Final Project

Goals and Background

The goal of this project is to use the geoprocessing skills I have acquired from tutorials and lectures to answer a spatial question of my choosing. The study area that is the focus of this project is Eau Claire County which is located in the state of Wisconsin. The question I have proposed for this project is where are suitable locations for deer ticks to survive within Eau Claire County, with restrictions to certain vegetation types and locations of forests. Deer ticks primarily thrive in wooded areas and areas of low-lying vegetation. This eliminates any areas that do not meet those criteria, including urban areas and along roads, where I have set a perimeter of 500 meters from any major road. The intended audience for this map is anyone who lives in Eau Claire County, this information could be used by the Wisconsin DNR and is important because deer ticks are the only known possible carriers of Lyme Disease and knowing the areas in which deer ticks can survive is helpful in preventing people from contracting Lyme Disease.

Methodology

I used data from a few different places. The first being the WI DNR dataset made available to me through the UW-Eau Claire Geography Department. The data I got from this dataset is the Eau Claire County outline, vegetation cover and county forest feature classes and the major roads feature class. The second place I obtained data from is the Geospatial Data Gateway through their website at http://datagateway.nrcs.usda.gov/GDOrder.apsx, from this website I obtained the urban areas feature class. The final source for my data came from accessing ArcGIS online. After a brief search for Wisconsin water bodies, I found a suitable layer that I clipped to fit to Eau Claire County. With my data being from multiple different sources a concern I have for my data is that not all of the data corresponds with each other in terms of accuracy in location. For example, the urban areas feature class may be in a different location if it were from the same dataset as the major roads feature class. In a way this reflects my concern with using data from multiple different sources.

To create the map for suitable deer tick habitats I started by connecting to the WIDNR2014 geodatabase. From this database I obtained the Wisconsin counties feature class, the Wisconsin major roads feature class, the Wisconsin county forests feature class and the Wisconsin original vegetation cover feature class. In order to be able to see the county boundaries beneath the other layers, I changed the transparency of the vegetation cover feature class to 50% for the time being. My area of interest was Eau Claire County, so I used the select by attributes tool to highlight Eau Claire, the query I used was COUNTY_NAM = ‘Eau Claire’, then I simply created a layer from the selected features and named it “Eau_Claire_Co”. I also needed to select the appropriate vegetation that deer ticks could live in; since deer ticks thrive primarily in wooded areas and low-lying vegetation, I needed to find areas where low-lying vegetation were in Eau Claire County. I searched Google to find information on the vegetation cover feature class I was using in my map and found the values that corresponded with low-lying vegetation to be scrub, prairie and brush. The values that represent these three vegetation types are 6, 12, and 13. So I again used the select by attributes tool to highlight these attributes. The query I used was VEG_TYPE = ‘6’ OR VEG_TYPE = ‘12’ OR VEG_TYPE = ‘13’. I also created a new layer from these selected features to use as the vegetation cover on my map and named it “DeerTickVeg”.

With my study area being Eau Claire County, it was obvious I needed to narrow down each feature class I was using so that each was contained within the borders of the Eau Claire County feature class. The first few feature classes I clipped were the major road feature class, the DeerTickVeg feature class, and the county forest feature class. Now I had all my feature classes contained within my area of interest, but I figured I needed more for this project than I had. So I downloaded data from Geospatial Data Gateway at their website http://datagateway.nrcs.usda.gov/GDGOrder.aspx, selected my state as Wisconsin and the county as Eau Claire county, and found that the Urban Areas of Wisconsin dataset was something that could be included in my map. I then downloaded the data and added the urban areas feature class to my map. However, the urban areas feature class was not specific to Eau Claire county, so I used the clip tool again to make it specific to only Eau Claire county. I also added data from ArcGIS online. This data was the Topper_Wisconsin_Water_bodies_Service layer and I added it to my map and clipped it to Eau Claire county to get the water bodies that are contained in my study area.

