Lab 4: Final Project

Introduction:

For this lab, you have to create and answer a spatial question by using the skills you've learned throughout the semester. The spatial question in this blog asks where is the best potential place to put a campground in Eau Claire county. You need to use at least three spatial layers feature classes and four tools. You can also pick one tool to perform a second time for it to count as the fourth.

Sources:



Data was found from the ESRI2013.DBO.USADATA database, and the Wisconsin DNR database.

Methods:

To obtain the best area for a campground in Eau Claire county you should find an area inside of a national park and a county forest as well as within 1 kilometer of a lake and 3 kilometers near a major road. The tools utilized to obtain those specific parameters are the buffer, dissolve, and intersect tools in order to find the most suitable area for a campground. 

Begin by clipping the Parks, Forests, Water Features, and Major Roads in Eau Claire. Next create a 1 km buffer for all the lakes, and dissolved them to get one polygon. After that make a 3 km buffer for all major roads, and dissolved that to get one polygon as well. Then I intersected the Lake buffer, the major roads buffer, the parks feature class, and the forests feature class which gives the areas you could put camp grounds.

Results:

Figure 1 below shows my flow chart of the tools and functions performed to get my potential campgrounds.

Figure 1 shows the Flow Chart for the Project.

Figure 2 below shows that map that I created showing where the best locations are for potential campgrounds. Those areas are represented in yellow.

Figure 2 shows the map in which I will propose to the audience

Evaluation:

My overall impression for this project was that it is a great way to show the Professor what we have learned. I liked that it allowed us to choose our own spatial question in which all steps were done on our own. I really wouldn’t change the project because I think it has a great layout. The challenges I faced were trying to find all the data I need from ArcGIS, after that the rest of the project flew by.

Lab 3: Vector Anaylsis with ArcGIS

Goals and Background:

The Goal of this lab is to use different geoprocessing tools for vector analyisis in ArcGIS to find suitable habitats for bears in the study area of Marquette County, Michigan. This lab report contains a final map layout and a data flow model. There are eight objectives listed below:

1. To map a GPS MS Excel file of black bear locations in the study area, central Marquette County, Michigan
2. To determine the forest types where black bears are found in the study area based on GPS locations of black bears.
3. To determine if bears are found near streams.
4. To find suitable bear habitat 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. To generate a digital data flow model of the workflow and cartographic output.
8. Use some python.

Methods:

Objective 1:

For this objective we unzip the marquette_bear_study file. Then add the bear_locations_geog% to the make, so you can add it as an event theme. Once its in ArcMap, export the data and save it in your lab 3 folder.

Objective 2:

In this objective you add all feature classes in the bear_management_area feature dataset, and change make the landcover symbology a unique color map for the "minor type" field. Now intersect bear_locations and landcover renaming the new feature class bear_cover. Next you summarize the Minor_type field with the FID_bear_locations (Max) and the Area (Max) statistics. Then open the attribute table where you will find the top 3 land covers with the most bears.

Objective 3:

This objective requires you to find out the how many bears are within 500 meters of a stream. In order to figure this out create a query by location. Create a buffer with a buffer with streams and a distance of 500 meters than dissolve the new layer to get one individual polygon. Add bear_locations to target layers, and use the dissolved stream buffer layer as your source layer. You will find that 49/68 (72 percent) of bears are located within 500 meters of streams. Note that the distance of streams is definitely an important habitat characteristic.

Objective 4:

For this objective you need to find suitable areas of bear habitat based on your research. Use the intersect tool, and input for the dissolved stream buffer layer and the suitable land cover top layer. Then dissolve the new layer to clean things up a bit.

Objective 5:

In order to make recommendations to the Michigan DNR for a bear management plan you need to find a bear habitat located in their management lands. Bring in the dnr_mgmt layer from the political boundaries dataset. Again use the intersection with the suitable land area near streams layer and the dnr_mgmt layer. Dissolve the internal boundaries for a better look.


Objective 6:

For this objective you need to find bear management areas that are away from Urban or Built up land. You need to exclude all areas that are within 5 kms of Urban or Built up lands in a 3 step process. First create a query by attribute to select Urban or Built up land then make it it's own layer. Second buffer urban or built up land with a distance of 5 km, and dissolve the boundaries when you're done to get a single polygon. Third and last, use the erase tool to get all of the suitable DNR managed land areas outside of 5 kms from urban or built up land.


Objective 7:

In this Objective you will create a cartographically pleasing data flow model of your workflow from all the steps/tools used as well as a cartographically pleasing map.

Objective 8:

In the final Objective you will work with Python Code.

Results:

Objective 2:

The three most populous land types with bears is listed below:

1) Mixed Forest Land - 31 bears
2) Forested Wetlands - 17 bears
3) Evergreen Forest Land - 14 bears

Objective 7: 

Your Suitable Bear Habitats in Marquette County, MI should like like Figure 1 below:

Figure 1 is the Final map you are proposing to the DNR for suitable bear habitats. It includes the location of the bears, streams, the three most populous bear land covers, and the DNR managed land that is more than 5 km from urban or Built-up land.

