Lab 4: Answering a Spatial Question

To begin lab 4 (the final project), the first step is to browse existing GIS data in order to come up with a spatial question. The first thing that needs to be done is to decide what data to use and what the spatial question will be. A new blank File geodatabase was created in the Lab 4 project folder. The next step was to browse the available GIS data that is in the “mgis” data folder on the campus computers. The state that was picked for this project is Oregon. The county that was picked is Harney, Oregon. The spatial question to be answered is; where is the best place to put a new hospital in the selected (Harney) county? In order to answer this question, it must be focused in on the area of interest for the project, which is Harney county in Oregon. When looking at a map of Oregon, it can be seen right away that there are not any hospitals located within Harney county, which had a population in 2000 of 7,609 and a population of 7,281 in 2010. It seems that placing a hospital within Harney county would be beneficial to residents, because there are not any hospitals located close to the county. Harney county is located in the southeast corner of Oregon, which is why it seemed to be a good place to look at when studying county proximity to basic needs like hospitals, airports, schools, etc., because this county is pretty far away from the more urbanized areas of the state. Criteria that will be used to answer this spatial question will include; placing the new hospital nearby the various schools in Harney county, placing the hospital nearby major roads/highways within Harney county, placing the hospital in somewhat close proximity to the nearest airport, and also placing the hospital and close proximity to any parks nearby Harney county, Oregon.

As seen in the map above, Harney county is located somewhat on the outskirts of Oregon, relative to airports, cities , schools, and parks. Because of this, it seems to be a good place to put a new hospital, it would provide new jobs, and easily accessible care to many people within this county who would have otherwise had to drive pretty far away to reach the nearest hospital. By determining how far away Harney county is from essential building structures it will then become much easier to pick the location for the new hospital. I will provide a completed map highlighting where parks, cities, airports, and schools are located within Oregon, and using various tools within ArcGIS, an analysis will be completed to determine the optimal place for a new hospital in Harney County. The completed map will include 4 different data layers, one outlining the optimal hospital location, one showing a locator map of where Oregon is located, and lastly, one showing the slopes of Oregon, and one with the Earth imagery base map. Showing the slopes of Oregon is important when considering where to build a new structure because this way, someone who is not on the site can still determine the optimal location for a new hospital simply using ArcGIS from anywhere (by utilizing the necessary data available). The new hospital will steer clear of any high slopes, which is very common all through Oregon, making it a very important consideration. The Earth imagery base map will help providing an appealing backdrop to the map as well.

The first tool that is used is the spatial join tool. To perform the join, the rail100k feature class was the join feature, and the highways were in the input feature. The spatial join was one to one. Once the join was completed, the new feature class was then saved and titled joinrailroadandhighway in the lab 4 file geodatabase. This join was completed in order to combine the railroad and highways on the map. Once these are combined, this makes is easier to see both of their proximities to where the new hospital will be. The second tool that was used was the dissolve tool. In order to complete this dissolve, the input feature was set to rivers and the dissolve fields was set as select all. The output was saved as dissolved rivers into the lab 4 file geodatabase. This tool was used in order to connect the rivers together and help in seeing the smaller fragmented rivers that are located within Harney county. When looking at the map of Harney county, it can clearly be seen that there are many small fragmented rivers all throughout the county. It will be important to place the hospital in a more central location within the county, so that it will be relatively close to all of the different rivers. Having a hospital nearby when at the beach or on the river would be an important safety consideration to take into account when building a hospital. The next tool to be performed is a buffer around the other hospitals in order to confirm and convey the far distances from Harney county and help in determining where the new and optimal location should be for the hospital. To complete the buffer, the input feature was set as hospitals, the buffer distance was set to 25 kilometers, and the dissolve type was set to all. Once completed, the new feature class was saved in the lab 4 geodatabase as bufferhospital. The color of this buffer was set to yellow as well. The next tool to complete is another buffer. This buffer will be around schools in order to find the optimal distance for the hospital to be from the different schools that are present within Harney county. To complete the buffer, the input features was set to schools, the buffer distance was set to 15 kilometers, and the dissolve type was set to all. Once completed, the buffer was saved in the lab 4 geodatabase and titled schoolsbuffer. The color of this buffer was set to rose pink. Once these tools were al completed, the next step was to choose the best place for the new hospital and create my final map that portrays the results. To complete the final map, it was also necessary to complete the three other data frames and make all of the coordinate systems the same, which was NAD 1983 Oregon Statewide Lambert (Meters). The completed map is shown below. A data flow model of the processes that I went through when using the tools during this lab was created as well. The end product is shown below, underneath the final map.

