Whenever there is a disease epidemic, CDC specialists track the origins of the disease and try to understand how it migrated over time. How can we provide a visual aspect to understanding how a disease migrates among the human population over time?
The goal would be to provide a disease tracker to record real-time occurrences and updates. If we could visualize where there's a influx of occurrences we could potentially contain the disease and prevent it from spreading.
The dataset is from Kaggle: https://www.kaggle.com/cdc/zika-virus-epidemic The dataset consists of the Zika virus migration across the world. I took the data concerned with Zika disease occurrences within the United States.
- Pre-processed the US Zika Disease occurrences to identify and provide state abbreviations so that the Plotly API understands where to plot a data point
- For every date, an image is produced showing all the Zika occurrences within the US.
- Python (Juypter notebook) - curate the data for each report date, feed the curated data for a Plotly API call to generate the Choropleth map.
- Plotly API - generate the Choropleth map
- Javascript/HTML/CSS - used for rendering the slideshow
- Fix image generation
- There exists geographic mapping libraries however due to the many different varibles you can have, there are a lot of geographic mapping libraries to search thru in order to find the one that suits your dataset.
Human Trafficking - Track when and where human trafficking activity happens and send law enforcers to arrest criminals. Crime - Track when and where crime happens across the world and send law enforcers to those areas. Finance - Where and when did somebody make a transaction? Fraud detection