Skip to main content
Solved

Neighborhood Aggregator not working properly?

  • February 7, 2019
  • 1 reply
  • 118 views

dataninja
Forum|alt.badge.img

I have an urgent question regarding the transformer "NeighborhoodAggregator".

The following numbers are used for the width and height which are equivalent to 0.0316897 (mile).

I also used 2D Gridder to visualize the width and height and it looks like the following image.

 

My question is that how come my 2 points which are 0.0316897 (mile) apart, do not get aggregated. I am using EPSG:4326 for my coordinate system. By the way, what is the difference between doing buffer/dissolve compared to using NeighborhoodAggregator?

Best answer by ebygomm

The neighbourhood aggregator doesn't work in the way you are expecting it to (or the way that would be most useful!). It effectively creates a grid of squares which are the size of the neighbourhoods and then groups features depending on which square their centre point falls within.

 

I expect a process where you buffer and dissolve to define your neighbourhoods will give an output closer to what you are looking for.

 

You can also use a neighbourfinder followed by a list combiner to create a common attribute for all features within a certain distance of each other

This post is closed to further activity.
It may be an old question, an answered question, an implemented idea, or a notification-only post.
Please check post dates before relying on any information in a question or answer.
For follow-up or related questions, please post a new question or idea.
If there is a genuine update to be made, please contact us and request that the post is reopened.

1 reply

ebygomm
Influencer
Forum|alt.badge.img+45
  • Influencer
  • Best Answer
  • February 7, 2019

The neighbourhood aggregator doesn't work in the way you are expecting it to (or the way that would be most useful!). It effectively creates a grid of squares which are the size of the neighbourhoods and then groups features depending on which square their centre point falls within.

 

I expect a process where you buffer and dissolve to define your neighbourhoods will give an output closer to what you are looking for.

 

You can also use a neighbourfinder followed by a list combiner to create a common attribute for all features within a certain distance of each other