Human-environment interactions in urban settings are complex

Big Data generated in the context of the Internet of Things (IoT) can provide real-time assessment of both individual and population health, based on existing and emerging knowledge about environmental and behavioral risks.

How We Work to minimize risk and maximize resilience

Risk comes in many forms in the city. We provide solutions to enhance safety while citizens and tourists traverse the city. We believe that is is in cities that solutions can be found and resilience can be scaled up.

We develop integrated data ecosystems, using mobile devices (smart phones, wearables), sensor systems (remote sensing, including satellites; fixed and mobile ground sensors) and mobile platforms, to generate risk and resilience models in each urban location.
We leverage real-time big data, predictive analytics, and DSS (decision support systems) to provide cities, citizens, community groups and businesses with the most accurate, valid and reliable information on environmental risks (e.g air pollution) and behavioral risks (e.g.sedentary lifestyle) for specific clinical conditions and population groups.

Rethinking Risk

The range of threats faced by urban populations is broad and diverse, and includes environmental risks, climate risks, and risks posed by human action (or inaction). These risks interact increasing the vulnerability of specific populations to both harm and injury

These multiple classes of risk can be described as follows

Modifiable risks defined as those that are amenable to change, and can be addressed via an intervention
Nonmodifiable risks defined as immutable (such as age or gender)
Continuous risks defined as those that occur irrespective of location or time, such as specific character traits
Discrete risks defined as risks that are time or location-dependent (such as exposure to air pollution at a busy intersection at rush hour)
Multidimensional risks defined as those risks that are within-person, between-person, person-environment, crowd-environment.

Our Work Builds On and Exploits

New models of public health preparedness aimed at addressing the challenges of climate change, and new understandings of public health aimed at promoting community health resilience (CHR)
New research frameworks that link big data to knowledge (BD2K) leveraging this to develop a new approach we call Big Data to Policy (BD2P)
Research in the field of computational behavioral science extending this to the new field of computational public health (and epidemiology)
Research on the significance of temporal spatial analyses and geo-localized data to tackle the social gradient of health (and health inequities)
New research in the field of exposomics aimed at modeling the impact of climate conditions and air quality on human health using remote sensing and mobile sensing technologies
Resilient Cities Frameworks to generate the ability of a ‘system of systems’ to function under stress