GIS I Lab 5 - Applying Geospatial Skills

Introduction:

This final lab called for the student to design their own research question that they could solve using geospatial tools within ArcGis. The research question that was thought of was; where is the best place to build a cabin in southwestern Wisconsin? In order to come up with the desired locations, there were a few parameters that needed to be set. It needed to be located in deer management zone 61, but only in Buffalo, Pepin or Trempealeau counties. The location also needed to be within 20 miles of the Mississippi River and at least 2 miles away from a major highway. People that may use this information may want to know where to build a cabin that would give them some solitude for a weekend get a way. Deer hunters who hunt in the deer management zone 61 may also be interested in this type of information.

Data Sources:

 In order to obtain an answer to this question, it was necessary to get data from a couple of different sources. The data was obtained by establishing database connections through ArcGIS. There were two different connections that needed to be made. The first was with the ESRI database and the second was with the Wisconsin DNR database. In order to get an answer to the question being asked, it was necessary to obtain data from both databases. The ESRI database provided the information regarding the state and county boundaries as well as the major highways. The Wisconsin database provided information that showed the different deer management zones as well as the Mississippi River.

Once all the data was on the map, it needed to be analyzed for possible problems it may have. The data sets appeared to be complete and it seemed to be reliable. After taking a closer look, the first concern that appeared was the consistency as to when it was updated. All of the data from the ESRI database is updated annually. There should be little concern with how up to date their data is, since it is updated every year. The Wisconsin DNR data on the other hand, did not specify when it was updated or how often. This could cause problems if some of the data sets had changed and there were different boundaries in present time. Without knowing how often this data is updated may cause for an irrelevant map to be made from outdated data.

The second major concern about the data is noticed by looking at the map. All of the boundaries do not line up perfectly. There also seems to be gaps within the southern portion of the map. This could cause for some areas to not be mapped that should have been included in the study area. 

Methods:

This is the work flow model that was used to obtain the answer to the question being asked.

The first step in answering this question was to narrow down the map to the selected area of interest. The states layer needed to be queried in order to make Wisconsin its own layer. Once that was done, it was clipped with USA counties in order to have the state of Wisconsin with all of its counties shown on the map.

 

The Deer management zones needed to be added to the map and queried as well. This allowed for zone 61 to be selected and shown individually on the map. Once a new feature class was made with management zone 61, it was then clipped with Wisconsin counties in order to get the larger area of interest. With the area of interest located, it needed to be narrowed down one more time. An attribute query needed to be done in order to locate where the desired counties were located within the deer management zone 61.

The next step was to select the Mississippi River from the Wisconsin Hydroflow data. Once it was selected, a new feature class of just the Mississippi River needed to be made for future use. A 20 mile buffer needed to then be made around the Mississippi River in order to see where a potential cabin could be built. Since the Mississippi River borders more than the three desired counties, the buffer needed to be clipped by the desired counties feature class. The result of this process only showed the buffer around the Mississippi within the desired counties.

The major highway feature class also needed to be narrowed down to the study area. The major highways were clipped by the desired counties in order to just have the highways in the desired counties visible on the map. Once the highways were clipped, a two mile buffer was needed around all of them.  This buffer showed where a cabin could NOT be located. An erase had to be used in order to eliminate the buffer around the highway, but show everything beyond 2 miles of a highway.

The result of the erase was then intersected with the Mississippi River buffer. It needed to be intersected in order to show where the ideal hunting cabin would be located in the deer management zone 61.

Results:

 This map shows the ideal hunting cabin locations within deer managment zone 61.

The results of this project are very interesting. Most of the ideal cabin locations are located in the central part of the study area. The highest amount of possible cabin locations are in Buffalo County. This is somewhat expected since it is the largest county in the study area. It is also interesting to see where the major highways were located and where the buffer from the Mississippi River cut off. There seems to be a major highway running along the Mississippi River with different highways branching off from it at different points. In the northeastern portion of the study area there is not many possible cabin locations. This is due to the Mississippi River buffer not reaching that far. Therefore, it automatically eliminates it from being a possible cabin site.

Evaluation:

This assignment allowed for the student to create their own project in their area of interest and use all the GIS tools they have learned throughout the semester. The assignment made the student come up with a real life geospatial question that needed to be solved. Critical thinking was needed in order to figure out which tools would yield the result the student desired in order to get the result that was wanted. If I were to do the assignment over, there would be more criteria involved. That would narrow down the ideal cabin locations and not give as general of a result. 