Deer ticks can live in both wooded areas and areas of low-lying vegetation. This meant that I needed to combine the DeerTickVeg feature class and the county forests feature class, and to do so I used the union tool in order to keep all features of both feature classes, not just what was common to both. I named the new feature “deertick_forestveg”. Also, in order to get rid of any boundaries within the feature class, I used the dissolve tool to make it one complete, smooth feature. This indicates on my map that deer ticks can survive in either wooded areas or areas of low-lying vegetation. Next I used the clip tool on the major roads feature class to get major roads contained in the deertick_forestveg feature class. I did this because the roads within the deertick_forestveg feature class represent an unsuitable area for any deer tick habitat. I named the new feature class “deertick_majroads”, then used the buffer tool and gave it a boundary of 500 meters and named the feature class that came from the tool “DT_MRbuff”. Then to get rid of any unwanted boundaries within the buffered feature class I used the dissolve tool and named the feature class that came from it “DT_MRdissolve”. The feature class had buffers that extended outside the Eau Claire county border so to get rid of those I clipped the DT_MRdissolve feature class and named the new feature class “DT_MRclip”. Then I intersected this feature class with the deertick_forestveg feature class. The final step in completing my map was to eliminate any unsuitable areas from the urban area in Eau Claire county. This involved the using the erase tool to get rid of any parts of the DT_MRclip feature class that were contained in the urban areas feature class, and the dissolve tool to get rid of any unwanted boundaries.

One extra step I took in creating a map of potential deer tick habitats was projecting the map in a projection I deemed appropriate for my study area. Eau Claire county is within the central zone of the state of Wisconsin, so the projection I chose for my map was NAD_1983_StatePlane_ Wisconsin_Central_FIPS_4802 and I applied this projection to each layer that was on my map.

Figure 1: Final Map

Figure 2: Work Flow Model

Results

The results of all of the data and tools used to complete my project is a map that shows potential areas suitable for deer tick habitat shown in green, areas that are not suited for deer tick to survive along the major roads in Eau Claire county, shown in orange and urban areas in Eau Claire county that would also be unsuitable for deer tick habitat shown in a salmon pink color. Streams, shown in blue lines on the map as well as Eau Claire county, shown in a light tan color serve as a backdrop to the features on the map. In layout view I created two other data frames to go along with the one containing my finished map (Figure 1). The second data frame contains a locator map that shows where my study area is located in the state of Wisconsin (Figure 3). The third data frame contains the data flow model I generated in ArcMap that shows the steps I took to complete my project. (Figure 2). The final product with all the data frames combined is shown in Figure 4 below.

Figure 3: Locator Map

Figure 4: Final Map Layout

GIS Lab 5

Lab 5 Technical Report

Goals and Background:
The goal of this lab is to enable me to select and apply geoprocessing tools discussed in lecture and practiced in tutorials in order to find suitable bear habitats in a study area in Marquette County, Michigan. This lab will also be an introduction to using geoprocessing tools through python scripting

Methodology:

Part One

Objective One:



To begin, I downloaded the data for Lab 5 from my instructor and extracted the zipped files into my lab 5 folder. The data was provided by the Michigan Department of Natural Resources and Esri. In a blank map on ArcGIS I set my workspaces to my lab 5 folder as should be done in starting any new project and connected to my lab 5 folder in the catalog window. Under part one of my lab 5 data I expanded the bear_locations_geo.xls excel file and found the bear_locations_geo$ sheet. In the next step I was to create a feature class from this sheet, to do so I right-clicked on the bear_locations_geo$ sheet and chose "create a feature class from XY table". This opened up a new window in which I set the X field to "POINT_X" and the Y field to "POINT_Y". The new feature class would need to be set to the same coordinate system as the rest of the data in the Marquette geodatabase that came with the dataset. To set my new feature class to the correct coordinate system I first clicked on the button underneath the Z field, "Coordinate System of Input Coordinates". This opened up a new window where I chose the import option under the "add coordinate system" drop-down menu. From there I navigated to the Marquette geodatabase and chose any one of the feature classes contained to import its coordinate system. The last step was to save the new feature class as a shapefile to the Marquette geodatabase under the name "bear_locations".

