Inspiration

Before we begin, we wanted to get everyone’s brain juices flowing: What is something common between your home, the coffee beans for breakfast, and that piece of clothing you just ordered online? There’s a good chance the materials used in building your home, agricultural commodities like coffee beans, and online retail goods were sea-shipped to your location

Seaborne trade is projected to grow by 39% until 2050. And with a global drive to reduce emissions, efforts have been made to establish what is known as green corridors: specific ‘shipping’ trade routes between major port hubs where zero-emission solutions are supported and demonstrated.

Sea trade is known to have extensive ecological and environmental impacts, and our solution aims to identify trade routes with the heaviest traffic and examine the potential to employ green corridors in them

What it does

Stage 1: Economic

• Purpose/Visual: Map, FILTER to indicate the top 5/10/20 travel routes ←> distinct routes separated by nautical mile/ by observation.

Stage 2: Environmental impact (GHG emissions, noise, invasive species)

• Purpose: To visualise and form relationships between multiple factors (GHG emissions, time, noise pollution, seasonal current & wind patterns, chemical & waste pollution)
• Visual: Different variations of dashboards (line graph, bar graph, force-directed graph etc.) to visualise the relevant data.
• Routes from Stage 1 will be given a colour grade (according to the env impact) with green (light), orange (moderate), and red (severe).
• Upon selection of individuals routes, a new display will indicate: -> A score (/100) with comparison to the other top 10-20 routes in terms of a) GHG Emissions b) Noise (ship vessel engine etc) c) coating on board to prevent invasive species d) intersections with marine superhighways

Stage 3: Intersection with marine superhighways

• Purpose: Gain a deeper understanding of marine movement and how they are affected by shipping.
• Visual: Coordinates of these superhighways will be plotted together with Stage 1 to visualise the intersections of shipping channels and marine movement.

How we built it

Stage 1: Economic

• Using PostgreSQL (long and lat of ship movement, port names, and vessels), we will plot shipping lines on PowerBI. Bolded lines will indicate busy shipping channels.

Stage 2: Environmental impact (GHG emissions, noise, invasive species)

• Using SQL to manage our database, we will visualise these datas using suitable formats (bar graph, line graph, gauge, heat map, force-directed graph) on PowerBI.
• Colour coding will grade the shipping channels (from Stage 1) based on their overall environment impact.

Stage 3: Intersection with marine superhighways

• Similar to Stage 1, we will plot the migration superhighways of whales on a map using PowerBI.

Challenges we ran into

1. Conflicting timezone as the biggest timezone difference in our team is by 15hours!
2. This is our first time dealing with large size of data. Hence, we are not experienced enough to clean data as fast as we expected ourselves to do.
3. All of us have our own job to work, hence we only have around 4 hours a day to work on this hackathon individually and as a team.

Accomplishments that we're proud of

1. We were able to assign taskings at appropriate hours with sufficient time to complete them. We also managed to have everyone onboard for multiple video calls together to complete the project.
2. Meet new people and make friends!!
3. Gain exposure to climate related to shipping and oceans.
4. Gain exposure to Microsoft tools such as Microsoft Azure and PowerBI.

What we learned

1. Gain more knowledge of oceans, marine biodiversity, shipping movement and shipping emissions.
2. Microsoft tools (Azure and PowerBI)
3. Working as a team with different schedules and timezones.

# What's next for Team Deep Green

Our team envisions ourselves gathering insights from complex data and deliver them to the masses with the following three criterias: up-to-date, useful for all stakeholders and insightful.

Time is an important factor when it comes to data relevance. Hence, keeping our database up-to-date is crucial in delivering relevant and critical insights for major stakeholders when they need to decide where to set up the necessary green corridors to mitigate the existing problems. We are looking into automating the process of updating our Azure Cloud database.

We are also looking into creating dashboards for different stakeholders. For the masses, we will want to provide more infographics to explain the insights of the data that we visualize. As for decision-makers such as governments and shipping companies, we promise to provide detailed reports on the insights of data to aid with their decisions.

Most importantly, we want our visualisation model to be insightful. Drawing of relationships between multiple complex factors will require time. With the aid of a machine learning model and time series data, we will be able to identify critical insights, unrealized patterns and even create forecast so that major stakeholders can anticipate and take the necessary actions.

Built With

• azure
• mysql
• postgresql
• powerbi
• python