Lab 4: Mini-Final Project

GOAL AND BACKGROUND 

This lab was oriented around a spatial question that used various GIS skills learned throughout Geography 335 to answer. The question was “Where are Non-Traditional Hiking Areas in WI?” The Area of Interest for this spatial question was the state of Wisconsin. The criteria used to base this question on was having the areas be 5 km away from a body of water such; as a stream, lake, river, or pond, not be located within a National Park, be 25 km away from secondary roads, be located in a less populated area, and have the Landcover consist of Forests. 


METHODS

  
The first step was gathering all the data for the criteria. The National Park Boundary layer and County Population layer was a zip file downloaded off of data.gov website. The NHD Water Body Area 24km was downloaded off of WI State Cartographer’s Office as a zip file. The landcover layer was also downloaded from the WI State Cartographer's Office and opened in ArcMap as a raster file that had to be converted into a vector file. The Major Road and WI State Boundary layers were used from a previous geodatabase in a lab.The second step was querying out a county with less than a 20,000 population. That layer was then joined by the National Parks Bdy layer where the erase tool was used to find areas that are not considered parks and have low population. The layer of 20,000 Pop. Without NP Bdy was then intersected with the NHD Body of Water layer, consisting of streams, rivers, lakes, and ponds queried out. Once joined, the buffer tool was used to get an area of 5km from any Body of Water. This layer was then joined back to the 20,000 Pop. Counties to be able to clip out the Water Bdy and dissolve unwanted boundaries.The third step started with the Major Roads layer and intersecting it with the WI State Bdy. From there only roads in WI could be seen that were then queried to find secondary roads to areas. The roads were buffered to create any leeway of hiking off of the roads 25km. This layer was then intersected with the Water Bdy layer of 5km. This created a layer that was in distance of secondary roads and water.The fourth step was converting the Landcover Raster into a Vector shapefile and then feature class. From there a query was made for ‘Forests’. This was intersected with the layer of secondary roads and water. A last query of Shape_Length for the options to Hike was made greater than 10,000 m.


RESULTS

Figure 1. Below shows the map created from the data gathered to answer the question of "Where are there Non-traditional Hiking Areas in WI?" Overall, the main AOI in Wisconsin was the Northwest and Northeast. It was found these areas are less populated, have more bodies of water, and are more prevalent to forests than the Southern Agricultural part of Wisconsin. Southeast Wisconsin also shows some areas due to the unique landscape being affected by Glaciers. The basemap that was used is an elevation relief shading layer that shows some of the physical landscape in WI.

Figure 1. Map of Non-traditional Hiking Areas in WI.

Figure 2. Below shows the Flowchart of the procedure for this spatial question. In total, 6 different data layers were used, and 4 different tools were used. The Flowchart was created using Microsoft's Program Visio Professional.

Figure 2. Flowchart

SOURCES

NHD Water Body Area 24km and Landcover http://www.sco.wisc.edu/ 
National Park Boundary and County Population https://www.data.gov/ 
Major Roads and WI boundary from UWEC server

Q:\StudentCoursework in geog$(//geog.servers.uwec.edu)

Lab 3: Vector Analysis with ArcGIS

GOAL & BACKGROUND

This lab consisted of finding a suitable habitat for bears in the study area of Marquette County, Michigan using geoprocessing tools in ArcGIS for vector analysis. Based on the results, a technical report containing a map and data flow model was created to illustrate the findings. Python was used as an introductory method rather than ArcMap’s interface procedures in creating feature classes. Data on the bear locations with a GPS MS Excel file was provided along with the feature classes of bear management areas and political boundaries.

METHODS

Mapping a GPS MS Excel file of black bears in study area

The first step was unzipping the file so it would be able to be used. However because the standalone table of bear_locations_geo$ was in an x,y location format it needed to be transferred to an ‘event theme’. From there the data was then able to be included in ArcGIS as points. Since ‘event theme’ cannot be edited it had to be exported into a shapefile.

Finding forest types of where black bears are found

This created an editable featureclass. After adding all of the data from the bear_management_area feature datatset, the intersect tool was used with the land_cover and bear_location features to create the bear_cover featureclass. To find how many bears were in each habitat, summarizing the FIPS_Bear_Location and Minor_Type created a standalone table. From that it gave the top 3 land coverings that were able to be queried in the Land_Cover featureclass and then created the new featureclass Bear Forests.

Determining if bears are found near streams

The next area that was examined for possible bear habitats were the streams area. Intersecting the bear_location and stream feature class created a layer. Then using the buffer tool on the stream layer gave the results of where bears were located within 500 meters of a stream. To further separate the bear population in 500 meters of a stream the intersect tool was made.

Finding suitable bear habitat based on land cover types and being within 500 meters of a stream

Using the Top 3 land covering layer and intersecting it with the results layer of bear’s location within 500 meters of a stream gave the Bear_Habitat.

Suitable bear habitats within the Michigan DNR area

Using the DNR Management area feature class and the study area feature with the intersect tool created a layer just showing DNR areas in the study area. Then using the dissolve tool, it merged together the parcels. In order to find the DNR areas within the bear habitats the clipping tool was used, this created the layer of DNR Bear Habitats.

