Communication is the key to alert people before the disaster, helping them out to moving to safer places during the disaster and to get them back to their lives. Hence time must be utilized wisely. Our inspiration is to reduce the time in reaching out to more people for help during the time of disaster. We wanted to create an application which would serve as a single hub to reach on for disaster-related relief services and contacts.

What it does

The application targets four main features:

  • SOS Service: Uses the geolocation of the user, the application sends alert/Help request to users first and second emergency contact, 911 and also based upon the disaster the user is stuck in or facing the terror, the application sends alert/request to organizations that helps in moving people to safe locations during disaster or provide disaster relief services. The application utilizes Google Geocoding API to find the specific location of the User, Twilio API to send Group Alerts/voicemails.

  • NGO's and EMA's search: Based upon user input the application provides the list of NGO's and emergency management agencies. The application uses Algolia API, Cloudant NoSQL DB for this feature

  • Analysis and Visualization of Flood Risk in the Various States of US: Application analysis the data which is collected and gathered from FEMA Website and cleaned in the pre-processing stage, Uses multiple analysis techniques using python packages and pandas, after analysis, Choropleth map visualization is made using the Plotly package representing various State maps of US.

  • Precautions and Remedies for Various Disasters: Application provides various remedy technique which could be very useful in the disaster situation. The Remedies are scrapped from FEMA and DataGov site, stored in the NoSQL DB then displayed.

How we built it

The application is built using python -flask framework, the IBM Cloudant Database is used for data storage. The application is built in phases, first phases start with the sos alert /help request service which is developed using the Twilio API and google geocoding and place-maps API. Second phases were to fetch and store the recommended remedies and precautions by government agencies to the NoSQL databases, retrieve it and display on the Users dashboard. The third phase was to collect the dataset on various disaster-related, relied program-related on and rescue related upon dataset from various source, clean and format the data to create a dataset for analysis, use traditional analysis techniques to visualize the flood risk in each state individually using the plotly package. The final stage was to develop a search engine for the user to search for their requirement based NGO's and EMA's, it was developed using the Algolia API and Cloudant database. The whole application is deployed on IBM Cloud which uses the bluemix services and cloud-foundry instance for the deployment.

Challenges we ran into

  • Using Cloudant NoSQL databases, the documentation of the IBM Cloudant is very limited but the database is very powerful.
  • Finding Dataset corresponding to the various disaster which occurred in the US.
  • Integration between application and the Twilio API service.
  • Finding the data based upon the county in states while performing the visualization.
  • Application deployment onto the IBM cloud due the health check error and timeout error.

Accomplishments that we're proud of

  • Using of as many API's as possible in the single application.
  • SOS push Service as it runs perfectly and to the expectation
  • Search engine using Algolia API
  • Embedding interactive Visualization on the US Map for different states

What we learned

  • Learned various tech -stacks and API:
    • IBM Cloudant
    • IBM cloud deployment
    • Twilio API
    • Algolia API
    • Google maps API
    • Choropleth Map Interactive Visualisations showing data on hovering.
  • Integration between python-flask application and Cloudant NoSQL database.

What's next for ResQU

  • Twitter-based real-time alerts using location.
  • More Analysis, on predict of various natural disaster.
  • Donation system integration to support the relief funds and the needy.
  • Analysis of the NGO's performance and function during the disaster relief scenario.
  • Custom analysis for the user based upon the neighborhoods.
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