Sources:

Esri Database

Wisconsin DNR Database

GIS I Lab 4 - Vector Analysis with ArcGis

Goal:

The goal of this lab was to use different geoprocessing tools within ArcGIS to find the best bear habitat for a particular study area in Marquette County, Michigan.

Background:

The Michigan DNR wanted to know what lands they own that are the best suitable habitat for black bears. There were certain criteria that had to be met to find the most ideal habitat locations.

Methods:

There was data provided that showed where the study area was located and the different features within it. A certain number of bears were being mapped and their locations were given in an excel file. In order to map this data, it needed to be imported into ArcGis as an event theme. Once the data were imported, it could then be exported as a feature class and added to the geodatabase. Once the bear locations were on the map, it was then necessary to put all the other necessary on the map for easier viewing and analysis.

 The assignment called for the top three habitats, based on bear locations, to be separated from the other land types. For this data to be obtained, the locations had to be spatially joined with the land cover types. Once the three habitats were found, a new feature class was made in order for the data to be analyzed with other data.

Biologists believed that bears may spend a fair amount of their time near streams. In order to find out if this was a true hypothesis, a 500 meter buffer was made around all the streams in the study area.  Once this buffer was made, it needed to be dissolved to make it easier to understand. This dissolved result needed to be clipped with the bear locations to find the number of bears near streams. The hypothesis turned out to be correct; 72% of the black bears that were being tracked were recorded within 500 meters of a stream. 

The next step was to find suitable bear habitat based on the stream buffer and bear locations. In order to do this, a feature class had to be created for the top three habitat types for the tracked bears. Once this was made, it was then necessary to intersect that feature class with the stream buffer. This result would give us the best habitat within 500 meters of a stream. Once these feature classes were intersected a dissolve was needed to make for continuous polygons rather than small individual polygons making up one larger feature.

The Michigan DNR then wanted to know what land that they owned that would fall into these areas of desired bear habitat. The DNR management feature class needed to be clipped with the study area in order to eliminate unnecessary data from the map. Once that data was clipped, it then needed to be dissolved to eliminate internal boundaries within the data. This dissolved result then needed to be intersected with the best bear habitat. The result from the intersect was the best bear habitat on DNR managed land.

Results:

The results of this map show the best black bear habitat for the Michigan DNR to improve. This assignment allowed for ArcGIS to be used in a real life situation to solve a real world problem.

This is a map of the results that were found while researching ideal black bear habitat. The map on the left shows the ideal habitat locations. The map on the right shows the study area and the different land cover types associated with the area.

This is a data flow model that was used to find the ideal bear habitat for the Michigan DNR.

 Source: Michigan Geographic Data Library

GIS I Lab 1: Base Data

Introduction:

The goal of this lab was to become familiar with using a GPS unit in the field. The objectives were to create a geodatabase prior to going to the field. The geodatabase that was created was deployed to the GPS unit being used, a Trimble Juno in order to collect the necessary information from around campus. Once the data was collected, it was necessary to export the data back onto the computer in order to make a map that would make sense to the viewer.

Methods:

The first step in completing this assignment was to make a folder for all of the information to be stored. Once the folder was, a new geodatabase was created in order to keep all the data in one place. After the geodatabase was created, new feature classes had to be created for the different features that were going to be mapped around campus. The first feature was simply, point. The coordinate system was set to NAD 1983 HARN Wisconsin TM meters and the tolerance and database configuration were left to the default settings. We added a field to this feature named “type” and set it to text, but left all the default field properties. Once those steps were completed, the first point feature class was created. It was then necessary to do the same steps to make a polygon and line feature class. These feature classes would be used in the field as the final product. The assignment then called for three more feature classes to be created. These were the practice points, lines and polygons. These feature classes were used while experimenting with the GPS unit to see how it worked. Once navigating through the different features of the GPS became easier, those practice points were no longer used in data collection.

In order to see what we were mapping and where we were on campus from an aerial photo, I was necessary to get that information onto the GPS unit. The first step was to import the shapefile of the campus buildings into the geodatabase. Once that was completed, we needed an aerial photo of campus. That image was imported into the geodatabase that we were going to use for mapping the different features around campus.

After all the features were created and images imported, the next step was to put them all into ArcMap. The only thing that showed up on the map was the aerial photo of campus and the different digitized buildings. Since some features were created for practice and others for actual use, it was necessary to symbolize them accordingly. They were symbolized based upon color so they would not get mixed up while in the field collecting data.