Lab 3: Vector Analysis with GIS

The goal of this GIS lab is to use various geoprocessing tools for vector analysis in ArcGIS in order to determine a suitable habitat for bears in the study area of Marquette County, Michigan. In order to accomplish this, the first step is to download the lab3.zip file from D2l and to unzip this file into the working folder for this lab 3 assignment. After unzipping the data, the next step was to add it into a blank map in ArcMap. Once that was completed, the next step was to use this X, Y coordinate data of bear locations and create an event layer with the data. In order to add the bear locations as an X, Y event theme, you go the File menu in ArcMap, then click Add Data, Add XY Data and then fill out the correct fields for the data wanted and the coordinate system that is needed (along with the X and Y fields as well). Once this step was done, the bear locations appeared on the blank map as points. Next step is to export the data and save the created event them into the lab geodatabase.

                The next part of the lab is objective two, where the first step involved adding all of the feature classes within the bear management area feature dataset to the data frame. After this, it was necessary to change the symbology for the landcover layer by creating a unique color map for the “minor type” field. After this, the next step was to perform a spatial operation tool to generate a new feature class that would be called bear_cover. The tool used was a spatial join. The target features are bear_locations, and then this was joined with landcover features. The join operation for this join is one to one. In order to complete the join, it first needed to be saved into the lab 3 folder and named bear_cover. A second operation needed to be performed in order to find out how many bears were found in each habitat type, using the minor type field. The top three habitat types are Evergreen Forest Land, Forested Wetlands, and Mixed Forest Land. There were 31 bears found in Mixed Forest Land, 14 found in the Evergreen Forest, and 17 found in the Forest Wetlands (queries in order to find the most common habitat types and the number of bears found in each).                 

                In objective three, the first step was to determine how many bears were found near (within 500 meters) of a stream when their GPS location was collected. In order to figure this out, a buffer must be performed. The input feature for the buffer was bear_locations, and the output feature class was the Marquette bear study geodatabase. The linear unit for the buffer was set to 500 meters, and the dissolve type was set to all and it was named streambuf. An intersect was then used to figure out the number of bears within 500 meters of a stream is higher than 30% (actual of 72) and therefore this indicates that this is a suitable area for bears to live.

                In objective 4 and 5, the goal was to find suitable areas of bear habitat based on the research that has already been done. The two criteria that this step is based off of is the suitable land cover types and the number of bears within 500 meters of a stream. The first step was to dissolve the DNR management areas, the new feature class was titled dnrdissolved.  In order to find separate the out the suitable land from the non-habitable, a query was done to get the top three habitats separated into a new feature class that is titled suitable land. Next, a clip was performed from DNR data to the study area. After this, an intersect was performed with the clipped DNR land and the suitable land feature classes (and named dnrandsuitableint). The first step in objective 6 was to exclude all areas that are within 5 kilometers from Urban and Built up lands. First, a query was performed to identify entries were urban or built-up land. The expression used was “MAJOR_TYPE = ‘Urban or Built-up Land’. Then, a layer was created from the selected features and named urbanandbuiltup. Next, a 5 kilometer buffer was run on this feature class. After re-arranging the feature classes so everything is visible on the map, the next step is to use the erase tool between the dnrsuitableintersect and the dissolved 5 km buffer feature class.  After doing the erase, the next step was to make a cartographically pleasing map with the data and research completed within the map in order to show suitable habitat land for bears in Marquette County, Michigan. My final map is show below.

                The last step of this lab was to work with the python application that is within ArcMap. Once in python, what needed to be done was typing out lines of computer code in order to perform tasks in ArcMap. During this step, it was necessary to do a buffer analysis, and intersect anlaysis, and an erase analysis using the research and data that was collected during this lab. The final result of the python coding is shown below.

     > import arcpy

>>> Arcpy.Buffer_analysis("streams","streams_buf", "1 kilometer", "FULL", "ROUND", "ALL")

>>> arcpy.Intersect_analysis(["streams_buf", "suit_land"], "land_stream")

>>> arcpy.Erase_analysis ("urbanandbuiltup" from "suit_land")            

I also created a data flow model to correlate with the steps taken throughout the lab. my data flow model is shown below. The source for all of the data used in this assignment is from the State of Michigan Open GIS data. (http://gis.michigan.opendata.arcgis.com/) Landcover data was from USGS NLCD (http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html). DNR management units: http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm. Stream data was from: http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html.