IN-CORE (Interdependent Networked Community Resilience Modeling Environment)

The Interdependent Networked Community Resilience Modeling Environment (IN-CORE) will have the capability of computing the proposed resiliency measures at the user-desired community level. The methodologies and algorithms will be developed by different research groups within the IN-CORE.

There are two different architectures of IN-CORE, a detailed explanation of each is provided below:

IN-CORE v1.0

The first version of IN-CORE is based on Ergo (previously referred to as MAEViz) - an open source multi-hazard assessment, response and planning tool for performing risk-based community resilience planning.

IN-CORE v1.0 is a Java application with a plug-in based architecture. This type of architecture allows researchers to extendIN-CORE's capabilities through the addition of new science/features. These features can be connected with the existing 40+ analyses to produce new results.

The core technologies of the Ergo framework include Eclipse Rich Client Platform (RCP), Geotools, Visualization Toolkit, JFreeChart, KTable and Jasper reports. These technologies make up the core of Ergo and provide capabilities such as; data management, visualization, analysis, etc. These components and their hierarchies are illustrated in the diagram below:

IN-CORE Diagram

Figure 1. IN-CORE v1 Architecture


IN-CORE v1 extends the capabilities of Ergo-EQ by adding new hazards such as Tsunami, Wildfire and Tornado. To see a brief demonstration of some of the capabilities of IN-CORE v1.0, see the video clips below:




IN-CORE v2.0

The second version of IN-CORE is currently under development. IN-CORE 2.0 will be a web-based application that brings the capabilities of the version 1.0 desktop client to the web while adding new capabilities that aren't available in 1.0 such as a REST API, support for tools in additional languages such as Python, support for spatio-temporal data, surface fragilities, communicating with external tools such as OpenSEES, overlaying data from web sources such as OpenStreetMap, NBI, NOAA, etc.