After all the features were symbolized and everything was put into ArcMap, it was then necessary to open up ArcPad Data Manager. Once ArcPad was opened, we had to get our data ready for ArcPad. Within the action menu, the background layer format had to be changed to an AXF layer and the background layer editing had to be changed to editing allowed. Without making sure the layers were checked out they could not ever be set to copyout. Once the data was ready to be deployed, it was necessary to save an .apm file to a desired location otherwise it would not allow for the map to be shown in ArcPad.

After all the data has been deployed, the next step is to transfer the data onto the Juno GPS. Once the GPS was connected to the computer, the folder that was created at the very beginning needed to be transferred to the GPS memory card. After all the data was transferred, the GPS was set to go start mapping the desired features of campus.

Following the collection of the campus features data, the data then needed to be transferred back onto the computer to be mapped in ArcMap.  Once the GPS was connected to the computer, ArcPad Data Manager needed to be accessed once again.  Navigating to the folder with the .axf file, it showed all the features that were collected. In this table, point, line and polygon were checked to be checked into the map. The reason only these features were included in the map is because they were the final product while mapping on campus. Once all the features were in ArcMap the next step was to build a map that made sense to the viewer and was easy to read. 

Results:

Figure 3.1 shows the results from taking the GPS around different parts of campus. Different areas that were included in the mapping were light poles, trees, the walking bridge and some grassy areas in the campus mall.

Sources: 

GPS data collected by: Matt Brueske

W:\geog\CHupy\geog335_s14\lab\lab3

GIS I Lab 1: Base Data

Introduction:

The goal of this lab was to become familiar with the different data within the United States Census Bureau. The state of Wisconsin was the selected area of study for this assignment. The total population of Wisconsin was the assigned map. The assignment called for another map to be made based on my own interests, and the data from the two maps were then to be analyzed for different spatial patterns and trends.

Methods:

The first step in being able to complete this assignment was to become comfortable using the data provided by the United States Census Bureau. Once at the site, the data dealing with total population was needed. By clicking on the topics tab on the left hand side of the page, this allowed for different categories of data to be displayed. When the topics were displayed, the category “people” was selected, followed by “basic count/estimate”. By choosing these categories, it allowed for accessing the necessary data dealing with the total population for the state of Wisconsin.

Once the data was located, it was necessary to choose the SF1 set of data. The reason for this is the assignment called for the basic census data. It did not require ACS, or American Community Survey data. After the SF1 data for total population was selected, it needed to be downloaded to the folder dealing with the data pertaining to this particular assignment.

When the data was downloaded to the desired location, it then needed to be unzipped so the data was easier to access. After being unzipped, the different comma separated values, CSV, files needed to be identified to see which file contained the metadata and the actual tabular data. Once the actual tabular data was located, that file was then saved into a MS excel file. Since this information only contained the different tabular data, it was necessary to go back to the United States Census website to download the information containing the spatial representations for the different Wisconsin counties.

The next task was to download the shapefile for the Wisconsin census data. By going back to the United States Census website, they had the information we needed. The first step was to click on the geographies tab on the left hand side of the screen. Instead of keeping the window on the list tab, the map tab needed to be selected. Once the selection was made, it showed the selected area of all the counties for the state of Wisconsin. After all the needed data was selected, it needed to be downloaded. By clicking on the download icon, it brought up a window with different options as to how the data would be stored. For the purpose of the assignment, the shapefiles selection was the necessary output of our data. The data was then downloaded to the desired location and unzipped, like was done with the previous data that was downloaded.

After all the data was downloaded and organized, ArcMap was needed to show the different data that was acquired. The shapefile for the Wisconsin counties was the first thing added to the map. After the shapefile, the table dealing with the total population was then added to the map. Since these two data sets had separate attribute tables, it was necessary to join the two tables by a common attribute. The common attribute in this case was GEO#id, so that is how the tables were linked together. Once they were linked, the data could then be viewed as one table, rather than two separated tables.

As of now, all the data has been added to the table, but it does not show any thing because of how it is currently mapped.  It was necessary to change the properties for the shapefile to make an understandable map. The assignment called for a graduated colors map to be made so the population distribution throughout Wisconsin could be easily recognized. Since there was only one variable to map, it was not necessary to normalize this data set.

The last task for the assignment was to create another map for a data set of my choosing. That data set I chose pertained to the occupancy percentage of houses, and that was divided into owner occupancy or renter occupancy.  

