Mini Final Project: Golf Course Site Selection

Introduction:

The main goal for this final lab was for us to choose a company, business or location and once we have chosen our study we are to find a new site for the next location. For my project I decided to find a new location for a new Golf Course here in Eau Claire County. In order to find this we are to use different data sets that are provided to us, and use ArcMap to map out the most suitable area for our study. I developed four requirements for a successful location for a new golf course, the questions are as follows;
1. Must be in a tract population of greater than 5,000.
2. Must be in a zone for commercial land use.
3. Must be at least 5 miles away from any other golf course.
4. Must be within 1 mile of a major road.
Through the project process my goal is to find the areas within Eau Claire County that match all of the requirements that I have developed. I will do this by using multiple tools and functions that are provided to me through the use of ArcMap and ArcCatalog.

Data Sources:

When it came to finding the data sources I was nervous that there wouldn’t be any data pertaining to the golf courses and if the data that was found I wondered if it would include all of the golf courses and not just the largest most popular locations. But we were given the link to an online database that provided numerous different types of data. To answer all of my questions the main data sets that I was looking for were the Tract population for each county, a map of the counties themselves so than I could single out my study area and do research on just that area of the world, a map of all the major roads that run through my study area, and I needed to find the most important dataset of all and that was of all the golf courses that are already of existence. There were a few concerns that I had about the datasets that were collected I already mentioned my concern about the golf course dataset already, but another concern that I had was on the dataset that provided me with all the information for each tract. The reason I had concerns about this data set is that it only had the information up to 2012, this is out of date and it would be much more helpful if I had data from 2015, because we don’t know what has happened to the population of each tract in the past three years, the areas that I have selected and labeled as suitable areas could be wrong since we don’t know the true population of that tract as of now.

Methods:

In order to start this project we first needed to make a file geodatabase so than we can store all of the data that we collect and create in one location rather than the default geodatabase that is provided by the program. Once we have created that we can now start uploading correct datasets that are needed in order to proceed with the project. The first datasets that were uploaded were those that represented the tracts and counties for the US. Than we found the datasets for the specific zones for the study area, as well as the datasets containing the information about local golf courses and the major roads that are located around and in the study area. Once we have all of the data that we needed we can now start the main portion of the project in order to do this we need to first define the study area on the map. In order to do this we need to click on the counties dataset and use the ‘select by attributes’ tool so than we can find where Eau Claire County is and export the data from the selected attributes and create a data layer that just represents Eau Claire County. The next step was to fill the first requirement, we do this by going to the properties of the Tract dataset and clicking on the query tab, this is where we build a query relating to the population that we are looking for to be in each tract. We need to find the tracts that are located in the range of 5,000 or more people, in order to do this we build a query that states the population of the most recent data is greater than or equal to 5,000 people and this should give us the tracts that contain 5,000 or more people and get rid of the rest.
The next step is regarding the major roads dataset, this is where we want to create a 1 mile buffer for all major roads in the study area, to do this we need to use the ‘buffer’ tool and specify the distance to be 1 mile. Then we need to use the dissolve tool so than we are combining all of the common areas in the major roads section and smoothen it out so than the final data will be easier to analyze. This process will help us fill another requirement for the new site location for a golf course, once we finish this process we want to move our attention towards the different zones that are located in the study area. Since we are looking for a commercial zone for our final location for the new golf course we would want to once again build a query for this dataset. In order to do this we want to go to the properties for the zone dataset and specify that we want only that area that represents commercial zones to be selected and export the data that is selected so than we have a layer that represents just that information. Since we have answered three of the requirements we can now move onto the next step before doing anything to the golf course dataset. The next step for us to complete is to intersect all of the known data with one another so than whatever the information that is being provided to us is now only located within the study area. Now the most important part is for us to fill the final requirement which is that the new golf course cannot be within 5 miles of another golf course. In order to do this we need to create a 5 mile buffer for the current already existing golf courses, to do this we would want to use the ‘buffer’ tool once again and specify in the distance section that we want it to buffer for 5 miles. Once we have this we want to use the ‘dissolve’ tool so than we combine all of the areas that are similar in the information that they are showing. Once we have this we want to use one final tool and that is the ‘erase’ tool, this tool will help us out by erasing all of the data that is outside of the study area and provide us with our final map. In order to do this we need to include the final output that we got from intersecting the major roads, tracts data, and the commercial zone information and use that final output as our erase feature and use the golf course data after buffering and dissolving the area and use that as our input feature so than it erases all of the areas that do not fill the four requirements and highlight the ones that do.

Results:

After going through the entire project and using all of the tools and functions that were needed to complete the project the final step was to create a map representing the most suitable area in your study area for in this case a new golf course to be built. We also needed to create a flow model of all the functions that we used and all the different outputs that were created. Below is the flow model of everything that was used in the project including all the functions and outputs of those functions as well as the final map that was created representing the suitable areas that we can build a new golf course.

Figure 1: Flow Model

Figure 2: Golf Course Site Selection

Once again the requirements that needed to be filled were as follows;

1. Must be in a tract population of greater than 5,000.
2. Must be in a zone for commercial land use.
3. Must be at least 5 miles away from any other golf course.
4. Must be within 1 mile of a major road.

