Lab 5 GIS 1

Goal

The goal of this lab was to solve a spatial problem of my own choosing. The spatial problem I chose was where I should look to live in Portland, Oregon.

Background

This fall I went to Oregon with one of my other geography classes. I fell in love with Portland and have talked about moving there after graduation. I chose some parameters for where I would live in Portland: proximity to bike lanes, average income of census tracts, not being near an airport, and living within Portland city limits. I do not have a license because biking is my main mode of transportation so being near a bike infrastructure is vital for me. I found data for bike lanes on Portland's public data website. As for income, I found data from the U.S. Census FactFinder. I also looked up the average income for a GIS technician on indeed.com and found that it was $37,000. As for airports, that was for personal reasons; I have lived near one before and I would rather not do that again. I found the data for counties in the MGIS folder on our class share drive.

Methods

Objective 1: Getting started
First I created a file database for the project called MultnomahCounty. I went to above websites and downloaded all data needed, including bike lane shapfile, Multnomah County census tract shapefiles, Portland city limit shapefile, ACS 5 year estimate census data table. Airports and County data was pulled from the Oregon folder from MGIS. I exported all of this data into the MultnomahCounty.gdb database I created. I then used the project tool and changed all of the files to the NAD_1983_UTM_Zone_10N projected coordinate system. I then took the ACS 5 year estimate income data and joined it to the income tracts. I added a field and used the field calculator to load household median income to the field. I then removed the join.

Objective 2: Beginning of Analysis

I then used select by attribute for the census tracts, and queried for tracts that had a median household income of less than $40,000 dollars, because the average income of a GIS technician is $37,000 dollars. I exported the selected data into a new layer. I then added a 100 meter buffer to bike lanes. I used intersect for both the buffered bike lanes and the queried income data, I called this layer WhereToLive. I then set a 4 mile buffer to the airport in the county. I used the erase tool to erase the airport buffer from WhereToLive. Finally, I took the city limits and used the clip tool to make WhereToLive only inside the city limits of Portland.  This gave me areas where I should look to live pictured in the map below.

Objective 3: Create a Cartograhically Pleasing Map

I exported the ArcMap file to an .AI document two times, one with a larger scale and one with a smaller scale. I then opened them in Adobe Illustrator and began my work. I changed the colors to make the selected areas stand out, by making it a bright orange against pastels. I also added an indicator map and a close up of the data. This gives the viewer some context as to what they are looking at. I chose a basic sans-serif font because I intend this map to be viewed on the computer. The main goal of mine when making a map is for it to be simple, clean and understandable, which I achieved in this map.

Results

This map shows the areas I should look to live. There is a large cluster of orange which is downtown Porland. This seems to be a good area to live for me because there are a lot of bike lanes, which means getting around across many tracts would be easy due to the large number of bike lanes.

Figures

This is the flow chart explained under methods.

Conclusion:

This project was a great way to end the semester. The goal of the end results is a easily understandable map. They look simple, clean, and not very complex to someone that does not know about GIS. Little do many know that the road to the end result is long and full of problems. My project may have seemed simple in the beginning, but proved to be very complex. I was pulling and downloading data from many different sources. Overall, this project was a fun way to pull together all of the thigns I have learned this semester.

Sources

U.S. Census FactFinder: Tracts, Income Data, City Limits
Civicapps.org: Bike Lanes
ESRI Software: Counties, Airports

Lab 4 GIS 335

Goal

The goal of this lab was to map suitable habitats of bears in Marquette County, MI using a variety of tools in ArcMap.

Background

In this scenario, the DNR wanted to figure out suitable habitats and management areas for bears. Data was downloaded from the Michigan Center for Geographic Information: Landcover, DNR managment units, and streams.

Methods

I started by adding the Marquette bear study from a non-spatial database using X, Y coordinates. File->add data > add x,y data. Once they were mapped I exported the data into a feature class and named it bear_locations. I then added all other shapefiles i would need including streams and landcover. I performed a spatial join between bear locations and landcover, with bear being the destination and landcover being the source. I named this new feature class bear_cover. Next, I figured out how many bear locations were within 500m of streams. I used select by location with streams and bear_locations within 500km. Over 70% of the bear locations were within 500km of streams which is significant. Bears live near streams for necessity like the food and water they can get, this means 500m within streams is suitable. To find complete land suitability for bears, I added a 500m buffer to streams.  I then used intersect between the buffered streams and a exported shapefile of the top 3 landcover types for bear locations and performed an intersect on the areas. After that, I dissolved the results to remove internal boundaries. This gave me Suitable_habitats. I used the clip tool to cut out the DNR management areas outside of the study_area. I took the DNR mangement boundaries and did another intersect on suitable_habitats and the clipped DNR_mgmt to find the suitable habitats that the DNR can manage in its jurisdiction. 

The DNR in this scenario liked the results, but wanted more. They wanted to look at management areas 5 km away from urban and built up areas. I went back to my data and selected by attribute all urban and built up areas and exported to its own shapefile. I applied a 5 km buffer on this area and dissolved the internal boundaries. I used the erase tool to delete the urban and built up areas with a 5 km buffer from the DNR managable habits I had found earlier. This gave me the final areas the DNR wants to look at. 

Results

Suitable habitat are areas where the top 3 landcover and 500m proximity to streams intersect. The bear managment areas are where suitable habitats and DNR management areas intersect that are 5 km away from urban and built up areas. What I have found potentially problematic is that almost no bears are located in the management areas. They are in suitable habitats, but bears for what ever have not chosen those specific habitats to live in. This will make studying these areas difficult because there are almost no bears to study. Perhaps expanding the study area to all of Marquette County the DNR would be able to cover more management areas that have bears to study.

Figures

Sources

Data was downloaded from the Michigan Center for Geographic Information: Landcover, DNR managment units, and streams.