Objective Two:

To start objective two I needed to add all of the feature classes from the "bear_management_area" feature dataset, including the bear_locations feature class. Then I needed to create a map showing the minor type field of the landcover feature class. In order to create this map I needed to change the symbology of the landcover feature class. I right-clicked on the landcover layer in the Table of Contents and chose "Properties", clicked on the "symbology" tab and chose "Unique values" under the categories section. Next I set the value field to "MINOR_TYPE", added all values, and chose an appropriate color ramp for the map, then clicked OK. Next I intersected the bear_locations feature class with the landcover feature class. This created a new feature class named "bear_cover". Finally I opened the Select By Attributes Table and found the top three habitat types in which bears could be found by selecting the layer as "bear_cover", and using the "MINOR_TYPE" field to get its unique values to find the top three habitats.

Selection of the top 3 Bear Habitats

Unique values map of the MINOR_TYPE field in the landcover data set.

Objective Three:

This section of part one involved finding the number of bears within 500 meters of a stream. To find the correct number of bears I used the buffer tool. Under the Geoprocessing menu in the toolbar, I chose the Buffer option. The Buffer window opened and I chose "streams" as the input feature and saved the output feature class in my lab 5 folder as "bearstreambuffer". Since objective three asked for the number of bears within 500 meters of a stream, the Distance value needed to be set to 500 meters. Everything else was left as its default and I clicked OK to create the new feature class. Then to clean up any overlapping boundaries between the buffers, I used the Dissolve tool which is located in the same place as the Buffer tool. Once the Dissolve tool opened I chose the input feature class as my "bearstreambuffer" feature class and saved it to my lab 5 folder. I left everything else alone and clicked OK to start the dissolve.

Objective 4:

Objective 4 asked to find suitable areas for bear habitats based on the two criteria of my choice, in this case being the dissolved bearstreambuffer feature class that showed the number of bears within 500 meters of a stream, and the top three habitats that I found in Objective Two from the MINOR_TYPE field. To isolate the top three habitats from the rest, I used the select by attributes table to create a query that highlighted the three desired habitats in the landcover feature class. From there I created a new layer from the selected features by right-clicking the landcover feature class in the Table of Contents and under the selection tab, choosing the "create layer from selected features" option. This created a new feature class that I named "suitable_habitats". The next step was to combine the two criteria and therefore their feature classes. To do so I used the intersect

tool, set the input features to "suitable_habitats" and "bearstreambuffer_Dissolve", saved the new feature class to my lab 5 folder and clicked Ok to perform the intersect. I named the created feature class "bear_habitats". Finally, to eliminate any boundaries within the bear_habitats feature class, I used the dissolve tool and created a new feature class "bearhabitats_final".

Objective 5:

The task for Objective 5 was to locate all suitable bear habitats within DNR management land. First I added the "dnr_mgmt." feature class from the Marquette geodatabase. From there, the task took only two steps, the first being to intersect the dnr_mgmt. feature class with the bearhabitats_final feature class. This gave me a new feature class that I named "dnr_bearhabitats" and saved it in my lab 5 folder. The final step was to dissolve the boundaries within the new feature class, and that was simply opening the dissolve tool and selecting the dnr_bearhabitats feature class as the input feature. A new feature class was created and saved to my lab 5 folder and I named it "dnr_bearfinal".

Dissolve tool to create the 'dnr_bearfinal' feature class

Objective 6:

Objective 6 is a second part to objective 5. for this objective I needed to exclude all areas for bear habitats that are within 5 kilometers of Urban or Built Up land. I began by finding where any Urban and Built Up land was by using the select by attributes tool and the 'Major Type' field in the landcover data set. After highlighting all of the Urban and Built up land on the map, I created a new layer from the selected features and used the buffer tool on the new feature class to create a buffer that was 5 kilometers. Finally I used the erase tool on the new buffered feature class to get the final version of the bear habitat in the study area.