       If you were to look at the map above you will see the area that is labeled in blue is the entire study area in question known as Eau Claire County. The next key part that one needs to look at is the area that is the color pink, this represents all of the suitable areas where we would want to build the next golf course. The areas that may fill some of the requirements but not all are located in the more orange color that is the area where it fills all of the other requirements but is not far enough away from the already existing golf courses.

Evaluation:

Overall I really enjoyed this project, it was a great experience and taught me a lot, most of the project was done on our own and that alone was an exciting aspect of the project being able to work by yourself and seeing where it would take you. If I were to repeat this project I would want to see if I could include a few more datasets so than I could try to find an area where the golf course would not only exist but prosper. I could have found datasets for average household income and average salary and put that into the project so than I could find an area in the Eau Claire County that not only has a large population but a wealthy one as well. At first just getting the entire project started was the largest challenge since its always hardest to try and complete the first step rather than the last, because once I got the first couple of steps done I basically just had to keep doing the same thing but for different datasets. Overall it was a very educational project and was faced with barely any challenges.

Lab 3 - Vector Analysis with ArcGIS

Goals:

The main goal of this lab was to better ourselves on the process of vector analysis in ArcGIS, we would do this by using various geoprocessing tools to help us find suitable habitat for bears in the area that we were given, or in this case the study area of Marquette County, Michigan. We were asked to complete multiple objectives in order to complete the lab, each objection using different tools and different feature classes.

Background:

            What we are trying to accomplish with this lab is finding the most suitable land areas in the study area for bears to live. We have many different forms of data to help us determine which locations would be most suitable. Some of the data that was provided are bear locations, streams, and the land cover types. All of these forms of data will make finding suitable living arrangements for these bears much easier than if we had nothing at all.

Methods:

            Throughout this lab we were asked to use multiple different tools for each function and were asked to create different maps showing different. All of the information that we were using had to do with the features that are located in Marquette County, Michigan and our job was to determine which areas in the study area would be best suited for bears to live in. The first step of the lab was to map the different x, y coordinates for the locations of each bear in the study area, and create an X, Y event theme out of all the points. Then we needed to export all of the data that we had just collected so than we could use it later in the lab. The next objective was for us to use spatial operation tools to generate new feature classes for the ID number of each bear and the land cover type where the bears reside. The next step in this objective is to perform the exact same procedure but this time we are using these spatial operation tools to help us find out what are the 3 top habitat types.

            The third objective is for us to try and locate the bears that are closest to the streams, we needed to determine how many bears are within 500 meters of a stream. In order to do this we used the “select by location” function, the “bear_locations” feature class and the “streams” feature class. Once we got all of that information entered into the function, the next step was to make sure that the features that are selected are either 500 meters or closer to the streams. Objective four asked us to do the exact same thing but not use the “select by location” instead we needed to use different tools such as the “buffer” and “dissolve” tools so than we can locate the land cover types that are most suitable for the bears by locating them on the map and bolding those areas in a different color. In order to do this we needed to create an actual feature class that showed the buffer for within 500 meters for a stream. After this step was complete we had to analyze the map, once we analyzed the map we noticed that all of the polygons were now combined from more than one layer. Our next task is to try to remove all of the internal boundaries that are within the answer that we just acquired, in order to do this we would need to dissolve the surrounding area to make a feature class that is showing only the area that are within the streams.

            The next two objectives that needed to be completed are objectives five and 6, for objective five we needed to add the DNR management area within the study area. Our main task for this objective is to find the areas of suitable habitat within the management areas that we just added to the map. Do accomplish this task we used the “select by attribute” tool and selected the bear_location feature class and dissolved that feature class so than we can see which areas are most suitable for bears to live in that are also near a stream. While objective six is asking us to use our answer from objective four, apply it to the DNR and use that data to help calculate the bear management areas and try to locate them five kilometers away from Urban or Built up lands. In order to locate the bear management areas that are five kilometers away from the Urban areas we need to first locate the Urban or Built up lands, we will do this by going to land cover and finding them by using “select by attributes.” Once we have them selected we can now create a feature class of that data, and then we use buffer, dissolve, and erase to the selected feature class in order to get our final image needed for the lab. After we created the map we were asked to do some python coding, in order for the python coding to work from the beginning we needed to make sure all of the correct feature classes were selected so than we wouldn’t get an error right away. We used many different functions and types within python so than we could import the correct information and perform the correct types of analysis. Below is the work flow model of all of the different procedures that I created in this lab, and the final output for the python coding.

Results:

                After looking at the final image we can tell a lot of different things about the map and about bear locations, which are best suited for the bears. If you were to look at map you would notice that all of the most suitable locations for the bears are right by streams which would be a fresh source of drinking water and a good source of food. As you can see most of the suitable areas for bears are not close to the Urban and Built up land areas because they want the bears to be in seclusion and to live in their natural habitat away from human life. Below is the final map of the lab, showing both the study area which contains all of the data on the bear locations, urban and built up land, and the most suitable areas for these bears to live. 

