Motivation

People are faced with more and more catastrophic disasters as the climate goes extreme. There are multiple tools trying to visualize natural conditions but they often failed to take individual differences in to account. Developers of those tools usually think with a scientist’s mind set, homogenizing the population by their geographic distribution instead of by there susceptibility. This results in poor localization of the affected and delayed evacuation. Besides, susceptibility is a relative concept depending on the encountered situations. Therefore, we think a tool that could stratify risks according to population characteristics and that could directly inform authorities concerned could significantly change the efficiency and effectiveness of disaster mitigation.

Object

1. Develop a tool with preset and customized susceptibility modules which provides better localization and visualization of likely affected people under certain natural conditions.
2. Inform authorities concerned and suggest dynamic disaster mitigation plans according available resources.

Data Source

From Government and NASA Data to Web application.

Data processing

Python to modify the Data to Shapefile. Input the Web application.

Decision model

Specifically feature get the model.

Alerts dissemination

1. After the operators identify their target group, they could access their contact number and inform them of the risk and coping strategies via SMS or voice message.
2. Central emergency response center could inform local governments, hospitals, fire departments and other authorities concerned prior to the disaster to optimize resource allocation via internal alerting system.

Video demo

Features

1. Susceptibility modelus
   ONE WAY TO EARTH provides susceptibility modules for common disasters based on pre-existing research, including high temperature, flood, tsunami, typhoon, and etc.. You can also adjust various parameters to customerize your own module and find your target population.
2. Environment data
   Environment data derive from NASA and other sources with high accuracy. Users could drag and drop different parameters onto the map. The two layers would merge into one that demonstrate the extent of impact of the environmental factors on the target group.
3. Evacuation plan simulation
   Human resources, emergency supplies and refuges data collected from public API are feed into our decision model for evacuation plan simulation. Artificial Intelligence (AI) and further incorporation of the Internet of Thing (IoT) continues to improve efficiency and effectiveness of the final predicted models.
4. Immediate reaction
   With one button, primary emergency response teams could send alerts to the susceptibles in their jurisdiction via SMS or voice message. Central government could notify the local authorities concerned more precisely according to the population at risk. This not only prepares the specific response teams in advance but also saves tons of money by avoiding activating local resources indifferently.

Source Code

https://github.com/geant44/One_way_to_earth

Slides

https://drive.google.com/open?id=1yDsL1rOZNAjzX5oD...