Final Term Project~~Tick Research Study Areas

Priority Areas of Study for Deer Tick Research

By: Andrew Evenson

 

Goals and Background: The goal of this mini term project was to be able to utilize all the knowledge and skills learned and used throughout this course, and then combining an area of interest and creating and answering a spatial question out of it.

The specific goal/question of my project was involving Deer tick densities and the prevalence of Lyme's disease amongst those ticks for my summer research with the UWEC Biology department in hand with the Eau Claire County Health Board. On top of I then found public lands that could be used for research, but then came the problem of there being so much land to study, what tracts should I study? So to answer that I came up with 3 ranks of 'High', 'Medium' and 'Low' priority areas based upon spatial and landscape criteria upon 1.) vegetation cover, 2.) distance from schools and major roads, and 3.) distance from the city of Eau Claire, WI. Then ranked into 3 categories: High, Medium, and Low. To earn a ‘High’ ranking a land must have vegetation cover >7, <1mi. from a school/major road AND <15 miles from Eau Claire. To earn a ‘Medium’ ranking a land must have vegetation cover>7, <1 mi. from a school/major road OR <15miles from Eau Claire. Finally to earn a ‘Low’ ranking a land must have vegetation cover<7, >1mi from a school/major road, but within Eau Claire County. Vegetation was selected to be >7 because we predict that more vegetation cover would relate to more Deer ticks. That will help prioritize areas to study, and help avoid testing areas that may be the same trying to accomplish as much diversity as possible. But also to gain spatial knowledge about where these Deer ticks are distributed and if Lyme's disease varies across the county, and finally to answer some environmental aspect questions like the effects of plant height/diversity on these Deer ticks.

Methods: I obtained data

through the UWEC Geography Department’s database and collected the Future Land Use layer to obtain parks and recreation land, however this did not fulfill all the public land that I wanted to include. I then found through the WI DNR geodatabase the county forests layer file that filled the last holes for land that I wanted to rank. I then needed the Eau Claire county boundary and some features classes of Eau Claire county, so I turned to ArcGIS data that was provided from the textbook and was able to add in counties, major roads and cities. I then selected by attributes and intersected them to get just those features within Eau Claire county. Finally I wanted to look at proximity to schools in the county, but no data was provided within the departmental data or mgis data, so I had to turn to ArcGIS Online, to which I found exactly Eau Claire County schools. I finally took all the new layer files and intersected them with Study_Areas layer and then with the aforementioned criteria I began to create layer and assign ranks of the public lands in Eau Claire County, giving me the desired results for my question (Figure 1 and Figure 2).

Figure 1: Data Flow Model for the project illustrating the geoprocessing tools and queries used to obtain the final results.




Figure 2: Second Data flow model illustrating the final steps to get the final ranked areas for research use.

 

Results: The results showed that even if the areas were within the 15 mile buffer of Eau Claire or a road that it didn't result in a 'High' ranking because it didn't meet the vegetation criteria (Figure 3). Also the map shows the fragmentation of the land with respect to vegetation cover that creates pockets of 'Medium' ranking land within a block of 'Low' priority land. Lowe's Creek County Park was the largest 'High' priority area given its vegetation cover, proximity to Eau Claire, schools and major roads, which should make it a top five site to look at this summer.

This map will be very critical for site selection, and after data is collected could be changed into an informational map for the public on areas to avoid, or to spread the word on prevention and early detection of Lyme's disease.

Figure 3: Map depicting public use lands of Eau Claire County, Wisconsin ranked based upon certain criteria for the research of Lyme's disease and Deer tick prevalence in the county.

 

 

 

References

1. ArcMap 10.2.2.(2014) [Software]. ESRI Inc., Redlands, CA. [accessed 5/2015].

2. Price, Maribeth. 2014. Mastering ArcGIS. 6th Edition data CD. McGraw Hill.

3. Wisconsin DNR. [Download]. Uncredited data. [accessed online 5/7/2015]

4. University of Wisconsin-Eau Claire, Geography Department geodatabase, [Download]. ArcGIS Online, ArcMap 10.2.2 (2014). [accessed 5/10-14/2015].