Eliminating areas near urban or built up lands

Finally, to create the DNR Bear Habitat 5 km away from Urban or Built up Land layer a query was used on the land cover layer for Urban or Built up land. Then the buffer tool was used to create a 5 km zone. From there the erase tool was used with the DNR Bear Habitat and Urban or Built up land buffer layer.

Using python

A short introduction to python was used in creating feature classes rather than using the interface of ArcMap. Figure 1. Below shows three codes used in replicating data already 

found. 

Figure 1.

RESULTS

Figure 2. below represents the Suitable Black Bear Habitat in Marquette County, MI. The Bear Location icon indicates where Black Bears have been seen in the Marquette County, MI. The Bear Habitat represents areas that are within the top three land covering with bears (Evergreen Forests, Forested Wetlands, and Mixed Forests) and areas within 500 meters of stream. The map shows that most Black Bears reside in the North and Center of Marquette County, MI.

Figure 2.

Figure 3. Below is a map of the three most common Land Coverings that Black Bears are located in. Comparing this map with Figure 1. It can be seen that Mixed Forests holds the largest population of Black Bears.

Figure 3.

Figure 4. Below shows the flowchart created to obtain the information of Suitable Habitats in Marquette, MI for Black Bears. As shown in the legend the blue circles were feature classes already provided with the geodatabase for this lab exercise. The yellow squares are the tools used to create the new information represented by the green circles. The Microsoft Program Visio Professional was used to create this flowchart.

Figure 4.

SOURCES

All Data is from the State of Michigan Open GIS Data

http://gis.michigan.opendata.arcgis.com/

Landcover 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/metadat/Marquette.html

Lab 2: Downloading GIS Data

GOAL AND BACKGROUND

The goal of this lab is to learn how to download, analyze, and map data from the U.S Census Bureau. We were to create two maps, one providing Wisconsin’s County Population and the other of our chosen variable. Additionally, we were to create a WebMap through ArcGIS and ArcMap.

METHODS

Creating Total Counties Population Map

The first three maps that were created used data obtained from the U.S. Census Bureau website:http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t. The data used was FSI 100% 2010 Census. The County Population data was retrieved from the U.S. Census Bureau website on the total population per county of Wisconsin. From the saved file the data was extracted into a shapefile that would present the counties data of Wisconsin’s population. This data was then mapped as the first map by total population count.

Creating Percent of Male and Female Population within Wisconsin Counties Map

The second and third maps were created similarly in the procedures used to obtain the data and map it. First, data was downloaded from the U.S. Census Bureau on Sex Population. This data provided age groups with their sexes and the total population of the sex in each county. The downloaded file had to be extracted from its zip file. Then it was saved as an Excel CSV file. In order to open up in ArcMap, the second line of the document had to be deleted because it was an extra line of worded information that is seen as a quantity in ArcMap. The naming of the headers also had to be changed into the correct ArcMap format with containing no (.) or (-) and were replaced with (_). After making those changes the file was saved as an Excel Workbook file and was able to be opened in ArcMap. Inserting the data file created a standalone table in ArcMap. Joining it with the Shapefile of Counties (created in the first map) allowed the data to be presented visually. Using the total sex population of males and then dividing or normalizing by the total population in each county created the male population percentage in each Wisconsin County. The same procedure was done with the female data. Once all three maps were created the components that make up a map i.e. legend, compass, scale bar, and title were added and also the World Light Gray Canvas Base. 

Creating a WebMap on ArcGIS

The WebMap was created from the Total Wisconsin Counties Population Map. The basemap and map components were deleted from the map frame. Before being able to publish the map to ArcGIS an analysis tool had to be used to make sure the data was correctly linked. After completion signing into ArcGIS through ArcMap created the ability to publish the Total Wisconsin Counties Population Map as a WebMap within UWEC organization on ArcGIS online. Once logged onto ArcGIS the map had to have a title, tag, attributes assigned, and a summary for mapping. After this the map was ready to be shared within the UWEC Geography and Anthropology Department.


RESULTS

Figure 1. Shows the results of the Total Wisconsin Counties Population compared to the Percent of the Male and Female Population within Wisconsin Counties. Looking at all three maps it can be deductive that more counties hold a greater female population than male population when looked at the concentrated populous counties. The male percent map shows that there is a larger concentrations of males in the northwestern half of the state. In contrast, the female percent map shows that there is a larger concentrations of females in the southeastern half of the state.

Figure 1. Map of Total Wisconsin Counties Population, Percent of Male Population within Wisconsin Counties, and Percent of Female Population within Wisconsin Counties.

Figure 2. Shows the WebMap produced through ArcMap and ArcGIS using the Total Wisconsin County Population. Each county in the WebMap shows the population and county names.

Figure 2. WedMap of Total Wisconsin Counties Population.

SOURCES

2010 Census Bureau

Figure 2. Shows the WebMap produced through ArcMap and ArcGIS using the Total Wisconsin County Population. Each county in the WebMap shows the population and county name.