This project is a team effort of Team ‘Vesper’ participated in the NASA International Space Apps Challenge'20. Our team took on the challenge of the automatic air hazard detection because we were inspired by the idea of building a tool that could potentially save many lives just by automatically analyzing data from a variety of sources and putting this analysis into the hands of key decision-makers, as well as the general public.

The objective is to report the prediction of the earthquake from the historical data. A machine learning model is trained with historical data of the world related to earthquakes from 1965-2016. The data includes geographical location and magnitude of the earthquakes (23.5k samples). The model predicts earthquake magnitude for the year of 2017. Finally, a dashboard is created to visualize the prediction in addition to the historical analysis on the data.

An interactive visualization dashboard including different states’ (of USA) confirmed cases and the number of deaths. The visualization presents different calculations such as top N states (i.e., N can be changed from the dashboard) with total confirmed cases and deaths, new cases and deaths; case fatality rates (CFR) on time series data collected from https://github.com/nytimes/covid-19-data. The interactive dashboard is created using Tableau Desktop. Different pie charts, maps, line charts, and stack bars are used in this visualization.

The dashboard presents an interactive visualization of the latest COVID-19 updates. It includes the trend line of the time series data, geographic map, cumulative & newly confirmed, active, death, and recovered cases. It also presents a calculation on Case Fatality Rate (CFR), confirmed & death cases in 100,000. The regular update is taken from https://github.com/datasets/covid-19. Data is preprocessed using Tableau prep builder. Tableau is used for the visualization. In this visualization, users can drill down to the country to see region-specific details (new cases and trends) and also overall comparisons among different regions.

The purpose of this project is to gain insights into the agricultural market data of different commodities, e.g., Wheat, Corn, Barley, Oats etc. and present them in a dashboard. The data includes different attributes such as consumption, production, import, export, stocks, yield of the commodities. The data reports attributes on 125 countries and over 20 years timeline. The UI supports responsive layouts. For developing the visualizations, PSD Grain Data_small.

The objective of this project is to develop a dynamic dashboard in Tableau which can update the data source. The dataset for this visualization includes wheat consumption (domestic, fsi, feed dom., etc) in Canada over 20 years timeline. XAMPP is used to host the data in a local server. The data is fetched from the local server into Tableau. The Tableau dashboard presents a crosstab and a line chart of the time series data.