GIS Final Project

Introduction:

 For the final project of the semester for GIS I, we were asked to come up with our own question to research. Throughout the semester, we learned a variety of different techniques and tools to facilitate answering spacially oriented questions. Vector analysis and geoprocessing helped us assess and answer these questions. For my project, I wanted to find potential areas in Minnesota my parents could retire too. I created a set of criteria for the location based on my parent’s interests and hobbies. For instance, my father really enjoys playing golf, so that was something I thought would be important to him as he transitions into the next portion of his life. Likewise, my mother likes to get out in nature, so state, and county parks in close proximity to where they may move would be important as well. Along with golf courses and parks, both my parents like boating, so for them it would be nice to live within a short drive of a lake to take the boat out on. The final thing I wanted to keep in mind, was proximity to an urban area with a population > 50,000, so they could do any shopping and have decent access to quality medical care which larger cities tend to offer.

Data Sources:

This project required me to gather data from a variety of sources. I connected to an Esri (2013) database through ArcMap 10.2.2. The data I needed to complete my project could be found inside this database with relative ease. Looking at the data I did have come concerns that I took into account for the final map. Firstly, in the “park. DBO”, included state and county parks, but made no mention of local or other types of parks in the surrounding area. Secondly, since I wanted to find places that were not federally owned in the counties. The public land I did find did not contain land use information. So if I wanted to make a more complete map of actual areas in county land that was for sale and not used for business or agriculture I would have to add those criteria into the mapping process.

Methods:

To begin, I wanted focus my AOI on the counties in Minnesota, I performed a select my attributes to get all the counties, made the selected features a layer, then performed a clip tool for all the feature classes (Cities, Lakes, golf courses, and Federal land). For my first three criteria points (proximity to cities, lakes, and golf courses), I performed a buffer around each for the desired area. After the buffer was completed, I intersected all three layers into one. For the locations were they could move, I used an erase tool on the federal land in the counties to get the areas that were not public land. After the erase on the counties, I used a second intersect tool to combine the (cities, lakes and golf courses) and the counties with federal land erased. This gave me the possible areas in Minnesota that would be suitable for my parents to look for homes to retire in.

Results:

 

 

 

Figure 1: The area in purple represents the possible area my parents my look into buying a home for retirement.

Evaluation:

In the beginning of the semester, this project seemed like a massive undertaking, and would provide quite a few challenges to overcome. As the semester moved on, and I learned a many more skills, I began to piece how to complete an open research project like this. I started realizing the power that GIS and ArcMaps contains. With all the power, comes limitations for someone like myself with partial experience and minimal analysis problems. With that being said, as I looked at the data and began to assess what I might be able to actually achieve, my project started taking shape. When I had all the datasets in ArcMap, my project unfolded in front of my eyes and I was able to take advantage of many of the tools I learned throughout the semester. This project has shown me how far I have progressed from just a few months ago. I can’t wait to see where I’ll be in another years’ time.

The Power of Geoprocessing and Vector Analysis

Goals: For the purposes of this lab, we were asked by the Michigan state Department of Natural Resources to examine possible suitable habitats for local bear populations in Marquette County, Michigan. This lab helped me become more familiar with data manipulation and organization, followed by geoprocessing and vector analysis to gain insights into the complex questions with multiple variables. Along with combining skills from previous MAG_ units, we were introduced to proper techniques like data-flow modeling and writing simple python scripts.

Background: The Department of Natural Resources (DNR) in Michigan has been tracking the locations of black bear populations in Marquette County near Lake Michigan. They contacted the University asking if we could use GIS to examine the local environment to obtain more detailed information about where the bear population are more likely to occur. Along with this information, the DNR wish's to establish where suitable habitats for bears under the management of the DNR. For this lab, we were given the GPS locations of 68 black bears, along with ground cover datasets and the study area the DNR operates in.

Methods:

 First, to determine what types of environments bears generally live in, i wanted to determine the top three forms of land cover where bears are found. To do this, I took the GPS locations of the bears, and performed a spacial join. Once i had the output feature class i used the summarize tool and found the top three types of land cover the bears were found on. (Evergreen forest, wetland forest, mixed forest).

