UW-Eau Claire Geography 335: GIS: GIS 1 Lab 5

Goals and Background

The goal of Lab 5 was to become a better user of various vector geoprocessing tools within ArcGIS. Lab 5 was completed using tools to find suitable habitats for bears within a study area of Marquette County, Michigan. The second part of the lab was done to get a basic understanding of python scripting for ArcGIS.

Methods

Bear Habitat

To start Lab 5, the bear location excel file was created into a feature class from the feature class from XY table selection. After that, all of the feature classes within the bear_management_area feature dataset into a data frame. At that point, the symbology for the landcover layer was set to view unique colors for the minor type field.

Once the feature classes had been added to the map, the first action taken was to intersect the bear location feature class was intersected with landcover to find the types of habitats where bears like to reside. The top three types of landcover turned out to be Mixed Forest Land, Forested Wetlands, and Evergreen Forest Land. After that, a 500 meter buffer was created around the streams within the study area. The buffer was set to dissolve all boundaries. The buffered streams was than intersected with the habitats where bears like to reside. This found the number of bears that are located near streams. This was important because it showed the correlation between bears and streams was quite significant as the percentage of total bears near streams was around 70%.

The landcover feature class was than chosen to use the select by feature class to extract the three suitable habitat types mentioned above. Those habitats were than intersected with the buffered streams. This showed the total potential bear habitat within the study area of Marquette County. This habitat was than intersected with the DNR_management feature class to find suitable habitat within DNR managed area.

The next process began with selecting Urban Areas within the select by attributestool from the landcover feature class. A new Urban landcover layer was than created from that selection. Than a 5km buffer was set around the Urban feature class and the buffer was intersected with the suitable habitat within the DNR management areas. This created a map with all of the suitable habitat, and the areas 5km around Urban areas. The intersection around the Urban Areas was than erased and helped to create the final map.

Part 2: Python Scripting

Finding Suitable areas for Development of Tourist Resorts

The first step in this task was to create a ten mile buffer around cities in Wisconsin. This was done by opening the Python Window in ArcGIS. Arcpy was then imported. After that, code was written to bring up the Buffer Analysis within the ArcPython library. In the buffer parameters, The WI Cities feature class was chosen to buffer from with a 10 mile buffer zone. The dissolve setting was set to ALL.

The next task involved writing python scripting that found all lakes within Wisconsin that are greater than 5 square miles. This was done by calling upon the select by attributes function with the arcpy library. The Lakes feature class was selected, with a parameter of area greater than 5 miles to create a new selection. The selected features were then copied and created into a new feature class which was called Lakes_resort_JF.

(Creating a feature class with lakes in Wisconsin that are greater than 5 square miles.)

Next, a clip was executed to find areas that fit both the city buffer zone and the lakes with greater than 5 square miles.

(Python scripting to create a feature class with lakes that could be a potential tourist resort.)

Modeling Air Pollution Impact Zones

The next task was to create a map with the potential zones of air pollution off of the major interstates in Wisconsin. Within the arcp library, the multiple ring buffer is called upon. Within it, the input feature class is set to interstates, the out put is set to be called Inter_mul_Buff_JF, and the buffer zones are set from 1-6 miles.

(Creating 6 buffer zones along the major interstates within Wisconsin.)

After this is completed, the zones are set to a monochromatic map showing the highest pollution zone in the darkest red, going to light read for low pollution. In the backdrop, Wisconsin counties, cities, and interstates are included.