Areas at Risk for Volcanic Activity in Washington State

Introduction

The question that I created and attempted to answer was, "What areas in Washington state are at risk for volcanic activity?" More specifically, I wanted to show possible associated earthquakes due to volcanic activity; and which counties and major roads were at risk. This research question was designed for the population of Washington state, as well as those officials who provide risk assessments for volcanic and earthquake activity. 

Data Sources

I used different data from the USA geodatabase to help answer my question. This data was provided through the GIS I course offered through the University of Wisconsin- Eau Claire. There were a few data concerns, specifically, the topographic base map used did not match up perfectly to the data used from the USA geodatabase. Also, earthquakes within a 40 mile radius of a volcano may not have been generated due to volcanic activity. Also, population density affected by a volcano may extend further than 20 miles. 

Methods

My process began by creating a personal geodatabase to work from, labeled Final Project. From here, I worked through ArcMap and ArcCatalog to add the appropriate data that I intended to use. The different data layers I used from the USA geodatabase were: States, Counties, Volcanoes, Quakehis, and Maj_roads. By using different queries (Query and Spatial queries) and analysis tools (Clip, Buffer, Intersect), I was able to generate a map showing different areas that are at risk for volcanic activity. The diagram below is the data flow model that I used to answer my research question.

Results

The results of my project shows population densities (one dot = 20 people) within a 20 mile radius of a volcano, earthquakes within a 40 mile radius of a volcano, major roads, and major roads within a 40 mile radius of a volcano. Counties that may be affected by a volcano include, Yakima, Whatcom, Snohomish, Skamania, Skagit, Pierce, Lewis, Klickitat, King, Cowlitz, Clark, and Chelan. West Crater, Rainier, and St. Helens appear to be the three volcanoes that pose the greatest risk to population density, and major roads.

Evaluation

My overall impression of this project was a positive one. I felt very satisfied with my overall map and was impressed that I was able to answer the question that I posed. If I were to do this project again I would have searched for a geodatabase specifically for the state of Washington, so as to have more accessible information, such as damage, pollution, eruption extent, etc. Some challenges that I faced while doing this project included finding data that would help answer my question. Again, my results could have been much more accurate or helpful if such data as damages or eruption extent were included. 

Vector Analysis With ArcGIS

Goals and Objectives

The goals and objectives of this lab were to learn and apply various geoprocessing tools for vector analysis in ArcGIS to determine suitable habitat for bears in the study area of Marquette County, Michigan. I used a GPS MS Excel file to map the locations of the bears, and used other data to determine forest types in which the bears were found, as well as bears found near streams. I combined this information to find an ideal habitat for the bears. I then used DNR management location data to create a new bear habitat area that fit the above criteria and was also in the DNR management area. I finally excluded all areas that were within 5 kilometers from Urban or Built up lands to produce a final ideal bear habitat that is within the DNR management area.

Method

The first objective was to add the bear locations from the MS Excel file as an XY event theme. An"event theme" is a temporary display of X,Y data in ArcMap. Once they were mapped, I exported them to bring them into my geodatabase as a feature class.

The second objective was to determine the forest types where bears are found in central Marquette County, Michigan based on GPS locations of bears. I began by adding several feature classes to my map from the Marquette_bear_study geodatabase. These feature classes were landcover, streams, and study area. I used the intersect tool between the bear locations and landcover classes and created a new feature class named Bear_cover. I summarized the Minor Type field in the new table that was created to determine the sum of bears within each forest type. The top 3 habitat types were Mixed Forest Land, Forested Wetlands, and Evergreen Forest Land.

The third objective was to determine if bears were found near streams. I began by creating a 500 meter buffer around all the streams (remembering to use the dissolve tool) and then intersected the new feature class (Bears_within_500m_of_stream) with the Bear_cover feature class. 72% of the bears were found near streams.

The fourth objective was to find suitable bear habitat based on the two above criteria: within 500 meters of a stream and within the suitable forest cover. I began by querying the landcover feature class to select and create a feature class (Suitable_landcover) with only the minor type covers that the bears were found in. There were 6 total types. I then performed an intersect between the Bears_within_500m_of_stream and suitable_landcover features classes to obtain a near feature class (Ideal_habitat). I then used the dissolve tool to remove any internal boundaries, and make the polygons look much cleaner.

