Lab 3: Introduction to GPS

Goals and Background
The goal of this lab was to learn how to use a GPS unit (Trimble Juno), how to create and upload a geodatabase onto a GPS unit and learn to collect data in the field using the Trimble Juno GPS unit. The aim of the lab was to create a map based on the data collected in the field (UWEC campus), as well as creating a cartographically pleasing map using digitising if necessary.

Methods
For this lab I had to create a geodatabase to collect the necessary data in the field as well as a map representing this data.

I created my geodatabase in ArcCatolog 10.2 with point, line and polygon feature classes (one practice and one real feature class for each type). Each feature class used the coordinate system NAD 1983 HARN Wisconsin TM (Meters). I also imported a shapefile of the buildings on campus and a raster image of UWEC campus to the geodatabase. Once I had created the geodatabase I opened ArcMap 10.2. In ArcMap 10.2 I imported the geodatabase created in ArcCatalog 10.2. To identify the different feature classes, different colours were assigned to each symbol.

To be able to use this geodatabase in the field, I used ArcPad Data Manager (Get Data for ArcPad button) in ArcMap to transfer ('check out') the geodatabase onto the Trimble Juno GPS unit. Once the data had been transferred onto the Trimble Juno GPS unit, I was able to go outside and collect the data. To make sure I was collecting the data correctly, I did a practice run, once satisfied, I collected the data as instructed by the professor. I collected 4 polygons, 1 line and 6 points whilst out in the field and entered attribute information for every feature I collected e.g. P1 for Polygon 1 and T1 for Tree 1.

After finishing collecting data in the field, the collected data was transferred back to ArcMap 10.2 using ArcPad Data Manager. This 'checked in' data was used to create my final map. Each feature was assigned a different symbol and colour for example a tree and the colour green for the tree feature class (3 of the points). To improve the quality on the polygons, I digitised them. I decided to change the basemap, to a more updated version of UWEC lower campus. I found this updated basemap using Add Data from ArcGIS Online, typing in the keyword Eau Claire. Once satisfied with the map, I placed the map on a single layout using layout view. To this map I added a title, scale, north arrow, legend, date, source and my name.

Results
Figure 3 shows the map I made for this lab. Figure 3 shows the problem with GPS, as data collected is not always that accurate. This is especially true for the polygons I recorded, as they were all slightly off the basemap below. To reduce this level of error I digitised some of the polygons to make them more precise, however they still are not perfect (as shown in Figure 3).

Figure 3

Sources

GPS Data Collected by Amelia Fitzpatrick 14/4/2014

Basemap: UWEC Campus



Lab 2: Downloading GIS Data

Goals and Background
The goal of this lab was to learn how to use the US Census Bureau and to be able to download and map data from this website. The most important aspect of the lab was learning to download data from the US Census Bureau and converting this data to a ArcGIS friendly format. The aim of Lab 2 was to create two maps, one following instructions provided to us (by our professor) and the other using data of our choice.

Methods
For this lab I had to create 2 maps on data regarding Wisconsin's counties. The first map I made was on total population, whilst the second map I made was on housing units. To create these two maps I used ArcMap 10.2. Each topic e.g. total population was put in a separate data frame. Each data frame used the basemap Light Gray Canvas and had the state of Wisconsin with counties shapefile (downloaded from the US Census Bureau).

To get the data needed to make these maps I went to the US Census Bureau website. For the first map in the Topics option section I choose People, Basic Count/Estimate, Population Total. To make the data specific to Wisconsin, I changed the Geography option section to County - 050, Wisconsin, All Counties Within Wisconsin. I choose to download the Total Population 2010 SF1 100% Data. This information was downloaded in tables as a zip file. To access the data I unzipped the data. To make the data in the total population table readable in ArcMap I saved the data as a Microsoft Excel Workbook. To be able to make a map I had to link the data table (total population) with the shapefile table using the GEO#id. Once these two tables had been linked I could map the data. To show this data I decided to use the graduated colour map (various shades of pink) in the Symbology tab of the Layer Properties tab. I decided to change the classification to Quantile as I felt this better represented the data.

After completing the first map I created another map as required in a separate data frame. For this map I decided to download housing units data. To find this data I changed the Topics option section to Housing, Basic Count/Estimate, Housing Units. I kept the Geography tab the same. I choose to download the Housing Units 2010 SF1 100% Data. After choosing the data I wanted to download, I followed the same process as above to create the map. Again I decided to change the classification to Quantile as I felt again this represented the data better.

Once I had made both maps, I placed them on a single layout using layout view. For both maps I added a title, scale, north arrow and legend. For the legend on both maps I decided to change the units to have no decimal places (only whole numbers) and used thousands separators to make the numbers easier to read. For both maps I changed the projection to NAD 1983 (2011) Wisconsin TM (Meters) as I wanted the maps to be projected using a more local projection. For the overall project I added the source of my information, the year of the source data and my name.

