#ARCMAP DISSOLVE HOW TO#
#ARCMAP DISSOLVE CODE#
#ARCMAP DISSOLVE DOWNLOAD#

No matter if we are targeting specific features right off the bat or we need to explore our data before making a selection, one of the most common vector layer tasks we do is extraction analysis. So many times we obtain vector layers that are way more data then we need, either by the fact itвЂ™s often easier to find a complete data set (such as all 50 states versus a Colorado specific layer or all the highways in the US versus Colorado specific highways) or we donвЂ™t know what data we need until we examine the attributes (such as cities with a population 50,000 or more). This was one form of extraction analysis, and in this section, we are going to briefly look at selections and exporting again, as well as some vector and raster geoprocessing tools which use spatial interactions instead of selections to create smaller datasets. In Chapter Five, we were first introduced to the idea of selecting data - by attribute, by location, and interactively - and then exporting that selection to a new output layer. Extraction analysis tools apply to vector, raster, and data tables. Quite often in GIS, we encounter datasets which contain way too much data for out needs, which leads to the need to make smaller, more concise data sets. Row.names(region) <- as.Extraction analysis tools are focused on converting large datasets into smaller ones. # If you want to recreate an object with a data frame If you want to do anything with the data or plot using ggplot you need to recreate the data frame. If you want to just plot this using base plot you can stop there. Region <- gUnaryUnion(region, id = $country) I use gUnaryUnion (and indeed I think unionSpatialPolygons in the maptools package uses this by default). Region <- spChFIDs(region, row.names(region)) Dissolve # Ensure shapefile row.names and polygon IDs are sensible I think the trick is to make sure the row names match exactly, and if you can match the polygon IDs as well with spChFIDs(). We should end up with one row per zone to dissolve, each with a reference for the relevant larger geography. We now need to merge the lookup table into our spatial object data frame. # Hopefully for your 'real' data you have a lookup table of all polygons and # For this we'll create a lookup table and merge with the spatial data

Plot(region) Regions # We're going to dissolve all regions in to one country (England!) # Check the shapefile has loaded correctly Unzip('lad-region-lookup.zip', exdir = '.')

# You can change this by modifying the next two lines

#ARCMAP DISSOLVE DOWNLOAD#

# and download and run everything from here. # The default behaviour of this script is to create a folder called 'dissolve-example' We need a shapefile of small geographies to вЂdissolveвЂ™, a lookup table to tell us which polygons dissolve into which, and we need a couple of R spatial packages to run everything. LetвЂ™s gather together everything we need to complete this example.

#ARCMAP DISSOLVE HOW TO#

This post is essentially a companion piece to Clipping polygons in R I wrote both because I often forget how to complete these tasks in R.

#ARCMAP DISSOLVE CODE#

With R this is can be a bit involved, but once done is fully reproducible and the code can be re-used. Dissolving polygons is another fairly elementary GIS task that I need to perform regularly.

Arcmap dissolve