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Data quality score

Understand the context behind your emission estimate with Lune’s Data Quality Score (DQS). This score provides insight into the type and completeness of the data used, highlighting where assumptions may have been needed to deliver the most accurate and precise estimate possible.

Pre-requisites

Familiarize yourself with the following guides:
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Calculate an emission estimate using the API.
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Calculate an emission estimate using the CSV.

1. Calculate an emission estimate

Each time an emissions estimate is calculated, a DQS is returned alongside it.The DQS is returned for every individual leg estimate, including:To calculate the overall score for a shipment, a weighted average of the DQS values across all legs is used.The Data Quality Score is returned for both CSV and API calculations, as outlined below. You can also view the score directly in the Lune Dashboard or on the shareable Analytics page for each estimate.Image of data quality score in the Lune dashboard
Using the API
When you make a POST request to /v1/estimates/shipping/multi-leg, the response will include several data_quality_score objects. There will be one for the entire shipment, and one for each leg. This object contains a letter grade indicating the quality of the input data for that leg.
  • DQS object
  • Using a CSV
    When you upload a CSV to calculate emissions, the results file will include a Data Quality Score for each estimate. The following columns in the results file indicate the calculated score:
    overall_data_quality_score
    The weighted average score across all shipment legs, representing the DQS for the entire shipment.
    legx_data_quality_score
    The score for an individual leg of the shipment, where X corresponds to the leg number.

    2. Understanding the DQS

    The Data Quality Score (DQS) reflects the precision of each emission estimate. Estimates based on high-quality, primary data provided directly in the request will receive higher scores. In contrast, if Lune’s emissions intelligence must rely on modelled or average emissions data to fill in missing or incomplete input, the score will trend lower.The DQS is returned as a letter grade ranging from A_plus to D, including plus and minus variations.
    • A_plus indicates excellent data quality.
    • A to C reflect reliable data quality.
    • D signals poor data quality, often due to low-confidence or invalid inputs, such as an unrecognized IMO number or a mass value of 0 tonnes.
    Calculating the DQS
    Lune’s DQS builds on the Smart Freight Centre’s Data Quality Index but takes a more granular approach. The DQS goes beyond the standard framework to provide deeper insight into the quality and completeness of the data behind each estimate.The score is calculated using a weighted average of three components:
    • Transport method/Emission factor: Determined from the data provided and inferred about the method of transport. Makes up 45% of the total score.
    • Route: Determined from the data provided and inferred about the total distance travelled. Makes up 35% of the total score.
    • Load: Determined from the data provided and inferred about the total mass of the shipment. Makes up 20% of the total score.
    The table below outlines how the components are graded:Table describing how the data quality score is determined.
    Improving your DQS
    To achieve the highest possible score for each shipment method, thus improving the accuracy of your emission estimate, aim to provide the most specific and accurate data available:
    • Specify the actual transport vessel:
      • Air shipments: Include the flight number or aircraft type.
      • Sea shipments: Provide the IMO number or ship name, along with the departure date to enable AIS tracking.
      • Road shipments: Share fuel usage details. If unavailable, include values for load_factor, empty_run_factor, gradient, or situation.
    • Provide additional details when using TEUs: When submitting estimates using TEUs, include the mass of the cargo if available. If mass is not known, specifying a cargo_type can help improve the accuracy of the estimate.
    • Improve route specificity:
      • If the vessel isn’t being tracked, provide coordinates, full addresses, or the exact distance traveled.
      • Ensure that origin and destination data are appropriate for the selected shipment method.

    What to do next…

    Now that you have your Data Quality Score, explore the rest of Lune’s features, designed to give you granular data and actionable insights about your shipments!