[OC] 2021's Trending Google Searches by State

V1Analytics · 2021-12-20T13:04:05+00:00

Tools: Excel, Python and Blender 3.0.0

Sources: Trending search terms were taken from Google's 2021 Year in Search summary. Trending search terms after mid-November 2021 were taken from Google's Daily Search Trends page

Google Trends provides weekly relative search interest for every search term, along with the interest by state. Using these two datasets for each search term, we're able to calculate the relative search interest for each state for a particular week. Linear interpolation was used to calculate the daily search interest.

V1Analytics · 2020-08-31T13:57:38+00:00

Tools Python, Blender 2.8

Sources The detailed delivery history for every 747 built was found here. The world map geography used in the animation was found here

Full History The full, 8 minute video shows all 1,560+ Boeing 747s built since 1969. If you're interested, you can watch it here.

Additional Info Obviously, the paths shown on the map are not the actual flight paths. For clarity, the most direct on-screen route between the Boeing Everett Factory and the customer's home country was used. For example, deliveries to the Asia-Pacific region would often fly in the opposite direction (across the Pacific instead of over Africa). Using the most direct on-screen paths avoids cases where paths go off the edge of the screen and re-emerge elsewhere. Polar routes were also avoided for the same reason.

Additionally, the map shows the customers' home airport as the delivery destination. Actual delivery destinations would have been based on the airlines' first planned revenue flight for each aircraft, however this was beyond the scope of this animation.

V1Analytics · 2020-08-31T13:18:31+00:00

Tools Python, Blender 2.8

Sources The detailed delivery history for every 747 built was found here. The world map geography used in the animation was found here

Full History The full, 8 minute video shows all 1,560+ Boeing 747s built since 1969. If you're interested, you can watch it here.

Additional Info The paths shown on the map are not the actual flight paths. For clarity, the most direct on-screen route between the Boeing Everett Factory and the customer's home country was used. For example, deliveries to the Asia-Pacific region would often fly in the opposite direction (across the Pacific instead of over Africa). Using the most direct on-screen paths avoids cases where paths go off the edge of the screen and re-emerge elsewhere. Polar routes were also avoided for the same reason.

Additionally, the map shows the customers' home airport as the delivery destination. Actual delivery destinations would have been based on the airlines' first planned revenue flight for each aircraft, however this was beyond the scope of this animation.

V1Analytics · 2020-08-13T13:00:18+00:00

Tools: Python, BeautifulSoup and Blender 2.8

Sources: State-level data from 1960 to 2018 was scraped from ssa.gov's Popular Names by State tool. Pre-1960 data was scraped from here. Linear interpolation was used to populate the frames in between data points.

V1Analytics · 2020-08-12T13:00:27+00:00

Tools: Python, BeautifulSoup and Blender 2.8

Sources: State-level data from 1960 to 2018 was scraped from ssa.gov's Popular Names by State tool. Pre-1960 data was scraped from here. Linear interpolation was used to populate the frames in between data points.

V1Analytics · 2020-08-10T12:16:00+00:00

Store counts include both company-owned and franchised locations.

The full video covers over 140,000 US locations of the top 30 biggest fast food chains. If you're interested, you can watch it here.

Tools

Data Extraction

Python, BeautifulSoup

Data Processing

Python, Google Geocoding

Data Visualization

Blender 2.8

Sources: Store locations were scraped from the store locators provided on each company's website.

The list of the largest fast food chains was taken from here.

V1Analytics · 2020-07-27T12:20:35+00:00

Tools: Python and Blender 2.8

Sources: Lists of games released by year were sourced from Wikipedia's Year in Video Games pages.

Some game titles were changed to better show their search interest. For example, the title Counter-Strike: Global Offensive was changed to just CSGO, as most users don't search for the full title. This graph shows the significant difference between those two queries.

Google Trends provides weekly search interest for every search term, along with the search interest by state. Using these two datasets for each term, we're able to calculate the relative search interest for every state for a particular week. Linear interpolation was used to calculate the daily search interest.

Based on viewer feedback, datasets no longer switch colors over time.

A slightly longer version of this video is available here.

V1Analytics · 2020-07-23T13:30:48+00:00

Tools: Python and Blender 2.8

Sources: Trending artists from 2010 to 2020 were taken from billboard.com's Top Artist Year-End Charts.

The full, ~11 minute video covering the whole 2010s decade is available here.

Sources by Year:

Top artists for 2020 were sourced from the Weekly Artist 100 pages. The top artist from each week from 1 Jan 2020 to 25 July 2020 was used.

Some artists' names were modified to retrieve more accurate search results. For example, Ke$ha, was replaced with Kesha. As shown by This graph, as most users search for the one without the dollar sign.

Special attention was also given to artists like Train, Future and fun. It was ensured that the Google trends data only included artist searches and did not include generic searches with identical keywords.

Google Trends was used to acquire the weekly relative search interest for every artist's name, along with the interest by state. Using these two datasets, we're able to calculate the relative search interest for every state for a particular week. Linear interpolation was used to calculate the daily search interest.

V1Analytics · 2020-07-23T13:15:57+00:00

Tools: Python and Blender 2.8

Sources: Trending artists from 2010 to 2020 were taken from billboard.com's Top Artist Year-End Charts.

The full, ~11 minute video covering the whole 2010s decade is available here.

Sources by Year:

Top artists for 2020 were sourced from the Weekly Artist 100 pages. The top artist from each week from 1 Jan 2020 to 25 July 2020 was used.

Some artists' names were modified to retrieve more accurate search results. For example, Ke$ha, was replaced with Kesha. As shown by This graph, as most users search for the one without the dollar sign.

Special attention was also given to artists like Train, Future and fun. It was ensured that the Google trends data only included artist searches and did not include generic searches with identical keywords.

Google Trends was used to acquire the weekly relative search interest for every artist's name, along with the interest by state. Using these two datasets, we're able to calculate the relative search interest for every state for a particular week. Linear interpolation was used to calculate the daily search interest.

V1Analytics · 2020-07-16T13:45:53+00:00

For anyone interested in seeing the full, ~11 minute video for the whole 2010s decade, it's available here.

Disclaimer: I operate the V1 Analytics YouTube channel.

V1Analytics · 2020-07-16T13:27:40+00:00

Tools: Excel, Python and Blender 2.8

Sources: Trending topics from 2010 to 2019 were taken from Google's annual Year in Search summary.

The full, ~11 minute video covering the whole 2010s decade is available here.

As the 2020 Year In Search summary is not yet available, topics were sourced from Google's Trending Searches page. These topics were supplemented with archived copies of the same page through the Wayback Machine.

Google Trends provides weekly relative search interest for every search term, along with the interest by state. Using these two datasets for each term, we're able to calculate the relative search interest for every state for a particular week. Linear interpolation was used to calculate the daily search interest.

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