Results
Figure 2 shows the maps I made in this lab. The total population map shows that Wisconsin is more densely populated in the South than in the North of the state. In counties where there are major cities such as Wasau there are higher populations. From further research the North of the state is predominantly forests explaining why the North of the state is less populated than the South of the state, fitting the pattern suggested above. The housing units map show that most houses are found in counties where there is a large city e.g. Milwaukee. However, compared to total population there are a lot more housing units in the Northern counties. This is probably due to many Wisconsinites having a second home, a cabin in this region.

Figure 2

Sources

US Department of Commerce, US Census Bureau. (2014). American Fact Finder Advanced Search. Retrieved from http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t.

Google. (2014) Google Maps. Retrieved from https://www.google.co.uk/maps/place/Wisconsin/@45.9383265,-89.981085,8z/data=!4m2!3m1!1s0x52a8f40f9384e3af:0xf2d5d5b8f88649d6.



GIS I Lab 1: Base Data

Goals and Background
For this lab I was working as an intern for the company Clear Vision Eau Claire. Clear Vision Eau Claire have created a partnership with local developers, UW-Eau Claire and the Eau Claire Regional Arts Center to create a new development called the "Confluence Project". The Confluence Project will include the following facilties: student housing, a community arts centre, a commercial retail complex and public parking, located in downtown Eau  Claire with construction beginning late 2013/early 2014. The goal of Lab 1 was to learn about spatial data sets used in land use, public land management and administration and to be able to create maps showing this various information specific to the Confluence Project.

Methods
For this lab I had to create 6 maps on various information to do with the Confluence Project including civil divisions, census boundaries, PLSS features, Eau Claire city parcel data, zoning and voting districts. To create these maps I used ArcMap 10.2. Each topic e.g. civil divisions was put in a separate data frame. Each data frame used the basemap Imagery and had the proposed site on it. For every data frame I put in the class features using ArcCatalog, using the data provided to us.

For the Civil Divisions map I used the civil divisions and county boundary (Eau Claire) feature classes . To show the different civil divisions I used the unique values map (using the municipality type value field) and set it to 70% transparency to be able to see the basemap below. To show the county boundary I used the colour 40% grey. For me to be able to see the whole city of Eau Claire I used the scale 1:40,000, and added a callbox to make the proposed site clear.

For the Census Boundaries map I used the tracts and BlockGroups feature classes. For the BlockGroups feature class I decided to use population per square mile (2007) to show the density of the area surrounding the site. For this data I used a graduated colour map with the natural breaks classification. To be able to see the basemap I set the transparency to 30%. For the tracts I used the colour black, to make them show up clearly.

For the PLSS Features map I used the PLSS Quarter Quater Section feature calss. To be able to see the basemap underneath I hollowed out the PLSS Quarter Quater Section feature calss and used a bright green colour as an outline to still be able to see the sections.

For the Eau Claire City Parcel Data map I used the centerlines, parcel area and water feature classes. To be able to see the centerlines and parcel area I used contrasting colours (pink and yellow). For the water I used a dark blue and set the transparency to 40%, to still be able to see the river underneath.

For the Zoning map I used the centerlines and zoning areas feature classes. To show the various zones I used the unique values map and the value field zoning_cla. I grouped the zones together based on the starting letter e.g. C = Commercial. Each zone on the map has a different colour. To contrast the zoning classes colours, I used yellow for the centerlines.

For the Voting Districts map I used the voting wards 2011 feature class. I changed the voting wards colour to orange, the outline to black and the transparency to 60% to be able to see the basemap below. I added the ward numbers using the labels tab and added a halo to the numbers to make them clear. For me to be able to see the proposed site I added a callbox.

Once I had made all 6 maps, I placed them on one a single layout using layout view. To every map I added a title and scale. For every map except Voting Districts I added a legend as well. For the overall project I added the source of my information and my name.

Results
Figure 1 shows the maps I made for this lab. The Civil Divisions map shows the Confluence Project site is located in the city of Eau Claire with towns surrounding it (according to the map). The Census Boundaries map shows that the Confluence Project site is situated  in a densely populated part of the city per square mile. The PLSS feature class identifies what PLSS Quarter Quarter the proposed site sits in. The Eau Cliare City Parcel Data map shows the relationship between the Confluence Project and other parcels in the city of Eau Claire. The Zoning map shows the Confluence Project site is located near the CBD and public properties district meaning this site would bring a new zone to the area (residential). The Voting Districts map shows that the Confluence Project site is located on the edge of voting district 31.

Figure 1

 

Sources

University of Wisconsin - Eau Claire. (2013, October 23). Frequently Asked Questions: The Confluence Project. Retrieved from http://www.uwec.edu/News/more/confluenceprojectFAQs.htm.