal descriptions contain information regarding the building (if any), the location, property class (commercial, residential, etc.), school district, and the value and sale history of the parcel.

Objective Five: Build a map of all the relevant base data for the Confluence Project

For this objective I created six maps in ArcMap, each of which contained different datasets regarding the Confluence Project. I started by creating 5 new data frames. From here, I changed the size and layout of the paper. I changed the size to 11x17 and the layout to landscape. I then resized and moved the 6 data frames so they were all the same size and evenly spaced. I put the world imagery as every base map of each data frame. To freshen up on how to create an appealing map, I watched the LAB1 demo video provided to me by Professor Hupy.

This video can be found here: http://youtu.be/p5UZYebNqJU


The first map was to consist of data of the Civil Divisions. The purpose of this map was to be a locator map of the proposed site. I added the civil divisions feature class and the pro_site feature class that I created in objective 2. I then added a title, legend, scale bar. When returning to the layout view of my 6 data frames, I realized that the scale of the map was so large that you could not see the proposed site on the map. To solve for this problem, I added a callout label indicating its location.

The next map was comprised of data referencing the Census Boundaries. Here, I added the BlockGroups and TractsGroup feature classes. In the Blockgroups feature class I chose to symbolize the variable of median age to show the average age of those located around the proposed site. This may help in determining what type of commercial places should be put into the project. This variable did not need to be normalized. I chose to symbolize median age as “unique values”. Because the median age was no more than 2 digits, I chose to use 3 significant figures in the labels to make it more eye pleasing when placed in the legend. Like in the previous map, I added a title, a legend, and a scale bar.

The next map was the PLSS features from objective three. This map just needed a scale, legend, and title.

The fourth map contains information regarding parcels for the city of Eau Claire. I added several feature classes; Parcel_area, centerlines, water, and pro_site. I made the parcel_area feature class hollow with bright outlines and changed the symbology of the other feature classes so that they were distinct from the imagery background. I then proceeded to make the map cartographically pleasing by adding a title, legend, and scale bar.

I then was asked to create a Zoning map. In a new data frame, after adding the world imagery basemap, I inserted the zoning_cla feature class. After looking at the attribute table, I pulled up the zoning codes for the city of Eau Claire so that I could better understand what each code stood for. I then went into the properties of this feature class and in the symbology tab I grouped the different zones based on the first letter of the code and created a unique symbol map. I also added the pro_site feature class along with the centerlines feature class. Just like the other maps, I added a title, legend, and scale bar.

For the final map, I was instructed to create a representation of Eau Claire’s Voting Districts. In the final data frame, I added the voting districts feature class and labeled each one by their designated ward number. I then changed the scale range of the labels and added a mask so that they were more legible upon the world imagery background. I added the pro_site feature class. This map did not need a legend, however, I did add a callout label to the proposed site because it was a larger scale map. I added a title and a scale bar to this map as well.  

Results

This lab provided me with a great deal of practice with map making. I was able to test my strengths in making an appealing map while also grasping a better understanding of my weaker areas to focus on for future assignments. Figure 1 shows the final product. As you can see, the compilation of the maps give a very general outlook on the Confluence Project. This is an advantage because then every citizen of Eau Claire should be able to look at them and understand their meanings. These maps also do a really good job of showing the location of the project and is a good representation of the area of Eau Claire surrounding the proposed site.

Fig1: This image is the final result from GIS I Lab1: Base Data

Sources

Hemstead, B. (2014). Plss - legal descriptions | plss. [online] Retrieved from:http://www.sco.wisc.edu/plss/legal-descriptions.html [Accessed: 01 Oct 2015].

Impressions, F. (2014). Eau claire confluence project | community involvment collaboration. [online] Retrieved from: http://www.eauclairearts.com/confluence/ [Accessed: 01 Oct 2015].

Legal Description and Permitted Encumbrances. (2014). [e-book] pp. B-1, B-2. Available through: Christina Hupy, Geog 335, UWEC [Accessed: 01 Oct 2015].

Lippelt, I. (2002). Understanding wisconsin township, range, and section land descriptions. [e-book] Madison, WI: pp. 1-4. Available through: Wisconsin Geological and Natural History Survey [Accessed: 01 Oct 2015].

ZONING DISTRICTS AND MAPS. (2011). [e-book] Eau Claire: p. 510. Available through: Christina Hupy, Geog 335, UWEC [Accessed: 01 Oct 2015].