Now that I had the types of land the bears were likely to live on, I wanted to know if proximity to streams was an important factor to a bear’s habitat. I used a buffer function of 500 meters around streams to determine how many of the bears were found within that distance from the streams. I found 49 out of the 68 bears were located near streams when they were tracked. Since the percentage of bears located near streams was high (72%), I concluded this to be an important aspect of the habitat.

Using my two criteria (top land cover, and proximity to streams), I performed a intersect of the two feature classes and produced a suitable habitat area for the bears. The next aspect specificed by the DNR was to determine which parts of the suitable habitat are located on DNR managed land. To do this, I took the DNR controlled area feature class and used the intersect tool to find where the areas of the two (bear habitat and DNR land) converged. After I had accomplished this, only a few more steps remained.

The DNR contacted us a second time, concerned about the proximity of bears to urban areas. They were concerned with increased populations becoming a hazard to local communities, and wanted to diminish the chances of this as much as possible. To accomplish this task, I got the Urban areas feature class and performed a 5 Km buffer around the areas. This gave me an output, which I used to intersect with the feature class for a bear habitat I just created.

Results: This gave me my final map, which contains the suitable habitat for black bears, under DNR management, and outside 5 Km from urban areas. The results displayed in brown are the areas that fit all the criteria specified by the DNR.

Figures: 

 

Figure 1: Suitable Black Bear habitat under control by DNR.

Work-Flow

Sources: USGS (Landcover)

Department of Natural Resources

http://www.mcgi.state.mi.us/mgdl/

Background:

For this project the goal was to become more familiar with data sources, and downloading particular datasets from online sources. In the aim of gaining experience that might transition into a job field outside of school, I choose to get the data from the US Government Census Bureau. The Census Bureau provides quality data about the nation’s people and economy. This data provides law makers with dynamic information to determine decisions that congress makes, as well as provide greater knowledge about community services, and distribution of federal funds. As an independent geospatial information systems analyst, quality data of this kind allows me to use Esri programming to spatially plot this detailed information to provide clarity and organization of the information for my employers depending on their specifications. For this mock exercise, I was contacted by a company specializing in retirement facilities. The company wants to expand business to other locations in Wisconsin, and would like my suggestions as to which counties may provide the most prosperous locations to build the new facilities.

Methods:

To begin this project for the retirement home, I first needed to download the correct data off the online server. I choose the US Census Bureau website. This website is accessible to anybody, but care must be taken to assure that the data that is needed the individual contains information of use. The data provided off the website comes in several varieties, differing in complexity and depth. The most basic of all the information is the 2010 SF1 100% data, this data provides accurate counts of all persons living in the US and if collected every 10 years. Information included is total population, age, household size, racial backgrounds, and housing units along with other relationships. A more detailed data set available is produced by The American Community Survey (ACS) Estimates, which contains data back until 2005. The content of the survey reveals data about employment, education, economic, and ethnic characteristics. Since all social data is 100% correct, the ACS is an estimate and the census provides the error.

After taking steps on the Census website (factfinder2.census.gov) to narrow the criteria search by geography, to Wisconsin. Then looking at population data for the counties in Wisconsin I found a SF1 dataset that contained information about the total population listed inside the counties in 2010. I next downloaded the data into an excel file and moved the set over to ArcMap for further use. I used the total population for each county to gain a picture of where population centers generally are located in the state. I did this to look for the biggest appropriate population of size. In general terms, the more people in a given area, the greater the amount of elderly people.

Using a base-map collected from Esri online, and a .shp file downloaded off the census website containing the counties of Wisconsin, I made a choropleth map of population totals per county. The next logical step was to gather another data set from the census containing information about the age of the population normalized by population totals and making another map to compare the already made map of population totals. I accomplished collecting the data in a similar way as the first set, and mapped the age of the population per county. This gave me a second map which to compare the first to and begin to piece together where the next move for a successful retirement village may go in the state.

During the process of making both maps, I learned a valuable skill which relates to using information downloaded of the internet. That skill is using table joins, to get the correct sets of data in the right place so I can effectively map the targeted information. Using this skill allows me a greater range of information distribution.