The fifth objective was to make recommendations to the Michigan DNR for a bear management plan. The DNR must find suitable bear habitat that is located on their management lands. I began by adding the dnr_mgmt feature class from the marquette_bear_study geodatabase, and then used the clip tool to include only DNR management areas within the study area. I then intersected the DNR_management and Ideal_habitat feature classes to obtain DNR management areas within ideal bear habitats (DNR_management_area_within_ideal_habitat). 

The sixth objective was to eliminate areas near urban or built up lands. I began by querying landcover to select the major type of cover "Urban or built up lands" and created a feature class (Urban_or_builtup_land). I then created a 5 kilometer buffer around this land (5km_buffer), and used the erase tool to remove the buffer and leave only the DNR management areas within ideal bear habitats at least 5 kilometers away from any urban or built up land.

The seventh objective was to create a cartographically pleasing map to display the different habitats and bear locations.

The eighth objective was to create a data flow model of the procedures used to determine suitable bear habitat in Marquette County, Mi.

Results

The area in light green is the ideal bear habitats based off of the two criteria; within 500 meters of a stream and within the suitable land cover types. The areas in pink are the DNR management areas within the ideal bear habitats. The brown circles are all the bear locations that were found through GPS. The upper right hand corner shows the state of Michigan as well as Marquette county.

Above is the data flow model I created on CorelDRAW X6 showing the procedure I used to determine ideal bear habitats in Marquette County.

Sources

Landcover data is from USGS NLCD
-http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html
DNR management units
-http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm
Streams from
-http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

S

Downloading GIS Data

Goals and Objectives

The goals and objectives of this lab were to learn how to download data from the U.S. Census Bureau and map the data using ArcMap. We were to download 2010 Census data, specifically total population of Wisconsin counties, from the U.S. Census Bureau, and then we chose our own data set to download from the same census. The final product is a map displaying both variables separately.

Methods

Our first objective was to download 2010 Census Data from the U.S. Census Bureau website. We narrowed down our search by using Advanced Search, and selecting Population total through the Topics option. We then chose Wisconsin counties (County 050), through the Geographies option. By adding these options to my selection window, I was able to view all the data available for me to download. On the third page, I found the P1 variable for TOTAL POPULATION from the 2010 SF1 Dataset, and downloaded it. I saved the downloaded file to the Lab2 folder I created for this project. From windows explorer, I found the data I had just downloaded (Lab2 folder) and extracted all the files to my folder. I then opened the two CSV files in the Lab2 folder in MS Excel. One of the files displayed the metadata while the other file displayed the actual tabular data. I saved the latter of the two files (DEC_10_SF1_P1_with_ann.csv) as an MS Excel Workbook file.

The second objective was to download the shapefile for the WI census data. I returned to the U.S. Census Bureau website, and chose the Geographies option again. I then navigated to the Map tab, and double checked that Wisconsin Counties was highlighted on the map shown. I then downloaded the map as a shapefile.zip through the spatial data formats. I saved this file in my Lab2 folder. I extracted these files as I had previously done.

The third objective was to join the data I had downloaded together in a table. I opened up a blank map in ArcMap, added the shapefile to the map, and viewed it's attribute table. I then added the P1 table to the map (the Excel file I had saved in my Lab2 folder) and opened this table as well. In order to view both table at the same time, I arranged the tables in a new vertical tab group. In order to map the total population, I joined the two tables together through a Table Join through the 050_00 shapefile , using GEO#id as the common attribute. I reopened the attribute table for the shapefile to double check that the both data was shown in the one table. The D001 field contained the population data.

The fourth objective was to map the population data. I navigated through the Properties of the shape file, to the symbology tab, and chose a graduated colors map for the D001 value field with 6 classes that best represented the population of WI.

The fifth objective of the lab was to download and map a variable of my choice from the U.S. Census Bureau website. I chose SEX BY AGE as the variable I was going to map. I followed the same workflow as in objective one to download my data. I added a new data frame to my previous map, added the shapefile, and the table I had just downloaded and saved as a MS Excel file in my Lab2 folder. I joined the two tables together, as done in objective three, and chose one specific variable I was going to map. Since the SEX BY AGE data was not labeled appropriately in the attribute table, I viewed the table from the website to have a better idea of how the fields were grouped. I chose the variable Females age 21 (D033) to map and normalized the data to Percent of Population (D001). I chose to display my data in a graduated colors map and use percentage labels.

The sixth objective was to build a layout displaying both maps I created with the data I downloaded from the U.S. Census Bureau. I changed the data frame projection for both maps to NAD_1983_2011_Wisconsin_TM_US_Ft. and included the Title, Legend, North Arrow, Scale Bar, source, author, and date. Lastly I added a base map, and exported the map as PDF document.

Results

The map on the left displays the percent of females age 21 per Wisconsin county. Northern Wisconsin seems to have the least percent of females age 21 with the exception of a few counties. A band of Central Wisconsin shows a higher percentage of females age 21 as well as a band of Southern Wisconsin which shows the same pattern. The map on the right shows the total population of each county. South-east Wisconsin seems to have the highest density of population per county, whereas Northern and some Central-western counties have the least density of population per county. The two maps show some correlation in regards to population densities of counties, and population density of females age 21.

Sources:

http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t
"U.S. Census Bureau, 2010 Census, Total Population and Sex by Age.

Collecting GPS Data on UW- Eau Claire Campus

Goals and Objective

The goals and objectives of this lab were to create our own geodatabase and prepare the geodatabase for deployment to the Trimble Juno GPS for field data collection using ArcPad Data Manager. Then loading the geodatabase onto the Trimble Juno and becoming familiar with the basics of the GPS unit and ArcPad through a demo. Actually going out onto the UW- Eau Claire campus to collect point, line, polygon feature data while using ArcPad on the Trimble Juno GPS, and finally, checking the collected data back into ArcGIS from the field.

Methods

To begin the first objective of this lab, I created a geodatabase from a pre-defined folder in my directory labeled 'lab3', using ArcCatalog. From the geodatabase, I created several feature classes that I planned on adding to while out in the field. The feature types were points, polygons, and lines. I then imported a shapefile of the buildings on UWEC campus to my geodatabase from a Lab3 folder created by my professor. I then imported a raster of campus to the geodatabase by inputting the CampusImage raster found in the Lab3 folder. I then opened this geodatabase in ArcMap, making sure all of the feature classes were added. I then went through and changed the symbology of the features to bright colors that would visibly stand out on the GPS unit while I was out in the field.

The second objective of this lab began by opening the ArcPad Data manager toolbar. I then clicked the 'Get Data for ArcPad' button to open the wizard that walked me through getting the data ready for ArcPad. I used the Action Menu to choose 'Checkout all Geodatabase layers' and 'copyout all other layers' and proceeded. I then specified a name for the folder (fuglesmalab3) and changed the path of where the file is stored to my lab3 folder. I then clicked 'create the ArcPad data on this computer' and clicked finish. After a few minutes, the deployment was successful.

The third objective of this lab included using a USB cable to connect the Trimble Juno to my computer. The Juno appeared as a drive in my windows explorer's catalog tree, and I then cut my lab3 folder I had created out of the directory and into the Juno storage card under my professor's directory. I then verified that all components were checked out properly, including the map and all of the layers and features I had previously added.

The fourth objective of this lab was to become familiar with the basics of the Trimble Juno GPS and ArcPad through a demo led by my professor. It included a handout that labeled all of the features and their functions on the Juno. We also went out into the field and went through the different methods used to collect data for points, lines, and polygons.

The fifth objective was to go out in the field and collect point, line, and polygon features using ArcPad on the Trimble Juno GPS. I began by opening my map through the lab3 folder, and activating the GPS, both under the Main Toolbar. I then began editing under the Edit Toolbar by adding a GPS vertex for the "lines" feature at one end of the campus foot bridge. I then walked across the footbridge, and when I reached the end I clicked 'proceed to attribute' which automatically opens the attribute table, and I labeled the feature "Footbridge". I then proceeded to walk around campus collecting "point" data for 3 different trees and 3 different light posts. For each, I used the 'add GPS vertex' button, and labeled each point accordingly (tree; light post) in the attribute table. Finally, I used the 'Add GPS vertices continuously' button to create a "polygon" by walking completely around a grassy area on campus, and clicking 'proceed to attribute' to label the feature. I used this method for 3 different grassy areas. I also used the 'add GPS vertex' button to create a "polygon" around a grassy area by adding a vertex to each corner of the area as I made my way around, and labeling it accordingly in the attribute table. I used this method for 3 different grassy areas as well. All of the collected data appeared almost immediately on the GPS unit that displayed my map. I then made sure to save my map.

