Lab 4: Final Project

Introduction:

As a geology major, it was my goal to propose a realistic question that would infuse both geology and geography. The world population continues to grow exponentially each year, causing waste and land management problems.  Americans produce more than 200 million tons of garbage a year, creating a need to find safe locations to dispose of the waste without jeopardizing the surrounding population.

The question I chose to answer was:  "What are the best locations in Travis County, Texas to create a new landfill?"  In real life, it is a requirement that the base of a landfill be lined with clay to prevent possible leakage from entering the groundwater.  As a result, one parameter considered was that the location had to be within a low permeability geologic unit such as clay.  In addition, it was also established that the location had to be 300m from any wells or rivers, and outside any federal reserve lands.  Furthermore, the location had to be economically reasonable so I created a parameter requiring the landfill to be within 1 mile of a highway.

The intended audience of this project is the county board of Travis County, Texas.  This information is incredibly useful when considering the safest, yet economic place to put a new landfill.

Data Sources:

I knew the hardest aspect of this project would be finding geology data, so I decided to search the Esri 2013 USA database.  There was no accessible geologic data, so I decided to utilize data provided with the Mastering Arcgis textbook by Marybeth Price. The Austin Geodatabase provided geologic data for seven counties in Central Texas, but I chose Travis County because it was the only county that also provided well locations in the area.  I also needed to find data regarding rivers, and obtained it from the Esri 2013 database for the entire continental U.S. This was an extremely large file which made it difficult to work with. Other data obtained from the Esri 2013 database included U.S highways, U.S Federally protected land, and counties.

My biggest concern with this data was mixing U.S data such as rivers and highways with local data such as Travis County wells and geology.  This presents a problem because it is likely that the U.S data was mapped at a very small scale and is over generalized when compared to the locally mapped well and geology data.  Another concern is with regards to the geologic data used.  One of the geologic units considered was titled "Del Rio Clay and Georgetown limestone; undivided".  This indicates that not all of the area mapped is necessarily the Del Rio Clay, and in reality would need to be double checked before deciding to place a landfill in that geologic unit.  Lastly, I was also concerned with the differences in map projection.  As a result, I decided to change all features to the NAD 1983 HARN, Central Texas State Plane projection.

Methods:

I chose to use model builder within Arcgis to complete project (Fig. 1). Even though it was not a requirement to project each feature class into the same projection, I did so anyway to mimic a real world problem.  I chose the NAD 1983 HARN, Central Texas State Plane projection because the area of interest (AOI)) fell entirely within that State Plane boundary.  Using a local coordinate system reduces map distortion and increases the accuracy of the data.  After projecting the data, I had to find a way to trim my U.S data to only the AOI.  To do so, I first created a new feature class by isolating Travis County from the U.S County attribute list.  Next, I intersected the Travis County Feature class with the highway, river, geologic, and federal preserve land data to cut out all data outside of the Travis County border.  This made small scale data easier to work with, and allowed me to proceed with buffering the highway, river, and well data according to the original parameters.  Furthermore, because the landfill was required to be within a clay unit, I had to isolate those areas (Walnut Clay and Del Rio Clay) from the rest of the geologic units by using the select by attributes tool.  From there, I was able to use the erase tool to remove the well and river buffer areas that overlapped the clay units. Similarly, I then used the intersect tool to remove the area of clay that did not intersect the highway buffer.  Lastly, I erased the clay area that fell within federal preserve land which produced the final objective.

Result:

As seen from figure 2, the resulting feature class (dark blue) shows area inside a clay geologic unit, within 1 mile of a highway, at least 300m away from rivers and streams, and outside of federal preserve land.  The data shows there are relatively few options that fit all the criteria, which in it of itself adds value to this project.  Placing landfills is a delegate situation due to the potential ramifications on environment and quality of life of those living near it.

Evaluation:

This project allowed me to utilize knowledge from two fields of science that I am interested in, while also creating a map that could be useful to the Travis County board.

