Merging a Million Tiny Polygons

Candy Williams

GEOG 375

May 15, 2015

Project Summary

Goal: Use Python to automate the merging of land use polygons

Maps have been developed that show 19 different types of land use for counties in California. These maps are periodically updated using National Agriculture Imagery Program (NAIP) data to check for and track changes in land use. After weeks of photo checking California counties for land use changes, the data (polygons) are used to generate land use conversion statistics. This can be a long tedious process especially if the geodatabases (GDB) do not line up very well, which creates many small polygons. ThsThis process is outlined below.

1. The first step of the process is setting up GDBs and datasets and photo checking (using NAIP) the county to produce a changes layer.

2. The second step is unioning the changes layer with the county land use layer. In some counties we also include other layers that also add Williamson Act properties and zoning.

3. The third step is unioning the updated county land use layer with a USDA soil survey layer. This can cause a large amount of sub-acre polygons or slivers (polygons less than 1 acre that are caused by unioning the new land use layer with the soil layer.)

In some cases there have been as many as 1,900 sub-acre polygons. Right now all of these sub-acre polygons have to be manually merged to an adjacent polygon by following a 3 page criteria sheet. (e.g. if Class L is adjacent to Class G, it becomes G followed by Class U, S, or P and so on). The following screen capture shows the polygon and the tools that I use to investigate and merge them.

This is also where mistakes are most likely to occur. For my project I would like to try to write a script that would merge these polygons automatically.

When first started I wasnt sure where to begin. I went to ESRI Help section and the explanation for merging polygons was very complicated. I tried 3 or 4 different flow chart of how to get the outcome that I wanted. There was so much data and so many different criteria that I kept getting overwhelmed.

For example, Grazing Land (G) category sub-acre polygons (