5. University of Wisconsin-Eau Claire, Geography Department geodatabase, [Server Access]. [accessed         5/10-14/2015].

 





Lab 5

Goals/Background: The goals of this lab were to efficiently utilize the geoprocessing tools ArcGIS for vector data. Specifically looking at suitable bear management study areas in Marquette county, Michigan, as well as looking at good tourist resort lakes, and interstate pollution hazard zones in Wisconsin. For these three main task many of the common geoprocessing tools were employed (merge, intersect, buffer etc...). We also utilized python scripting, as another option to use these geoprocessing tools. Finally after the maps were created we also had to create a data flow model to depict the steps that we took to reach the final maps in a simple fashion.

Methods: Part 1(Figure 1) entailed finding suitable areas for bear management in Marquette county in Michigan. First the bear locations had to be added from an excel sheet by mapping the X-Y values, then merged with the landcover feature class to create the bear_cover feature class, merging both feature classes together. After the streams were found to be suitable habitat for the bears, so to include this in the management plan a 500 meter buffer was created around the streams, which then was dissolved to delete any internal boundaries. The dissolved layer underwent a select by location with the bear_cover as the target layer, then create layer from selected features resulting in the streams_buffer feature class with the bear locations found within the 500 meter buffer. Landcover then underwent a select by attributes/create layer from selected features to create the landcover_selection feature class, which only included the top three minor_type landcovers for bears. Landcover_selection was intersected after with the Streams_Buffer feature class to create the Suitable_Habitat feature class, which was dissolved to make the final Suitable_Habitat_Dissolve feature class that had the bear locations and suitable habitats that fell within the stream buffer. The DNR_mgmt feature class then had to be intersected with the Suitable_Habitat_Dissolve layer to bring in the rest of the state/data creating the Suitable_Study_dnr class, which was dissolved, to make the Dissolve_Suitable_Final layer. To then eliminate the urban areas from the study/management areas the landcover feature class had to again undergo a select by attributes creating Urban_Areas class, then a buffer around to create Urban_Buffer. Finally the Urban_Buffer and Dissolve_Suitable_Final classes were clipped together to exclude the urban buffer areas from the management area creating the final map product (Figure 2).

Part 2 (Figure 3) was broken up into two parts, the first part was to create a map that showed the lakes around Wisconsin cities that would be good for tourist looking to resort on lakes in Wisconsin. The first part included the buffering of the cities feature class that would pick the lakes that were close to cities, but the lakes as well had to be selected by their attributes to be bigger than 5sq.miles and a layer created from those selected features called Lake_ResortAE. After the two resulting class (WI_cities_buffered, Lake_ResortAE) were created they then were clipped to excise the leftover lakes not found within the buffer of the cities resulting in the final feature class: lakes_resort (Figure 4). The final part to Part 2 (Figure 3) had us creating a multiple ringed buffer around the interstates of Wisconsin, then creating 6 zones of Hazard levels from the multiple rings in the buffer (Figure 5). Python Scripting was used for all of Part 2 instead of the normal ArcToolbox or SQL expression query windows (Figure 6).


Results:


Figure 1: A data flow model showing the steps taken and geoprocessing tools used to create the final map.





Figure 2: Map depicting suitable habitats for bears in the study area in Marquette County, Wisconsin.





Figure 3: Data flow models for both scenarios in Part 2 of Lab 5, showing the utilization of the geoprocessing tools.

Figure 4: Map showing the lakes that would be ideal for tourists looking to resort in Wisconsin.



Figure 5: Map displaying the air pollution hazard zones for Wisconsin Interstates.







Figure 6: Example of Python Scripting used for Part 2.

Sources:

1. ArcMap 10.2.2.(2014) [Software]. ESRI Inc., Redlands, CA. [accessed 4/20,23/2015].

 

2. Michigan Department of Natural Resources (DNR).

 

3. Price, Maribeth. 2014. Mastering ArcGIS. 6th Edition data CD. McGraw Hill.

 

4.  Wilson, Cyril 2012, A comprehensive Lake features for

Wisconsin, Unpublished data. [accessed 4/21/15,4/23/15].