Downloading GIS Data

Goal

The goal of this lab was to introduce the different ways you collect data sets and how to download that data from the U.S. Census Bureau. The U.S. Census Bureau is one of the largest sources of quality data in the nation, all of the data is relevant to the nations people and economy. 

Methodology/Results

In order to start this lab we first had to collect two data sets, one of which was required while the other was our choice. The first data set that we had to collect was the total population for each county in the state of Wisconsin. While the other data set that was needed to be collected was our choice, for this lab I wanted to choose a data set that would be relevant to the total population and that would create a good and reasonable comparison. That is why I chose the total number of housing units in each county for the state of Wisconsin. After we collected the data the next step was to download the shape file for the state of Wisconsin, after that step was complete we needed to map the data by adding both data sets to different data frames on Arc Map. In order to get the data on the map for each county we needed to join the data so than the map could correctly show the data within each county. Once all of the data was joined with the map we could then start to create the layout for the final map, to do this we needed to add a legend, north arrow, our name as the designer of the map and a title for the entire map. Once this step was complete we were on our final step of the lab was to publish our final map onto ArcGIS Online, we had to go through many steps in order to properly upload our map as a web map. But after we got everything finished our final map looked like the figure below. 

Figure 1: Population vs Housing Units

As you can notice on the map the amount of housing in each county is almost directly related to the total population in each county for the state of Wisconsin. The only area where it might differ is the more northern part of Wisconsin because that is a much less populated area than that of the southern part of Wisconsin. As expected though, the more populated counties do indeed have the most housing since the population is so large in Milwaukee so are the amount of housing units. 

Sources

 http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_P1&prodType=table

http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_H1&prodType=table

http://uwec.maps.arcgis.com/home/webmap/viewer.html?webmap=3453e8379eb142ddb0c42dc47cfa1913

Eau Claire Confluence Project

Lab 1: Confluence Project

Introduction

            The future of Eau Claire is very important to most people that live there, as an intern at Clear Vision Eau Claire I would like to strive to make this city one of the greatest. In order to do that my company announced a plan in 2012 for a public private partnership between local developers. This development is referred to as the “Confluence Project” this project is supposed to be completed by the beginning of 2014 and will contain three performance spaces, galleries, offices, classrooms, studios and much more. What I have to do for this project so than it will succeed is to create an initial report or relevant information of the layout as well as including base maps for each data set. I will create this relevant information and base maps using ArcMap and ArcCatalog.

Methodology

            In order to create the relevant information and base maps we first had to create the initial data for the confluence project. First we opened ArcCatalog so than we could create the data, after this program was opened we had to create a geodatabase and a feature dataset for that database. After the feature dataset was created we could move on to mapping the confluence project while being displayed at its real life location. ArcMap is the next program that is needed to be used so than we can create the maps. Once this program is opened we need to upload a world imagery base map so than we can see where the actual confluence project is going to take place. The next objective was to find the parcel information for each of the parcels that were being used for the confluence project. In order to find out this information we have to use the “identify tool” in ArcMap, this tool helps us get vast amounts of information about each location. But the only information that we wanted was the parcel number so than we can get the full parcel description of each location. Another tool that was very useful in this project was the “editor tool” and the “snapping tool” both were very helpful in defining where the proposed site for the confluence project actually was in relation to real world features.

            The next step after collecting all of the relevant information for the confluence project is to start creating all of the maps that are needed to make this project successful. The task at hand is to create six different maps all for the same location but each map representing a different variable. The first map represents “Civil Divisions” in order to create this map we had to change the symbology of the map and add the civil divisions feature class so than the information on the map is relevant. We also had to create maps that represented other variables, for instance we had to create maps for “Census Boundaries”, “Zoning”, “PLSS”, “Voting Districts” and “EC City Parcel Data.” Each of these maps required adding different types of data and different feature classes. For example, the “Zoning Map” needed the zoning_areas feature class to be added to it since that has information on it relevant to that map, while the “Census Boundaries Map” needed the BlockGroups and Tract Group feature class to be added since that has information relevant to this map. Each map needed different feature classes to be added to each one some required the same since they are showing somewhat similar information. After we made all of the maps and included all of the information that was needed all we have to do now is change the view to layout and start to construct the map correctly making each data frame the same size, while including a legend, scale and title for each map.

Results

            After looking carefully at all of the information and analyzing each one of the maps carefully you can see that the proposed site is placed at a perfect position. Especially if they are trying to draw in a younger crowd, if you were to look at the figure below at the Census Boundaries map you would see that the area that the Confluence project is in is among the youngest in Eau Claire. Also one of the goals of this Confluence project is to try to have local businesses work together to help promote Eau Claire. If you were to look at the Zoning map below you would see that the proposed site is in the Central Business District for zoning classes making it a great spot for local businesses to meet up and share ideas with each other. Each map tells vast amounts of information about the confluence project but the one thing that all maps are telling us is that it is in a very good position for accomplishing its goals. 

Figure 1: Confluence Project Maps