32:T604,### Movie Gross The total revenue generated by a movie at the box office. This figure includes ticket sales from cinemas around the world but does not account for other sources of revenue, such as home video sales, streaming rights, merchandise, or other ancillary revenues. Movie gross is an important metric in the film industry as it helps gauge a movie's commercial success and popularity with audiences. ### Domestic Movie Gross The movie gross within a country, in the context of our third visualization, it refers to the US domestic movie gross. ### WorldWide Movie Gross The movie gross across all countries in the world. ### Yearly Movie Gross The movie gross over a calendar year. ### Yearly Movie Gross by Country The total movie gross for all movies over a calendar year in a country. ### Yearly Movie Gross by Country Per Thousand People The total movie gross for all movies over a calendar year in a country, divided by the country's population and then multiplied by 1000. ### The COVID-19 Pandemic Also known as the coronavirus pandemic (we may refer it as "the pandemic" in this notebook). It was caused by a coronavirus called SARS-CoV-2. First identified in December 2019, it quickly spread across the world and caused numerous fatalities, institutional shutdowns, and economic declines. Although the pandemic has officially "ended," the virus is still mutating and transmitting. It was one of the most severe pandemics in the human history. (Reference: [Wikipedia page](https://en.wikipedia.org/wiki/COVID-19_pandemic))33:Tb75, ## Dataset Our dataset contains worldwide box office data for movies, including rankings, titles, worldwide gross revenue, and domestic (US) gross revenue, and distributing countries as well as the domestic(US) distributor. These datasets, collected via web crawler(scraper) that scrape data from Box Office Moja and Wikipedia, offer a comprehensive view of the evolving film industry trends, audience preferences, and the economic landscape of cinema from 2019 to 2023. Links to origins of dataset:
https://www.boxofficemojo.com/year/?area={code}, where {code} is country code by alpha-2. (collected from 97 countries)
[2019 in film](https://en.wikipedia.org/wiki/2019_in_film)
[2020 in film](https://en.wikipedia.org/wiki/2020_in_film)
[2021 in film](https://en.wikipedia.org/wiki/2021_in_film)
[2022 in film](https://en.wikipedia.org/wiki/2022_in_film)
[2023 in film](https://en.wikipedia.org/wiki/2023_in_film)
[List of countries and dependencies by population](https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population)
[Domestic Box Office For 2023](https://www.boxofficemojo.com/year/2023/?grossesOption=calendarGrosses)
[Domestic Box Office For 2022](https://www.boxofficemojo.com/year/2022/?grossesOption=calendarGrosses)
[Domestic Box Office For 2021](https://www.boxofficemojo.com/year/2021/?grossesOption=calendarGrosses)
[Domestic Box Office For 2020](https://www.boxofficemojo.com/year/2020/?grossesOption=calendarGrosses)
[Domestic Box Office For 2019](https://www.boxofficemojo.com/year/2019/?grossesOption=calendarGrosses)

We primarily based our visualization on data from the website [Box Office Mojo](https://www.boxofficemojo.com). This website includes movie sales data from around the globe. However, as we implemented the visualization using this dataset, we found some issue regarding its data accuracy. The data for domestic yearly gross in the US is actually the sum of gross from the US and Canada. Therefore, we used yearly gross data from 2018 to 2023 in Canada from the Movie Theatre Association of Canada (as referenced from the bar chart at the top of page 18 from [Cineplex 2022 Report](https://mediafiles.cineplex.com/Annual%20Report%202022%20-%20FINAL-signed.pdf)), and did a subtraction on the original US yearly gross from 2018 to 2023 in our dataset. For Data from Wikipedia, we used it to double-verify the data from Box Office Mojo, and we also used the country population data from Wikipedia in the calculation of the yearly movie gross by country per thousand people. To create the choropleth map, we used [this topo json file](https://unpkg.com/world-atlas@1/world/110m.json) for the world map, and [this csv file](https://gist.githubusercontent.com/jashkenas/59c7c820265537b941251dabe33a8413/raw/7ccd0d24ef50b3152ce848e7c3f9ce21a0d75af6/country-codes.csv) to