Buffer tool to with distance set to 5 kilometers



Erase tool that created the final bear habitat feature class.

Objective 7:

To complete part one of my Lab 5 assignment I had to create a cartographically pleasing map displaying my final product. This map needed to include the locations of the bears, streams, the suitable habitat I created for the bears, the results from objective 6, a legend, a locator map that showed where in Marquette County the study was done and a workflow model that shows the steps used to create the final product. To start off I needed to create the map in an 11X17 frame. Then I added two other data frames for the locator map and for the workflow model. The only data frame that was filled at the time was the first data frame with my finished map. To add in the legend I went to the insert tab at the top of the page and chose the legend option from the dropdown menu. I selected the appropriate items to include and made sure to change the name of each item to an appropriate title. I then added the locator map and that was simply adding the Marquette County shapefile to one of the empty dataframes and then adding the study area shapefile over it in a different color. The next part was generating the workflow model for the third data frame. To do this I changed from layout view to data view so I could make any necessary changes without affecting the layout of my map. I clicked on the Model Builder button on the main toolbar and this opened a new window for me to start building my model. Before I began building however, I saved the model to my lab 5 folder in a new toolbox by clicking on the New Toolbox button in the Save window. I named my model and saved it in that toolbox. In the blank Model Builder window I first added a new feature layer by choosing ArcToolbox, Data Management Tools, Layer and Table Views, and Make a Feature Layer then dragging the Make a Feature Layer tool into the empty window. I set the input feature as landcover and the output feature as Top 3 Bear Habitats. I then added the Buffer tool by doing the same process with the previous tool, by dragging the buffer tool from Arctoolbox into the Model Builder window. I set the input feature to Streams and the output feature as Streams Buffer. Then from the Streams Buffer feature I added a new feature layer and named the output Bears Near a Stream. Next I added the intersect tool and set the input features to Top 3 Bear Habitats and Bears Near a Stream, and the output feature was named Bear Habitats. Finally I used the Dissolve tool, with Bear Habitats as my input and Bear Habitats (2) as my final output.  

Work flow model created for Lab 5

Results: The final result of all of the objectives is a map of a study area in Marquette County that shows bear locations, bear habitats, DNR management areas and streams. The map also features a legend with the symbols of these features displayed, a locator map of where this study area was in Marquette County and finally, the map shows the workflow model I created symbolizing the steps put into creating the final bear habitat feature class.

Final product of Lab 5 part one.

Part Two

Modeling air pollution impact zones

This section created a scenario in which I needed to develop an index model that will show zones of potential air quality problems at a one mile interval in the state of Wisconsin. To do so I needed to use python scripting. To start, I launched the python window by clicking the python window button in the main toolbar. This opened a small window that would later contain the code needed to create the correct map for this scenario. The beginning of the code starts with importing python for ArcGIS and that is done by typing "import arcpy" into the python window. Pressing enter starts another line of code, which will be used in the creation of the map. In this part of the code I called the multiple ring buffer tool from ArcGIS by entering "arcpy.MultipleRingBuffer_analysis". The next part of the code was adding in the input feature. I entered the input feature as "Interstates" followed by a comma. Next was to name my output feature that would result from using the multiple ring buffer tool on the input feature. In the same parentheses as the input feature I named the output feature "Inter_mul_Buff_RL" followed by a comma. Next I needed to enter the intervals for the distance units. Since the scenario called for 6 zones to be created, numbers 1 through 6 needed to be entered. Still in the same parentheses as the other two parts of the code, I entered [1,2,3,4,5,6,] as the intervals followed by a comma. After, I specified the distance unit and entered it as "Miles", followed by a comma, a pair of quotation marks and another comma as the optional parameters that take on a default value. Finally to finish the code I called the dissolve ALL function by entering "ALL" as the final part of the code and closed it off with the ending parenthesis. I then checked for any errors in the code by pressing F2, found that there were no errors and pressed enter to run the code. Lastly I created a map of the results with Wisconsin counties and cities as a backdrop and the interstate results as the main part of the map.