The sixth objective was to check the collected data back into ArcGIS from the field. I reconnected the Juno to my computer, and under windows explorer I copied the fuglesmalab3 folder from the Juno and pasted it back in the lab3 folder in my directory. I then opened the lab3.mxd into ArcGIS and used the ArcPad Data Manager toolbar to "get data from ArcPad tool". I then navigated to folder I had copied from the Juno which was an .axf file. In the feature class table, I checked the point, line, and polygon feature classes in, and double checked that the check in was successful. The data I collected then appeared in ArcMap. I then proceeded to build a cartographically pleasing map of my collected data which will be displayed below.

Results

The result of my map shows the different data I collected while out in the field (footbridge, point features, and grassy areas) while the Campus Building features were previously added to the geodatabase in objective one. While viewing the map, some components may seem confusing; such as why are grassy areas overlapping with the campus buildings. Well, this aerial photo was taken previous to our recent campus "face lift" which destroyed our old student center, and created an even greater one in a new location. Also, the old education building was destroyed in order to make room for an even larger education building in it's place. Construction of the UW- Eau Claire campus began in the Summer of 2012, so the aerial photo was taken sometime before this period. The trees are all located on grassy areas while the light posts are situated along the sidewalks on campus.

Sources:
GPS data collected by Marisa Fuglestad
Aerial photo is from the National Air Photography Program (NAIP 201X)
http://help.arcgis.com/en/arcpad/10.0/help/index.html#/Overview_of_ArcPad_toolbars/00s1000000wn000000/

GIS I Lab 1: Base Data

Goal and Background: 

The Eau Claire Confluence project is a public-private partnership to redevelop a part of Downtown, Eau Claire at the confluence of the Eau Claire and Chippewa Rivers. Their vision will be a centerpiece of Eau Claire including the construction of a community arts center, public plaza, and mixed- used project that includes housing, commercial/retail space, and parking. Eau Claire has always embraced and cherished different creative talents ranging from music, art, dance, theatre and much more, and this venue will provide a facility to support and serve that talent for the future.

The goal of this project for Geography 365 GIS I, was to become familiar with various spatial data sets used in public land management, administration, and land use and, in this case, to prepare base maps for the Eau Claire, Wisconsin Confluence project set to begin in 2013 and end in 2016. I used ArcGIS to produce base maps of the projected site area, as well as include different features pertaining to each map. The different types of maps include Civil Divisions, PLSS features, Census Boundaries, EC City Parcel data, Zoning, and Voting Districts.

Some specific objectives of this project, besides those stated above, was to learn about the Public Land Survey System, create a brief legal description of the two parcels (projected site area) and generate a basic report describing the site, and build a layout with each of the major thematic feature classes.

Method

The first objective was to become familiar with various data sets for the City and County of Eau Claire. I obtained information through two different sources titled 2009_07_13_Eau Claire geodatabase and City of Eau Claire geodatabases. The first included different datasets such as Census Features, Development, Environmental, Parcel Features, PLSS, Political Features, and Transportation with each containing different feature classes. The latter included feature classes labeled Centerlines (streets), City Limits Area, Parcel Area, PLSS qq (quarter-quarter), PLSS Sections, PLSS townships, Private Streets, Railroad Lines, Right of Ways, Voting Wards, Water, and Zoning Areas. Using ArcCatalog, I viewed many of these feature sets and read their descriptions in order to obtain specific information pertaining to this lab.

The second objective was to digitize the site for the proposed Confluence Project. I began by creating a geodatabase in ArcCatalog labeled EC_Confluence and adding a feature class name pro_site, standing for proposed site. I used the Census_Features dataset, specifically BlockGroups, to import the parameters of the Eau Claire County Coordinate System. I then began a new map in ArcMap 10.1.

I used World Imagery as my base map and added the pro_site feature class right away. I then zoomed to the area of the Confluence Project and added the Parcel Area feature class to the data frame. I used the Identify Tool to locate the two parcels that had been purchased by UWEC; 128 Graham Avenue and 202 Eau Claire Street. I then began editing the proposed site feature class from the editing toolbar with the Polygon tool.