Objective 8:

Your Data Flow Model should look like Figure 2 below:

Figure 2 is a Data Flow Chart of all the functions performed to reach the final product

Your Python Code should look Figure 3 below:

Figure 3 shows the Python Code you should have done to introduce yourself to the Python tool

Sources:


Data Management:

The data for this lab is downloaded from the lab3.zip file.

Data Sources: 

State of Michigan open GIS data.

USGS NLCD

DNR Management Unit

Lab 2: Downloading GIS Data

Goals and Background:

The goal of Lab 2 is to learn how to download and map data from the U.S. Census Bureau. In this lab there are 8 objectives displayed below:

1) Download 2010 Census data (total population) from the US Census Bureau.
2) Download a shape file of the 2010 Census boundaries from the US Census Bureau.
3) Combine the downloaded data to the Census shapefile.
4) Map the data.
5) Download and map a variable of your choice.
6) Make a layout with both maps
7) Create a web map with one of the variables.
8) Indite a technical report and post it to this blog.

In preparation for the lab read the definitions of US Census Bureau, Census Boundaries, Statistical Boundaries, Census Tract, Block Group, Census Block, 2010 SF1% Data, and lastly American Community Survey Estimates in order to learn some background knowledge.

Methods:

Objective One: Download 2010 Census Data:

Begin by creating a lab 2 folder in your the workspace for the class. The first step is to choose a data set form with advanced search on the US Census Bureau Fact Finder Website. Click on the Topics option, choose People , Basic Count/Estimate, and lastly population total. Now choose all counties within Wisconsin in the Geography option. Find the P1 for Total Population from the 2010 SF1 Dataset, download the data, and then unzip it in your Lab 2 folder. Next, extract all the files from the new unzipped folder in order to open the CVS files. Find the DEC_10_SF1_P1_with_ann.xlsx file in order to save it as a Excel Workbook. Then go back to the Census website to download the shapefile.

Objective Two: Download the shapefile for the WI census data:

Start by downloading the Map in the geographies option, and again unzip the file. You will need all the files, so don't move anything around. Use ArcCatalog to see and manage shapefiles.

Objective Three: Join the data together

Create a blank map in ArcMap titled Lab 2, and rename the Layers tab "Population". Bring in the 05000.shp shapefile, and the DEC_10_SF1_P1_ann$ attribute table. Join the tables using the common attribute field "GEO_ID".

Objective Four: Map the data

For this section a quantities graduated colors value for D001 needs to be made. However one must complete various steps to fix the original string field type. In order to fix this, create a new field named D001 in the 05000 attribute table. Go to the new field, and use the field calculator. Double click D001 then hit "Okay". Now you can create a map with graduated colors.

Objective 5: Map a Variable of your choice

Going back to the census website, choose a variable to download and map. Repeat the same steps as before in order to create the newly mapped variable.

Object 6: Build a Layout

For this section you will create a cartographically pleasing layout with both maps of Wisconsin. Put the two maps from Objectives 5 and 6 into two separate data frames in a landscape layout. Make sure each map includes a title, legend, north arrow, scale, date, source, and author. Include the year of the census from which you gathered the data.

Objective 7:

The last part of the lab involves sharing and configuring a map by creating a web map service in ArcGIS online. You also need to create a web map, and lastly configure that web map.
Publish the finished population map, and name it Wisconsin_Demographic_Information_Morgan in the UWEC Geography and Anthropology GIS online account. is complete you are asked to add a pop up in the Configuration window.

Results:

Objective 4:

After creating the new D001 Field you can create a quantities graduated color map that should like the map in Figure 1.




Figure 1 is a map of the D001 field
for Total Population

Objective 5:

Just like objective 4 you used D001 to create a quantities graduated color map, but this time it was with a variable of your own choice. Figure 2 shows displays a map of the number of housing units in Wisconsin.

Figure 2 displays a map of D001 field
for total number of Housing Units

Objective 6:

This portion of the lab requires you to make the two maps from Objective 5 and 6 visibly pleasing (as shown in Figure 3.




Figure 3 shows two cartographically pleasing maps

Objective 7:

Once you configure the pop-up in the Configuration window it will appear for any county you click on (as shown in Figure 4). This is the hyperlink to show proof of finished product: http://uwec.maps.arcgis.com/home/item.html?id=fa3a20e0336b488a95c51ee20bcd657a

Figure 4 shows the pop-up configuration for the uploaded map

Sources:

All Data was acquired from the US Census Bureau in 2010

GIS Extra Credit: Geography Bee

Thursday, November 17th, 2016

Overview: 

The Geography Bee was a trivia challenge between 10 groups of pairs that lasted about an hour and a half. Question were based off of different Geographical studies with Multiple Choice and Fill in the blank. Each multiple choice question had 5 answers, A through E. There were a total of 3 rounds, and after the first round groups were eliminated. This event was ran Ari Anand, Ryan Weichelt, and Ezra Zeitler. 