Results:
The results of this scenario is a model of six zones of potential air quality problems along interstates in Wisconsin. I used python scripting to call geoprocessing tools and features that created a cartographically pleasing map of the scenario required. Both the script for the scenario and the results of the script are shown below.

Python script code for creating the scenario

Final result is the state of Wisconsin, with its counties, cities and interstates, along with the zones of potential air quality problems along those interstates.



GIS 1 Lab 4

Goals and Background:

The goal of this lab was to use the skills I learned from lectures and tutorials in class to develop queries that returned the desired results in the instructions and assess my knowledge of attribute queries and spatial queries. A combination of Boolean expressions, operators and parentheses were used to develop the correct multiple criteria queries that the instructions requested.

Part 1

Query 1

Methodology:

Part one focused on the United States for building multiple criteria queries. From a blank map in ArcMap I added the counties shapefile from the USA geodatabase in my mgisdata folder. To begin building my first query I opened the selection drop-down menu from ArcMap and chose the 'Select by Attributes' tool. This opened a new window in which I chose my layer to select attributes from, in this case counties. The method of choosing the attributes remained at its default 'Create a new selection'. The next step was to begin building query 1 and to do so I first added parentheses to my query to separate one part from the other, then I scrolled through my options until I found the expression that represented the 2010 population 'POP2010'. Double clicking on the expression added it to the query box. In order to get a result that returned a population between 3000 and 4000 I added a greater than or equal to sign to include 3000, then I added the AND operator to include all attributes between the two numeric values: 3000 and 4000 and selected 'POP2010', a less than or equal to sign and entered in 4000 as the final numeric value in this part of the query. Another parenthesis closed off the first part so I could begin on the second part of query 1. The second part was left out of parentheses because they were not necessary as the first part was already separated from the second part. The second part of query 1 required the use of a new Boolean expression "POP10_SQMI. This expression would return results of the 2010 population in persons per square mile and for this particular part of the query, I wanted the population per square mile greater than or equal to 1000. First I had to use an OR operator, this operator selects records for which either expression is true, that is it would select records that have a population between 3000 and 4000 and it would select records that have a population per square mile over 1000. After the OR operator I added the 'POP_SQMI' expression, the greater than or equal to sign and 1000 as the final numeric value. In order to execute the query to actually return any records, I first verified it to check for any errors and then clicked okay. The counties highlighted were the records returned from the query. From the counties layer in the Table of Contents, I right-clicked it to open a new menu and under the selection tab I chose to create a new layer from the selected attributes. This created a  new layer so I could create a cartographically pleasing map instead of having all of the selected counties outlined by the query.

Query 1: Multiple criteria query that shows counties with a population between 3000 and 4000 people in 2010 as well as counties in 2010 that had a population density greater than or equal to 1000 persons per square mile.  

Query 1 results

Results:

The results from query 1 are shown above. The query I developed was a multiple criteria query that returned counties in the United States with a population between 3000 and 4000 (including 3000 and 4000) and counties that had a 2010 population density of at least 1000 persons per square mile.  The resulting query and map are shown above with the desired counties highlighted in yellow on the map.

Query 2

Methodology:

Query 2 focused again on the United States when returning desired records. I began first by clearing the first query from the map and from the select by attributes window. The first step to creating Query 2 was similar to Query 1 in that I used the selection tool to open the select by attributes window. The layer stayed the same, counties and the method also stayed the same, create a new selection. For this query I wanted counties within specific states so to start the query I needed to specify which state I wanted. To do so I entered the expression 'STATE_NAME', an equal sign to indicate the exact state I wanted results from and the state name. After I used the AND operator to isolate specific results within that state. To follow that I added parentheses to separate one part of the query from the other, and in the parentheses I added the male population greater than the female population. The last part of the query, followed by another AND operator was intended to return results of counties with a senior population greater than 6500. to add this to Query 2 I found the 'AGE_65_UP' expression, added a greater than sign and added the 6500 value.