The third objective was to learn about the Public Land Survey System. My professor supplied a PDF for us titled "The Public Land Survey System" which assisted in the following objective. I began by adding a new data frame to my map and World Imagery as my base map. I added the PLSS_Townships from both geodatabases, and the PLSS_Sections feature class from both geodatabases. I chose stretched color schemes for them to look for patterns. I then added the PLSS_Quarter_Quarter_sections and PLSS_qq from the two geodatabases. I used the attributes table to build the description of the proposed Confluence Site as digitized.

The fourth objective was to create a brief legal description of the proposed site. I used the Identify tool to find the Parcel ID for 128 Graham Avenue and 202 Eau Claire Street. I used the City of Eau Claire's Property Assessment Search Website to obtain the legal property descriptions.

The fifth objection was to build a map of all relevant base data for the Confluence Project. I began by building a data frame of the Civil Divisions as a locator map. I added the country boundary of EC and set the symbology so as to see the different divisions and the aerial behind it. I then built a data frame of Census Boundaries by adding the BlockGroups feature class and Tracts Group and zooming into the downtown around the Confluence Project. I also included the data frame of the PLSS. I then built a data frame of parcels and associated data for the City of Eau Claire. I added Parcel_area, Centerlines, and Water feature classes from the City of Eau Claire geodatabase. I then built a data frame of Zoning by adding the zoning _areas feature class. I symbolized unique values based on zoning_cla (zoning class). I grouped values together based on their zoning code. Lastly, I built a data frame for Voting Districts by adding the voting districts class for the city. I labeled the voting districts by their ward number. I then displayed all 6 maps onto a layout and added a title, legend, scale bar, and north arrow.

Results

(Below: Final map of all relevant base data for the Confluence Project; ArcMap 10.1)

Above is the map I constructed by using the method discussed above. The proposed site is in the center of Eau Claire City at the confluence of the Eau Claire River (directly above the proposed site) and the Chippewa River (to the left of the proposed site) (See Civil Divisions map) . It is located in a Commercial zone which will be ideal for goals and visions of this project since it is the site of many local businesses. It is also surrounded by many residential areas which will bring many families and people to the new Confluence site (See Zoning Districts map).

Below is the legal descriptions for each of the parcels of interest. Which each description is an aerial photo with the parcels shown in red, and the parcel of interest highlighted in blue.

LOCATION OF SITE IN PUBLIC LAND SURVEY SYSTEM:

I. PARCEL 1

Parcel Number :  02-0365

PIN: 1822122709200042068

Street Number : 128

Street Name : Graham Avenue

Owner's Name : Haymarket Concepts LLC

Owner's Address : 3506 Oakwood Mall Dr

Owner's City, State, Zip: Eau Claire, WI, 54701

Legal Descriptions: LOTS 1-2-3-4-5-6-7-8 BLK 62 & THE 26 FT W OF E 84 FT OF LOTS 9 & 10 & EX E 140 FT ALL OF LOTS 9 & 10 BLK 62 VILLAGE OF EC ADD TID 8

II. PARCEL 2

Parcel Number : 02-0357

PIN: 822122709200042063

Street Number : 202

Street Name : Eau Claire St

Owner's Name : Haymarket Concepts LLC

Owner's Address : 3506 Oakwood Mall Dr

Owner's City, State, Zip: Eau Claire, WI, 54701

Legal Descriptions: LOT 1 AND W ½ OF LOT 2 BLK 58 AND A PC OF LAND BEG AT SW COR OF SAID BLK 58 THC W ON N LN OF EC ST 45 FT THC N PRLL WITH W LN OF SAID BLK 145 FT THC E PRLL WITH N LN OF SAID BLK 106 ½ FT THC SLY PRLL WITH W LN OF SAID BLK TO N LN OF LOT 2 OF SAID BLK THC WLY ON THE N LN OF SAID BLK TO N W COR OF SAME OR LOT 1 BLK 58 THC SLY ON W LN OF SAID LOT 1 TO POB AND ALSO THE LAND BETW THE ABOVE DES LAND AND EC RIVER VILLAGE OF EC TID 8

Sources

http://www.eauclairearts.com/confluence/
http://www.uwec.edu/News/more/confluenceprojectFAQs.htm
http://volumeone.org/news/1/posts/2012/05/15/3134_arts_center
http://www.sco.wisc.edu/plss/legal-descriptions.html
http://www.bis-net.net/cityofeauclaire/search.cfm