4

I have a dataset with point data within a given country. Let's say my dataset looks somewhat like this:

tree_id | species | age | geom
------------------------------
   0    |   Ash   | null| ...
   1    |  Beech  |  70 | ...
   2    |   Ash   |  10 | ...
   3    |  Beech  |  70 | ...
   4    |  Beech  | null| ...
   5    |  Beech  |  60 | ...
  ...   |   ...   | ... | ...

As you can see the dataset has some missing data. For instance, tree_id 0 has no age. Therefore I would like to interpolate those missing values from a 100 meter radius.

I am looking for the mean of the species. The result should also include the number of sample trees used. A result table could then look like this:

tree_id | age_avg | samples
---------------------------
   0    |   11.8  |  113
   3    |   12.2  |   97
   5    |   50.7  |  272
  ...   |   ...   |  ...

Could you get me started with some PostgreSQL query code, please?

  • ST_DWithin will get you the within 100 meters part. Nearest neighbours can be done with a new Postgres operator, see this post. Median is trickier to do, as not built in, see this post for user defined functions. You have asked quite a lot of questions, so I suggest you look at those, and maybe refine it a bit. – John Powell Oct 7 '15 at 11:18
3

Ok so let's start again this is the answer to do what you want, but this will only be useful in a non-meanigfull context. For instance to render a 3D scene where some data are missing and you want to draw a "local medium tree" for each species.

I'm assuming your original table is called "mytrees".

Create two alias a & b from your table mytrees ,join b to a table if in your search radius, then summarize data for each point using aggregates.

SELECT a.tree_id, a.species, avg(b.age) as age_avg, count(*) as sample, a.geom
FROM mytrees a LEFT JOIN mytrees b
ON ST_DWithin(a.geom,b.geom,100) AND a.species = b.species
GROUP BY a.tree_id, a.species, a.geom
ORDER BY a.tree_id

Again a last warning, it will work but DONT USE IT for meaningful data-analysis. Only for rendering or as proof of concept.

Edited : using ST_DWithin as suggested by John Barça, way easier

  • Ok so I think I'll just delete the other one it's pretty redundant as you mention. New here still have to figure out a lot of things ;) – MarHoff Oct 8 '15 at 7:04
  • It works! First the query took forever - even when using LIMIT 1. So I suggest you add WHERE a.age IS NULL. Thank you again! – n1000 Oct 8 '15 at 9:49
  • This is dependent or your dataset and what you want to achieve. If you want to compute only for missing values, yes this the way to go. Please dont forget to mark answer as solved if you are happy ;) – MarHoff Oct 8 '15 at 10:42
  • Can you please mark you question as answered if it's the case? It will help users with similar question selecting interesting answers when searching ;) – MarHoff Oct 30 '15 at 8:11

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