Lab 5 Final Project

Introduction:

Recently a friend from college has called and informed me of his plans for him and his family to move to Eau Claire to begin a new job.  Nobody in his family has ever been to Eau Claire before and they will not have much time to house hunt ahead of time.  Seeing as how I have lived in Eau Claire, he thought he would feel more comfortable finding a home sooner with my assistance finding the right neighborhood for his family, which consists of two children, 4 and 7, and a third due on the way.  He has said he can commute a little ways to work, so not to worry about that as long as it is within Eau Claire County.  He does, however, have a few criteria he would prefer to satisfy in order to be in the best situation for his family. 

- They would like to live on a block where there is a possibility of other children, but not a block that is really crowded.  With that in mind, he is estimating a block population of one hundred to one hundred and fifty people to be ideal.

- His kids can be pretty wild and like to horseplay.  Broken bones is nothing new and another baby will be coming soon.  It is very important that the families new house falls within close proximity to a hospital.  He has heard good things and was recommended to Luther Hospital. He does not want to be more than 2.5 miles away from this hospital.

- With his second child set to be in kindergarten next year, and his first child still in elementary school for a few more years, he says it would be also be really helpful to be near an elementary school where his kids can easily get to school.  He wants to know if there are any schools that satisfy his block population and hospital distance criteria and are within a quarter mile so his kids can walk.

He has asked for your assistance in narrowing down his possibilities for blocks to go house hunting based on these criteria.  He has additional desires with his new home, but those are flexible and can be dealt with once these three criteria are satisfied.  His one final question is how far away Lake Eau Claire County Park would be from the suitable block, as he likes to be outdoors with the kids.

Data:

The data that I used to identify the best possible scenario for a suitable block within one quarter mile of an elementary school and 2.5 miles of a hospital was used from http://www.esri.com/data/find-data.  From ESRI, I used data from the Schools, Counties, Hospitals, Parks, and Blockpop layer frames.  A couple of concerns passed over me as I performed my analysis.  For instance, some schools may not be fit for everyone and may not provide the most beneficial education.  This is something that was not looked at in my solution.  Additionally, crime rates and actual children presence on the suitable block was not measured, but rather just the population. 

Methods:

To begin, it was necessary to connect to the ESRI server to acquire the US data.  Once connected I determined the layers I would be using included Hospital, Schools, Blockpop, and finally the County layer as my area of interest.  First, I added the county layer to the map and exported Eau Claire County to a new layer.  Once this was done, I was able to add my additional layers and perform the Clip operation to limit them to my area of interest.  At this point, I was ready to begin my spatial analysis to determine if any blocks meet all criteria.  I performed a query to acquire a layer with just the blocks that have a population between 100-150 people.  Next I selected Luther Hospital and created a layer from the selection that I proceeded add a 2.5 mile buffer around.  I also created a layer based just on the selection of Eau Claire County Park as well as one for just elementary schools.  I proceeded to create a buffer of one quarter mile around the schools and then intersect that buffered layer with the Luther Hospital buffer.  Finally, to determine if any of the intersected buffers held a block within them I performed another intersection of blockpop and the buffer intersection.  This gave me one value that satisfied these requirements (Figure 1).  I performed a spatial join to determine the distance between my suitable block and Lake Eau Claire County Park.  A summary of my strategy used to determine my spatial question answers appears in Figure 2.

 

Figure 1. Green Star representing suitable block

 

 

Figure 2. Data Flow Model

 

 

Results:

Since my friend had a requirement of a 2.5 mile radius of Luther Hospital, it all but eliminated potential blocks anywhere outside of the city of Eau Claire.  It was found that eight potential elementary schools were within the required hospital radius, but a block with the population desired fell within the desired distance of only one of these eight schools.  From the spatial join, it was determined Lake Eau Claire County Park is a distance of 38,145 meters or about 38 km away (Figure 3).  A final map showing overall results is depicted in Figure 4.

 

 

Figure 3. Distance to Lake Eau Claire County Park

 

Figure 4. Finished Map

 

 

Evaluations:

 

I thought the project was a great way to require thinking and figuring on the students' own behalf.  I found difficulties along the way when I wanted to distinguish and figure something out, but there wasn't always the data I would need to proceed in that direction.  If I were to repeat the project, I would use a bigger county and try to develop a spatial question that involves more of the county rather than have almost all the results in one small part of the county that happened to be the City of Eau Claire. 

Lab 4 - Suitable Bear Habitats

Introduction:

Lab number 4 examined a situation where multiple criteria needed to be met in order to satisfy the predetermined requirements established to find suitable bear study areas.  These predetermined objectives included the popular forest types bears are found if they are near streams.  Then it was necessary to find areas that satisfied both those criteria and additionally areas that are managed by the DNR and are at least 5 km from urban or built up lands.  A numbered list of objective steps is as follows:

1. To map a GPS MS Excel file of black bear 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 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. Generate cartographic output

8. Generate a digital data flow model of the procedures used to determine suitable bear habitat in Marquette County, Mi

Following these steps will allow us to use various geoprocessing tools in vector analysis to gain our desired result of suitable bear habitat areas for bears in an area of Marquette County in the state of Michigan.

