mia VS tinyflux

At our university years ago we had a research group for Internet of Things, and we wrote a PoC for IoT Platform which was open sourced here. This PoC is written in Python and don't support vertical scaling, but we tried to manage things models using python classes, which is its main feature. I will be glad to have your opinions on it and if you have any idea we will be glad to follow it and improve our PoC.

Here's the GitHub repository: https://github.com/citrusvanilla/tinyflux

As Californians enter the height of summer (along with all west-coasters in general), you’d expect our days to be filled solely with sun, sand, and beautiful alpine hikes but the truth is that in the last couple of years, we almost expect a fire to break out at a moment’s notice during these months. As an asthmatic that lives off the 5 in Los Angeles and recently experienced his first asthma attack, I wanted to explore whether or not the air quality improved in 2020 versus 2019 due to Covid and the associated reduction in commuting, but what I found was that the average air quality index for the year dramatically increased (a higher index means worse air). As someone who is definitely not a climatologist, I am declaring without evidence that almost all (if not entirely all) of this increase is due to the worst fire season in recorded history- a season in which nearly 10,000 individual fires burned about 4.4 million acres of land- a full 5% of the entire state. It’s hard to fathom but that’s about the size of Connecticut and Rhode Island, combined! Of course the acute effects of fire are devastating- lives and homes, businesses, entire towns lost forever. What I learned in making this visualization is that the secondary effects (in this case, increases in various particulate matter in the air) lead to many more deaths than fire itself. A recent study from the Harvard T.H. Chan School of Public Health estimates “the cumulative number of COVID-19 cases and deaths attributable to daily increases in PM2.5 from wildfires {in 2020} was… 19,700” for the state of California. I actually made this chart/animation as part of a demo on how to use a new open-source Python time-series/IOT database I just released called [TinyFlux](https://github.com/citrusvanilla/tinyflux), but the animation turned out to be interesting enough to share on its own. However, if you are interested in time-series and/or IOT data, I encourage you to take a look at TinyFlux! If you like what you see, please leave a star on the GitHub repository so that others may also one day potentially use the tool. Data: - Air Quality Index data - [EPA](https://www.epa.gov/outdoor-air-quality-data) - Location Data - [US Census Bureau] (https://catalog.data.gov/dataset/tiger-line-shapefile-2019-nation-u-s-current-metropolitan-statistical-area-micropolitan-statist) Analysis Tools: Python, [TinyFluxDB](https://github.com/citrusvanilla/tinyflux), Jupyter Notebook Visualization Tools: Python, Plotly, PIL, MapBox/OpenStreetMaps (base map), Adobe Illustrator, FFMPEG See the analysis from start to finish on GitHub [here](https://github.com/citrusvanilla/tinyflux/blob/master/examples/2\_analytics\_workflow.ipynb).