Healthy Regions + Policies Lab

What are Healthy Neighborhoods?

We’re interested in how place drives, interacts with, and influences health for different people, in different ways. To explore this further, we look at neighborhoods as complex systems with spatial signals that help decode their stories.

What Drives Disparities?

We look at multiple dimensions of social vulnerability, differences in built and physical environments, and historical context to better measure and understand disparities in the communities we work with. To address this intersectional challenge, we partner with multidisciplinary teams.

Extending 'Metrics with Spatial Effects

We model spatial effects explicitly to uncover new understanding, and are pushing the boundaries of what’s possible in evaluation research and social-spatial network analysis, as well as design-driven infrastructures.

Join our Seminar Series to learn about the latest work in the field.

 Welcome to HeRoP

The Healthy Regions & Policy Lab is housed at the Center for Spatial Data Science and integrates innovative GIScience, public health, and statistical approaches to explore, understand, and promote healthy regions and policies.

Spatial Data Science For Good

For each project we take on, we commit to both research and translation for policy & public use. We’ve collaborated with health departments, community organizations, and national healthcare groups to translate research for good.

We likewise maintain a commitment to Open Science.

News

Big Data, Government Policy and Defeating COVID

Big Data, Government Policy and Defeating COVID

This month, Dr. Kolak of HEROP connected with leading scholars to discuss how data can be used to inform policy to defeat COVID, the emerging wave in Hong Kong, what happened in the US via the Covid Atlas, & brainstorming international, evidence-based cooperations...

Facilitating STEM Research Experiences

  • Graduate Students 56% 56%
  • Undergraduate Students 37% 37%
  • High School Students 7% 7%

Research Experiences Since 2016

%

Women

Scroll to Top