Lab 4

Multiple Criteria Queries

Lab #4

Andrew Evenson

4/3/15

 

 

Introduction: The goal of this lab was to utilize queries and Boolean expressions, which we have been covering in class, and then adding on the concept of multiple criteria queries. Learning this skill is very useful for selecting features with minimal amount of work/steps and Boolean expressions, making us more efficient queries. Then to build on the multiple criteria aspect we worked with spatial and attribute queries during this process, as well as mapping the results of the queries we performed onto maps that already had other data.

 

Methods/Results: The first query involved finding counties with a specific population range and population density per square mile (Figure 1). To do this I first set up the population ranges with the 'AND' expression (to make the results only from that range), and then added the population density with the 'OR' expression.

 



Figure 1: A query box showing the SQL and Boolean expressions used for the multiple criteria query.
 

Then after the query results appeared a layer was created from the selected features from the counties shapefile, after which the answers were obtained from the resulting tables, resulting in the first map (Figure 2)


Figure 2: A map of the United States depicting the counties that met the multiple criteria query.

The second query dealt with selecting multiple states and characteristics based upon them (Figure 3). Which required the use of 'IN' as the operator, then adding the 'AND' operator to finish up the rest of the query.


Figure 3: Query window showing the necessary language to extract the desired information.



After which, I repeated the same steps as the first query of creating a layer from the selected features, resulting in a map of the desired attributes. (Figure 4).

Figure 4: Map of the United States with counties highlighted that matched the multiple criteria query.

The third query was and addendum to the query for question 2, which added in 5 states. This was achieved by taking the original expression from question 2 and adding the 'OR' expression to include the new states and criteria for the query (Figure 5).

Figure 5: Added criteria from the previous query.

After, when creating another new layer, the number of  counties that fulfill the new criteria can be acquired; and a new map of the new results (Figure 6).

Figure 6: Map of the United States showing the additional counties that met the new criteria, which was built on from the previous question.

The fourth query involved the combination of spatial and attribute queries. First to get the correct cities that are within 2 miles of a lake I had to perform a spatial query, setting cities as the target layer. After which the attribute query can be performed to get the final results (Figure 7).

Figure 7: Attribute query performed after the spatial query for question #4.

 After the query was performed a map was again created using the same techniques as described in the prior queries (Figure 8).

Figure 8: Map showing cities that are within 2 miles of a lake.

 

The final query was finding rivers that matched certain criteria (names) to do this the same 'IN' expression was used (Figure 9), just like previous questions dealing with multiple states, after which a map was created showing the rivers that matched the criteria (Figure 10).

Figure 9: SQL of the multiple criteria query.

Figure 10: Map of the multiple criteria results.

 



 

Conclusion: This lab helped employ queries, specifically multiple criteria queries, in an efficient manner for further use, as well the combination of using attribute and spatial queries together to obtain the desired selected features of the data.



Sources
1. Mastering ArcGIS (2014) [Book]. Price, Maribeth. McGraw Hill, New York, NY.
2.  ArcMap 10.2.2.(2014) [Software]. ESRI Inc., Redlands, CA. [accessed 4/2/2015].

Lab #3 Blog

Lab #3

Goals: The goals of this lab were for the use of obtaining GIS data and standalone tables from other mediums (US Census Bureau) than the mgisdata already downloaded from ArcGIS. Then with that data being able to join them with other tables, and being able to manipulate data, excel sheets, to make them joinable and readable by the software. Specifically the goal was to find the total population by county in Wisconsin, then to find a variable of our own choosing to map as well (Total households per county).

Methods:To start this lab I had to obtain census data from 2010 specifically in Wisconsin, so to do that I had to do an advanced search through the American Factfinder website that the US Census Bureau operates. Starting with topics and choosing the year, then to geographies to pick specifically counties, then Wisconsin and finally I downloaded the zipfile. After which I opened the shapefile of Wisconsin and its' counties, but the county population data was not joined yet from the excel sheet. So I had to manipulate the data sheet so ArcGIS would read it as a number file not a string file, so it could be joined to the shapefile and mapped onto the layer. Once properly formatted and joined I opened up the data's properties/symbology to map the total population per county, then I classified the breaks by quantiles. After that I copied the Wisconsin county map to a new data frame to map the other variable (Total households per county). To get that data I had to again go to the American Factfinder website that the US Census Bureau and repeat the process except I downloaded the total household file. Again I had to reformat the excel sheets so they could be read as number files and able to be joined and mapped on ArcGIS. I then repeated the process in the symbology tab to map the total household data by quantities per county in Wisconsin, again by quantile breaks again. I then formatted the maps in the layout view to exported.