do look up countries by country codes.34:T517,### Visualization 1: ### World Movie Yearly Gross Choropleth Map (2018 - 2023) This choropleth map builds off of this public code example: [Building the 'World Life Expectancy 1960–2016' map using the vega-lite-api](https://observablehq.com/@chrispahm/building-the-world-life-expectancy-1960-2016-map-using-the-v). The reason we used yearly gross by thousand people is to mitigate the bias naturally brought by choropleth maps, since the size of population can play a dominating part in the total gross of a country. The map covers yearly total movie gross per thousand people for 97 countries from 2018 to 2023. The amount of gross is encoded via the darkness of the green color. Countries with missing data on that year are colored white, and countries with a per thousand people gross larger than the maximum on the spectrum are colored black. You can select the year that you want to investigate using the slider below. Also, you can hover over a country to see its yearly movie gross per thousand people. This visualization helps answering the question about how cinemas in different countries around the world are affected by the pandemic. Also, "ab uno disce omnes," we can see how different policies and regulations for pandemic control affect a country's economy by how they affect cinemas.35:T41b,This bar chart presents the annual worldwide gross revenues for the top 20 highest-grossing movies from 2019 to 2023. Each vertical bar signifies the cumulative earnings for that year, with the height reflecting the total gross revenue. The chart shows considerable year-to-year variation, indicating the different levels of commercial success attained by movies in each respective year. A notable feature of the chart is the interactive "Worldwide Gross Threshold" slider at the bottom, denominated in US dollars. This slider tool allows users to adjust the data displayed by setting a minimum gross revenue threshold. As the threshold is increased, movies with lower earnings are filtered out, thus narrowing the focus to the more financially successful films. This interactivity enhances the user's ability to conduct a targeted analysis of the impact of the pandemic on the market's top performers. This visualization answers the question that how the movie market, especially the top performers in the movie market, are impacted by the pandemic.36:T482,{ const movieGross = vl.param('movie_gross_slider') .value(0) .bind(vl.slider(0, 2800000000, 10000000) .name('Worldwide Gross Threshold (by US Dollars): ')); return vl.markBar({width: 50, stroke: 'black'}) .title('Top 20 Best-Selling Movie Gross (2019 - 2023)') .data(singleMovieGross) .params(movieGross) .transform( vl.filter('datum.worldwide > movie_gross_slider') ) .encode( vl.x() .fieldT('year') .title('Year') .scale({domain: ['2018', '2024']}), vl.y() .aggregate('sum') .fieldQ('worldwide') .axis({format: "$,.0f"}) .title('Worldwide Gross'), vl.color() .fieldQ('worldwide') .scale({scheme: 'blues', type: "log"}) .title('Worldwide Gross') .legend({format: '$,.0f'}), vl.tooltip([ {field: 'title', type: 'nominal', title: 'Movie'}, {field: 'worldwide', type: 'quantitative', title: 'Worldwide Gross', format: '$,.0f'} ]) ) .height(600) .render(); } 37:T567,This composite visualization is an interactive tool that analyses the performance of the top 50 highest-grossing movie releases in the U.S. from 2019 to 2023. The upper part is a scatter plot displaying the U.S. domestic gross of each movie against its release date. Each dot represents a movie and is color-coded according to the distributor, as defined in the legend on the right. This plot reveals fluctuations in revenue and patterns in the timing of movie releases. The interactive element of the scatter plot allows users to select specific time intervals. Upon making a selection, the data displayed is dynamically filtered to only show movies released within that timeframe. This selection simultaneously updates the lower bar chart, which shows the number of top-selling movies by distributor for the chosen period. The lower bar chart illustrates the number of top-selling movies each distributor has released within the selected time interval, visually indicating the market influence and dominance of certain