Know Ais

Overview

The KnowAIS project emerged victorious at the UN Big Data Hackathon 2022, securing first place and Best Visualization in the Big Data Expert Track. This innovative self-service analytics platform is designed specifically for optimizing the global shipping network by leveraging vast amounts of Automatic Identification System (AIS) data to generate crucial insights and support decision-making. The application not only demonstrates the power of big data in enhancing shipping operations but also highlights the collaborative efforts of a talented team from various organizations, including BPS - Statistics Indonesia and Tokopedia.

KnowAIS aims to facilitate the processing of AIS data quickly and effectively, with a focus on the relationship between global shipping networks and essential statistical indicators of the world economy. By utilizing a structured approach in data filtration, analysis, and visualization, KnowAIS provides an invaluable resource for stakeholders in the shipping industry.

Features

• Real-Time Insight Generation: Enable leaders to make informed decisions by quickly processing and visualizing AIS data in real-time.

• Comprehensive Data Analysis: Conduct thorough analysis connecting global shipping networks with economic statistical indicators, fostering a deeper understanding of market dynamics.

• Efficient Data Filtration: Implement an initial filtration process that ensures the most relevant information is captured, streamlining data handling.

• Robust Sampling Techniques: Maintain high vessel coverage greater than 80% during data sampling, ensuring representativeness and relevance for analysis.

• Advanced Port Activity Identification: Enhance efficiency by joining AIS data with port activity data using innovative approaches that reduce reliance on complex coordinate systems.

• Geospatial Analytics: Utilize H3 indexing for trajectory identification across specified zones, facilitating clear insights into maritime patterns and activities.

• Historical Data Contextualization: Leverage historical data from 1990 to 2018 for comprehensive comparisons and trend analysis, enhancing the accuracy of current assessments.

• User-Friendly Visualization: Provide intuitive visual representations of data to aid comprehension and facilitate better presentations of findings to stakeholders.