The goal of this lab was to learn to use various geoprocessing tools for vector analysis in ArcMap 10.2. The aim of Lab 4 was to determine suitable habitat for bears to live in, in Marquette County, MI, and to create a map based on this vector analysis.
Methods
For this lab I had to create a map regarding the vector analysis I had done on bear habitat in Marquette County, MI. I also had to include a locator map to enable the reader to understand where the analysis took place. To create the main map and locator map I used ArcMap 10.2. I put the main map and locator map in separate data frames. Each data frame used the basic study area shapefile provided to us. The locator map also used the state of Michigan shapefile and Marquette County shapefile.
Before I started my vector analysis, I had to give the bear_locations_geog$ excel sheet XY coordinates to be able to use the data in ArcGIS. I was able to do this by adding the XY coordinates as an "event theme" in ArcMap 10.2. I exported this 'new' data to my lab4 geodatabase, titling the feature class bear_locations. In ArcMap 10.2 I added all the feature classes in the bear_management_area feature dataset to the data frame with just the study area shapefile, and changed the symbology of the landcover feature class to a unique colour map based on the MINOR_TYPE field.
Now I was able to start my vector analysis. To start my analysis I joined the bear_locations and landcover feature classes using a spatial join. I then used summarise to determine the top 3 land cover types in which bears were found; these were mixed forest land, evergreen forest land and forest wetlands. After finding out the types of land cover the bears lived in, I wanted to find out if bears lived near streams or not. In this lab, near was defined as within 500m. To find out this data I used the buffer tool on the streams feature class. Within the buffer tool, I set the distance field to 500m and dissolved all intersecting boundaries. This new feature class (streams_buff) was intersected with the bear_cover feature class to determine the percentage of bears within 500m of a stream. In total 49 out of 68 bear locations were within 500m of a stream (72.1%), making it an important habitat characteristic.
Once this information had been processed I had to find the most suitable areas for bear habitat. I already knew the top 3 land cover types for bears, however I needed to create a separate feature class. I created this feature class (landcover_select) using the select by attribute tool. To find out if any of these suitable areas were within 500m of a stream, I used the intersect tool. I intersected the landcover_select and stream_buff feature classes. To remove internal boundaries in this newly created feature class (suitable_habitat) I used the dissolve tool.
After gathering this information, I had the task of finding the most suitable bear habitat within the Michigan DNR management lands. To find out this information I used the clip tool. Within the clip tool, I set the input feature as the dnr_mgmt and the clip feature as suitable_habitat (feature class made in objective 3), creating the feature class dnr_mgmt_clip. I used the dissolve tool on the dnr_mgmt_clip feature class to remove internal boundaries, creating the dnr_mgmt_habitat feature class.
The final task of this lab was to find bear management areas away from urban or built-up land. In this lab, bear management areas had to be at least 5km from an urban or built-up area. For this final task, I first used the select by attribute tool to determine where the urban and built-up areas of land were. This data was made into a feature class (landcover_select2). To determine areas further than 5km away from an urban or built-up area, I used the buffer tool on the landcover_select2 feature class, creating the feature class landcover_select2_buff. Finally to find the ideal bear habitat I used the erase tool, as this would exclude areas within the buffer zone. Within the erase tool, I set the input feature as dnr_mgmt_habitat and the erase feature as landcover_select2_buff. After this tool had run, all that was left was the ideal bear habitat (final_dnr_mgmt_habitat feature class).
After completing the vector analysis, I created a data flow model (Figure 4.1) and two maps; a main map with the vector analysis and a locator map. In my main map I included the study area, bear locations, stream, ideal and all bear habitat feature classes. In a separate data frame I created the locator map, including the state, county and study area feature classes. Once I had made both maps, I placed them on a single layout using layout view, with the main map being significantly larger than the locator map. For both maps I added a legend and used the NAD 1983 HARN Michigan GeoRef (Meters) projection. To make the maps look more cartographically pleasing I changed the symbology of the represented data. To the main map I added a title, north arrow and scale. For the overall project I added the source of my information and my name.
Figure 4.1
Figure 4 shows the ideal areas for bear habitat in Marquette County, in which the DNR could establish bear management areas. Figure 4 shows that there are very specific areas in the county where there are ideal bear habitats. The majority of bears according to my map are found in neither ideal nor all bear habitat areas, which is slightly concerning as it reduces the protection of the animal.
Figure 4
Source
Michigan Department of Technology, Management & Budget. (2002). Michigan Geographic Data Library. Retrieved from: http://www.mcgi.state.mi.us/mgdl/.
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