studios. This interactive functionality enables users to perform a focused analysis on different periods across the pandemic and observe the impact of seasonal releases, market trends, or other temporal factors on the movie industry's competitive landscape. This visualization mainly answers the question on how major distributors perform across the pandemic.38:T86c,{ const timeInterval = vl.selectInterval('selected_interval').encodings('x'); const moviesScatterPlot = vl.markCircle() .title({ text: 'Timeline of Movie Releases and Movie US Domestic Gross (2019 - 2023)', subtitle: 'Top 50 High-Grossing Movies Included For Each Year | Select a Time Interval to Explore' }) .params(timeInterval) .transform( vl.filter('datum.Rank <= 50') ) .encode( vl.x().fieldT('Date') .axis({format: "%b %Y"}), vl.y().fieldQ('Gross') .axis({format: "$,.0f"}) .title('Domestic Gross'), vl.color() .fieldN('Distributor') .scale({scheme: 'category10'}) .title('Distributor'), vl.opacity().if(timeInterval, vl.value(1.0)).value(0.2), vl.tooltip([ {field: 'Release', type: 'nominal', title: 'Movie'}, {field: 'Distributor', type: 'nominal', title: 'Distributor'}, {field: 'Gross', type: 'quantitative', title: 'Domestic Gross', format: '$,.0f'}, {field: 'Date', type: 'temporal', title: 'Release Date', format: "%b %d, %Y"} ]) ) .width(500) .height(260); const moviesBarChart = vl.markBar() .title('Number of Top-Selling 50 Movies in the US Across Distributors') .transform( vl.filter(timeInterval), vl.filter('datum.Rank <= 50'), vl.aggregate([{op: 'count', as: 'NumMovies'}]) .groupby(['Distributor']) ) .encode( vl.x().fieldN('Distributor') .title('Distributor'), vl.y().fieldQ('NumMovies') .axis({format: "d"}) .title('Number of Top-selling Movies'), vl.color() .fieldN('Distributor') .title('Distributor'), vl.tooltip([ {field: 'Distributor', type: 'nominal', title: 'Distributor'}, {field: 'NumMovies', type: 'quantitative', title: 'Number of Top-Selling Movies'} ]) ) .width(500); return vl.vconcat(moviesScatterPlot, moviesBarChart).data(movieList).render(); }39:T742,## Reflection In this report, we analyzed the impact of the COVID-19 pandemic on the global film industry. We used a mix of visualizations, including a choropleth map, a scatter plot, and bar charts, to effectively illustrate the pandemic's uneven impact across different countries and highlighted the cinema market and film industry's performance from 2019 to 2023. Our notebook not only made complex data comprehensive but also demonstrated the importance of data selection, cleaning, and visualization in data analysis. To approach this, we applied data scraping libraries in Python, Scrapy and BeautifulSoup to collected and filter our datasets. We used the Vega-Lite and D3 API to create interactive and compelling data sets. Looking back from this valuable journey, we explored new areas and got familiar with syntax and functionalities of various programming tools that are related to data analysis and visualization, which can be helpful for our future study or career. Our examination of industry trends and the recovery process revealed how a significant external factor, the COVID-19 pandemic, affected movie gross. In the first visualization, we explored how Movie Gross in different countries changed from year 2018 to 2023. In the second visualization, we explored the change in Movie Gross of the Top 20 Best-Selling movies during these year, showing shifts in market dynamics and competitive positioning. In the third visualization, we explored how major distributors performed across the pandemic, specifically the distributors of the 50 Top-Selling movies. Our visualizations are user interactive and visually compelling with detailed tooltips. Overall, this report has not only broadened our understanding of the pandemic's impact on the film industry but also underscored the value of visualizations in the process of data analysis.3a:T46c,// This part of code builds off of this public code example: https://observablehq.com/@chrispahm/building-the-world-life-expectancy-1960-2016-map-using-the-v choroplethData = { const [worldTopo, movieGross, population, countryCodes] = await Promise.all([ d3.json('https://unpkg.com/world-atlas@1/world/110m.json'), FileAttachment("MoviesYearlyGross_PerCapita_2018_2023_Corrected_WithCorrectedPop.csv").csv({typed: true}), FileAttachment("Modified_Population@2.csv").csv({typed: true}), d3.csv( "https://gist.githubusercontent.com/jashkenas/59c7c820265537b941251dabe33a8413/raw/7ccd0d24ef50b3152ce848e7c3f9ce21a0d75af6/country-codes.csv" ) ]); const worldGeoJSON = topojson.feature(worldTopo, "countries"); return worldGeoJSON.features.map(countryFeature => { const iso3166Code = countryFeature.id; const alpha3 = countryCodes.find( codes => codes['country-code'] == iso3166Code )?.