Query 2: Multiple criteria query that shows counties in Wisconsin where the male population is greater than the female population and where the population of seniors (ages  65 and up) is greater than 6500. Other queries were created with the same structure but for different states: Texas, New York, Minnesota and California.



Query 2 results

Results: The query I developed was a multiple criteria query that returns counties in a state that have a male population that is greater than the female population and counties that have a senior population, ages 65 and up, that is greater than 6500. The states I used for this query were Wisconsin, Minnesota, California, Texas and New York, the counties in each state are highlighted in different colors: Wisconsin counties in orange, Minnesota counties in purple, California counties in pink, Texas counties in yellow and New York counties in green.

Query 3

Methodology:

Similar to the first two queries, Query 3 focused on the United States counties. From the select by attributes window, the layer remained on counties and the method remained on create a new selection. I also maintained the use of the 'STATE_NAME' expression, as I wanted results from specific states. The query begins by naming the state I wanted to isolate then using the AND operator to separate the first part of the query from the second part. Parentheses were also used for the second part to further isolate that specific part of the query from the first and third parts. After the AND operator I added the parentheses and within those I added the 'AGE_65_UP' expression followed by the greater than sign and 6500 as a numeric value. I ended that part of the query with a closing parenthesis and another AND operator to indicate a new part of the query. The final part made use of the 'HSE_UNITS' expression which indicates the number of housing units per county. After this expression I added a greater than sign and 30000 as a numeric value.

Query 3: Multiple criteria query that shows the counties in a state containing seniors ages 65 and above and containing more than 30,000 housing units. The same structure of the query was used for multiple other states, including Washington, Maryland, Illinois, Nebraska and District of Columbia.

Query 3 results

Results: The query I developed was a multiple criteria query similar to the first two. This query returned results for counties in the states of Washington, Maryland, Illinois, Nebraska, District of Columbia and Michigan. The query intended on returning results for counties where the senior population was greater than 6500 and those counties also contained more than 30000 housing units. Washington counties are shown in dark orange, Illinois counties are shown in pink, Michigan counties in yellow, Nebraska counties (though very few) are shown in a pale orange, Maryland counties in green and unfortunately, the District of Columbia counties were too small to be seen on the map.

Part 2

Query 1

Methodology:

This query had multiple parts using two different tools. I started by using the select by attributes tool and entered in the query " 'POP2007' > 15000 AND 'POP2007' < 20000 AND 'AREALAND' >= 5 AND 'FEMALES' > 'MALES' " This part of the query was separated by the AND operators So it would return population data, city area data and the ratio of females population greater than the male population. After applying that part of the query I opened the select by location tool and changed the selection method to "select from the currently selected features in". This would ensure that the results returned from this part of the query stemmed from the first part. I chose the target layer as WI_cities, the source later to be Lakes, and the spatial section method to be "are within a distance of the source layer" with a distance of 2 miles. Finally, I clicked the OK button to apply this part of my query to the first part.

Query 1: Selecting by both attributes and location to return a result of the cities in Wisconsin that have a population between 15,000 and 20,000, where the female population is greater than the male population, the area of the city is greater than or equal to 5 square miles and the city is within 2 miles of a lake.

Query 1 result

Results: The results of Query 1 returned data of cities in Wisconsin that have a population between 15000 and 20000, with a female population greater than the male population and the area of those cities being greater than or equal to 5 square miles. This was part one of Query 1. Part two of Query 1 was a location query that returned results of the cities selected in part one that were within 2 miles of a lake. A map of the results and the two queries are shown above

Query 2

Methodology:

This query was simple to construct but involved selecting multiple rivers. To do so I simply added the 'PNAME' expression an equal sign, and the name of the river I wanted to select. To include other rivers in the same selection I would add the AND operator and use the same structure as the first part of the query: 'PNAME' = "river name". I got the river names from the unique values list to save time instead of typing out all the names.

