31:T465,viewof regionheatmap2 = { const future = vl.markCircle({tooltip: {"content": "data"}, clip: true}) .data(coreregions) .transform( vl.filter('datum.Time > 2099') ) .encode( vl.y().fieldO("Time"), vl.x().fieldN("Location"), vl.color().average("PopTotal").scale({ scheme: "redyellowblue", reverse: true }), vl.size().average("PopTotal") ) const past = vl.markCircle({tooltip: {"content": "data"}, clip: true}) .data(coreregions) .transform( vl.filter('datum.Time < 1951') ) .encode( vl.y().fieldO("Time"), vl.x().fieldN("Location"), vl.color().average("PopTotal").scale({ scheme: "redyellowblue", reverse: true }), vl.size().average("PopTotal") ) const present = vl.markCircle({tooltip: {"content": "data"}, clip: true}) .data(coreregions) .transform( vl.filter('datum.Time == 2020') ) .encode( vl.y().fieldO("Time"), vl.x().fieldN("Location"), vl.color().average("PopTotal").scale({ scheme: "redyellowblue", reverse: true }), vl.size().average("PopTotal") ) return vl.vconcat(vl.hconcat(past, present, future)).render(); }32:T423,{ const brush = vl.selectInterval() .encodings('x'); const years = vl.markBar({width: 20}) .data(countrypop) .transform( vl.filter('datum.Variant == "Medium"'), vl.filter('datum.Time > 2070'), vl.filter('datum.PopDensity < "1000"') ) .select(brush) .encode( vl.x().fieldO('Time'), vl.y().count("Time") ) .width(760) .height(50); const ratings = vl.markCircle() .data(countrypop) .title("Population density versus population 2070 onwards") .transform( vl.filter('datum.Variant == "Medium"'), vl.filter('datum.Time > 2070'), vl.filter('datum.PopDensity < "1000"') ) .encode( vl.y().fieldQ('PopTotal'), vl.x().fieldQ('PopDensity'), vl.tooltip(['Location', 'Time']), vl.color().fieldO("Time").scale({ scheme: "redyellowblue", reverse: true }).legend({ orient: "right" }), vl.opacity().if(brush, vl.value(0.75)).value(0.05) ) .width(760) .height(350); return vl.vconcat(years, ratings).spacing(5).render(); }33:[{"id":"086633a3d9b38c976789d21b0c2f8bc1570abcc08c2551893bb5dfa500326c773b5e5374a16b54d1554868916a496265e9a47c9a713507795a3ce998f79ec181","href":"https://static.observableusercontent.com/files/086633a3d9b38c976789d21b0c2f8bc1570abcc08c2551893bb5dfa500326c773b5e5374a16b54d1554868916a496265e9a47c9a713507795a3ce998f79ec181","name":"WPP2019_REGION.csv","type":"text/csv","lastModified":1613153854298.752,"size":4812773},{"id":"cde71bf5fce57f4adb639f44abce1e19526d35d4248f9e726a59c2f35927d57c33802b948a5f9972abb0cf34170d3d84d889f18c69cf5e7656ab1c32e3da4dee","href":"https://static.observableusercontent.com/files/cde71bf5fce57f4adb639f44abce1e19526d35d4248f9e726a59c2f35927d57c33802b948a5f9972abb0cf34170d3d84d889f18c69cf5e7656ab1c32e3da4dee","name":"WPP2019_TOTAL.csv","type":"text/csv","lastModified":1613153856637.76,"size":8115052}] 2f:{"title":"Exploring World Population Prospects 📈🧑‍🤝‍🧑","cells":[{"id":0,"value":"md`# Exploring World Population Prospects 📈🧑‍🤝‍🧑\n\n

\n\nDiving into population statistics from the [World Population Prospects](https://population.un.org/wpp/). \nTo quote from the source:\n\n*The 2019 Revision of World Population Prospects is the twenty-sixth round of official United Nations population estimates and projections [].The main results are displaying key demographic indicators [] for selected periods or dates within 1950-2100.*\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":102,"value":"md`## Societal notes\n\nThe numbers we refer to in this notebook are individual people in their *thousands*. \n\nIt's quite exciting and also somewhat concerning to ponder about all the potential individuals we could be sharing the world with in the future. \n\n#### Specifcally I wanted to understand and explore: \n- What are the population trends?\n- Are there any insights in population density?\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":106,"value":"md `## Understanding the data\n\nHere we're loading the data and exploring the general shape, structure and differences between the data sets.\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":236,"value":"md `### Country data\n\nIn the country data we can see the *Location* is the key identifier, along with data in time, gender splits, totals and the density.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"viewof countrypop = aq \n .fromCSV(await FileAttachment('WPP2019_TOTAL.csv').text())\n .view(1) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"md `### Region data\n\nIn the region data it's the same story with regional groupings in *Location*.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"viewof regionpop = aq \n .fromCSV(await FileAttachment('WPP2019_REGION.csv').text())\n .view(1) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"md `## What are the population trends?\nIn this section we're mainly exploring the main population totals across time. What's fairly clear is that the world's population has been growing steadily.