['alpha-3']; const countryMovieGross = movieGross.find( country => country['CountryCode'] == alpha3 ); countryFeature.properties = countryMovieGross; return countryFeature; }); }3b:[{"id":"d2f51f1429d4cdb42555ecf536ee2e5533cc1be8e8447fceeed9ff6cc77e8abb1516b4a7be99a26cf9ec21f72f579d65f8774db748516efa856efaace0ee535d","href":"https://static.observableusercontent.com/files/d2f51f1429d4cdb42555ecf536ee2e5533cc1be8e8447fceeed9ff6cc77e8abb1516b4a7be99a26cf9ec21f72f579d65f8774db748516efa856efaace0ee535d","name":"Top20Movies_2019_to_2023.csv","type":"text/csv","lastModified":1708231306338.914,"size":3799},{"id":"f4da5be2fd257c8fa2b272deb1a60d3be594dfed1cceac2e0b57893e29f6959b497abd680044ab1f637b26d97d0b1817d43f1c1c233c76ce1bee480f6f1d572b","href":"https://static.observableusercontent.com/files/f4da5be2fd257c8fa2b272deb1a60d3be594dfed1cceac2e0b57893e29f6959b497abd680044ab1f637b26d97d0b1817d43f1c1c233c76ce1bee480f6f1d572b","name":"Modified_Population@2.csv","type":"text/csv","lastModified":1709357417969.087,"size":3965},{"id":"de211ab0dd4657d84d47dfc9858aaaa959e08a59437b81f9fa87c34eb35b10c45c265d2ae7b335f91fbd0ad9ae7ccee42c41befa916e7af4d649d55c65e84ff1","href":"https://static.observableusercontent.com/files/de211ab0dd4657d84d47dfc9858aaaa959e08a59437b81f9fa87c34eb35b10c45c265d2ae7b335f91fbd0ad9ae7ccee42c41befa916e7af4d649d55c65e84ff1","name":"MoviesYearlyGross_PerCapita_2018_2023_Corrected_WithCorrectedPop.csv","type":"text/csv","lastModified":1709359365095.178,"size":11205},{"id":"02953d50484b9d4ec642ace16bf247d6f6be179e550fd7b61ff946a715712dd7821cc3e02f6409982c8d9e78833ecbe050fc4f55902aad79858190c27f6c0dca","href":"https://static.observableusercontent.com/files/02953d50484b9d4ec642ace16bf247d6f6be179e550fd7b61ff946a715712dd7821cc3e02f6409982c8d9e78833ecbe050fc4f55902aad79858190c27f6c0dca","name":"USGross.csv","type":"text/csv","lastModified":1709421121511.404,"size":84401}] 30:{"title":"The Impact of the COVID-19 Pandemic on Cinemas","cells":[{"id":0,"value":"# The Impact of the COVID-19 Pandemic on Cinemas","pinned":false,"hidden":false,"mode":"md"},{"id":228,"value":"The global film industry and the movie market has faced significant challenges and undergone major changes due to the impact of the COVID-19 pandemic. The visualization of our selected data unfolds against the backdrop of an unprecedented global event: the COVID-19 pandemic. Our data cover the critical period of the film industry from 2019 to 2023 (some data from 2018 included to show the trend), allowing us to observe the impact of the epidemic on the film industry and the recovery of movie sales after the epidemic. Visualizations include a choropleth map, a scatter plot, and a line graph, ranging from yearly movie sales by country to individual movie sales.","pinned":false,"hidden":false,"mode":"md"},{"id":233,"value":"The impact of the COVID-19 pandemic on the film industry is complex and multifaceted, touching upon every aspect from the filmmaking process to cinemas. As the world continues to navigate the pandemic and its aftermath, the film industry will likely continue to evolve, facing new challenges and seizing new opportunities.","pinned":false,"hidden":false,"mode":"md"},{"id":346,"value":" \n## Key Concepts","pinned":false,"hidden":false,"mode":"md"},{"id":337,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":32,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":647,"value":" \n\n## Technology Used","pinned":false,"hidden":false,"mode":"md"},{"id":707,"value":"To collect the dataset, we mainly used two Python libraries, namely **Scrapy** for dynamic web contents and **BeautifulSoup** for static websites. To visualize our data, we used Vega-lite and D3 APIs in the Observable notebook.","pinned":false,"hidden":false,"mode":"md"},{"id":19,"value":" \n## Interactive