NSF award 2521573 · Texas A&M University

Explainable Spatial Machine Learning Methods for Complex Socioeconomic Data

Final report due

29 December 2028

in 803 days · The award ends on 31 August 2028.

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Decide about an extension3 May 2028in 563 days
award ending 2028-08-31 · Research.gov
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Extension request17 Jul 2028in 638 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2028in 673 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 August 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2028.

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Award card

Award number: 2521573
Period of support: 1 Sep 2025 – 31 Aug 2028
Amount: $199,944
Awardee: Texas A&M University, College Station, TX
Program: Methodology, Measuremt & Stats
Directorate: SBE

Abstract

The aim of this project is to develop advanced explainable spatial machine learning-based artificial intelligence models and algorithms for clustering and predicting complex spatial data, focusing on problems involving socioeconomic data. These methods help uncover complex spatial patterns and provide a deeper understanding of how socioeconomic factors relate to outcomes across different areas. They are applicable…

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