Data Gathering

Introduction

In order to understand the impacts of frac sand mining in western Wisconsin, data from various amounts of sources needs to be obtained to have sufficient and effective results. The goals of this lab is to promote the skill of file management, use python coding to create a geodatabase from the downloaded data, and be able to extract that data from various sources.

Methods

The beginning stage of this lab was to extract data from multiple sources on the internet. Extracting this data includes downloading, and unzipping the files into the correct folders for working purposes.

·         The first downloaded data came from the US department of Transportation. This included a Railway Network ZIP from the Bureau of Transportation Statistics website.

·         Second, the USGS National Map Viewer allows you to download data that includes the National Land Cover and Elevation information from their website.

·         Third, the USDA Geospatial Data Gateway provides land cover data in their website.

·         Fourth, the Trempealeau County Land Records division website holds various amounts of data such as boundaries, emergency, transportation, streets, etc.

·         Lastly, the USDA NRCS Web Soil Survey data for Trempealeau County Wisconsin was downloaded from their website.

Data Accuracy

Scale - relationship between depiction on map and its actual size in the real world

Effective Resolution – The smallest identifiable object on a map at a specified scale.

Minimum mapping unit – A features ability to be reasonably represented by a line and an area by a point at a given scale. 

Planimetric Coordinate Accuracy – The closeness that objects are to the real location on the Earth. 

Lineage - Documentation that shows how the data was collected, used, and by whom.

Temporal accuracy - How up to date the data is, and when it was published. 

Attribute accuracy - How accurate the data is represented when compared to real world.  The data is then recorded as a specific number for metric attributes and as an accuracy percentage for categorical data.

Conclusions

This lab was pretty challenging but gave me great experience on manipulating the data to correctly display it in a map.The classification list for Land Use/Land Cover was quite large so I grouped multiple values together in the legend. At first I had trouble organizing the data into a correct folders and geodatabase, I realized how important it is to keep certain files separated from one another. It especially makes a difference if you take a break from it a day or two and have to find where the data you need is located. I learned quite a few skills from this lab and will probably use them with future projects.