Visualizations","pinned":false,"hidden":false,"mode":"md"},{"id":184,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":264,"value":"// This part of code builds off of this public code example: https://observablehq.com/@chrispahm/building-the-world-life-expectancy-1960-2016-map-using-the-v\nviewof year = slider({\n min: 2018,\n max: 2023,\n value: 2019,\n step: 1,\n title: \"Year Selected\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":268,"value":"// This part of code builds off of this public code example: https://observablehq.com/@chrispahm/building-the-world-life-expectancy-1960-2016-map-using-the-v\nlegend({\n color: d3.scaleSequential([0, 25000], d3.interpolateGreens),\n title: `Yearly Movie Gross Per Thousand People by Country in ${year} (in US Dollar)`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"// This part of code builds off of this public code example: https://observablehq.com/@chrispahm/building-the-world-life-expectancy-1960-2016-map-using-the-v\nmap = vl\n .markGeoshape({ stroke: 'black' })\n .data(choroplethData)\n .encode(\n vl\n .color()\n .fieldQ(`properties.${year}`)\n .scale({\n scheme: 'greens',\n domain: [0, 25000]\n })\n .legend(null),\n vl.tooltip([\n {\n field: 'properties[\"CountryName\"]',\n type: 'nominal',\n title: 'Country'\n },\n {\n field: `properties.${year}`,\n type: 'quantitative',\n title: 'Yearly Movie Gross Per Thousand People: ',\n format: '$,.0f'\n }\n ])\n )\n .project(\n vl\n .projection(\"naturalEarth1\")\n .translate([450, 300])\n .rotate(0)\n .scale(200)\n )\n .width(1000)\n .height(500)\n .config({\n view: { stroke: null }\n })\n .title('Yearly Movie Gross by Country Per Thousand People:')\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":188,"value":"### Visualization 2: \n### Top 20 Movie Global Gross Bar Chart (2019 - 2023)\n","pinned":false,"hidden":false,"mode":"md"},{"id":753,"value":"$35","pinned":false,"hidden":false,"mode":"md"},{"id":388,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":419,"value":"### Visualization 3: Timeline of Top 50 High-Grossing Movie Releases and US Domestic Gross (2019 - 2023)","pinned":false,"hidden":false,"mode":"md"},{"id":421,"value":"$37","pinned":false,"hidden":false,"mode":"md"},{"id":575,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":778,"value":"$39","pinned":false,"hidden":false,"mode":"md"},{"id":784,"value":"## Role Breakdown \n\nTank Wang: Creating all 3 visualizations, processing data, and editing the Observable notebook\n\nZiwen Chen: Data collection and cleaning\n\nClaire Xu: Creating slides for presentation, writing reflection\n\nZhenyang Li: Idea brainstorming, writing introduction","pinned":false,"hidden":false,"mode":"md"},{"id":66,"value":" \n## Imports","pinned":false,"hidden":false,"mode":"md"},{"id":254,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":395,"value":"singleMovieGross = FileAttachment(\"Top20Movies_2019_to_2023.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":584,"value":"movieList = FileAttachment(\"USGross.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"import {vl as api} from \"@vega/vega-lite-api-v5\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":72,"value":"import {printTable} from \"@jonfroehlich/data-utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":76,"value":"import {printTableTypes} from \"@jonfroehlich/data-utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":149,"value":"import {printVegaLiteJSON} from \"@jonfroehlich/vega-lite-utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":164,"value":"import {concious_analytics} from \"ff90685a875a43ac\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":171,"value":"vl = {\n await addExtendedProjections(api.vega);\n return api;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":247,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":257,"value":"topojson = require(\"topojson-client\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":249,"value":"import {concious_analytics} from \"ff90685a875a43ac\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":244,"value":"import {addExtendedProjections, extendedProjections} from \"@vega/vega-utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":261,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":252,"value":"import {slider} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3b","stdlib":"1","titling":"auto"}