Data Gathering

Goals and Objectives

This semester we will be working on a project evaluating the suitability and risk of sand mining in western Wisconsin in our chosen study area of Trempealeau County. The first step any GIS based project is to acquire data, either by personally collecting data in the field, or by accessing pre-existing data sets.  In this project, we will be using publicly available data, downloaded from a variety of organizations. 

 The goals of this lab are to:

• Download data from various organizations
• Keep data organized and properly labeled
• Find metadata for what we downloaded
• Use Python to project and clip rasters

The objectives of this lab are to: 

• Gain familiarity with a wide array of data sources
  
  
• Develop skills to be more organized and efficient when downloading data
• Become more familiar coding in python

Methods

Figure 1. General data flow model for downloading data, provided by Professor Hupy

The steps in the data flow model shown in Figure 1 offer a good outline for the process of data collection and data management. Below I list main parts of the exercise

• Project and clip/extract data and load data into a geodatabase, then delete redundant data
• Produce a table showing metadata

Download zip files to a temporary directory and extract files to a working folder

• We downloaded the following data from the websites that are respectively listed:

National Elevation Data: http://nationalmap.gov/viewers.html

Trempealeau County Land Records Geodatabase: http://tremplocounty.com/landrecords/

Soils Data: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

National Land Cover Data: http://datagateway.nrcs.usda.gov/

Railroads:  http://rita.dot.gov/bts/sites/ridta.dot.gov.bts/files/publications/national_transportation_atlas_database/2013/index.html

• The zip files downloaded from these websites were then into a temporary folder, and then extracted into our working folders.  This was done to reduce the amount of files we would have in our working folders.

Project and clip/extract data and load data into a geodatabase, then delete redundant data

• Projecting and clipping rasters was done utilizing python.  Redundant data was deleted to save space and keep folders organized.

Data Accuracy (Produce a table showing metadata)

It is important to have metadata for the data used in this project.  It is needed in order to understand how valid any results that we drive from analysis can be.  For instance, if a data set that we have downloaded is particularly old, we may not be able to make many accurate statements about what the current condition of a feature might be.  Also, it is important to have a sense of how accurate and precise the data is, so that we can describe how precise and accurate our results are.  

For these reasons, a metadata table (Figure 2.) was produced in order to have a simple reference that we can look back on as we continue forward in this project

Data	Scale	Effective resolution in meters (Approximation)	Minimum mapping unit (acres)	Planimetric Coordinate Accuracy	Lineage	Temporal accuracy	Attribute accuracy
Railway Network	1:100,000	50	N/A	N/A	U.S. Department of Transportation	2015	N/A
National Land Cover Data set	1:60,000	30	1	N/A	U.S. Geological Survey National Map Viewer, Multi-Resolution Land Characteristics Consortium	2011	N/A
National Elevation Data	1:60,000	30	N/A	N/A	U.S. Geological Survey National Map Viewer	2011	N/A
Cropland Data	1:60,000	30	N/A	N/A	U.S.D.A. National Agricultural Statistics Service, National Resource Conservation Service	2006	85-95% accurate, data standard
Soil data	1:12,000	10	3	N/A	National Resources Conservation Service	2004-2014	N/A
Trempealeau County Land Records geodatabase	The geodatabase downloaded from the Trempealeau County Land Records website contains 71 feature classes organized into 6 feature datasets.  Instead of listing all of these out in this metadata table, I will check metadata periodically for individual feature classes that are utilized in the project

r     Figure 2.  Table showing metadata for various data sources
   

     Results

      The following map (Figure 3) shows the rasters that were downloaded, projected and clipped during this activity.  The elevation is shown in meters.  The crop cover map from the National Agriculture Statistics Survey, and the land cover map from the Multi-Resolution Characteristics Consortium show different land use in different colors.

Figure 3.  Maps showing various rasters

     Conclusions

      Successfully downloading and organizing data is a fundamental part of any GIS project.  Understanding what the data is, and being able to find metadata characteristics are essential if you want to be able to make any claims on your results after analysis.  In this activity, we downloaded data from various different organizations that utilized different ways of formatting their download process and also had metadata of variable completeness and accessibility. We also projected and clipped data using python, and learned the problems that can be associated with improper coding or labeling of files. 

Understanding where this data came from, how its accuracy has been evaluated, and how to utilize python are all valuable to the project of evaluating suitability and risk in sand mining in Trempealeau County.  

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