Choropleth Map Visualization of the Zika Disease Epidemic within the US

Link below provides slideshow: http://htmlpreview.github.io/?https://github.com/rxl7906/DiseaseEpidemicVisualizer/blob/master/slideshow.html

Problem

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?

Proposed Solution

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.

Dataset

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.

Implementation

1) 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 2) For every date, an image is produced showing all the Zika occurrences within the US.

Technologies/APIs used

• 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

Future

• Fix image generation

Issues faced

• 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.

Other applications

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?

Built With

• backplane-javascript
• css
• html
• jupyter-notebook
• plotly
• python