Query 2: The same structure was used for different rivers listed.

Query 2 result

Results: The results returned a record of the rivers I selected from the instructions given to me. In the map above the results are shown with a backdrop of major roads, lakes and counties in Wisconsin. The rivers selected are: The Chippewa River, Eau Claire River, Embarrass River, Fisher River, Hunting River, Kinnickinnic River, Maunesha River, Milwaukee River, Moose River, Namekagon River, Pelican River, Platte River and Potato River.


GIS Lab 3

Lab 3 technical report

Goal and background:

The goal of this lab was to create a static map and a dynamic map of the state of Wisconsin. The static map includes information about the population per county, and a variable of my own choosing, in this case the number of housing units per county. All of the data contained within the static map was derived from the U.S Census Bureau as part of their 2010 Census data. The dynamic map was produced online, through the ArcGIS website, and serves as a way of sharing the information I obtained pertaining to the number of housing units per county in the state of Wisconsin.

Methodology:

 To begin building the static map, containing two data frames for the population and housing units of Wisconsin, I went to the US Census Bureau website. From there I chose an advanced search and narrowed my selections down to the total population of Wisconsin by choosing “2010 SF1 100% Data” as my topic, “County” as my Geography, “Wisconsin” as my state and “All Counties Within Wisconsin” to ensure I would obtain data on all Wisconsin counties. From there I found the “Total Population variable and downloaded it to my lab 3 folder. The download came in as a zip file, so unzipping it was necessary to obtain the CSV files needed for my maps. The P1 table contained in one of the CSV files I needed would not be compatible with ArcMap if I left it as is, so I saved it as a MS Excel file instead by changing the file type from the save as tab as Excel Workbook.

The next step was to download the shapefile of Wisconsin from the Wisconsin Census data. From the Geographies tab, I clicked on the map tab to show the state of Wisconsin highlighted since that was what the data I had chosen previously pertained to. I downloaded the file to my lab 3 folder and formatted it to a shapefile so it would display on my map correctly. This file also came in as a zip file so unzipping it was necessary as well.

Finally I was able to start building my maps, now that I had all the necessary components for the population portion. I connected ArcMap to my lab 3 folder and added the Wisconsin shapefile and the P1 table to my blank map. The next task was to join a standalone table and an attribute table. In this case, the standalone table was the P1 table and the attribute table was the Wisconsin shapefile table. I right-clicked on the shapefile in my table of contents and highlighted the joins and relates tab, a sidebar opened and from there I chose join. A join window opened and the field I based my join off of was the GEO ID field because both the standalone and the attribute table had this field. Next I chose the table to join to the shapefile table, in this case, the P1 table and the field in that table to base the join on was the same, GEO ID. Then I validated the join and clicked OK. This joined table needed to be displayed as its own shapefile so I exported the data by right-clicking on the Wisconsin shapefile feature and pointing to Data and then Export Data. Then I saved the data as a shapefile with a name of my own choosing and the new shapefile of Wisconsin was displayed on my map.

The next task was representing the attribute table on the map. To do this I opened the properties for the new Wisconsin shapefile and chose the symbology tab. Since the value I needed, D001, was a set as a string type I had to go back into the attribute table, create a new field which I named “newD001” and added the same data from the D001 field using the field calculator and saving it as a long integer type instead of string type. Then I went back to the symbology tab and chose D001 as my value in the graduated colors section under quantities.