It is acknowledged that not all the aspects of selecting a landfill location have been acknowledged in this project due to lack of available zoning data.  As a result, it is very possible that a large majority of the area within the Del Rio Clay is within the city of Austin, which would not be a good location for a landfill.  Furthermore, for the sake of this project, it was established that the landfill location had to be within a clay geologic unit.  Although this is convenient, it is not always true.  In reality, County boards will often use clay from another location to line the base of the landfill.  If I were to do this project again, I would set the parameter that the landfill must be within two miles of a clay unit, which would drastically increase the available land area.  In addition, I would need to obtain zoning data from the city of Austin so that it is not accidentally recommended that a landfill be built downtown.

My biggest struggle with this project was working with the large River and Highway data sets.  Projecting the data into a new coordinate system caused the program to stop responding on multiple occasions, and took a very long time when it did work.  If this were an actual project, I would use locally obtained data regarding streams and highways to provide a more accurate map and make  the data easier to work with.

Sources:


Price, M., (2014).  Mastering ArcGIS, Sixth Edition.  McGraw-Hill Companies, Inc., mgisdata.

ESRI Database

www.EPA.gov/epawaste/nonhaz/municipal/

Lab:3 Vector Analysis with ArcGIS

Goal: 
To become familiarized with geoprocessing tools in commonly used vector analysis in ArcGIS to determine suitable habitat for bears in the study area of Marquette County, Michigan

Background: 
The Michigan DNR requested information regarding the location of suitable black bear habitats to aid the development of a bear management plan. The DNR asked that habitat recommendations on DNR management land, and 5km from urban land coverage.

Methods: 
 Preliminary research in the determination of suitable bear habitat required the analysis of current known black bear populations within the study area.  I added a table of X,Y coordinates showing bear locations (provided) as an event theme and displayed them in ArcMap as a feature class.  Next, I made a spatial join between the bear locations and the land cover data to show which land cover types current black bears were living in.  Summarizing the resulting data provided the top three land cover types black bears preferred.

Biologists in the area indicated that black bears are most often found near streams due to the vital resources they provide.  I investigated the theory and took proximity to streams into consideration by creating a 500m buffer around all streams within the study area.  I then clipped all areas of the newly created layer that fell within DNR management land.

The DNR also requested that all proposed habitats be 5km away from urban or built-up land. To do so, I first selected all urban and built-up land from the land cover feature class and created a new layer.  Next I created a 5km buffer around urban and built-up land and used the erase tool to eliminate all proposed habitat area that fell within the buffer.

A map was created showing the following layers: Suitable bear habitat according to preferred land cover and proximity to streams, suitable bear habitat that lies in DNR management land and is 5km from the urban development, streams, current bear locations, and bear locations within 500km of a stream.  A locator map was also included to show the study area within the larger scale of the State of Michigan. In addition, the process of data manipulation is visually represented in the data flow model that I have generated.

Figure 1:Sample python script used in conducting the Arcgis tools

Results:
Analysis of bear populations within the study area show prove that bears are typically found near streams. Biologists consider a habitat characteristic a defining criteria important if it is represented by 30% of the population. Of the current bear locations in the study area, 72% are within 500m of a stream showing that proximity to streams plays a vital role in habitat suitability.    Unfortunately, even though there is a large proportion of suitable bear habitat land in the study area, only a small portion is found on DNR management and 5km away from urban development.

Figure 2:  Final map displaying suitable bear habitat according to land cover, proximity to streams, and distance from urban development

Figure 3: Data flow model displaying the steps taken to obtain the final product

Sources: Michigan Center for Geographic Information
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

Lab 2: Downloading GIS Data

Introduction: The goal of this lab was to learn how to download and map data from the U.S Census Bureau

Methods:
I first learned how to search for specific data from the U.S Census website according to topic and geographies using the bureau's FactFinder search engine.  I then downloaded attribute data for both total population and household size in Wisconsin counties.  In addition, the shapefile for the Wisconsin census data was also downloaded from the website.  Before the data could be linked together with the shapefile, I had to prepare the attribute data to be used in Arcmap by first saving it as a Excel workbook file.  Next, the shapefile and total population attribute data were joined based off the Geo ID so that the populations would be linked to the proper counties.  After the attribute data tables were linked, it was then mapped using a graduated color scheme. The same steps were followed to construct the map displaying percent of two-person households.

Results:
The map displaying population shows that counties with smaller total populations tend to be focused more in the north, while the more populated areas tend to be in the southeast portion of Wisconsin where larger cities such as Green Bay, Milwaukee, and Madison are located.  When placed side by side with the map displaying the percent of two person households, it is seen that less populated areas tend to have a higher percentage of two person households then more urban areas.

Sources: All data was obtained from the U.S Census Bureau website

Lab 1: Base Data

Goals and Background:

The confluence project is a plan to revitalize downtown Eau Claire by constructing a community arts center in additon to a mixed-use apartment-commercial building in order to foster economic growth. The purpose of this assignment was to compile a series of basemaps to show civil divisions, census boundaries, PLSS features, parcel divisions, zoning, and voting districts in the area surrounding the proposed location for Eau Claire confluence project. In addition, this project introduced me to the Public Land Survey System and how to build a layout with major thematic feature classes.

Methods:

Before major feature classes could be mapped individually, I created a blank geodatabase using ArcGIS software to serve as the storage location for data I wished to add.  This allowed me to digitize the area of the proposed site and add it as a feature class in the geodatabase.  Next, I created six separate dataframes to highlight one of the desired feature classes (civil divisions, census boundaries, PLSS features, parcel divisions, zoning, and voting districts) in addition to the newly digitized proposed site.

The first map displays civil divisions.  In order to create this map, I first added a basemap of world imagery to serve as a spatial reference for the data.  Next, I added the county boundary as a hollow feature with a bright outline to contrast with the darker basemap.  The civil divisions data were added to the map as a transparent feature so that the basemap could still be made visible for reference.  Lastly, the proposed site layer was added on top and the legend and scale were added.

The second map shows the 2007 population density of the area around the proposed site.  I added the World Imagery basemap for reference, the block groups dataset as a transparent layer, and the tracts data as a hollow layer so that all features could be seen.  The block groups feature class was symbolized by 2007 population and normalized by square miles in order to represent population density. Lastly, the proposed site layer was added on top and the legend and scale were added.

The third map illustrates the Public Land Survey System quarter-quarter lines with reference to the proposed site.  The World Imagery basemap was placed underneath the hollow PLSS quarter-quarter lines so both features could be represented.  Lastly, the proposed site layer was added on top and the legend and scale were included.

The fourth map represents parcel divisions surrounding the proposed site.  First, the water, centerlines, and parcel area layers were added as transparent features on top of the World Imagery basemap.  The water layer was made a dark blue because the color is most often associated with water on maps and because it stood out against the green land surface.  Parcel divisions and centerlines were changed to a bright yellow color so that they could be best distinguished from all other features.  Lastly, the proposed site layer was added on top as a bright red and the legend and scale were included.

The fifth map shows zoning in the Eau Claire area and is the most visually complex map of the six.  To create this map, Zoning data was added on top of the World Imagery basemap.  This feature was symbolized according to the zoning classification by using information from the attribute table. Data was grouped into six major zoning classes: Commerical, residential, transportation, industrial, public property, and central business district.  Each class was represented by a distinguishing color before the centerlines and proposed site layer were added.

The sixth map displays voting districts around the site. This map was created by adding voting districts as a transparent, dark blue layer to the World Imagery basemap.  Voting districts were labeled by ward number, and the district boundaries were made orange to contrast with the blue fill color. Lastly, the proposed site layer was added on top as a bright red and the legend and scale were included.

Results:

Together, the series of maps shown in Figure 1 illustrates a number of features surrounding the proposed site.  Based on the census boundaries maps, it is seen that the confluence will be at the heart of the most densely populated part of the city.  The zoning map shows that it is also in the middle of the central business district where it will likely enhance tourism, the tax base, and economic and cultural growth.

Figure 1

Source: City of Eau Claire and Eau Claire County 2013