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EMBERWATCH
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THANKS!
🔥Inspiration🔥
Fire. The fuel that feeds humankind. The power that razes cities, topples nations and forces the strongest of materials to bend before it. A double-edged blade that brings life to those who wield it properly, and destruction to those who don’t.
In recent years, the climate on our beloved Earth has started to change for the worse. Climate change has been shown to affect wildfires, due to the increased amount of droughts, and higher overall temperatures. Studies have shown that in more recent years, fires have been getting larger, more severe, more frequent, and more dangerous.
This dilemma that the world has to face is what inspired us to create EmberWatch, an extremely helpful fire visualization tool capable of displaying wildfires that have happened all around Canada throughout the past century. Canada is one of the world's leading producers and exporters of wood, with the industry accounting for about $23.7 billion of the country's GDP in 2019. Due to the natural risk that comes with such an important industry, we felt that our project would be able to make an impact on local enterprises, and populations who live in high-risk areas.
🔥What it does🔥
In short, EmberWatch is a data visualization program that plots out the information on known wildfires throughout Canada given a location, and a time range. The map starts the user off at the IP location of their device, and then simply allows the user to explore the map while they change up the time ranges and watch the patterns of wildfires through the years.
🔥How we built it🔥
Our project started off as a simple idea to map out wildfires over an unknown expanse of land. In order to execute this, we first determined the number of active tasks and roles that needed to be fulfilled. These include, but are not limited to: finding data and information about fires, converting hundreds of thousands of lines of data into a usable format, creating a map that could display all of this data, actually displaying the data, and finally, creating an elegant webpage that would be user friendly, and functional. In order to meet all of these tasks, we decided to divide the project into its frontend components, backend components, and a mix of both in the middle. In the frontend, we used HTML, CSS, JavaScript, and a bit of BootStrap to help get the main webpage working. On the backend side, we used Python to manage and organize all of the data, while also using DCP to calculate the distance values between all fires and the user location. Finally, between the two, we had the creation of the map, and its inputs and interactability.
🔥Challenges we ran into🔥
One notable challenge we came across throughout the project was the integration of that data into the interactive map. We wanted to make the map easy on the eyes and easy to use, while also having large amounts of data flowing into it in order to draw out proper representations of the fires in the area. However, this led to many efficiency problems, as the sheer amount of items being rendered and information being sent would drastically reduce load speeds and increase the lag while using the map. To remedy this, we wrote a few functions to sort through the data not only given the time range but also on the size and locations of the wildfires in the datasets.
Other challenges we came across included the difficulty of using DCP towards the beginning of the hackathon, as well as our uncertainty when we started out, which led us to switch from using ReactJS to pure HTML, CSS, and JS.
🔥Accomplishments that we're proud of🔥
Using DCP
We used DCP in order to calculate distances from 400 000 different datapoints to a automatically detected location from your IP
Using Jupyter Lab + Python
We used Jupyter Lab to scan through hundreds of thousands of lines of text to reformat them.
Interactive map
We created a fully interactive map that displays a massive number of fires given their range
Fast refresh
Our map refreshes at a face pace without the need for redrawing the map or refreshing the page.
Image carousel
We created a nice image carousel on our home page that will present a set of pictures automatically.
🔥What we learned🔥
We learned that there is a substantial increase in national disasters in the past decade, with many being related to the weather or fire. Visualizing data makes understanding it much easier Though a project may seem simple in the beginning, it may prove to be much harder to finish in practice
🔥What's next for Ember Watch🔥
EmberWatch has the potential to improve and become a much larger scale product, and a more highly functional product. A first step could be to add more functionality to the map, possibly with elevation controls, average humidity levels in specific areas, the severity of fires, and the paths of the fires.
Furthermore, the project can be added to an extension or mobile application, which would then further increase the ease of access and user-friendliness.
🔥Persona 0 -> Julia from British Colombia🔥
Julia is an elderly woman from British Columbia. She is concerned about the fires that are raging near her home and fears that she will be caught in one. She is becoming increasingly concerned about residents who refuse to follow and obey evacuation orders. She wants a way to track where the forest fires are occurring and be more aware of the damages caused. Other than being concerned for herself, she is also an environmentalist and is worried about the impact of climate change.
Due to the recent events in British Columbia with the forest fires, we were inspired to create a web app that would create a map visualization of all the fires that occurred in Canada. This helps keep the damage caused transparent and allows more people to be aware. It also functions as a forest fire tracker, where people can be aware of the nearest forest fires to them.
🔥Persona 2 -> Joe from the Philippines🔥
We think that we are capable of helping out Joe in his home in the Philippines. Our program can easily be adapted to suit different regions, and it would be quite easy to display all of the fires in Joe’s area. This can help him greatly because Joe can learn where fires commonly break out and he can locate much safer places to be around. This program will not remove all of the risks from Joe’s community, nor will it completely solve his issue, but it does present as a way to keep Joe as safe as possible, at least from fires. As we know, since the first step to solving any problem is acknowledging that there is one, it’s possible that Joe’s community can become better suited to fighting fires given the data we present, such as by developing larger and more efficient water sources near high-risk areas over the span of a decade, which would greatly reduce any future damage done by fires.
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