GIS Mini Final Project

Introduction:
The spatial question I chose for this final project is "Where is a good place for me to live in Saint Croix County?". The objectives for this project are to live within 10 miles of a park, 5 miles of urban areas, and 25 miles from a beach. Also, the location needs to be 2 miles away from any previous fire occurrences. For this map in particular, I would be the main intended audience because it was created by my preferences. Although, I am sure there are other people that might have similar preferences that could use the map I created as well to find a suitable place to live. Anyone who prefers living close to urban areas, and enjoys parks and beaches would benefit from this information. Or, anyone who wants to be aware of areas where fires have occurred to hopefully help prevent a house fire as well.


Data Sources:
To create the map based on my spatial question, fire occurrences, urban areas, parks, and recreation data were used. I started by clipping each of these features so only Saint Croix County would show data. I retrieved this data from a few locations. Fire occurrences were from the Wisconsin DNR database and urban area, parks, and recreation data were used from Esri database. The only concerns I have relating to the data is if there has been any changes since that information has been released. Although the Wisconsin DNR is from 2014 and the Esri data is from 2013, there are changes made all the time and new locations being created as well. Also, the fire occurrences data was taken between 1982-2008 which leaves around 8 years that fires could have happened but not been recorded. There is always an issue with coordinate systems and how well they will relate in the data frame as well. However, I did not come across any problems while working.


Methods:
To start off, I clipped all of the data I retrieved so that it would just be shown in Saint Croix County. The four tools I used were buffer, intersect, union and erase. First, I completed a query through recreational areas to find any beach locations, in which Browns Beach was the only one selected. I created a new layer for Browns Beach and set a buffer to 25 miles and then intersected the buffer to Saint Croix County. Next, I set buffers for urban areas (5 miles), parks (10 miles), and fire occurrences (2 miles) while dissolving to combine the internal boundaries for each buffer layer. Then, I intersected each buffer with Saint Croix County once again so the buffers would only be shown within the county. After that, I used the union tool for the parks buffer and the urban buffer and then intersected that with the beach buffer. Lastly, I used the erase tool with input feature being the parks, urban and beach buffer layer and the erase feature being the fire occurrences buffer. This final layer showed the area best suited for me to live based on my criteria.

Figure 1: Data Flow Model

Results:
After completing all of the steps in my methods section, it appears that the best area for me to live within Saint Croix County would be on the Western side of the county. This area is closer to urban areas, parks, and Browns Beach. This area is also 2 miles away from any fire occurrences that had happened between the years of 1982-2008. The map below shows in yellow the results of where the best area to live would be. I included parks, urban areas, Browns Beach, fire occurrences, interstand and highways so that the viewer would have a better understanding on why the results are located in the area that they are. I also included a locater map so that one can see where Saint Croix County is located in the state of Wisconsin.

Figure 2: Map of the best area to live within Saint Croix County

Evaluation:
I really enjoyed completing this project overall. It was nice to be able to use the information I learned throughout this course to complete a map on my own spatial question. If I were to repeat this project again, I would have liked to be able to use other data as well like where certain restaurants were located or where hiking trails are. I think I would be able to access this data in the future when I know how to download alternate data correctly. The biggest challenge I faced throughout this process was probably just trying to stay organized. I wrote down each step to make sure I wouldn't forget anything when trying to describe my methods. I also constructed the data flow model in my notes as I went to make it easier to complete in Microsoft Word. I am very pleased with how this project turned out because it was nice to be able to put my own project together and produce a map of the results that reflected my criteria.


Sources:
ArcGIS Data
Wisconsin DNR
Esri-GIS Mapping, Software, Solutions, Services, Maps, and Data.

GIS Lab 3: Vectory Analysis with ArcGIS

Goal:

The goal of this lab was to determine suitable habitat for bears living in the study area of Marquette County, Michigan, while using different geoprocessing tools for vector analysis in ArgGIS.

Background:

The idea for this lab was to determine what land in the study area of Marquette County would be most suitable for bears while keeping in mind which type of land bears prefer,  location to streams, the distance to urban areas, and where bears have been located in the past.

Methods:

Throughout the lab, one encounters numerous steps and methods while utilizing previous skill sets and advancing with them.


Objective One:
Objective one looks at the type of files and data one will be working with throughout the lab. There are X,Y coordinates of where the bears are located in the bear_locations_geog$ excel file but they need to be added as an "event theme" to be mapped. An "event theme" is just a temporary display of X,Y data in ArcMap. Once the coordinates are mapped, they are exported to ensure they will be added to the geodatabase as a feature class.


Objective Two:
Objective two requires adding all of the feature classes that are within the bear_management_area feature dataset. One of the feature classes, bear_locations, has information on what type of land cover the bears were located on when there whereabouts were recorded. So, bear_locations is intersected with landcover to produce a new layer that shows how many bears were located in each land cover type. Then, a query is used to determine which top three landcover types had the most bears located on them. The results of the top three land types were: Evergreen Forestland, Forestland Wetlands, and Mixed Forest Land.


Objective Three:
In objective three, information is given on how bears might be found most often near streams. The next step is to create a 500 meter buffer around the streams to determine how many bears were located within that distance. The results showed that in fact 72% of the recorded bears were located within 500 meters of a stream. One can tell that the close proximity towards streams is a very important characteristic in bear habitat. The buffer leaves a lot of remaining polygons. To clean them up, the dissolve tool is used to dissolve all the stream buffers into one remaining polygon which is now its own layer.


Objective Four:
Using the information produced by the past two objectives, one must perform an intersect between the land cover types layer and the stream buffer layer to produce a new layer that is suitable areas for bear habitat. The dissolve tool will also be used again to remove the internal boundaries.


Objective Five:
Objective five explains that the DNR wants to find suitable bear habitat that is located within their management lands. So, the dnr_management feature class must be added but it includes information of all of Marquette County so one is required to intersect and dissolve the dnr_management feature class and the studyarea layer to ensure that the data only applies to the study area. After that, another intersection is performed with the bear habitat layer and the new DNR management layer that only includes data located in the study area.


Objective Six:
Objective six requires that the bear management areas be away from urban or built up land. There are three different steps to complete this process. The first step is to perform a query to select only urban or built up land to create its own layer. Secondly, create a 5 kilometer buffer surrounding the urban and built up land layer as well as dissolving once again. This buffer helps to show the land that will not be used for bear management areas. Lastly, the erase tool will erase the 5 kilometer land cover buffer of the urban and built up land and leave only the bear management areas.


Objective Seven:
Create a cartographically pleasing map and data flow model that shows the steps throughout the lab.

 

 

Figure 1: Map of Suitable Bear Habitat within Marquette County, MI

Figure 2: Data Model of steps used throughout the lab.

Objective Eight:

Objective eight introduces the functionalities of Python. One is required to write a few simple commands in order to perform geoprocessing operations. Here in screenshot of some of the commands:

Results:

Figure 1 shows the map results of this lab. The map displays the study area which is Marquette County, MI and it shows information on bear locations, streams, bear habitat and DNR management land. The DNR management lands are located in the Northwest region of the study area because the urban and built up land was located in the Southeastern part of the study area. Not all of the bears are located in the suitable bear habitat regions but they are located near streams. Figure 2 shows a model of what tools where used throughout the lab and the outcomes that each created. This lab was very helpful in the fact that it helped strengthen the knowledge of tools and it provided and introduction to Python.

Sources:


USGS NLCD
State of Michigan Open GIS Data
DNR management units, land cover and stream data from: https://www.mcgi.state.mi.us/mgdl/