GIS II Post 2: Data Collection and Geodatabase Design

Goals:

Data provides the foundation for all GIS projects. So naturally finding different ways to acquire this data is very important, as many GIS professionals discovered with the shut down of the government websites during the shutdown. So knowing a wide variety of geoportals is important in acquiring data. This lab is designed to familiarize ourselves with acquiring data and knowing how to correctly process the data for later use.

Objectives:

• Download Data from four geoportals
• Import and process data
• Build a geodatabase for AOI (Trempealeau County, WI)
• Create table of data quality measures

Methods:

Data Acquisition

National Atlas -

• Railroad.shp at the 1:1,000,000 scale

USGS National Map Viewer - 

• NLCD 2006 Land Cover Data 
• National Elevation Dataset (1/3) arc Second - Staged in ArcGrid format

USDA Geospatial Data Gateway -

• LULC data for the state of Wisconsin

USDA NRCS Web Soil Survey -

• SSURGO data for Trempealeau County
• SSURGO is an acronym which stands for "Soil Survey Geographic Database" or "Soil Survey Geographic." SSURGO comes in the form of digital soils data produced and distributed by the Natural Resources Conservation Service - National Cartography and Geospatial Center.

Once the data was acquired it needed to be properly prepared. The raster of the elevation dataset needed to first build pyramids in ArcCatalog to speed up viewing. This caches tiles at different viewing extents to speed up loading and processing. Once both rasters were viewed in ArcCatalog and added to ArcMap, they were mosaicked together using the "Mosaic to New Raster" tool. Looking at the elevation you can determine the unsigned (no negative values) pixel depth type needed. Since the data has decimals I decided to use the pixel depth floating point, with supports decimals. I also went with the bit depth of 32, because the decimals are enabled; and, even though their is only a change in elevation of 236 m, the values with decimals account for many more than 236 values. So the 32-bit depth is necessary or else data will be lost. Since the rasters are a grey scale only one band is required. I also changed the mosaicking scheme to "mean" so that the overlapping zones were averaged instead of just taking the values from one or the other. Figure 1. shows the two rasters before and after the mosaic. In the mosaic tool I made sure to change the mosaic colormap mode to "match," so the two rasters would look more uniform.



Figure 1. A comparitive view of the two rasters before (left) and after (right) the mosaic process. NOTE: the more uniform coloring of the raster after the mosaic process.

Next the various remaining datasets were downloaded. To prepare the data a geodatabase for Trempealeau County was created and a feature dataset. The projection for the feature dataset was chosen as NAD_1983_HARN_StatePlane_Wisconsin_Central_FIPS_4802_Feet because of the location for Trempealeau County had in the state. Then SSURGO feature class was imported into the geodatabase and a relationship class was created to reference the data using "mukey" as the primary and foreign key. From here on out it was smooth sailing and all that remained was to make sure all the data was in the same projection and the maps were clipped to the outline of Trempealeau county using a projected shapefile downloaded from the Census Bureau. Figure 2 shows all five datasets illustrated by four maps.


Figure 2 All the downloaded data sets.

Data Quality Measures

Key Terms and Definitions:

Scale - in geography scale represents the relationship of the distance on a map/data to the actual distance on the ground.

Effective resolution - In GIS, the smallest allowable separation between two coordinate values in a feature class. The smaller the effective resolution the more precise your data can be.

Minimum mapping unit - For a given scale, the size in map units below which a narrow feature can be resonably represented by a line and an area by a point.

Planimetric Coordinate Accuracy - Is determined by the root mean square error (RMSE). RMSE is the cumulative result of all errors including those introduced by the processes of ground control surveys, map compilations and final extraction of ground dimensions from the map. Limiting RMSE is crucial to data credibility and accuracy.

Lineage - A collection of states representing the changes that have occurred over time in a versioned geodatabase.

Temporal Accuracy - How accurate data is in relation to time; the frequently of data collection.

Attribute Accuracy -  An assessment of the accuracy of the identification of entities and assignment of attribute values in the data set.

Sources:

Definitions taken from ESRI's GIS Dictionary.
http://support.esri.com/en/knowledgebase/Gisdictionary/browse

National Atlas Data
http://nationalatlas.gov/

USGS National Map Viewer
http://viewer.nationalmap.gov/viewer/

USDA Geospatial Data Gateway
http://datagateway.nrcs.usda.gov/

USDA NRCS Web Soil Survey

http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx

Drainage Index Data from
http://foresthealth.fs.usda.gov/soils/

http://foresthealth.fs.usda.gov/soils/MoreInfo