Summary of Questions:

The first round's questions dealt with statistics. For example what are the top 5 largest lakes in the world. I answered 2 out of 5 of these questions with lake superior and lake Michigan. The remaining 3 were Lake Victoria, Lake Huron, and the largest of all, the Caspian sea. Another question was what are the top 5 largest. My partner and I again only guessed 2 out of 5 with the largest Tokyo and Shanghai. The other three were Delhi, Seoul, and Jakarta.  

The second round also dealt with statistical questions. The first question asked what the top 5 most popular parks are. We got Yosemite, Yellowstone, and the Grand Canyon right. The other two were the Rocky Mountain national park, and the largest of all, the Great Smoky Mountain national park. The next question was what is the coldest temperature ever recorded. The answer was -162 degrees Celcius or 128.6 degrees Fahrenheit, and was recorded on July 20th, 1983 at Vostok Station Antarctica.

The final round consisted of two matching questions, and one statistical question. The first matching question asked to match up 5 physical features with 5 states. My partner and I got all 5 of the following correct answers right: Crater Lake is in Oregon, Mammoth Cave is in Kentucky, Carlsbad Cavern is in New Mexico, Shenandoah Valley is in Virginia, and Tortugas bay is on the coast of Florida. The second matching asked to match up monuments with their states. The only one we got right was Fort Sumter with South Carolina. The other four were Homestead to Nebraska, Dinosaur park to Colorado, Pipestone to Minnesota, and lastly Murr woods to California. The very last question asked what are the 5 oldest national parks. I guess Crater Lake, Yellowstone, and Yosemite correctly. The other two parks were Sequoia and Mt. Rainier.

Conclusion:

By the end of it all there was one winner who received the Goat Trophy. All participants received prizes. Although I did not win, I learned new facts that I would not be aware of if I hadn't gone. It was a great time, and I will definitely be in attendance next year. 

Lab 1: Confluence Project

Thursday, October 27th, 2016

Goals and Background:

For Lab 1, the student is "working" as an intern at Clear Vision Eau Claire in order to help construct a new development for the confluence of the Chippewa and Eau Claire Rivers in the downtown of Eau Claire called the Confluence Project. The project plans involve creating a new community arts center/ university student housing as well as a commercial retail complex in downtown Eau Claire that began in 2014. This new Art Center will include three performance spaces along with offices, studios, classrooms, galleries, and more. The first task is to prepare a basic report containing all relevant knowledge and base maps for the Project. The Goal of this project is to become familiar with different spatial data sets used in administration, land use, and public land management in order to prepare for the Confluence Project.

Methods:

This lab teaches various skills in order to reach the end goal. The first objective required the user to get used to the two Eau Claire geodatabases which were used for all the basemaps. One was for Eau Claire county, and the other for the city of Eau Claire. After that I explored the geodatabases's data in ArcCatolog. These geodatabases contained feature datasets, parcel feature classes, and common feature classes. These various features helps you settle in, and prepare for the base maps prior to their creation.

The next objective requested me to create a geodatabase, titled EC_Confluence,  as well as make numerous feature classes. The most important feature class created was Proposed Site, and the directions tell the intern to name it "pro_site". This specific feature class is in every single one of the maps that are created because it was the area in which the intern would propose their idea for Clear Vision. The directions tell you to add the Block Groups from the CENSUS_FEATURES feature dataset in to the Eau Claire Geodatabase which enabled it to have the Eau Claire County Coordinate System from the existing geodatabases.  The next step was create a blank map in ArcMap and add World Imagery as my base map. Then you put the pro_site and parcel feature classes into the data frame. Moving on, the intern identified the two parcels of 128 Graham Avenue and 202 Eau Claire Street in order to digitize them.

Figure 1 shows the locations that I identified the Parcels in order to Digitize them.

The third objective teaches the worker about the Public Land Survey System. After reading some background information you are asked to create a new data frame. Then add PLSS_Township to the Eau Claire geodatabase as well as PLSS_townships from the city geodatabase.  The intern must also add the PLSS_Sections from both databases. Lastly PLSS_Quater_Quarter_sections from the counties geodatabase and PLSS_qq from the city geodatabase are added to the new data frame. After observing the attribute data one is able to form a description of the two parcels. This was found in the City of Eau Claire's Property and Assessment Search Website: http://www.bisnet.net.cityofeauclaire/search.cfm.

Finally, you start generating the maps shown in figure 2. The intern must make a document with landscape layout. Then create the six data frames labeled Civil Divisions, Census Boundaries, PLSS, City of Eau Claire, Zoning, and lastly Voting districts. They need appeal to the eye, and include all the requested elements: title, legend, scale bar, map creator, and source.

Results:

Figure 2: Shows base data for the proposed site of the Confluence Project, Wisconsin created in 2014

All of these proposed sites for each data frame are located in the same districts/sections. It is easy to tell they are all located in the city of Eau Claire with a single census boundary and voting district. There are no roads dividing them, and there is a good population density in the area. Most importantly they are located on public property, so no residents or commercial industries will be able to acquire land there.

Sources:

City of Eau Claire. (2013). Retrieved from http://ci.eau-claire.wi.us/

Eau Claire County. (2013). Retrieved from http://www.co.eau-claire.wi.us/