https://github.com/renzozuk/spatial_interpolation-java-spark

https://github.com/renzozuk/spatial_interpolation-java-spark

apache-spark concurrency interpolation inverse-distance-weighting spark sql

Last synced: 29 days ago
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• Host: GitHub
• URL: https://github.com/renzozuk/spatial_interpolation-java-spark
• Owner: renzozuk
• Created: 2024-07-03T17:38:18.000Z (8 months ago)
• Default Branch: spark-sql
• Last Pushed: 2024-07-16T21:49:42.000Z (7 months ago)
• Last Synced: 2024-11-16T12:04:33.253Z (3 months ago)
• Topics: apache-spark, concurrency, interpolation, inverse-distance-weighting, spark, sql
• Language: Java
• Homepage:
• Size: 11.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
 Spatial Interpolation 


 In this repository, the inverse distance weighting algorithm is used concurrently with the Spark Framework. 


There are 3 branches in this repository: Spark RDD, Spark Dataframe and Spark SQL.


## The algorithm

The Inverse Distance Weighting (IDW) algorithm is a type of interpolation method used to estimate unknown values based on known values at surrounding points. The key idea is that points closer to the location of interest have a greater influence on the estimated value than points further away. The influence of each known point is inversely proportional to its distance from the location of interest, typically raised to a power (often 2, but it can vary). This method is commonly used in geographic information systems (GIS) for spatial interpolation.




Where:

- \( Z(x) \) is the estimated value (in this case, temperature) at the location \( x \).

- \( N \) is the number of known points.

- \( Z(x_i) \) is the value (in this case, temperature) at the known point \( x_i \).

- \( d(x, x_i) \) is the distance between the location \( x \) and the known point \( x_i \).

- \( p \) is the power parameter that controls the rate of distance decay (commonly set to 2, but in this repository is set to 3).

## Application

Let's pretend the following situation: you know the temperature of N points, but you don't know the temperature of a specific point. Based on the temperature of the N points that you already know, you can predict the temperature of the mentioned specific point.