Goals and Objectives
This exercise is the first step in the sand mining semester project for University of Wisconsin-Eau Claire's Geographic Information Systems II course. The exercise involves downloading data for the project from several internet sources, including the USGS, USDA, USDOT, as well as county-based files.In addition to importing the geospatial files for the sand mine project, this exercise will also help familiarize one about manipulating the data in ArcGIS; for example, by joining tables, projecting data into the appropriate coordinate system, building and designing a geodatabase to store the data, and checking all data for accuracy.
The location with which the geospatial data for the exercise relates is Trempealeau County in west-central Wisconsin (fig.1).
Figure 1. Location of Trempealeau County in west-central Wisconsin (red).Methods
Data Acquisition
The first section of the exercise involved acquiring data from different sources. Six data sets were imported from four websites:- USGS:
- National land cover data (NLCD) that differentiates the general types of land found in Trempealeau County; e.g. cropland, open water, hay/pasture land, etc. (fig. 2).
- Digital elevation model (DEM) which shows the differences in elevation throughout the county (fig. 3).
Figure 2. National land cover data map created for Trempealeau County, Wisconsin.
http://nationalmap.gov/viewer.html
- USDA:
that it too differentiates the land use for whatever area of interest (AOI) it covers (fig. 4). How-
ever, NASS data differentiates land use based on crop land, and is thus more detailed than the
generalized NLCD from the USGS site.
4. Web soil survey displays the soil map units (MU) for Trempealeau County (fig. 5).
Data Source:
NASS http://datagateway.nrcs.usda.gov/
Soil Survey http://www.nrcs.usda.gov/wps/portal/nrcs/site/soils/home/
- USDOT:
Data Source:
http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html
- Trempealeau County Land Records (TCLR):
specific to Trempealeau County; for example, county boundaries, roads and highways, etc.
Data Source:
http://www.tremplocounty.com/landrecords/
Data Manipulation
Once data were imported into the appropriate location from the various sources discussed above, it was manipulated using Python Scripting (PS; see Post 2: Python Scripting for more details). Such manipulation was needed in order to create the maps displayed in figures 2-6 above. Manipulation of the datasets included using PS software included:- Projecting data into the appropriate coordinate system (i.e. NAD_1983_HARN_WISCRS_Trempealeau_County_Feet)
- Extracting raster data to the Trempealeau County boundary
- Importing raster data into the appropriate geodatabase
Data Accuracy
An assessment of data accuracy was conducted on the datasets used for the project (table 1). While challenging, it is important to examine all relevant geospatial metadata to ensure that is accurate, precise, and up to date. Some of the information in table 1 is directly dependent upon what is contained in a datasets metadata, e.g. temporal accuracy, scale, and minimum mapping unit. However, several of the fields in table 1 can be calculated indirectly. For instance, planimetric coordinate accuracy (PCA) could be calculated using an equation derived from an Excel graph (fig. 7). Once the PCA is determined, the effective resolution could be determined; being half the PCA.
Table 1.
Summary dataset accuracy from the five sources used on the project thus far.
Conclusions
Determining the accuracy of the datasets was one of the most tedious steps of the exercise, but also one of the most important. By using the most accurate data that is required by a given project we are ensuring that our products are accurate as well. The accuracy portion of the exercise worked well because it forced the user to investigate the metadata from various data sources.The exercise also did a good job of exposing the user to new software like Python Scripting. While Python Scripting is rather tedious and cumbersome to work with as a beginner, it definitely has potential as far as a data-processing tool. For example, creating a data-processing loop for rastersets was useful because once a user identifies a few parameters in the scripting code, like file location and definition of data, all valid raster data will be processed in succession.
