Govan Graving Docks

Built between 1869 and 1898 by the Clyde Navigation Trust, Govan Graving Docks are the best preserved example of the Clyde’s glorious industrial maritime heritage. The docks initial purpose was to…

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A Predictive Earthquake Damage Model written in Python

I have been working on a project over the last couple of years in my role as a Data Scientist at New Light Technologies to develop a model that predicts damages to structures in near-real-time following earthquakes in the USA using nationwide building centroids and Hazus damage functions.

There are several important data sources used in this predictive model. The GitHub repo contains all required tables, however, some of the external data sets must either be downloaded and/or generated by the user with some basic geoprocessing steps, which are outlined in the GitHub readme file.

Sample of Hazus General Building Stock table. — Sample of Hazus General Building Stock (GBS) indicating the percentage of structures within each Structure Type per Tract.

Hazus Damage Function graphs for 36 Structure Types — Set of damage functions for each Structure Type + Seismic Design Code. For example, C1HHC can be interpreted as C1 (Concrete Moment Frame), H (High Rise), HC (High Code). The colored lines represent Damage Curves for the following Damage Categories: Blue — Slight Damage, Green — Moderate Damage, Yellow — Extensive Damage, Red — Complete Damage. For each graph, the X-axis is Peak Ground Acceleration (PGA) (%g), and the Y-axis is Probability of Meeting or Exceeding the Specified Damage State.

Other supplementary data sets used in the code (for doing things like spatial filtering, spatial joins, etc) include:

The program calls the USGS ShakeMap API to detect new or recent earthquake events in the USA. If the earthquake occurred in the US, and GIS files are available through the API, data is downloaded to the ShakeMaps subdirectory.

USGS ShakeMap (MMI) GIS data for the Napa 2014 M 6.0 Earthquake. MMI represents the intensity of ground shaking following an earthquake.

Sometimes following an earthquake, the ShakeMap data will be updated by USGS as the epicenter is relocated or more data comes available. In this case, the program will update the model results based on the most up-to-date ShakeMap data.

The ShakeMap GIS files are then processed and clipped to census geographies (counties, tracts), with all hazard information spatially joined to each Tract, County, and building centroid. All output files are saved into a local geodatabase built inside the ShakeMap folder for the event.

The building centroid count for each tract is used to estimate the number of buildings within each tract, and to calculate the breakdown of structures using the Hazus GBS table. Below is a breakdown of all the files pulled from USGS, as well as the output file generated by the Earthquake Damage Model:

Data sets produced by the Earthquake Damage Model for a single earthquake event.

Earthquake Model Results vs. Hazus Model Results for the Napa 2014 M 6.0 Earthquake (Red/Destroyed Buildings per Tract)

There are a few significant differentiators between this code and the Hazus model itself which results in slightly different model outputs (a difference of less than 10 buildings per tract in this test case):

Future iterations of this work will include:

Govan Graving Docks

A Predictive Earthquake Damage Model written in Python

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