Lab 4

Introduction: The goal of this lab was to find a suitable area in Vilas or Oneida counties for a golf camp. The objective was to find an area in close relation to a number of different things. The camp needed to be inside of a forest, within a half mile of a lake, 15 miles of a golf course, and 15 miles of a hospital. The intended audience for this project would be an entrepreneur looking to start a golf camp in Northern Wisconsin. The entrepreneur would use the data to find what land to buy to establish the camp. 

Data Sources: The data which was used was from the 2014 Department of Natural Resources of Wisconsin's survey, and a 2014 Esri Data; both bought by the University of Wisconsin-Eau Claire for academic uses. 

Methods: The first operation executed was to clip all of the features used so that the only features displayed were within the county. However the hospitals and golf courses were not clipped until a buffer was given, because some were outside of the county but within a distance of 15 miles. Next was selecting the lakes in the vegetation cover to create a new class of lakes. Then a 1/2 mile buffer was given to the lakes to find land close enough to have campers walk to the lake on an average day. An intersection of the 1/2 mile lake buffer, 15 mile golf course and hospital buffers, and forests, was completed. Lastly an analysis of unavailable land was done. For the purpose of this lab US and Wisconsin parks were erased from the previously executed intersection. This was to show that this land could not be purchased, and therefore should not be included. If given more data more area would be erased, per land available for purchase.

Results: The results showed areas touching lakes in both counties, that fulfilled all of the previously mentioned criteria. Although not an incredible large selection was given, the areas were large enough to contain camp necessities, ie. mess halls, dormitories, infirmary, and other classrooms. The result would most likely be larger if more accurate data for golf courses was given. Although plenty of courses were shown on the report, in reality there are more courses. 

Evaluation: This project was very interesting given the ability to pick ones research topic. If done again more accurate data would be beneficial. More golf courses would be present and therefore more areas for the camp would be available. More in depth analysis would be able to be done, for example areas where multiple golf courses are present would allow for campers to experience a larger number of courses, hence yielding a better result to their game. Some challenges faced were long loading times for some of the overlays used, namely the intersection of all of the criteria.  

Lab 3

Goal: The goal of this lab is to demonstrate skills in spatial analysis in ArcGIS. During the lab we used overlay tools to display bear habitats

Background: The lab looked at a study of bear populations, and worked to find an optimal habitat for black bears in Marquette county, Michigan. Using vector analysis tools I determined the preferred habitat for black bears. The criteria for the habitat was, in the preferred ecological cover, within 500 meters of a stream, within DNR land, and at least 5 km away for urban or built up areas.

We began by taking a zipped file of X-Y coordinates of tracked bears. Once we had our data displayed on a map we began to run analysis of the bears population, and their habitats. By querying the data, it was determined that the optimal location for black bears was in Mixed, Deciduous, and Evergreen forests. I than buffered the streams 500 meters, and ran a intersection to determine that over 30% of the bears were within 500 meters of a stream, in fact 72% were within a stream. Forrest type, and stream proximity were then added as criteria for the bear habitat.

The next step was to intersect the buffer of streams, with the suitable habitats. This generated our first look at where the bear habitat would be. Although this showed where bears could live in the area, it did not display whether or not the data was inside of a DNR managed area. To solve this problem, I ran a clip of the DNR zones within the study area. Next I ran a intersect of the DNR zones, and the previously determined habitat zones. This showed us areas where the bears habitat could be, within the DNR zones. The last objective was to ensure that the bear habitat was outside of 5 kilometers of urban, or built up areas. I began by creating a feature class from the selected "urban or built up" areas in the landcover data. Next I created a 5 km buffer around those urban areas. Lastly running a intersection, of the urban buffer and the DNR_bear_habitat I determined the areas too close to civilization.

The data flow model below shows the steps, and order of the steps, which I used to generate my map of potential habitat areas.

Data Flow Medel

The following maps shows the areas of Marquette county which meet all of the qualifications of a bear habitat, as defined by the lab. The Blue areas show possible habitat locations that a too close to an urban area. The green shows the habitat areas which are outside of an urban area, and inside DNR land. The red shows the urban areas. Dots and lines represent bear location, and stream location respectively.

Python coding for certain operations used in Lab 3.

>>> import arcpy

>>> arcpy.Buffer_analysis("streams", "steams_buf", "1 kilometer", "FULL","ROUND","ALL")

<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\steams_buf'>

>>> 

>>> arcpy.Intersect_analysis(["steams_buf", "suitable_habitat"], "land_stream")

<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\land_stream'>

>>> 

>>> arcpy.Erase_analysis(["Urban_Buffer_All", "Suitable Habitat"], "Erase_Buffer")

<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\Erase_Buffer’>

>>> 
Sources

http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_uni ts.htm  http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

Lab 2 - Basics in Joining

Lab Introduction:

The Goal of this lab was primarily to demonstrate skills in Joining. We began by acquiring new zipped data from the US census bureau. By downloading a shape file, I was able to join the county shape data with each county’s population. Once we joined the data together we created a new field which calculates the total population, and divided that by the state population, to display percentages. After the first objective was completed we returned to the Census to find a variable of our own. I choose to display the median age, of the population of each county, for Wisconsin. Once both maps were created the next objective was to create a web map. The most difficult detail of this objective was the errors which we had to solve and delete. After solving the errors and running an analyze on the data a web map could made.

Methods:

The Methods which we used in this lab was join and relates. We were able to retrieve our own data, unzip it for use, join the data together, and display a map from the excel data table which previously would have been of no use in ArcMap. To reach my goal I used the lab instructions as well as previous knowledge of joins and data management which was taught in the ARC mag book.

 Results:

The results are shown on the maps below. I found that the 2 variable which I had used are inversely related. As the population percentage of a county increases, the median age decreases. This shows an older population in the less populated north of the state, as well as a younger populous in the larger cities. We can also look to areas with a large population from Universities; Dane, Eau Claire, and Milwaukee are the most obvious of these.

 Sources:

The source of my data came from the US Census Bureau at the URL below. http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t this site shows a litany of data which when joined to spatial data can be represent as a map.

Introduction 

This lab covers the skills learned so far in GIS I. The lab was meant for the purposes of demonstrating skills in displaying data, analyzing how data should be represented, and making decisions on how to display the data. The instructors objectives were as follows.

    1. Explore various data sets for the City and County of Eau Claire and answer some basic                   questions about the base data. 

    2.  Digitize the site for the proposed Confluence Project.

    3. Learn about the Public Land Survey System. 

    4. Become familiar with the WebGIS for the county and legal descriptions for parcels.

    5.  Build a layout with each of the major thematic feature classes. 

    6. Write a technical report and post it to your blog. 

Methods

In this lab we focused on key terms for creating maps for city and counties. Working with, zoning features, parcels, land use, land cover, political features, US census features, and hydrologic features; the objective of the lab was to create a Cadastral survey of the proposed site for a UW- Eau Claire expansion hereafter referred to as the confluence project.

Results

The goal of this lab was to create a series of maps for what would be used for the planning of the confluence project. The confluence projects goal was, to construct a new development at the confluence of the Chippewa and Eau Claire Rivers. The data which we used came from the City of Eau Claire geodatabase, and a 2009 Eau Claire database.  The maps displayed below show, civil divisions, voting districts, parcel data, PLSS features, district zoning, and Census Boundaries. In the real world this data would be used by city planners to determine the validity of the proposed project.