Results: 


Figure 1: Maps showing the total number of households per county in Wisconsin, and the total population per county in Wisconsin.

 

The results show that some counties in Wisconsin that are in the highest quantile for total population are not in the highest quantile for total number of households per county, which might suggest that some counties may have larger families.

 

Sources:

 

 

1. Price, Maribeth (2014). Mastering ArcGIS 6th ed. Retrieved 3/11/15.

2. US Census Bureau (2010). 2010 SF1 100% Data.                                                                                                                                                http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml. Retrieved on 3/11/15

3. US Census Bureau (2010). 2010 SF1 100% Data Households and Families: 2010,                                                    http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.                                                                                   Retrieved on 3/12/15

 

 

Lab 1

LAB 1:
Andrew Evenson
Geog335 GIS1
University of Wisconsin- Eau Claire.

Goals: The of this lab were to organize a collection of maps, as well as defining coordinate systems for shapefiles in different geodatabases to match each other so the maps will be proper and with minimal distortion. Finally to also learn some basic essentials of the data management tools in ArcMap.

 Figure 1: A map of West Central Wisconsin with counties and major rivers in the area.

Methods: I Obtained this map by first downloading the data from D2L (1). Then once downloaded I extracted and added the data into ArcMap (2) into a data frame. I connected two shapefiles from the Central Wisconsin geodatabase that was downloaded on D2L (1) and added them to the layer on ArcMap. However the shapefiles that were added (rivers and county lines) were not projected the same, or had the same GCS, so I then opened up the data management tools in ArcMap and formatted them all into the same GCS (NAD_1983) and same PCS (North American Equidistant Conic). I chose those coordinate systems because of the location for the GCS, and since some of the counties go farther out east and west I chose a conic projection. Then once everything was formatted properly I changed the view from data to layout view and inserted text boxes for the counties and legend, a scale bar in miles, and as well as a North arrow (ESRI North Arrow 3) into the top right corner for reference. Finally I chose colors for the different layers that would make the map easy to read.

Figure 2: Maps of the World's countries, the USA, and Wisconsin in different projections.

Methods: This map was made by first downloading the data from D2L (1). The world maps were made all in different data frames, the Geographic projection was made by connected the country shapfile from the downloaded data and the geogrid shapefile. The Mollyweide Projection map was obtained like the Geographic, but the projection was then changed for the data frame to 'Mollyweide'. The Mercator Projection was also started like the Geographic in a different data frame, then the projection for the data frame was changed to Mercator (World). The Sinusoidal was again started off like the other world maps, but the projection was changed to Sinusoidal (World). The final world map's (Equidistant Conic Projection) data frame was changed to Equidistant Conic. The 'States' map shapefiles came from a different folder (states.shp and a roads shapefile). The road shapefile was though not in the same coordinate system. I then used the project tool to change the projection to North American Equidistant Conic; the road shapefile then was in its proper location. Finally I changed the color of the reformatted roads layer to green to stand out more. The Wisconsin UTM map was obtained by repeating the first part in the 'States' map, then using the select attribute tool I chose Wisconsin and added it to its on layer, then turning off the states layer leaving Wisconsin. I then changed the projection to North American UTM Zone 16N.
Finally for both the separate maps I exported the map files as a .jpg onto the Q-drive.

Final products were also under the guidance of Mastering ArcGIS textbook (3)

Citations

 

 

1. ArcGIS 10.2.2 for Desktop, Version: 10.2.2.3552, 1999, ESRI Inc.

 

 

2. Wilson Cyril, in class materials at University Of Wisconsin- Eau Claire (2/15/2015), retrieved by instructor's permission.

 

 

3. Price Maribeth, (2014), Mastering ArcGIS sixth edition.