Post 3: Data Gathering

Goals and Objectives: 
The goal of this lab is to become familiar with the process of downloading data from different sources that are available on the internet, importing the data into ArcGIS, and joining data. Lastly, being able to project that data from these different sources into one coordinate system and building and designing a geodatabase to store the data in. The data that will be obtained will be part of the multipart project in that class that will build a suitability and risk model for sand mining in Western Wisconsin. The study area for this lab focuses on Trempealeau County, WI. This lab will focus on gathering and exploring the data.

General Methods:
The data obtained for this exercise was from multiple sites. The US Department of Transportation provided the polyline data for the Railway Network. The rail line data was later clipped to only include the rail lines in Trempealeau County. On the USGS website there is a National Map Viewer. From there the National Land Cover Database (NLCD) was downloaded, specifically two data sets that included the NLCD 2011 Land cover and NLCD 2011 Percent Developed Imperviousness. Using the Multi-Resolution Land Characteristics (MRLC) site, a legend was given to describe the data. The National Map Viewer was also used to download the Elevation Products (3DEP) 1/3 arc-second DEM. Two different DEM's were needed to cover the entire Trempealeau County area. They were then created as a new raster to combine the TIFs. The USDA includes a section on their site called the Geospatial Data Gateway. Land cover can be categorized by state. Getting more specific with Trempealeau County, WI, this site had the Cropland Data by state. More land records were obtained from the Trempealeau County Land Records. The database created in the TMP downloaded data will be the workspace for the data in this lab. The last web site to gather data from was the Web Soil Survey, which is part of the USDA NRCS. This website can be zoomed not only to a specific state or county, but specific area to get the soil classification.
The SSURGO data needed to be imported in order to access the tables. By opening the folder filled with files in Access, it will import the files to be used in the TMP database. Next the component table from the Web Soil Survey database needs to be imported to the TMP database and have a relationship class, and joined.

Data Accuracy:
When there is downloaded data, the accuracy and precision are a large part of the credibility in results of ones data collection. Accuracy is the degree to which the data agrees or how close the data matches true values. Positional accuracy looks at the closeness of coordinate values in the database versus the real work. This includes the scale, effective resolution, minimum mapping unit and planimetric coordinate accuracy. The temporal accuracy is the measure of data quality in relation to the time of the creation of the data. Lastly, attribute accuracy needs to be assessed. This looks at the closeness of the descriptive data to see if the values represent the ones in the real-world. This includes metric data (DEM and TIN) or categorical data. The accuracy of the data downloaded is shown in Table 1 below.

Table 1. Accuracy of the metadata.

	Geospatial Data Gateway (USDA NRCS)	Web Soil Survey (USDA NRCS)	Trempealeau County Department of Land Records	Bureau of Transportation Statistics (US DOT)	Elevation: National Map (USGS)	Land Cover: National Map (USGS)
Scale	1:3998		1:4000		1:4000	1:4000
Effective Resolution	19.99		20		20	20
Minimum Mapping Unit	1.999-mm		2-mm		2-mm	2-mm
Planimetric Coordinate Accuracy	0.9996		1.0000361538		1.0000361538	1.0000361538
Temporal Accuracy	2009	2016	2000	2015	2011	2011
Attribute Accuracy (Kappa values, etc.)

Discussion and Conclusions:

After assessing the accuracy of the different data sets, a few concerns pose for the future. A main one would be the temporal accuracy. In other words, all of the data sets are from a different time. Some of them are from 2009, others 2011 or 2015. When compiling the data, some aspects could be more updated and other data sets would be outdated. This is why it is always important to use the most recent set of data, which was done for this lab. Figure 1 has a few of the layers of data. Each are from a different site. When importing the data into ArcMap, a few of the rasters and shapefiles were in different projections. This impacts the change in scale when they had to be reprojected. In the end the projection used was a County Projection Coordinate System: NAD 1983 HARN WISCRS Trempealeau County (feet). This way the county was displayed the same in each layer that only had the county showing. 

Figure 1. Different layers from the various data sets representing the structure of the land for Trempealeau County, WI. 

References: 

CP Lo, AKW Yeung - 2003 - Pearson Prentice Hall
ESRI. 2014. What is Python? March 3. Accessed March 13, 2017. http://resources.arcgis.com/en/help/main/10.2/index.html#//002z00000001000000.

Multi-Resolution Land Characteristics Consortium (MRLC). 2016. National Land Cover Database (NLCD). August 26. Accessed March 8, 2017. https://www.mrlc.gov/nlcd11_leg.php.

Natinoal Resource Conservation Service (USDA). n.d. Geospatial Data Gateway. Accessed March 6, 2017. https://datagateway.nrcs.usda.gov/GDGOrder.aspx.

NRCS (USDA). 2016. Web Soil Survery. August 10. Accessed March 6, 2017. https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx.

Trempealeau County . 2015. Department of Land Records. Accessed March 6, 2017. http://www.tremplocounty.com/tchome/landrecords/data.aspx.

United States Department of Transportation. 2015. Bureau of Transportation Statistics. Accessed March 6, 2017. https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2015/polyline.

USGS. 2016. The National Map. December 16. Accessed March 6, 2017. https://nationalmap.gov/about.html.