Mini Term Project

Mini Term Project

New Locations for Hospitals in Brown County, Wisconsin

Jake Charneski

2016

Geog 335, Geographic Information Systems

Dr. Cyril Wilson

Objective:

                Our target location will be Brown County, Wisconsin. Brown County is located in north eastern Wisconsin. Brown County is commonly know because it contains Green Bay which is home to the Green Bay Packers. We are going to look for a good location to build a new hospital.  We want to find an appropriate place to build a new hospital that will allow the hospital to be economically efficient and also serve the most people as possible. The targeted audience are urban developers and the general public who will potentially need access to the hospital. The development of a new hospital will help further meet the needs of the growing population within Brown County. The hospital will also help to keep the population safer and healthier. We will specifically look for locations that are within the urban populated area and within 0.25 miles of a major road and within 1 mile of a school. The hospital will also need to be 2 miles away from any existing hospitals. We will use different tools and queries to select the correct locations and present a map showing the locations.

Data:

                The data referenced will be taken from ESRI 2013 DBO USA data. We will look at Schools, major roads, hospitals, counties, and urban populated areas. This data can be found on the University of WI- Eau Claire’s geography file server. The data can also be found at http://www.ArcGIS.com as downloadable content. The concerns I have with the data are the fact that the data was taken in 2013 or earlier. The data that ESRI collected also came from many third parties at different times. This could lead to accuracy problems leading to some inaccuracies in the final result.

Methods:

                First we need to select the target location of Brown County. We will import the counties layer form the ESRI 2013 DBO USA data. We will make sure to project the data in the correct coordinate system. For Brown County, we will use the coordinate system of NAD_1983_StatePlane_Wisconsin_Central_FIPS_4802_Feet to best represent Brown County. We will perform a select by attributes within in the counties layer, first selecting Wisconsin and then only Brown County within Wisconsin. From this we will create a layer just for Brown County that we will clip all other layers too.

                Next we will add all the layers that we will use to analyses where to place a new hospital. We will add a school, urban area, major roads, and hospital layers. We will make sure to project each layer in the NAD_1983_StatePlane_Wisconsin_Central_FIPS_4802_Feet coordinate system to match that of our created Brown County Layer. Next we will clip all of the projected layers to the created Brown County layer so only data shows up for Brown County.

                Next we will create a buffer of 0.25 miles around major roads and a 1 mile buffer around all schools in the area. We will also create a buffer around existing hospitals that is 2 miles. We will perform an interest between the major road and school buffer and the urban area clip within Brown County. This will yield good potential area to build a new hospital but we have not taken account the hospitals. We will now perform an erase action on hospitals buffer to eliminate the 2 mile radius around hospitals. This will ensure that the new potential hospital to not fall within 2 miles of existing hospitals. This erase combined with the intersect action will yield the best location for hospitals based on the stated criteria. Here was what the data flow model will look like for a visual explanation.

Results:

                The area to build a hospital is neatly labeled on the given map, FIG 3 below. This data will help urban developers better decide where to build a new hospital without having to do the research. The area meets the criteria laid out perfectly. If the city needed to vote on a new location for a hospital they can reference this map to make sure it meets the criteria stated. If more criteria is needed this map can provide a great reference to build further criteria off of.

Evaluation:

                The project overall did not seem that difficult. After doing all of the labs and tutorials throughout the semester the tools and queries that should be used are easy to think of. I enjoyed coming up with our own research topic. Coming up with the initial idea was slightly difficult because I could not find data for my original idea. After changing my idea I was able to find sufficient data to complete the project. If I were asked to do the project again I think it would be interesting if we were all giving a problem to solve from the professor. I found it interesting to solve giving problems, though coming up with our own problems leads to more creativity. It also allows to really test the skills we learned. I would not change much for the project and keep it the same as it is now.

References:

http://geonames.usgs.gov/domestic/download_data.htm

http://www.ArcGIS.com

                

GIS 335 Lab 5

GIS 335, Lab 5

Goals:

The goal of this lab is to use different geoprocessing tools to determine suitable habitats for Bears in Marquette County, Michigan. The lab will also teach some basic python scripting tools and model building.

Methods:
Part1:

First we need to create a shape file of bear location from the bear_locations_geog$ sheet. Right click the sheet and select create feature class from XY table. Make sure you save the file as a shape file and name it bear_locations. Next open up arc map and add all the feature classes within the bear_management_area dataset and your bear_locations shape file. Give the landcover symbology a unique color based on minor type. Then perform a intersect between bear_locations and landcover. Next perform a summation on minor type in the new attribute table to figure out the top three habitats.

Next you will create a new feature class from selection that only contains the top three habitats by performing a query in the landcover attribute table.

Now perform a buffer action on streams of 500 meters. We will use this buffer to intersect with bear locations to figure out which bears are with 500 meters of streams. You will figure out that over 70% of bears are within this distance.

We then combine all these to find a target area of bears yielding the total area suitable for bears. Then we will compare this to DNR management area by clipping the dnr first to the study area. Next intersect it with the total suitable area for bears. Then we perform a buffer of 5 km for urban areas in the landcover feature. Then perform an erase of the dnr intersect and buffer to get only the area managed by the dnr that lies in the bear habit area and is 5km away from urban area. You will have a map and data flow like this.
Results:

Part2: Section 1.
Next we will create a map to show areas in wisconsin to build new resorts. They must be on lakes that are 5 square miles and also not more than 10 miles from a city. We will use python for these actions.

This will produce a map that looks like this.
Results

Part 2 section 2

We will create a map that will show potential for pollution levels near interstates. We will use a multiple ring buffer on the interstates from 1 to 6 miles away. You will use these python functions.

Then create a map that shows the levels ranging from high to low.
Results:

Sources: Price, Maribeth. 2016. Mastering ArcGIS. 7thEdition data. McGraw Hill.
Wilson, Cyril 2012, A comprehensive Lake features for Wisconsin, Unpublished data
Michigan Department of Natural Resources (DNR)

GIS 1 Lab 4

GIS 1 LAB 4


Goal:


The goal of this lab is to use multiply queries to obtain answers from arcmaps.


Methods:


Part 1: United States
 Question 1: First add the counties layer from the USA geodatabase. Write a multiple query to find that will return counties that show a population between 3000 and 4000 people in 2010 and also all counties in 2010 that had a population density of at least 1000 persons per square mile. Do a select by attributes query that will look like this:

This will tell you how many counties were selected. Next you can build a summarize table of state fips and state name first to yield how many counties were in each state. From the table you will be able to figure out which state hat the most and which state had 8 counties.  Then use a statistics function to figure out the mean and standard deviation of the counties in the criteria. Your map should look like this

Question 2:
 Write a multiple criteria query to show the records for counties in ( Wisconsin, Texas, New York, Minnesota, California) where male population is greater than female population and has population of greater then 6500 for seniors over 65. Your query should look like this:
You will repeat the steps from problem one to figure out how many counties were selected, which state had the highest, and which counties were only in Wisconsin. Your map should look like this:

Question 3:

 Next create query that will add the state of Washington, Maryland, Illinois, Nebraska, District of Columbia, and Michigan who reside in counties with more then 30000 housing units and includes all seniors citizens. Your query table and map will look like this:

Part 2: Wisconsin

Question 4.

Develop a query that will show cities in Wisconsin with a population between 15000 and 20000in city that is at least 5 square miles in land, and has more females then males and are within 2 miles of a lake. This will tell you how many cities are in the search and their names. You can then do a statistics function to figure out the total population.  Your map and query will look like this:

Question 5.

You will make a query that will yield the total length of the following rivers: Chippewa R, Eau Claire R, Embrarrass R, fisher R, Hunting R, Kinnickinnic R, Maunesha R, Milwaukee R, Moose R, Namekagon R, Pelican R, Platte R, and Potato R. This will select all the rivers and then you can perform a statistics tool to find the total length of the rivers. Your query and map will look like this: