{"id":15208685,"url":"https://github.com/renzozuk/spatial_interpolation-java-spark","last_synced_at":"2026-03-09T11:31:54.016Z","repository":{"id":248766661,"uuid":"823778165","full_name":"renzozuk/Spatial_Interpolation-Java-Spark","owner":"renzozuk","description":null,"archived":false,"fork":false,"pushed_at":"2024-07-16T21:49:42.000Z","size":12,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"spark-sql","last_synced_at":"2025-01-17T04:45:34.852Z","etag":null,"topics":["apache-spark","concurrency","interpolation","inverse-distance-weighting","spark","sql"],"latest_commit_sha":null,"homepage":"","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/renzozuk.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-03T17:38:18.000Z","updated_at":"2024-07-16T21:49:46.000Z","dependencies_parsed_at":"2024-07-17T02:11:57.670Z","dependency_job_id":null,"html_url":"https://github.com/renzozuk/Spatial_Interpolation-Java-Spark","commit_stats":null,"previous_names":["renzozuk/spatial_interpolation-java-spark"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/renzozuk%2FSpatial_Interpolation-Java-Spark","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/renzozuk%2FSpatial_Interpolation-Java-Spark/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/renzozuk%2FSpatial_Interpolation-Java-Spark/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/renzozuk%2FSpatial_Interpolation-Java-Spark/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/renzozuk","download_url":"https://codeload.github.com/renzozuk/Spatial_Interpolation-Java-Spark/tar.gz/refs/heads/spark-sql","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242276886,"owners_count":20101528,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["apache-spark","concurrency","interpolation","inverse-distance-weighting","spark","sql"],"created_at":"2024-09-28T07:01:36.507Z","updated_at":"2026-03-09T11:31:53.961Z","avatar_url":"https://github.com/renzozuk.png","language":"Java","readme":"\u003ch1 align=\"center\"\u003e Spatial Interpolation \u003c/h1\u003e\n\n\u003cp align=\"justify\"\u003e In this repository, the inverse distance weighting algorithm is used concurrently with the Spark Framework. \u003cbr\u003e\nThere are 3 branches in this repository: Spark RDD, Spark Dataframe and Spark SQL.\u003c/p\u003e\n\n## The algorithm\n\n\u003cp align=\"justify\"\u003eThe 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.\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"ql_f0a999afcd9cc442cdeda04af2e8f3ec_l3.png\" /\u003e\u003c/p\u003e\n\nWhere:\n- \\( Z(x) \\) is the estimated value (in this case, temperature) at the location \\( x \\).\n- \\( N \\) is the number of known points.\n- \\( Z(x_i) \\) is the value (in this case, temperature) at the known point \\( x_i \\).\n- \\( d(x, x_i) \\) is the distance between the location \\( x \\) and the known point \\( x_i \\).\n- \\( p \\) is the power parameter that controls the rate of distance decay (commonly set to 2, but in this repository is set to 3).\n\n## Application\n\n\u003cp align=\"justify\"\u003eLet'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.\u003c/p\u003e","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frenzozuk%2Fspatial_interpolation-java-spark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frenzozuk%2Fspatial_interpolation-java-spark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frenzozuk%2Fspatial_interpolation-java-spark/lists"}