As outlined above, the first step was to go to the United States Census website to access the information. SF1 data was then selected again in order to have all the necessary information for the different counties. I chose a data set that looked interesting and fulfilled all the necessary expectations. Once the data was chosen, it was then downloaded to the desired location and unzipped into that folder. Since this was going to a new map, the first thing I did was create a new data frame in ArcMap to keep the data separated from the first map. Then the tabular data then needed to be saved into an MS excel file. Once it was converted into an MS excel file, it could then be added to the new data from in ArcMap. Since the shapefile for the Wisconsin counties was already made, it was not necessary to repeat that step again, rather just add it to the map with the household occupancy data.

Once both data sets were added to the map, the attribute tables then needed to be joined based on a common attribute. Once again, the common attribute was GEO#id. Once the tables were joined, a variable needed to be chosen in order to map the data. The data I chose to map showed the owner occupancy of households for the state of Wisconsin. Since there were three different variables to choose from, it was necessary to look back at the metadata table to see what variable was being mapped.

After both maps were made, the data frames needed to be changed to be more local over the state of Wisconsin. It was not necessary to use the project tool in this case; all that was needed was to change the projection for each individual data frame. 

Results:

Figure 2.1 These are the two maps of population. The map on the left shows the number of housing units occupied by the owner. The map on the right shows the total population of each county in Wisconsin.

The graphic above shows the two different maps made of the state of Wisconsin. The pattern between a higher population and higher amount of home owner occupancy are almost directly related. The counties that have a larger population typically also have a higher amount of homeowners that occupy their own home.

Sources:

 United States Census Bureau. (2014, March 6). Retrieved http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

GIS I Lab 1: Base Data

Goal and Background:

The purpose of this assignment was to show the different features surrounding the area of the confluence project. The confluence project is a county wide project that will provide student housing and create a new art center in downtown Eau Claire. It is intended to break ground in 2014. The goal of this assignment was to show the location of different features of the city that surround the confluence project.

Methods:

The first thing that had to be done was to create a geodatabase for the proposed site of the confluence project.  Once the geodatabase was made, a feature class for the proposed site needed to be added. This allowed that specific feature class to be applied easily to all the maps. Once the feature class was made, we had to digitize the site so it was easier to identify. After the site had been digitized, new individual maps had to be made to highlight different features around the area of the confluence project.

The first map made dealt with the PLSS or public land survey system. A new data frame was created in order to keep all related information together. A basemap of the world was added in order to locate the proposed site and see the surrounding area. The PLSS section feature class was added to the map from both the county and city data. Once that was added, the PLSS quarter quarter sections were added to the map from the county and city data. This allowed for a more specific area of where the proposed site was located.

 Once the site was identified, the next step was to build a very brief legal description for each of the two parcels. Once the legal description was made, the next step was to build multiple relevant maps of the area surrounding the confluence project.

The first map was dealing with civil divisions. The first step was inserting a new data frame to keep all the information together. Then the civil divisions feature class was added to the map along with the proposed site feature class. This showed what civil division the confluence project was located in. The next map showed where the different census boundaries were located. A new data frame was inserted for this data. The block and tract groups feature class was then added to the map. The tracts had to be moved on top of the block group in order to see the both of them. The block group was then set to represent the population of 2007 for the City of Eau Claire. The next data frame needed was with the data for the City of Eau Claire. Once the new data frame was made, the parcel area, centerlines and water feature classes were added to the map. An aerial map was added in order to more easily identify the different parcel areas.

The next map that was made was for the different zoning classes. A data frame was made for Zoning and the zoning areas feature class was then added to the map. Due to the large amounts of different zone classes, the assignment called for the classes to be grouped based on similar symbols. Since the data was narrowed down, it made the map much easier to understand for the viewer.

The last map dealt with the different voting districts for the City of Eau Claire. A new data frame was inserted, and the voting district feature class was added to the map. The districts were labeled so it would be known what zone the confluence project would be located in.

Results:

Figure 1.1 This is a compilation of all the base maps for the area surrounding the Confluence Project.
 

There is no significant pattern with all these different maps. There is something interesting that is happening in the same block group as the confluence project. The zoning map shows that it is mainly central district zoning where the confluence project is located. Even though it is not strictly residential, there is still a fairly large population living in that particular block group.

 

Sources:

W:\geog\CHupy\geog335_s14\lab\lab1: 2009-07-13_EauClaire.gdb and City of Eau Claire.gdb

 

Property search. (2014, 2 17). Retrieved from http://www.bis-net.net/cityofeauclaire/search.cfm