# Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research

> **NIH NIH R01** · UNIV OF MARYLAND, COLLEGE PARK · 2020 · $329,703

## Abstract

PROJECT SUMMARY/ABSTRACT
This proposal represents a vertical advancement in neighborhood effects research, producing for the
first time, national neighborhood indicators of the built environment. Thus far, only local studies have
been conducted due to the resource-intensive nature of site visits to conduct assessments of
community features and also manual annotations of street images. With the recent advancement of
computer vision and the emergence of massive sources of image data, we will leverage our team’s
abilities to develop a data collection strategy utilizing geographic information systems to assemble a
national collection of Google Street View images of all road intersections and street segments in the
United States. We will utilize this data bank, and develop informatics algorithms to produce
neighborhood summaries of built environment that have been theoretically and empirically identified
to be important for health outcomes. After the creation of Neighborhood Looking Glass, we will
conduct investigations into the impact of neighborhood environments on health utilizing medical
records from hundreds of thousands of patients and accounting for predisposing characteristics in
analyses. Our investigative team—comprised of experts in the field of epidemiology, computer vision,
bioinformatics, and computer science—is uniquely suited to implement the study aims. Our Specific
Aims are: 1) Develop informatics techniques to produce neighborhood quality indicators; 2) Measure
the accuracy of data algorithms and construct an interactive geoportal for neighborhood data
visualization and data sharing, 3) Utilize Neighborhood Looking Glass and a large collection of
medical records from Intermountain Healthcare to investigate neighborhood influences on the risk of
obesity and substance abuse. The epidemic rise in chronic health conditions is recent and as such
suggests its cause is social, cultural, and constructed rather than purely biological. Thus, we have the
possibility of intervening on the environment to better support health. Recent studies suggest that the
current cohort of young adults may face historically high cardiovascular disease risk and chronic
disease burden. Our substantive investigation of the impact of neighborhood factors on chronic
conditions will contribute further to the understanding of contextual influences on the health of this
cohort at the forefront of a chronic disease epidemic. Moreover, the dramatic rise in overdoses,
accidental poisonings, and mental health issues contributing to premature mortality warrants further
investigation into risk-inducing environmental factors for substance abuse. Neighborhood Looking
Glass will be a significant benefit to neighborhood effects researchers, harnessing the largely
untapped potential of street image data to capture built environment characteristics. Results can be
utilized to inform population-based strategies to reduce health disparities and improve health.

## Key facts

- **NIH application ID:** 9979947
- **Project number:** 5R01LM012849-03
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** QUYNH NGUYEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $329,703
- **Award type:** 5
- **Project period:** 2018-08-06 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979947

## Citation

> US National Institutes of Health, RePORTER application 9979947, Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research (5R01LM012849-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9979947. Licensed CC0.

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