Data Gathering

Introduction:
The goal of this lab was to gather a variety of data on Trempealeau County from different sources to develop file management and data acquisition skills.  This data was downloaded from five different sources then compiled into a database that could be used for further analysis.  Pyscripter was used to project, clip, and load rasters into the geodatabase.

Methods:
-Railway data was collected from the US Department of Transportations website.
https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

-Landcover/Landuse and elevation data was collected from the USGS National Map Viewer.
http://nationalmap.gov/about.html

-Landcover Crop Land data was collected from the USDA Geospatial Data Gateway.
http://datagateway.nrcs.usda.gov/

-A Trempealeau County Geodatabase was collected from the Trempealeau County Land Records.
http://www.tremplocounty.com/landrecords/

-Soil data was collected from the USDA NRCS Web Soil Survey.
http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

Data Accuracy:
Each data set comes with meta data that provides information about the data.  This information includes:
Scale-

Effective Resolution- Resolution or scale that the data was taken in.  This has to do with camera height and lens focal length.

Minimum Mapping Unit- Smallest depictable or plotable object on a map.

Planimetric Coordinate Accuracy- How close the objects in the data set are to the real world position.

Lineage- Documentation of source materials, whats been done to the data by who and who collected it.

Temporal Accuracy- How relevant the data is today.

Attribute Accuracy- Closeness of descriptive data to real world.  Magnitude of gross error.

The table below shows how each of the data sets fall into the categories above.  This data was taken from the meta data for each of the data sets.

The following maps were created to display the data collected:

Conclusions:
Based on just the metadata provided with the data sets lots of important information was not included.  Resolution and scale should be two things that every data set clearly provide.  It is important for using the data with any confidence.  If I was using this data for a project I would have to keep in mind that some of the data may not be temporally accurate.  The earth is constantly changing and up to date data is important for doing accurate analysis.