\n\nAccording to our data there from 2.5 billion people in 1950 at the end of 2019 we had over 7 billion people in the world.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"viewof worldview = vl\n .data(regionpop)\n .transform(\n vl.filter('datum.Location == \"World\"'),\n vl.filter('datum.Time < 2020')\n )\n .layer(\n vl.markArea()\n .title(\"World Population between 1950 - 2019\")\n .encode(\n vl.x().fieldO('Time'), \n vl.y().fieldQ('PopTotal').title('Population in thousands'),\n vl.color().fieldN(\"Variant\").legend({ orient: \"top\" }),\n ),\n vl.markCircle()\n .encode(\n vl.x().fieldO('Time'),\n vl.y().fieldQ('PopTotal'),\n vl.color().fieldN(\"Variant\").legend({ orient: \"top\" }),\n vl.tooltip().fieldQ(\"PopTotal\") \n )\n ) \n .width(width/1.2)\n.render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":485,"value":"md `Looking into the future we can see that population continues to grow, with all [projection variants](https://population.un.org/wpp/DefinitionOfProjectionVariants/) reporting similarities till around *2030*. \n\nThese variants are interesting and offer a variety of additional possibilites with changes in assumptions to *fertility*, *migration* and *mortality*. I'm interested in primarily overarching trends so have only included a few of these to get a guage of what might happen. To explore them more the source have provided more [documentation](https://population.un.org/wpp/DefinitionOfProjectionVariants/). \n\nThe core insight for me is that by 2050 the world population is predicted to be anywhere between *8.9* billion to *10.5* individuals. By 2100 anywhere between *21* to *7* billion.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":171,"value":"viewof worldview2 = vl\n .data(regionpop)\n .transform(\n vl.filter('datum.Location == \"World\"'),\n vl.filter('datum.Time > 2020')\n )\n .layer(\n vl.markLine()\n .title(\"World Population projections up to 2100\")\n .encode(\n vl.x().fieldO('Time'),\n vl.y().fieldQ('PopTotal').title('Population in thousands'),\n vl.color().fieldN(\"Variant\").legend({ orient: \"top\" }),\n ),\n vl.markCircle()\n .encode(\n vl.x().fieldO('Time'),\n vl.y().fieldQ('PopTotal'),\n vl.color().fieldN(\"Variant\").legend({ orient: \"top\" }),\n vl.tooltip().fieldQ(\"PopTotal\") \n )\n ) \n .width(width/1.2)\n.render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":528,"value":"md `Looking just at countries we can see the top 4 most populated countries between 1950-2019 are *China, India, United States and Indonesia*.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":81,"value":"viewof layeredchartcountry = vl\n .data(countrypop)\n .transform(\n vl.filter('datum.Time < 2020')\n )\n .layer(\n vl.markLine()\n .title(\"Populations in countries between 1950 - 2019\")\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopTotal\"),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n ),\n vl.markCircle()\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopTotal\"),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n )\n ) \n .width(width/1.2)\n.render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"value":"md `This chart looks to the future using the medium variant only. \n\nWhat's interesting to observe projection wise, is the pattern of growth and then decrease for some countries. *Nigeria* is intersting, showing high population growth, reaching the rank of third most populated country by 2100. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"viewof layeredchartcountry2 = vl\n .data(countrypop)\n .transform(\n vl.filter('datum.Variant == \"Medium\"'),\n vl.filter('datum.Time > 2020')\n )\n .layer(\n vl.markLine()\n .title(\"Populations projections up to 2100\")\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopTotal\"),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n ),\n vl.markCircle()\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopTotal\"),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n )\n ) \n .width(width/1.4)\n.render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":738,"value":"md `This chart uses all variants to highlight the shift in the most populated countries from 1950 to 2020 to the projection in 2100.\n\nWhat's interesting to observe is that China and India continue to have had the strongest presence in population. Hover your mouse over the countries for more information.