${dataBi.title[0]}${labelsBi[j] && ` (${labelsBi[j]})`} ${dataBi.title[1]}${labelsBi[i] && ` (${labelsBi[i]})`}

2b:null 2c:["$","title",null,{"children":"Population Demographics of Germany | Countryman, Eian | Observable"}] 31:T49c,## Introduction This project aims to visually explore different population dynamics of the country Germany. Population dynamics are important when it comes to a healthy society and economy. Thus it's essential to understand different phenomenon occurring within a country. Tools one can use to examine population dynamics include a variety of things such as, but not limited to, population pyramids, youth dependency ratios, elderly dependency ratios, total dependency ratios, Crude Birth Rate (CBR), Crude Death Rate (CDR), and Rate of Natural Increase (RNI). All of these can paint a picture of what a society and economy look like now and what they might possibly look like in the future. Germany, among many European countries, is vital to understanding how the United States' (US) population might look in the near future. Germany's population is aging just as the US population is. However, the median age of Germany is 47.8 years while in the US it is only 38.5 (CIA World Factbook). With Germany's population being almost 10 years older this puts the US in a unique position to see what will happen as the population ages and prepare or mitigate any observed catastrophes.32:T548,The interactive map above shows the percent of people migrating in each German region by age group. All of this data is from the year 2011 and includes three age ranges, 0 - 19, 20 - 59, and 60+. Age group 1 represents the age range 0 - 19, group 2 is 20 - 59, and group 3 is 60+. In map showing group 1, ages 0 - 19, the north east has the least amount of people migrating. The only part of the north west where there is a higher number of migrants is in Berlin. Group 2, ages 20 - 59, shows a very similar map to group 1. The north east still has the lowest amount of migrants but Berlin has one of the highest in the country. Group 3, 60+, is where the map starts to look different. There are still more migrants in the south, south west than in the north east but not as much as groups 1 and 2. The spatial distribution shown in these maps indicate that people of all ages are not migrating in the north east, except for Berlin. It also shows that people between 0 - 59 are migrating more than people 60+. This could be due to the Berlin Wall and Germany's reunification in 1990 because when the Berlin Wall fell and Germany was reunited, many Germans migrated to the West for better jobs. This phenomenon can still be seen from the migration maps above. More information about this can be found under the "Importance of the Berlin Wall" section. 33:T84a,Germany, among many European countries, is vital to understanding how the United States' (US) population might look like in the near future. Studying Germany's migration and population patterns is key to predict and mitigate any possibilities that might happen to the United States. These visualizations showed that Germanys population over 65 years old is higher than their children 0 - 17 years old. Indicating that their population is decreasing. They also showed the migration of different age groups and genders in Germany. In the population pyramids, Germany's older median age could clearly be seen. A higher population size in the older groups is pulling the median age up, especially since there's lower population size in the younger groups. Whereas the US has a more stable population at the moment with most age groups being roughly the same number. From the population pyramids we can see that the US population is growing and Germany's is declining. These are important to look at because the US could soon be in a state of population decline and seeing how it affects Germany's society and economy could prepare the US. The maps of German population makeup by age show that the German population of 65 year olds and older is higher than the German population of 0 to 17 year olds in both 2010 and 2021. In the interactive map of average age, the increase in average age in most regions from 2011 to 2021 can be seen. This again proves that the German population is growing older. In the interactive map showing migration of different age groups, it can be seen that people between 0 - 59 years old are migrating more than people 60 years old or older. It also showed that people of all ages are not migrating in the north east, except for Berlin. A possible reason for less migrants in the north east is the Berlin Wall and Germany's reunification in 1990 because when the Berlin Wall fell and Germany was reunited, many Germans migrated to the West for better jobs. Higher migration rates everywhere in Germany except the north east can also be seen in the 2011 map of net migrations within Germany. 34:T5f7,legendBi = () => { const k = 24; const arrow = DOM.uid(); //Creates the arrows seen above that indicate low to high return svg` ${d3.cross(d3.range(nBi), d3.range(nBi)).map(([i, j]) => svg` ${dataBi.title[0]}${labelsBi[j] && ` (${labelsBi[j]})`} ${dataBi.title[1]}${labelsBi[i] && ` (${labelsBi[i]})`} `)} ${dataBi.title[0]} ${dataBi.title[1]} `; } //This function creates the legend seen above. The function utilitzes our "colorBi" function containing the 4 color schemes to fill the small boxes with the proper color. The function is quite complex and specifies a variety of things such as the text style, position, and boldness as well as the arrows color and size. 35:T552,// choropleth = { // const svg = d3.create("svg") // .attr("viewBox", [0, 0, width, height]); // svg.append("g") // .attr("transform", "translate(225,20)") // Controls the position of the legend (x, y) x-left and right, y-up and down // .append(() => // legend({ // color: color, // Sets the colors on the legend from our color object earlier // title: data.title, // Sets the title of the legend using the data object we created earlier // width: 300, // Controls the width of the legend // tickFormat: ".1f" // Controls the decimal place of the numbers used on the legend (ie. 3.1 versus 3.112) // }) // ); // // This function creates the legend graphics , or ("g"), while the functions within control aspects of the legend // svg.append("g") // .selectAll("path") // .data(regions.features) // .join("path") // .attr("stroke", "Black") // .attr("stroke-linejoin", "round") // .attr("stroke-width", .5) // .attr("fill", d => color(data.get(+d.properties.schluessel))) // .attr("d", path) // .append("title") // .text(d => d.properties.gen + " Population Percent: " + data.get(+d.properties.schluessel)); // return svg.node(); // }36:T55e,// choropleth_2 = { // const svg = d3.create("svg") // .attr("viewBox", [0, 0, width, height]); // svg.append("g") // .attr("transform", "translate(225,20)") // Controls the position of the legend (x, y) x-left and right, y-up and down // .append(() => // legend({ // color: color_2, // Sets the colors on the legend from our color object earlier // title: data_2.title, // Sets the title of the legend using the data object we created earlier // width: 300, // Controls the width of the legend // tickFormat: ".1f" // Controls the decimal place of the numbers used on the legend (ie. 3.1 versus 3.112) // }) // ); // // This function creates the legend graphics , or ("g"), while the functions within control aspects of the legend // svg.append("g") // .selectAll("path") // .data(regions.features) // .join("path") // .attr("stroke", "Black") // .attr("stroke-linejoin", "round") // .attr("stroke-width", .5) // .attr("fill", d => color_2(data_2.get(+d.properties.schluessel))) // .attr("d", path) // .append("title") // .text(d => d.properties.gen + " Population Percent: " + data_2.get(+d.properties.schluessel)); // return svg.node(); // }37:T55e,// choropleth_3 = { // const svg = d3.create("svg") // .attr("viewBox", [0, 0, width, height]); // svg.append("g") // .attr("transform", "translate(225,20)") // Controls the position of the legend (x, y) x-left and right, y-up and down // .append(() => // legend({ // color: color_3, // Sets the colors on the legend from our color object earlier // title: data_3.title, // Sets the title of the legend using the data object we created earlier // width: 300, // Controls the width of the legend // tickFormat: ".1f" // Controls the decimal place of the numbers used on the legend (ie. 3.1 versus 3.112) // }) // ); // // This function creates the legend graphics , or ("g"), while the functions within control aspects of the legend // svg.append("g") // .selectAll("path") // .data(regions.features) // .join("path") // .attr("stroke", "Black") // .attr("stroke-linejoin", "round") // .attr("stroke-width", .5) // .attr("fill", d => color_3(data_3.get(+d.properties.schluessel))) // .attr("d", path) // .append("title") // .text(d => d.properties.gen + " Population Percent: " + data_3.get(+d.properties.schluessel)); // return svg.node(); // }38:[{"id":"f20092adfa8ef4b618ea46e87d260d5ea130cd7bc8fcdd00109c51f7262528387ff55cc8875dac43f7b8808dbc349cb25a75b693d40c0e70c4eefb9ad79e6987","href":"https://static.observableusercontent.com/files/f20092adfa8ef4b618ea46e87d260d5ea130cd7bc8fcdd00109c51f7262528387ff55cc8875dac43f7b8808dbc349cb25a75b693d40c0e70c4eefb9ad79e6987","name":"Ages0to19.json","type":"application/json","lastModified":1681916480327.133,"size":1632432},{"id":"d4d0aeadfa0ed23a53c0c0268e5e27790f60fa8547ec9b4173079d3c83d3bf8733711105a8bdc5df963ebe8537d35aac24e960857671c776828e5ba03ad039d7","href":"https://static.observableusercontent.com/files/d4d0aeadfa0ed23a53c0c0268e5e27790f60fa8547ec9b4173079d3c83d3bf8733711105a8bdc5df963ebe8537d35aac24e960857671c776828e5ba03ad039d7","name":"Ages0to19_Regional.csv","type":"text/csv","lastModified":1681919619841.08,"size":837},{"id":"98064d1fe17e6dd9f96e8e43632123b4311c91ea02b2bb0de66eca96cbe31115f0fd118c048be32f8fd0b86322423f883e325e29f8994e6dee8352e4deba2e3f","href":"https://static.observableusercontent.com/files/98064d1fe17e6dd9f96e8e43632123b4311c91ea02b2bb0de66eca96cbe31115f0fd118c048be32f8fd0b86322423f883e325e29f8994e6dee8352e4deba2e3f","name":"Regional60up_choro.csv","type":"text/csv","lastModified":1682562204939.836,"size":750},{"id":"c84fa93f042c2e3113c33a9a2d224ca2502fb00d76a2468f824e250a843a252684085f19ca3cae298f8f08b3910e66c49a885b31ae9167d499cd287273d16c3f","href":"https://static.observableusercontent.com/files/c84fa93f042c2e3113c33a9a2d224ca2502fb00d76a2468f824e250a843a252684085f19ca3cae298f8f08b3910e66c49a885b31ae9167d499cd287273d16c3f","name":"germ_data.zip","type":"application/zip","lastModified":1683578233948.627,"size":26701474},{"id":"c5423e3450640637d8adb4f8e87e3760aceafebaa5cb8f75d5da148959ae7c1bfa2113cdaad7b17296f875fcc679b898171835bab7315f33a7458791f31b9957","href":"https://static.observableusercontent.com/files/c5423e3450640637d8adb4f8e87e3760aceafebaa5cb8f75d5da148959ae7c1bfa2113cdaad7b17296f875fcc679b898171835bab7315f33a7458791f31b9957","name":"choro_all_ages_slide.csv","type":"text/csv","lastModified":1683584264038.386,"size":2430},{"id":"1f5bb2ff3fe9159c543c65051d8d27d0ca0bbde5dd8fe5787b0af7626b0b3a599bedee9fb32d668081fdfa6a6b64b49148558c5b9d5286870793496799ecb444","href":"https://static.observableusercontent.com/files/1f5bb2ff3fe9159c543c65051d8d27d0ca0bbde5dd8fe5787b0af7626b0b3a599bedee9fb32d668081fdfa6a6b64b49148558c5b9d5286870793496799ecb444","name":"migration.png","type":"image/png","lastModified":1683320978475.837,"size":312300},{"id":"d16d176d92ffa06a88ceaab6e5ebb30ce0536af47a5dc06a708f446f2b7e27933bb90a3e5bfc99b63b4436e2062bf75dcfdda0ecfccef0e465089644b78b2b9f","href":"https://static.observableusercontent.com/files/d16d176d92ffa06a88ceaab6e5ebb30ce0536af47a5dc06a708f446f2b7e27933bb90a3e5bfc99b63b4436e2062bf75dcfdda0ecfccef0e465089644b78b2b9f","name":"age.png","type":"image/png","lastModified":1683320978479.374,"size":293363},{"id":"21188ca9d025dcf4e64a45ce2e19c28ed9b1c4ac8c0d5360a1da161db45ec5348d44af317ea8acc022af2106ead8b71d97670e7a2c205a6b1e0ba0adc0e14f60","href":"https://static.observableusercontent.com/files/21188ca9d025dcf4e64a45ce2e19c28ed9b1c4ac8c0d5360a1da161db45ec5348d44af317ea8acc022af2106ead8b71d97670e7a2c205a6b1e0ba0adc0e14f60","name":"netmig_fem.csv","type":"text/csv","lastModified":1682802637428.932,"size":12509},{"id":"5711b575cc40f47a2980334b3b0d81919a589eac20f4154cbd52c3cb8bc4447b6a4d182b1131d0486c3a1aebc40b8a6707bc45ff30e870852d98101c8be57bde","href":"https://static.observableusercontent.com/files/5711b575cc40f47a2980334b3b0d81919a589eac20f4154cbd52c3cb8bc4447b6a4d182b1131d0486c3a1aebc40b8a6707bc45ff30e870852d98101c8be57bde","name":"All_Ages.csv","type":"text/csv","lastModified":1682919060044.977,"size":1672},{"id":"dd8fcd82eeeba6debfa2837e4cefc6617ace869f0e40f5f66061ab0e3942b5c854d8e57b781c6b7477e15b340cd0c20e0eada6b3c0ca1d30b3c6732cae9c4f53","href":"https://static.observableusercontent.com/files/dd8fcd82eeeba6debfa2837e4cefc6617ace869f0e40f5f66061ab0e3942b5c854d8e57b781c6b7477e15b340cd0c20e0eada6b3c0ca1d30b3c6732cae9c4f53","name":"PopPyramid_Germany.jpeg","type":"image/jpeg","lastModified":1682921809919.714,"size":36603},{"id":"d040a64dc8096bed9246d68fc162edffbd42f2dd21a942a8e1b4849c19fd998217a3aacc59de59dd042cdd2b28cb8d0a74613b9cf93307c94611363655d4d828","href":"https://static.observableusercontent.com/files/d040a64dc8096bed9246d68fc162edffbd42f2dd21a942a8e1b4849c19fd998217a3aacc59de59dd042cdd2b28cb8d0a74613b9cf93307c94611363655d4d828","name":"PopPyramid_US.jpeg","type":"image/jpeg","lastModified":1682921809945.017,"size":39198},{"id":"d27dc5f043100225e939983a70e8ac0ee17345b665209af468093231ba8c6ee6f57c50b782060c091930dd4fb06d07bb463f731a8966369557ccf86abc3a0191","href":"https://static.observableusercontent.com/files/d27dc5f043100225e939983a70e8ac0ee17345b665209af468093231ba8c6ee6f57c50b782060c091930dd4fb06d07bb463f731a8966369557ccf86abc3a0191","name":"Regional20to59_choro@2.csv","type":"text/csv","lastModified":1682737446909.736,"size":758},{"id":"08898a8a6733e88b183985638ce3e1085ede45db566f7882c7b55cf4bbc1371f957e70881ea18d87023f95bf8699448ca25cc113ba9982a2f94da366bb0a5591","href":"https://static.observableusercontent.com/files/08898a8a6733e88b183985638ce3e1085ede45db566f7882c7b55cf4bbc1371f957e70881ea18d87023f95bf8699448ca25cc113ba9982a2f94da366bb0a5591","name":"2000_0-17_histo.csv","type":"text/csv","lastModified":1682737945767.555,"size":798},{"id":"969cb68cf30631a5a31d25037ae77266dd81983d6e9d85e0860da7b8bcc2ede259b731990874334dbdd52e0083fe8374b46e09959797bcf1d095a322af8c1865","href":"https://static.observableusercontent.com/files/969cb68cf30631a5a31d25037ae77266dd81983d6e9d85e0860da7b8bcc2ede259b731990874334dbdd52e0083fe8374b46e09959797bcf1d095a322af8c1865","name":"AveAgesdiff.csv","type":"text/csv","lastModified":1683410841520.483,"size":10190}] 2f:{"id":"d610efd981f56da9","slug":"germany-final-project","title":"Population Demographics of Germany","thumbnail":"3ba3d17a43f7d09cb2a7e142f2c9add81207c5d92a11defef3cb9a8f89b66f2d","access_level":"public","create_time":"$D2023-04-19T14:18:05.261Z","update_time":"$D2023-05-12T18:18:11.043Z","view_time":null,"first_public_time":"$D2023-04-19T14:18:05.261Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"a6450b8113fc64e9","login":"ecountryman","name":"Countryman, Eian ","avatar_url":"https://avatars-next.observableusercontent.com/71e33126deac679283b00a95302babf4acf525938d8d096599a46e0451848fbb","tier":"starter_2024"},"version":2726,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"34f43699b8220c9e","login":"ecountryman","name":"Countryman, Eian ","avatar_url":"https://avatars-next.observableusercontent.com/71e33126deac679283b00a95302babf4acf525938d8d096599a46e0451848fbb","tier":null}],"members":[{"type":"individual","id":"34f43699b8220c9e","login":"ecountryman","name":"Countryman, Eian ","avatar_url":"https://avatars-next.observableusercontent.com/71e33126deac679283b00a95302babf4acf525938d8d096599a46e0451848fbb","tier":null,"role":"editor"},{"type":"individual","id":"decb771ea2603899","login":"ecphillips-uiowa","name":"Ella Phillips","avatar_url":"https://avatars-next.observableusercontent.com/ea66556f03eb291f0d609a24ea7870f72d89cfd44d6a8478077684825ebadefd","tier":null,"role":"editor"},{"type":"individual","id":"17b84184deb69d2f","login":"lauren-pearson","name":"Lauren Pearson","avatar_url":"https://avatars-next.observableusercontent.com/66554395231b6d4d2772af7ca5912a1c425600fbe0d9f9d5d549fb4ada2b0c74","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"Population Demographics of Germany","cells":[{"id":0,"value":"# Population Demographics of Germany\n*Created by: Ella Phillips, Lauren Pearson, and Eian Countryman*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":125,"value":"Data obtained via *Statistische Ämter des Bundes und der Länder* \n[Regionalatlas Deutschland: https://regionalatlas.statistikportal.de/#]","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":465,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":570,"value":"## Importance of the Berlin Wall\n##### Source: [https://www.history.com/topics/cold-war/berlin-wall]\nThe Berlin wall was erected in 1961 and was torn down in 1989. The reunification of Germany followed in 1990. The Berlin Wall was a gruesome but effective tool used to keep East German citizens from migrating from East Germany (GDR) to West Germany (FRG). It's important to note that GDR was under control of the Soviet Union and it's policies reflected that. While the FRG was under control from the Allied powers of World War II and their policies reflected that as well. While the GDR and it's citizens were separated from the FRG their economies developed very differently. The FRG flourished and modernized while the GDR lagged and developed quite slowly. So, when the Berlin Wall fell and Germany was reunited many Germans migrated to the West for better jobs. This phenomenon can be seen still from the migration maps.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":478,"value":"## Population Pyramids\n*Figures obtained via CIA World Factbook*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":518,"value":"Germany(left), United States(right)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":509,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":522,"value":"One of the easiest ways to visualize the age structure of a population is through population pyramids. As mentioned earlier, Germany has a older median age and that is clearly visible above. You will see higher population size in older groups that is pulling that median age higher with lower population size in younger groups. Thus the population looks less like a pyramid and more like a tree. With high populations at the top being the foliage and low populations at the bottom or trunk. This is likely due to Germany's RNI of -2.95, meaning for every birth about 3 people pass away per 1000 individuals. When examining the US it looks more just like a rectangle. This means the population is fairly stable at the moment and most age groups have roughly the same age. Also, the US RNI is 3.79 so this indicates our population is currently growing. The US population pyramid may eventually look as Germany's does now and it's important to understand what that means for our society and economy. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":504,"value":"## **Small Multiple Maps of German Population Makeup by Age**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":494,"value":"Use interactive elements of this visualization here: https://geog3540.github.io/ecphillips/germ_data/age.html","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":499,"value":"md`${await FileAttachment(\"age.png\").image()}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":809,"value":"## Discussion of Age Distribution Maps","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":782,"value":"The youngest population group (0-17) is much less than the oldest percentage of the population in 2010 and 2021. But at the beginning of the century the youngest percentage of the population was higher than that of the oldest percentage of the population. This shift in population speaks to the population pyramids above. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":774,"value":"## Interactive Map of Average Age","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":766,"value":"viewof Select = Inputs.range(d3.extent(csv_datai, d => d.year), {label: \"Year\", step: 1, value: d3.max(csv_datai, d => d.year)})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":769,"value":"Choroplethi = Plot.plot({\n projection: projectioni,\n marks: [\n Plot.geo(polygonsi, {\n stroke: \"white\",\n fill: (d) => Age_fill.get(d.properties.gen),\n title: (d) =>\n `${d.properties.gen} ${formati(Age_2021M.get(d.properties.gen))}` \n }),\n Plot.geo(polygonsi, { strokeWidth: 0.05 }),\n Plot.geo(polygonsi, { stroke: \"#fff\", strokeWidth: .5 })\n ],\n color: {\n scheme: \"Blues\",\n type: \"quantile\",\n label: \"Average Age by region\",\n legend: true,\n domain: [40, 50]\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":793,"value":"The interactive map above shows the average age of the German population by region. The map spans from 2011 to 2021. If you start the map on 2011 and work your way through the years you will notice that the average age of most regions are increasing. Meaning that the German population is growing older. In 2011 the average age of the northeast, excluding Berlin, is older than the south. As time progresses the south is starting to grow older along with the northwest. As this is happening the northeast remains fairly steady in average age. not increasing much nor decreasing.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":538,"value":"## Bivariate Dependency Ratio Map [2021]\nThere is a clear geographic phenomenon present in the bivariate map with the Elderly ratios being high in the northeast, excluding Berlin, and the Youth ratios being high in the southwest. While both appear to be medium to high in the northwest. Another phenomenon present is that major cities such as Berlin, Hamburg, and Frankfurt have high Youth ratios with the exception of Leipzig in the East. One possible cause of these distinct geographic distributions is the Berlin Wall and Germany's reunification in 1990. More information about this can be found under the \"Importance of the Berlin Wall\" section. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":456,"value":"viewof colors = {\n const form = html`

color scheme`;\n form.i.selectedIndex = 1;\n form.oninput = () => form.value = schemes[form.i.selectedIndex].colors;\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":429,"value":"chartBi = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, widthBi, heightBi]);\n\n svg.append(legendBi)\n .attr(\"transform\", \"translate(670,100)\"); //This adds the legend to the map at the coordinates and ensures it's not buried underneath the map.\n\n svg.append(\"g\")\n .selectAll(\"path\")\n .data(topojson.feature(polygonsBi, polygonsBi.objects.Ages_0to19_wgs84).features)\n .join(\"path\")\n .attr(\"stroke\", \"white\")\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-width\", .5)\n .attr(\"fill\", d => colorBi(dataBi.get(d.properties[idAttribute])))\n .attr(\"d\", pathBi)\n .append(\"title\")\n .text(d => `${d.properties[idAttribute]}, ${formatBi(dataBi.get(d.properties[idAttribute]))}`);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":546,"value":"## Interactive Map Showing Migration of Different Age Groups\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":912,"value":"viewof Select_slide = Inputs.range(d3.extent(slide_csv_data, d => d.year), {label: \"Age Group\", step: 1, value: d3.max(slide_csv_data, d => d.year)})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":910,"value":"Choropleth_slide = Plot.plot({\n projection: projection_slide,\n marks: [\n Plot.geo(polygons, {\n stroke: \"black\",\n fill: (d) => Age_fill_slide.get(d.properties.gen),\n title: (d) =>\n `${d.properties.gen} ${format(youth_map.get(d.properties.gen))}` \n }),\n Plot.geo(polygons, { strokeWidth: 0.05 }),\n Plot.geo(polygons, { stroke: \"#ff\", strokeWidth: .5 })\n ],\n color: {\n scheme: \"Greens\",\n type: \"quantile\",\n label: \"Number of People Migrating (%)\",\n legend: true,\n tickFormat: \"1.0f\",\n domain: [1.4, 39.8] \n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2668,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":484,"value":"## **Small Multiple Maps of Net Migrations Within Germany**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":507,"value":"Use interactive elements of this visualization here: https://geog3540.github.io/ecphillips/germ_data/migrate.html","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":480,"value":"md`${await FileAttachment(\"migration.png\").image()}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":788,"value":"## Discussion of Net Migration Maps ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":790,"value":"Although the data displayed in these maps is row count data for the net-migration rate you can see that in 2011 the migrations to the southern part of Germany have a greater influx of migrants of males and females. This is the same for females in 2021, when net migrations are higher across the board for females in 2021 than they were in 2021. In 2021 the majority of regions are losing males, which is shown by the majority negative net migration rate across Germany. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":801,"value":"## Conclusion and Closing Statements\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2696,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2721,"value":"## Discussion","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2724,"value":"Some limitations we had while completing this project was finding the correct data. All of our data was in German so there was dedicated time for translation and the site was difficult to navigate because of it. We also had to use raw counts for one of the net migrations small multiple maps because we didn't have access to alternate data. In the future, looking into different demographics or linking it to economic challenges due to Germany's older/declining population would be very interesting.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2716,"value":"## Divided Work Load","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2718,"value":"For this project we each worked on our own parts, brought them together, and then fixed any problems we had together. We had multiple meetings over the semester to update each other on our progress and see if we needed to add or change anything. Lauren worked on the Interactive Map Showing Migration of Different Age Groups, choropleth maps, and conclusion. Eian worked on the Bivariate Dependency Ratio Map, Interactive Map of Average Age, introduction, and importance of the Berlin Wall. Ella worked on the population pyramids, Small Multiple Maps of Net Migrations Within Germany, and Small Multiple Maps of German Population Makeup by Age. \n\n \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":778,"value":"## All Map Code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":387,"value":"#### Start of bivariate map code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":425,"value":"// http://www.joshuastevens.net/cartography/make-a-bivariate-choropleth-map/\nschemes = [\n {\n name: \"PuOr\", \n colors: [\n \"#e8e8e8\", \"#e4d9ac\", \"#c8b35a\",\n \"#cbb8d7\", \"#c8ada0\", \"#af8e53\",\n \"#9972af\", \"#976b82\", \"#804d36\"\n ]\n },\n {\n name: \"GnBu\", \n colors: [\n \"#e8e8e8\", \"#b5c0da\", \"#6c83b5\",\n \"#b8d6be\", \"#90b2b3\", \"#567994\",\n \"#73ae80\", \"#5a9178\", \"#2a5a5b\"\n ]\n },\n {\n name: \"BuPu\", \n colors: [\n \"#e8e8e8\", \"#ace4e4\", \"#5ac8c8\",\n \"#dfb0d6\", \"#a5add3\", \"#5698b9\", \n \"#be64ac\", \"#8c62aa\", \"#3b4994\"\n ]\n },\n {\n name: \"RdBu\", \n colors: [\n \"#e8e8e8\", \"#e4acac\", \"#c85a5a\",\n \"#b0d5df\", \"#ad9ea5\", \"#985356\",\n \"#64acbe\", \"#627f8c\", \"#574249\"\n ]\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":423,"value":"labelsBi = [\"low\", \"med\", \"high\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":421,"value":"nBi = Math.floor(Math.sqrt(colors.length))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":419,"value":"xBi = d3.scaleQuantile(Array.from(dataBi.values(), d => d[0]), d3.range(nBi))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":413,"value":"yBi = d3.scaleQuantile(Array.from(dataBi.values(), d => d[1]), d3.range(nBi))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":411,"value":"pathBi = d3.geoPath().projection(projectionBi)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":433,"value":"projectionBi = d3.geoTransverseMercator().rotate([-5,0]).fitExtent([[10, 10], [widthBi, heightBi]], polygon_featuresBi);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":409,"value":"polygon_featuresBi = topojson.feature(polygonsBi, polygonsBi.objects.Ages_0to19_wgs84)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":407,"value":"dataBi = Object.assign(new Map(d3.csvParse(await FileAttachment(\"All_Ages.csv\").text(), ({gen, wert_17, wert_W, wert_65p}) => [gen, [100*(+wert_17/+wert_65p), 100*(+wert_65p/+wert_W)]])), {title: [\"Youth Ratio %\", \"Elderly Ratio %\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":405,"value":"idAttribute = \"gen\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":403,"value":"polygonsBi = FileAttachment(\"Ages0to19.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":401,"value":"heightBi = 610","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":399,"value":"widthBi = 850","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":397,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":395,"value":"colorBi = {\n return value => {\n if (!value) return \"#ccc\";\n let [a, b] = value;\n return colors[yBi(b) + xBi(a) * nBi];\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":393,"value":"formatNumBi = d3.format(\".1f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":389,"value":"formatBi = (value) => {\n if (!value) return \"N/A\"; //If there isn't a value it will display \"N/A\"\n let [a, b] = value;\n return `${formatNumBi(a)} ${dataBi.title[0]}${labelsBi[xBi(a)] && ` (${labelsBi[xBi(a)]})`} \n${formatNumBi(b)} ${dataBi.title[1]}${labelsBi[yBi(b)] && ` (${labelsBi[yBi(b)]})`}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":583,"value":"#### End of Bivariate map code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":597,"value":"#### Start of Interactive Choropleth code - Average Age","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":627,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":679,"value":"d3i = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":602,"value":"csv_datai = FileAttachment(\"AveAgesdiff.csv\").csv().then(data => data.map(d => ({region: d.gen, year: +d.jahr, age: +d.wert})))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":606,"value":"Germanyi = FileAttachment(\"Ages0to19.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":599,"value":"polygonsi = topojson.feature(Germanyi, Germanyi.objects.Ages_0to19_wgs84)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":751,"value":"Age_2021 = csv_datai.filter(d => d.year === 2021)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":753,"value":"Age_2021M = new Map(sliderYear.map(({region, age}) => [region, age]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":755,"value":"formati = d3.format(\".1f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":760,"value":"projectioni = d3i.geoTransverseMercator().rotate([-5,0]).fitExtent([[10, 10], [850, 400]], polygonsi);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":764,"value":"sliderYear = csv_datai.filter(d => d.year === Select)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":762,"value":"Age_fill = new Map(sliderYear.map(({region, age}) => [region, age]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":748,"value":"#### End of Interactive Choropleth Code - Average Age","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":825,"value":"#### Start of Interactive Choropleth Code - Migration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":831,"value":"slide_csv_data = FileAttachment(\"choro_all_ages_slide.csv\").csv().then(data => data.map(d => ({region: d.regionaleinheit, year: +d.age, age: +d.wert}))) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":833,"value":"polygons = topojson.feature(Germany, Germany.objects.Ages_0to19_wgs84)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":835,"value":"youth = slide_csv_data.filter(d => d.year === 1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":837,"value":"youth_map = new Map(slideYear.map(({region, age}) => [region, age]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":839,"value":"format_slide = d3.format(\".1f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":843,"value":"projection_slide = d3.geoTransverseMercator().rotate([-5,0]).fitExtent([[10, 10], [850, 395]], polygons);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":845,"value":"slideYear = slide_csv_data.filter(d => d.year === Select_slide)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":848,"value":"Age_fill_slide = new Map(slideYear.map(({region, age}) => [region, age]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":852,"value":"#### End of Interactive Choropleth Code - Migration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":579,"value":"#### Start of Choropleth map code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":11,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":13,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":16,"value":"simple = require(\"simple-statistics@7.0.7/dist/simple-statistics.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":18,"value":"format = d => `${d}%`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":20,"value":"topojson = require(\"topojson-client@3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":22,"value":"Germany = FileAttachment(\"Ages0to19.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":24,"value":"regions = topojson.feature(Germany, Germany.objects.Ages_0to19_wgs84)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5,"value":"csv_data = d3.csvParse(await FileAttachment(\"Ages0to19_Regional.csv\").text(),({schluessel, wert}) => [+schluessel, [+wert]])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"data = Object.assign(new Map(csv_data), {title: \"Population 0 to 19 by Region (%)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":9,"value":"PopPercent = Array.from(csv_data.values(), d => d[1][0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":27,"value":"WtLgGr = [d3.color(\"#edf8e9\"), d3.color(\"#bae4b3\"), d3.color(\"#74c476\"), d3.color(\"#31a354\"), d3.color(\"#006d2c\")]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":47,"value":"Equalinterval = [d3.min(PopPercent),d3.max(PopPercent)]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":29,"value":"color = d3.scaleQuantize()\n .domain(Equalinterval)\n .range(WtLgGr)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":32,"value":"width = 800","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":34,"value":"height = 550","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":36,"value":"margin = 100","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":38,"value":"projection = d3.geoMercator().fitExtent([[margin, margin], [width - margin, height - margin]], regions)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":41,"value":"path = d3.geoPath().projection(projection);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":173,"value":"csv_data_2 = d3.csvParse(await FileAttachment(\"Regional20to59_choro@2.csv\").text(),({schluessel, wert}) => [+schluessel, [+wert]])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":182,"value":"data_2 = Object.assign(new Map(csv_data_2), {title: \"Population 20 to 59 by Region (%)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":187,"value":"PopPercent_2 = Array.from(csv_data_2.values(), d => d[1][0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":191,"value":"Equalinterval_2 = [d3.min(PopPercent_2),d3.max(PopPercent_2)]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":194,"value":"color_2 = d3.scaleQuantize()\n .domain(Equalinterval_2)\n .range(WtLgGr)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":214,"value":"csv_data_3 = d3.csvParse(await FileAttachment(\"Regional60up_choro.csv\").text(),({schluessel, wert}) => [+schluessel, [+wert]])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":217,"value":"data_3 = Object.assign(new Map(csv_data_3), {title: \"Population 60 and Older by Region (%)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":222,"value":"PopPercent_3 = Array.from(csv_data_3.values(), d => d[1][0])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":224,"value":"Equalinterval_3 = [d3.min(PopPercent_3),d3.max(PopPercent_3)]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":226,"value":"color_3 = d3.scaleQuantize()\n .domain(Equalinterval_3)\n .range(WtLgGr)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":581,"value":"#### End of Choropleth code","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":561,"value":"### Migration population 0 to 19","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":43,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":143,"value":"### **Migration Population 20 to 59**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":146,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":206,"value":"### **Migration Population 60 and Older**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":210,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 30:"$2f:body"