The goal of this lab was to measure the completeness of two different road datasets, Street Centerlines and TIGER Roads, and see which one gives a more accurate picture of the county’s road network. To do this, I compared total road lengths and also broke the analysis down by grid cells to see where one dataset might be stronger than the other.
I started by projecting the TIGER Roads dataset so it matched the coordinate system of the Street Centerlines. Both layers were then clipped to the county boundary. Using Calculate Geometry, I created length fields in kilometers and summed them to get the total lengths. This showed TIGER Roads had about 11,382.7 km compared to 10,805.8 km for Street Centerlines, meaning TIGER had around 577 km more roads overall.
To dig deeper, I used the Intersect tool to slice both datasets by the county grid and calculated road lengths for each cell. With the Summary Statistics tool, I totaled the road length per cell, then joined the results back to the grid. From there, I created new fields to calculate the difference and percent difference between TIGER and Street.
The results showed TIGER Roads was more complete in 162 grid cells, while Street Centerlines was more complete in 134 cells. One grid cell had no roads at all, so it was considered a tie. This pattern shows that TIGER is slightly more complete overall, but Street still covers nearly half the cells better.
For the final step, I made a map layout that displays percent differences using a diverging color scheme. Purple shows cells where Street is more complete, green shows where TIGER is more complete, and beige marks cells with less than 5% difference. This map clearly highlights areas where the datasets agree and areas where one is much stronger than the other.
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