The first part of my static map was finished, containing the population of Wisconsin by county. The next map required me to make a map of a variable of my own choosing, in this case I chose to make a map of the number of housing units per county in Wisconsin. This procedure followed the exact same steps as the previous map. I downloaded and unzipped the census data from the US Census Bureau website and converted the desired CSV file to an Excel file, added it to the new data frame on the same map along with the original Wisconsin county shapefile and joined the standalone Excel table to the shapefile table. I then exported the data as a new shapefile and changed the symbology accordingly for the new map.

Next was designing the layouts for the finished maps. This required a change in projection more appropriate for the state of Wisconsin and in this case I chose to do a NAD 1983 Wisconsin Transverse Mercator projection. To do this I opened the data frame properties for both frames, clicked on the coordinates tab and changed the projection to NAD 1983 Wisconsin TM (Meters). Next was an addition of a title, legend, north arrow, scale bar, date, source and my name as the author of the map. All of these could be found in the insert tab, under their corresponding names. Finally I simply had to move and scale everything so the map looked organized and appealing.

Part two of lab 3 involved creating a dynamic map displaying the data derived from the static map.  For this part I used the map containing my variable choice, housing units. To begin, I removed the population data frame from the map document, leaving the housing map. From ArcMap  I signed into my ArcGIS online account that’s through UW-Eau Claire so I would be able to share my map with the UW-Eau Claire geography department. Since dynamic maps are online, and I needed to share my map online, I needed to create a feature service for my map. To do this I clicked the file drop-down menu and chose the share as tab. A sidebar appeared giving me two choices: “Map package” and “Service., I chose service. A new window opened up that would take me through the share as service process. In the first window I chose to publish my map as a service, then clicked next. The next step was to choose where to publish my service and a drop-down menu appeared giving me the choice to publish my service to “My Hosted Services” in this case UW-Eau Claire Geography and Anthropology. Below it I created the name for my service and clicked continue.

The next window to open was the service editor window, this allowed me to choose the capabilities of my map, give it a description, a summary and tags and allowed me to choose who I was sharing my service with. Finally, I clicked analyze and a pop-up window appeared showing me any warnings or errors that appeared. I resolved any errors I had and clicked the publish button to publish my service. Once the publishing was complete I signed in to the ArcGIS website and viewed my service under the contents page. The last thing I needed to do before I saved and shared my map was edit the data that could be viewed by clicking on each county. I found the feature layer of my service and clicked the arrow to display a drop-down menu and from there I chose “add layer to map”. This displayed my map of Wisconsin over a base map of the U.S. Since I only needed two attributes to be shown, county and number of housing units, I clicked on the content button to the left side of my map and clicked on the three dots underneath my service name to expand a menu. In that menu I chose the “Configure Pop-Up” option that opened a window allowing me to select what attributes are being displayed on the map and what their names are. I chose the “NAME” attribute, and the attribute for my housing units labeled “newD001”. I renamed them “County” and “Housing Units”. I then clicked OK to save the changes and clicked the “Save Pop-Up” button near the bottom of the window.

The last step to completing part 2 was to save and share my map. First I saved my map by clicking the save button near the top of the page and chose the “save” option. This opened a new window allowing me to enter a title, tags and a summary for my map and where to save my map. Once all the information was entered correctly I clicked the “Save Map” button. Lastly I needed to share my map and to do so I went to my content page and clicked on the name of my web map to open its properties. From there I clicked the share button which opened a new window in which I checked the UW-Eau Claire- Geography and Anthropology box. This allowed me to share my map with that specific group.

Results:

The results I obtained were 2 maps, one static and one dynamic that displayed the skills I acquired from this lab and previous labs and tutorials. One map is the static map of the state of Wisconsin, showing housing units in the top data frame and population in the bottom data frame. Each data frame has its own legend, scale bar and north arrow. The dynamic map is a screenshot I took of the final product of my published service in the form of a web map. It shows the housing units data frame from the static map over a base map of the U.S. This map is displaying the number of housing units in each county, with an example being shown of 23,996 housing units in Door County.

Dynamic Map

Static Map

Credits: US Census Bureau (2000). American FactFinder . Retrieved from http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml