# Big Data and Network Analysis of Children's Health

> **NIH NIH K01** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $137,660

## Abstract

Project Summary
 Decades of research suggest that neighborhood socioeconomic disadvantage increases children's
health risk. This proposed project seeks to address two major weaknesses in conventional neighborhood
effects research and interventions: a) the assumption that residential neighborhoods function independently of
each other - ignoring that risk factors in areas where people work, learn, and play away from home may
interact with residential factors; and b) as importantly, insufficient understanding of neighborhood effects
mechanisms and heterogeneity in effects. To systematically address these critical barriers in the field, I
propose a research and training program that will enable me to learn, use, and adapt recent advancements in
Big Data analytics. I plan to model hidden interdependencies among individuals and neighborhoods and
operationalize mechanisms of neighborhood effects by drawing on multiple large datasets (demographic,
geospatial, networks, population flows), with several hundred million observations across multiple states, cities,
and years, and match them to locally and nationally representative restricted survey data. The massive
volume, great variety, and unique complexity of such data, such as relational data on inter-neighborhood
dependencies and interactions, pose a challenge to the standard capabilities of hardware, algorithms, and
analytical methods and models of social and population science. The proposed training program in Big Data
analytics and machine learning will enable me to overcome computational and conceptual challenges and
uniquely position me to: a) examine the ecological inter-neighborhood networks (econetworks) to which
population groups are differentially exposed to across space and time; and b) test new contextual mechanisms
underlying children's exposures to health risks. Specifically, I propose to: a) develop computational models of
dynamic large scale econetworks to assess population differences in exposures to health risk factors, as they
commute daily between home and workplaces; b) examine heterogeneity in econetwork effects on child health
using a hybrid design that links Big Data to local and national surveys; and c) model child health risk
mechanisms and causal effects using natural experiments on Big Data. The proposed training program will
enable me to learn and adapt Big Data analytics, draw on its strengths, but also address some of its key
limitations. With the support of a unique team of distinguished mentors and advisors, established experts in Big
Data analytics, spatial demography, network analysis, child development and health risk, neighborhood
change, and population heterogeneity, I will embark on a training program that will uniquely enable me to
address these research goals and position me to become an independent scholar and a leader in the field.

## Key facts

- **NIH application ID:** 10003037
- **Project number:** 5K01HD093863-04
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Corina Graif
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $137,660
- **Award type:** 5
- **Project period:** 2017-09-21 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003037

## Citation

> US National Institutes of Health, RePORTER application 10003037, Big Data and Network Analysis of Children's Health (5K01HD093863-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10003037. Licensed CC0.

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