Mini Term Project

Goal and Background

The goal of the Mini Term Project was to use all of the techniques and skills learned throughout the semester to create a map that answered a spatially related question. It allows for the conceptualizing of how GIS can be utilized to decipher practical problems in the World. The Mini Term Project allowed one to develop both an issue with spatial dimensions and the solution to the problem.To create the map, criteria had to be formed to solve the problem and data had to be collected from a variety of sources. This was then modeled out to show the geoprocesses in action.

Methods

Model Used to Create Map for Potential Land Development
Dark green circles indicate shapefiles that were left on the final map.



The project was started with Wisconsin's DNR County Boundaries, the WI DNR National Forests, and the Topper's Wisconsin Water Bodies (only the Lakes shapefile) feature classes being added to a blank map in ArcMap. After that, a ten mile buffer was created around the National Forests. Then the Lakes shapefile  is intersected with the National Forest buffer to find potential lakes within ten miles of National Forests. The Wisconsin counties were then selected with the select by polygon selection feature to determine which counties the lakes and forests are located in. The National Forests were then clipped to only allow the National Forests within the counties of interest. At this point, a 2 mile buffer was created around the lakes to determine potential land that could be determined to be suitable for development. To determine if the potential land is suitable, land use data was then brought in. Raster land use data was then brought in from the Geospatial Data Gateway. It was converted from raster data to vector data to use within the current map. After the vector data was added, it was intersected with the two mile buffer of the lakes to determine the land use around the lakes of interest. At that point a select by attribute query was done to select the High Density Development, Medium Density Development, and Cropland land use data.  That selection was then created into a new feature layer, and then erased from the two mile buffer around the lakes. This allowed for viewing of all of the potential land that could be used for development of a hotel or resort.

Select by Attribute Query to select area around lakes that should be erased.



Results

Final Results map to display potential resort locations in Northern Wisconsin.

Citations

ESRI. (2014). [Wisconsin County Boundaries].

ESRI. (2014). [WiDNR2014.DBO.National Forest].

UW Eau Claire Geography. (2015). [Topper Wisconsin Water Bodies Service].

Bureau of Land Management. (2000). [Public Land Survey System (PLSS) Townships and Sections].

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.

Results

Potential Bear Habitat within a study area in Marquette County, WI.

Model used to deduce bear habitat.

Lakes suitable for a tourist resort in Wisconsin.

Model used to deduce potential tourist resort locations.

Wisconsin air pollution impact zones caused by interstate traffic,

Model used to deduce air pollution zones. 

Citations

Michigan Department of Natural Resources (DNR) 

Esri

Price, Maribeth. 2016. Mastering ArcGIS. 7th Edition data. McGraw Hill. 

Wilson, Cyril 2012, A comprehensive Lake features for Wisconsin, Unpublished data. 

GIS 1 Lab 4

Goals and Background

The primary goal of Lab 4 was to gain skills in using and implementing the search query on ArcGIS maps to extract components of data from a database. The lab helped to assess how we understand attribute and spatial queries. Lab 4 was completed using Boolean expressions, operators, parenthesis, etc.. to help create multiple criteria queries that allowed us to extract the data we were interested in. Along with the attribute queries, spatial queries were used to execute specific tasks in extracting data. Once the queries were completed, the data that was extracted was mapped for viewing.

Methods

United States County Queries

The county data from the US database within the Price mgisdata was added to the map to create the query from. Once added, a multiple criteria query was completed to extract data that allowed one to view counties with a population between 3000 and 4000, as well as counties that had a population density greater than 1000.

(Specific Query Used to extract data.)


Once that was completed, the data was used to find specific pieces of information, such as: the amount of counties that met the criteria (194), how many states were in the selection (35), which state had the highest amount of selections (Virginia), which state had 8 counties that met the criteria (California), and the selection was also used to find the mean (554924) and standard deviation(1014847) of the population of the counties. 
 
The second activity was to write another multiple criteria query for the US county data. This query involved writing an expression where the male population is greater than the female population and the population of seniors (greater than 65) is over 6500. This query was all within the states: Wisconsin, Texas, New York, Minnesota, and California.

(Query used to extract data on counties.)

Once the data had been extracted, questions were answered to further explore the results of the data. These questions were: how many counties met the above criteria in the five states (46) , which state had the highest counties that met the above criteria and how many counties (Texas with 14), and to list the counties in Wisconsin that fulfilled the criteria (Grant, Waupaca, Sheboygan, Columbia, Dodge).

After this query had been completed, another set of conditions were added to the multiple criteria query. This addition was to find counties in Washington, Maryland, Illinois, Nebraska, District of Columbia, and Michigan who live in counties where there is over 30,000 housing units. This was used to find the new amount for the counties that had been fulfilled by the query (128).

(Multiple Criteria Query to add additional counties.)

Wisconsin Queries

The second part of the lab was an activity that allows one to return cities within a Wisconsin dataset that has data collected from 2007 (Wilson). The purpose was to find what cities in the state have a population between 15000 and 20000, the area is at least 5 square miles, the female population is greater than the male population, and the cities are within 2 miles of a lake. To do this a multiple criteria query and a location search needs to be done.

(Multiple Criteria Query)

(Select by Location)

After this was completed, the results are used to determine how many cities in Wisconsin met the criteria above (3), and which cities they were (Middleton, Beaver Dam, Menasha). The total population of these cities was 48,914.


Another multiple criteria query was used to search for a set of rivers within Wisconsin.
(Multiple Criteria Query which lists the rivers searched for.)


The goal of this query was to determine the amount of rivers found with these names in the state of Wisconsin (80), and the total length of the rivers, which amounted to 137,937 miles. 

Results

(Counties with population between 3000 and 4000, and counties with pop. density greater than 1000.)

(Counties within Wisconsin, Texas, New York, Minnesota, and California that has a male population greater than females, and a senior citizen population over 6500.)

(Counties within Wisconsin, Texas, New York, Minnesota, and California that has a male population greater than females, and a senior citizen population over 6500. As well as counties within Washington, Maryland, Illinois, Nebraska, District of Columbia, and Michigan where there are more than 30,000 housing units.)

 

 

 

Part 2

 

 

(Cities in Wisconsin that have a population between 15,000 and 20,000, an area greater than 5 square miles and a female population greater than a male.)

 

 

 

 

( Rivers in Wisconsin: Chippewa, Eau Claire, Embarrass, Fisher, Hunting, Kinnickinnic, Maunesha, Milwaukee, Moose, Namekagon, Pelican, Platter, Potato.)

 

 

Sources

Price, Maribeth H.. (2016). Mastering ArcGIS. Mcgraw Hill Higher Education.

 

ESRI - Wisconsin Cities, Interstates, Rivers, Roads, County Shapefiles.

 

Dr. Wilson. -Wisconsin Lakes.