Results:

A.                                                             B.

Figure 1: The map labeled A displaying the Percent of population over the Age of 65. The increasing darkness of the color blue represents a higher percentage of peoples aged 65 years and older. Compared to map B, map A shows a high percentage of elderly people located in the far north of the state. Map B shows the total population of the state of Wisconsin. The highest counties are in the southern part of the state, containing the large cities Madison, and Milwaukee.  Looking at the north of the state, one can gather that there is less population overall, but a higher percentage of the occupants are of greater age.

Confluence Project 2015

Background: In the spring of 2015, I was asked by Clear Vision Eau Claire to help develop a cooperative plan for the Confluence Project. I utilized an ESRI designed ArcGIS computer program to work with Clear Vision Eau Claire and public-private partnerships between local developers to provide a comprehensive plan for the intended Confluence Project. The Project intends to develop an area at the confluence of the Eau Claire and Chippewa rivers in downtown Eau Claire. The new building project envisions a new community arts center, housing for University of Eau Claire students, a complex of retail stores, offices, classrooms and studios as well as 3 performance spaces. My goal in participating in the project was to gain insight into working with spatial datasets commonly used in land management and administration and how to manipulate map projections to provide useful information for the clients.

Methods: Various tools were used during the map making process. The ArcGIS system allowed me to download and organize several county and city of Eau Claire datasets with the end goal of creating 6 maps to represent different aspects important to the Confluence Project.

I used a base map of world satellite imagery as the backdrop for all my maps. This was intended to give the map viewer easy correlation with the area in reference and solid topographical guidelines to support a cohesive map experience.

The successive maps I wanted to create all involved easily identifying the location of the parcels of land to be developed by Clear Vision. To achieve this I added the parcel_area feature class to a blank map to facilitate easy access to the location for all my maps. I first located the sites by using identify tool in ArcMaps. Next I digitized the parcels, and saved the file, after that was accomplished could use the editor tool to add the digitized polygons and saved the file for later use.

I decided to include six maps, all of which contained information I thought would be useful to the project. The maps contained information about civil divisions, census boundaries, public land surveys (PLSS) features, parcels located around the confluence, zoning, and voting districts.

The first map was civil divisions, used as a locator map. I added the Eau Claire county boundary, along with civil division’s dataset. In order to see the imagery underneath the data I adjusted the transparency levels. Along with transparency I highlighted the proposed land parcels to be developed in red for easy readability.

The second map contained information about census boundaries or population density. To create this map I added block groups and Tracts to an empty data frame. In an effort to show distinction between the boundaries I symbolized the block groups as a variable of the 2007 population, and normalized the data by Sq./Mile. Finally I set the transparency and zoomed in on the highlighted (red) confluence project area.

The next map I wanted to create, highlighted the PLSS data in relation to the confluence project. This was achieved by adding Parcel_area to a blank data frame. I hollowed out the parcel area, and changed the outline to a bright color. I included center lines and water feature classes to add to the aesthetics of the map. Finally I adjusted the transparency to show the relation to the base map imagery.

Another map I wanted to include contained information about Zoning. I started by adding the zoning_areas into a new data frame with base map imagery. To show the distinction between the zones, I decided to symbolize each specific zone by its distinctive color and provided a Key on the map. I also added center lines to make a cartographicly pleasing map.

A final, and important map was added containing Voting district information. This was achieved by adding the voting districts for the City of Eau Claire to a blank data frame. I labeled the districts by ward number in the map for easy legibility, and so I did not need to include a legend.

The last step for the project was to include all six maps onto one page, adjusting ratios so all maps were of equal size. Adding Labels for the individual maps provided the map viewer easy navigation, legibility, and a clear map.

Results:

Figure 1 displays six maps: Civil Divisions, Census Boundaries, PLSS Features, Eau Claire City Parcel data, Zoning, and Voting Districts. The Civil Divisions map shows the different civil divisions of Eau Claire (town vs. city). Census Boundaries shows population density per square mile. The PLSS map shows townships split into quarter quarter sections and zoomed in on the confluence project area of downtown Eau Claire. EC city parcel data displays a number of land parcels around Eau Claire including the Confluence project proposed site. The Zoning map shows commercial, industrial, and business districts around Eau Claire. The Finals map; Voting Districts shows the various voting districts around Eau Claire city.

Sources: County and City of Eau Claire, 2013