GIS 4930, Lab 3: Data Quality Assessment
This was a fairly straightforward lab. In it, we had a county boundary, a gridded layer within the county, and two line shapefiles of road data. Our goal was to determine which set of road data was more comprehensive, both overall and within each grid cell provided.
To do this, I edited the attribute tables of each to add a length in km field, then used calculate geometry to have the road values in kilometers instead of feet. Then, I exported the tables to Excel and summed the values. From there, I clipped each road layer by the grid layer to remove roads outside the grid, calculated geometry again, and then used the Summarize Within tool to get the length of road inside each cell, then joined them together and used a python expression to determine the percent difference. I probably wasted the most time trying to symbolize it in a map -- there are several large outliers, and several grids with no roads from either dataset. I ultimately decided to mark the null cells as 0/no difference and not make a special category for the large outliers.
To do this, I edited the attribute tables of each to add a length in km field, then used calculate geometry to have the road values in kilometers instead of feet. Then, I exported the tables to Excel and summed the values. From there, I clipped each road layer by the grid layer to remove roads outside the grid, calculated geometry again, and then used the Summarize Within tool to get the length of road inside each cell, then joined them together and used a python expression to determine the percent difference. I probably wasted the most time trying to symbolize it in a map -- there are several large outliers, and several grids with no roads from either dataset. I ultimately decided to mark the null cells as 0/no difference and not make a special category for the large outliers.
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