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":708,"value":"viewof countrysizecards = {\n const future = vl.markCircle({tooltip: {\"content\": \"data\"}, clip: true})\n .data(countrypop)\n .transform(\n vl.filter('datum.Time > 2099'),\n vl.filter('datum.PopTotal > 550000')\n )\n .encode(\n vl.y().fieldO(\"Time\"),\n vl.x().fieldN(\"Location\"),\n vl.size().average(\"PopTotal\")\n )\n const present = vl.markCircle({tooltip: {\"content\": \"data\"}, clip: true})\n .data(countrypop)\n .transform(\n vl.filter('datum.Time == 2020'),\n vl.filter('datum.PopTotal > 128080')\n )\n .encode(\n vl.y().fieldO(\"Time\"),\n vl.x().fieldN(\"Location\"),\n vl.size().average(\"PopTotal\")\n )\n const past = vl.markCircle({tooltip: {\"content\": \"data\"}, clip: true})\n .data(countrypop)\n .transform(\n vl.filter('datum.Time < 1951'),\n vl.filter('datum.PopTotal > 45000')\n )\n .encode(\n vl.y().fieldO(\"Time\"),\n vl.x().fieldN(\"Location\"),\n vl.size().average(\"PopTotal\")\n )\n\n return vl.vconcat(past, present, future).render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"md `### What are the projection variants of some interesting countries?\nIn this area we're exploring just those mentioned above *China, India, USA, Indonesia and Nigeria*.\n\n#### Select the location using the drop down input to see the variant projections.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":408,"value":"{ \n const selectLocation = vl.selectSingle('Select')\n .fields('Location') \n .init({Location: top5[0]}) \n .bind(vl.menu(top5)); \n \n return vl.markCircle()\n .data(chinaindiaus)\n .transform(\n vl.filter('datum.Time > 2020')\n )\n .select(selectLocation)\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopTotal\"),\n vl.color().fieldN(\"Variant\").scale({ scheme: \"category10\" }).legend({ orient: \"top\" }),\n vl.tooltip().fieldN(\"Variant\"),\n vl.opacity().if(selectLocation, vl.value(0.75)).value(0.05)\n )\n .width(width/1.2)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":440,"value":"md `### What are regional population projections?\nIn this section we are exploring the core geographic regions defined in the source [metadata](https://population.un.org/wpp/Download/Metadata/Documentation/). \n\nWhat's highlighted is that Asia and Africa are projected to grow, however Africa's growth is almost linear increasing from aorund 1.5 billion to 4.3 billion by 2100. Where as the growth in Asia appears to peak around 2050 before appearing to decrease slightly.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":346,"value":"viewof coreregions = regionpop\n .filter(d => {\n return d.Location === 'Africa'\n || d.Location === 'Asia'\n || d.Location === 'Europe'\n || d.Location === 'Latin America and the Caribbean'\n || d.Location === 'Northern America'\n || d.Location === 'Oceania'\n ;\n })\n .reify()\n .view(1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":126,"value":"vl.markCircle()\n .data(coreregions)\n .transform(\n vl.filter('datum.Variant == \"Medium\"'),\n vl.filter('datum.Time > 2020')\n )\n .encode(\n vl.x().fieldO('Time'),\n vl.y().fieldQ('PopTotal'),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category10\" }).legend({ orient: \"top\" }),\n vl.tooltip().fieldQ(\"PopTotal\")\n )\n .width(width/1.2)\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":764,"value":"md `This chart is similar to our previous splits by time. Ot highlights the dramatic shifts in the most populated regions from 1950 to 2020 to the projection in 2100.\n\nWhat's interesting to observe is the significant growth in Africa.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":667,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":443,"value":"md `#### What are the World Bank income groups regional trends?\nIn this section we filter out to the core income groups defined by the world bank.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":446,"value":"viewof incomeregions = regionpop\n .filter(d => {\n return d.Location === 'High-income countries'\n || d.Location === 'Middle-income countries'\n || d.Location === 'Low-income countries'\n ;\n })\n .reify()\n .view(1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":491,"value":"md `What's highlighted is the core bulk of population is retained in those middle income countries.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":449,"value":"vl.markCircle()\n .data(incomeregions)\n .transform(\n vl.filter('datum.Variant == \"Medium\"'),\n vl.filter('datum.Time > 2020')\n )\n .encode(\n vl.x().fieldO('Time'),\n vl.y().fieldQ('PopTotal'),\n vl.color().fieldN(\"Location\").legend({ orient: \"top\" }),\n vl.tooltip().fieldQ(\"PopTotal\") \n )\n .width(width/1.2)\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":371,"value":"md `## Are there any insights in population density?\nIn this section we're exploring the population density data. This is defined as the *[Persons per square Kilometer](https://population.un.org/wpp/GlossaryOfDemographicTerms/)*.\n\nUp to now Monaco, Macao, Singapore and Hong Kong show the highest population density historically and projected. This is fairly clear as the overall area of those locations are small in comparison. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":374,"value":"vl.markCircle()\n .data(countrypop)\n .title(\"Population density up to 2019\")\n .transform(\n vl.filter('datum.Variant == \"Medium\"'),\n vl.filter('datum.Time < 2020')\n )\n .encode(\n vl.x().fieldO('Time'),\n vl.y().fieldQ('PopDensity'),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n )\n .width(width/1.2)\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":378,"value":"viewof layeredchartcountry3 = vl\n .data(countrypop)\n .transform(\n vl.filter('datum.Variant == \"Medium\"'),\n vl.filter('datum.Time > 2020')\n )\n .layer(\n vl.markLine()\n .title(\"Population density projections up to 2100\")\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopDensity\"),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n ),\n vl.markCircle()\n .encode(\n vl.x().fieldO(\"Time\"),\n vl.y().fieldQ(\"PopDensity\"),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" }),\n vl.tooltip().fieldN(\"Location\")\n )\n ) \n .width(width/1.2)\n.render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":584,"value":"md `### What can we observe when we plot the population density against Population total?\nIt's quite nice to see a sort of explosion effect happening across the data set by country. \n\nWhat's interesting to note is the pattern that can be observed of large step increases in population density, followed by patterns of slower increases and decreases in density. Perhaps this is caused by natural limitations of the region.\n\n#### Click and select years using your mouse on the count of records see specifically the data for that time range.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":458,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":792,"value":"md `Because the data is somewhat tricky to see we filtered the population density to see some of the most interesting countries. \n\nThis helps highlight how for some countries population density increases much more dramatically, for example China compared to Nigeria. This makes sense as we consider the overall land area that each region has.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":502,"value":"vl.markCircle({tooltip: {\"content\": \"data\"}, clip: true})\n .data(countrypop)\n .title(\"Population density < 1000 versus population\")\n .transform(\n vl.filter('datum.Variant == \"Medium\"'),\n vl.filter('datum.PopDensity < \"1000\"')\n )\n .encode(\n vl.x().fieldQ('PopDensity'),\n vl.y().fieldQ('PopTotal'),\n vl.color().fieldN(\"Location\").scale({ scheme: \"category20\" }).legend({ orient: \"right\" })\n )\n .width(width/1.4)\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":845,"value":"md `The chart below from the [World bank](https://data.worldbank.org/indicator/AG.LND.TOTL.K2?end=2017&locations=NG-CN-IN-MC&start=2017&view=bar) helps us understand the effects of land area in population density. For example we can see that *Monaco* would have had a very high population density given it's small land area compared to countries like *China*, *India* and *Nigeria*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":788,"value":"md ``","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"md`\n## Reflections and summary.\n- Data Insights:\n - The total population number of the world is growing and continues to grow.\n - By 2050 the world population is predicted to be anywhere between *8.9* to *10.5* billion individuals.\n - By 2100 anywhere between *21* to *7* billion.\n - In 2100 China, India and Nigeria are projected to be the most populated locations in the world.\n - China and India have held the highest population numbers for a significant period of time. \n - Population density increases and decreases for a number of countries.\n - Monaco, Macao, Singapore and Hong Kong remain as the most densly populated locations.\n\n- Observable Insights:\n - I'm really enjoying using Arquero and Vega Lite together to filter, fold to present charts. \n\n- To do: \n - Experiment further with interactivity.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"md`\n## Appendix & Resources.\n\nBig thanks to the authors of these note books: \n - https://observablehq.com/@uwdata/interaction\n - https://observablehq.com/@uwdata/introducing-arquero\n - https://observablehq.com/@uwdata/data-transformation\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":387,"value":"viewof chinaindiaus = countrypop\n .filter(d => {\n return d.Location === 'China'\n || d.Location === 'India'\n || d.Location === 'United States of America'\n || d.Location === 'Indonesia'\n || d.Location === 'Nigeria'\n ;\n })\n .reify()\n .view(1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":211,"value":"top5 = ['China', 'India', 'United States of America','Indonesia','Nigeria']","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs"},{"id":13,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}