Making Resilience Measurable
The world has changed. It’s time our approach to climate and disaster risk changes too. As climate change and extreme weather accelerate each year, traditional models of hazard exposure are inadequate to handle the increasing vulnerability of assets, infrastructure and communities.
Without understanding both the direct and indirect consequences of climate and natural disaster risk for businesses, it’s impossible for asset owners, property managers, and insurers to effectively manage and mitigate their risk exposure.
Recent extreme environmental events demonstrate that comprehensive, network level data are key to uncovering risk exposure and building resilience. Most assessments of asset risk consider only building-level data, obscuring the broader vulnerability of a property. One Concern not only models the climate risk and disaster exposure of a single building, but also all the networks it depends on, including highways, airports, and power grids and so on.
In the US alone, One Concern has collected, curated, and analyzed trillions of data points to create our digital twin, including data on 160 million buildings, 45,000 power substations, and 2.7 million highway segments. We have 57 million data points on windstorm risk, 630 million data points on earthquake risk, and 1.8 billion data points on flood risk.
The volume of empirical data we’ve recorded helps us to build synthetic datasets, powered by interpolation, inference, and advanced machine learning for globally scalable and relevant analyses. Our synthetic datasets leverage the best of ground truth and machine learning to fill in gaps in knowledge via inference and trained models.
Meet Data Science
One Concern data scientists interrogate datasets to find the most useful subsets by utilizing parameters like location or building characteristics to identify core vulnerabilities. We perform regular quality control on our data to solve for outliers and systematic data errors, and continually review data sources to find new information and keep our data inputs relevant.
We offer formatted and joined datasets by building, so clients can easily plug the information into existing risk analysis processes for a more complete picture of property resilience.
Statistics and Analytics
One Concern offers a series of resilience statistics and metrics in addition to our curated datasets to help users interpret the risk vulnerability of prospective and owned assets, and make effective plans to mitigate or transfer related risks. We transform datasets through algorithms and modeling to tell a clear story of risk exposure. Our resilience statistics are intermediate calculations that can be plugged into a clients’ existing analysis workflow, while our resilience metrics are complete stand-alone measurements.
All of our analytics can be collapsed or expanded to be conditioned to only one hazard or return period, or multiple, depending on the needs of our clients. Our analytics are also designed to allow users to consider climate change in their calculations by applying the conditions of RCP 4.5 to analyses.
The downtime of an asset in hours or days due to a hazard.
The financial loss conditioned on an event that causes the property to be unusable.
The probability of downtime due to any event that makes the property unusable.
A probability-adjusted downtime loss estimate.