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Impact on Airport Traffic due to COVID-19

Airports are crucial for the financial advancement of urban communities, nations, and locales. They straightforwardly add to economies by offering types of assistance to carriers, moving travelers, and shipping freight. The development of products and individuals additionally helps states, customers, and businesses.

However, the COVID-19 outbreak has hit airports hard—which could stall development in emerging markets.

As a result, air travel has fallen sharply, which has prompted airlines to cut capacity. Many have been closed by governments to contain the spread of the virus. The result is a sharp fall in revenues. Given the importance of airports to the economic development of cities, countries, and regions, the broader impact of COVID-19 on the global economy is enormous.

So Lets Do the Simple Data Analysis with Geo-Spatial Data to know witht the visualization how COVID-19 impact Airport Traffic.

Data

This dataset shows traffic to and from the Airport as a Percentage of the Traffic volume during the baseline period. The baseline period used for computing this metric is from 1st Feb to 15th March 2020. The dataset gets updated monthly.

Download Data. This data is publicly available from Geotab (geotab.com).

Lets Understand the Columns in Dataset:

AggregationMethod :- Aggregation period used to compute this metric
Date :- Date in format YYYY-MM-DD
Version :- Version # of this dataset
AirportName :- Name of airport
PercentOfBaseline :- Proportion of trips on this date as compared to Avg number of trips on the same day of week in baseline period i.e 1st February 2020 – 15th March 2020
Centroid :- Geography representing centroid of the Airport polygon
City :- City within which the Airport is located
State :- State within which the Airport is located
ISO_3166_2 :- ISO-3166-2 code representing Country and Subdivision
Country :- Country within which the Airport is located
Geography :- Polygon of the Airport that is used to compute this metric

<h4 class="item-title">Shreya Sinha</h4>

Shreya Sinha

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3 Comments

  • Dabiv Matina November 15, 2021

    Compellingly innovate effective data without cross functional information. Dynamically empower tactical internal or “organic” sources rather than cooperative partnerships. Professionally expedite resource-leveling networks and adaptive.

    • Dabiv Matina November 15, 2021

      Compellingly innovate effective data without cross functional information. Dynamically empower tactical internal or “organic” sources rather than cooperative partnerships. Professionally expedite resource-leveling networks and adaptive.

      • Dabiv Matina November 15, 2021

        Compellingly innovate effective data without cross functional information. Dynamically empower tactical internal or “organic” sources rather than cooperative partnerships. Professionally expedite resource-leveling networks and adaptive.

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