Methods:

Determining that 49 of the 68 bears located were within 500 meters of a stream was done by a join of the bear locations feature with streams feature.  Since this amounts to well over thirty percent, it told us this was an important feature and we should map it in our final resulting answer.  Also, it is necessary to create a new layer of the three most populated forest types: Mixed Forest Land, Forested Wetlands, and Evergreen Forest Land.  This can be done by a summary of a join between bear location and landcover.  To determine the areas that satisfy both those requirements, buffer the streams and dissolve all. Next, to find land suitable for both requirements, intersect the created forest layer with the stream buffer layer and dissolve the result.  To determine which parts of the area you have found is in DNR managed area, you will need a few steps.  First, add the DNR management layer to the data frame. Then clip that to the study area layer to only examine the DNR land in our area of interest.  Next, dissolve the DNR layer and intersect it with the previous suitable area followed by a dissolve of the resulting area after the intersection.  Finally, to satifsfy our last requirement of getting rid of the area within our suitable result that is 5km or less from urban or built up areas,  first select urban and built up areas and create a layer from the selected features.  Dissolve the new layer.  Finally, use the erase tool to erase this new layer from the most recent suitable area layer.  Figure 1. shows a data flow model of these steps and how the final answer is reached.



Figure 1. Data Flow Model

Results:

After all the analysis has been completed, a map of Marquette County, Michigan, showing bear location, streams, all suitable habitat, and suitable habitat that was eliminated from its proximity to urban and built up areas (Figure 2.).  It appears as if the majority of suitable bear habitat falls within the southwestern area of our overall study area.

Figure 2. Final Map
 

Sources:

http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

GIS 1 Lab 2: Downloading GIS Data

Introduction:

Lab number 2 is designed to increase knowledge in regards to downloading and mapping data sets acquired from the United States Census Bureau.  The Census Bureau provides a variety of collected data in order to adapt policies, distribute funds, and apply Congressional seat proportions.  Census data comes in two formats, SF1 and ACS.  SF1 data is gathered in ten year increments and counts total population and the distribution of the population in the United States.  ACS data is now gathered on a yearly basis and provides more detailed information on the population, including employment, ethnicity, economic, and educational characteristics.  Using this data to determine the total population by counties as well as percent of households with a member over the age of 65 by county will be the final objective of this lab.

Methods:

To begin, access to the Census Bureau website at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml and it will provide data to be downloaded for the map.  Once a topic is chosen it is important to determine the geography of where the desired resulting map is intended, in this case each county within the state of Wisconsin.  Once the data for the correct location is downloaded in a zip file, the user will need to locate the file and unzip it.  The unzipped table with the necessary data needs to also be converted to an Excel file so it can be imported to ARCMap. 

This download only gives us raw data.  To display it geographically, we need to also download a shapefile that will provide a spacial reference for the data mapping.  This is done by viewing the Map tab within the Geography section of the Bureau website.  A download and another unzip is necessary for the shapefile. 

With the files downloaded in an ARCMap connected folder, dragging the shapefile and Excel file into a blank map will not map the data to the spatial reference.  To fix this, it is necessary to join the files using a common attribute.  We wish to join the Excel table to the shapefile, so clicking the join option from the shapefile will allow for this.  Next join the files by their common attribute and double check the shapefiles attribute table to confirm the join was successful. 

Next the data can be mapped.  Since total population is an entire statistic, it does not need to be normalized, but the second example of this map where we are looking at the amount of households with a member 65 years of age or above will require normalization versus the total amount of households.  This is all done by right clicking the shapefiles and creating a map in the symbology tab of the of the properties option.  Also within the symbology tab it is important to clean up numbers in labels and determine a proper break point method to provide a reasonable picture. 

Once the maps are created, the user will need to switch to layout view to edit and make the map appealing from a cartographic stand point.  Using grids, rulers, and snap points, both maps were lined up side by side.  To ensure both pictures of Wisconsin are not distorted, the data frames should have a state projection that can be observed and changed by right clicking each data frame and choosing the properties option.  To ensure equal sizes, find the right size on one state and copy the scale to the other.  Additional features to make the map more appealing include adding a title, legends, scale bars, north arrows, author, the source, and if desired, a basemap.  In this exercise, it is more efficient to also be in landscape view versus portrait to line the maps side by side.

Results:

The result of these methods will provide two side by side, equally sized respresentations of Wisconsin and the individual counties.  Using the legend, it is apparent from the left hand picture that population density is heaviest in Southeastern Wisconsin, but based on the right hand picture, it also appears to be somewhat younger (Figure 1).  For example, the selected county in Figure 2 shows that it has one of the highest percentages of households with a member 65 years or older, but is also the third lowest populated county.

                                            Figure 1. Total population by county and percent of households by county with a member age 65 or older.

                                          
                                          Figure 2. Iron County

Sources:

U.S. Census Bureau. (2010). Retrieved from
http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml