# Hierarchical Modeling and Analysis for Large Spatially and Temporally Misaligned Data in Environmental Health Applications

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $315,846

## Abstract

Project Summary/Abstract
 The last decade has seen an explosion of interest in statistical modeling and analysis of spatiotemporally
misaligned data and change-of-support problems, where different variables of scientific interest are observed
at disparate scales making them difficult to be coherently modeled. This is especially relevant in environmental
public health, where exposure data may be based upon data from monitoring data networks, while climate
data are usually available as rasterized outputs from numerical models. The situation is further compounded
by our objective of associating these factors with health outcomes (e.g. disease incidence, hospitalizations,
mortality and so on), which are reported by public health sources as aggregated data over regions rather than at
points. Furthermore, public health researchers today routinely encounter datasets exhibiting high-dimensional
spatial misalignment or change-of-support, where “dimension” refers to one or all of the following: (a) the
number of spatial units (e.g., geographically referenced coordinates), (b) the number of temporal units (time
points) at which the variables have been observed, and (c) the number of outcomes and other variables being
studied. We propose a versatile collection of easily implementable and innovative Bayesian statistical methods
that, in conjunction with appropriate software, will offer more comprehensive and statistically reliable mapping
and analysis for misaligned spatiotemporal data in high-dimensional settings. Our methods and software will
help spatial analysts to establish relationships among health outcomes and environmetal and climate-related
predictors. Our dissemination efforts will deliver our methodology to a far broader audience of health and
environmental researchers and administrators than is currently accessible.

## Key facts

- **NIH application ID:** 9850257
- **Project number:** 5R01ES027027-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Sudipto Banerjee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $315,846
- **Award type:** 5
- **Project period:** 2017-05-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9850257

## Citation

> US National Institutes of Health, RePORTER application 9850257, Hierarchical Modeling and Analysis for Large Spatially and Temporally Misaligned Data in Environmental Health Applications (5R01ES027027-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9850257. Licensed CC0.

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