ArcGIS Polygon Area Calculation – How to Determine Area Within Polygon Boundary

arcgis-desktopareapolygonunion

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I have a series of maps representing a 1000m diameter around a point. These maps contain 40 different types of polygons that represent a land use (ex. Corn crop, Pasture).

I want to select all polygons that are within the 1000m boundary and find out each of their areas.

I've attempted to create unions and delete the exterior polygons, but I'm only limited to performing a union on 2 features which would make that task very tedious. Is there a faster and simpler way of performing this task?

My goal is to create an attbiute table, that will have a unique identifer for each polygon and then a column with the SHAPE_AREA that I can export to excel.

I created the maps using ArcGIS, I have access to all licenses and other GIS programs.
All polygons are shapefiles, including the boundary circles.

Best Answer

Stated in the comments.
You want to use the intersect command.
possibly the summarize command
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And then depending on the use case perhaps
Join the two output (at separate times) back to the original and calculate some of the generated data into some existing or new fields.

Related Solutions

[GIS] Intersect (analysis) of polygons generating empty output

Follow these steps:

Visually verify that the polygons do indeed intersect using Arcmap
Restart Arcmap
Ensure that features are not selected
Re-run the intersect

[GIS] Group and union polygons that share a border in R

Here's a function that takes an sf polygons object and clusters all features within a threshold distance, then merges the features. So starting with N features you end up with M<=N features:

library(sf)
library(sp)
library(rgeos)
clusterSF <- function(sfpolys, thresh){
    dmat = st_distance(sfpolys)
    hc = hclust(as.dist(dmat>thresh), method="single")
    groups = cutree(hc, h=0.5)
    d = st_sf(
        geom = do.call(c,
            lapply(1:max(groups), function(g){
                st_union(sfpolys[groups==g,])
            })
            )
        )
    d$group = 1:nrow(d)
    d
}

As an example, I'll create some circular polygons based on some points. I'll have to use sp classes and the rgeos package until I figure out the sf versions. So I set a seed, then create 20 random points, make buffer polygons round them, and convert to sf object:

set.seed(123)
pols  = st_as_sf(
    gBuffer(SpatialPoints(cbind(100*runif(20),100*runif(20))), width=12, byid=TRUE)
)

plot(pols)

Now the function. I'll compute clusters within 1 unit. The function returns an sf object with a group column:

polcluster = clusterSF(pols, 1)

In this case its down to 5 clusters:

polcluster
  Simple feature collection with 5 features and 1 field

which look like this:

plot(polcluster, col=polcluster$group)

Now I think method="single" is the right clustering method. It should merge points such that all clusters are at least thresh distance from any other cluster...

Best Answer

Related Solutions

[GIS] Intersect (analysis) of polygons generating empty output

[GIS] Group and union polygons that share a border in R

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