2d:null 2e:["$","title",null,{"children":"Exploring Philadelphia Artist Data | Nabil Kashyap | Observable"}] 33:T59d,md ` Transcription: The Philadelphia Artist Census 2018 Data: * Looking at the demographics of artists * Average income level or distribution * Level of education * Geographic distribution? -- map of Philly, concentration Questions one could ask variables are highlighted * neighborhoods that attract artists and speculation about why ___ of those who live in a certain region, how many of them are above or below a certain income percentile? Latitude, Longitude, and Geo group * of those who live in a certain region, how many are of a certain race and income? This could be posisble evidence of a gentrified area, but we would need past data to check * How old is the art community in Philly? --> Look at distribution of years lived in Philly <-- variables: "3-5 years", "5-10 years", "10-20 years", "20-40 years", "40+ years" * What do artists do? How to they form connections and social circles? <-- "Where do you go to see art? Financial situation of artists in Philly: * is there a correlation between Pieces sold, Last 3 Years (Centroid) and Avg Price Category (Centroid) or Avg Price (Adj Centroid) * What % are employed part vs full time? What is your current employment status? / Employment Status Code ` 34:T548,html` "What is your current job title?"

I decided to keep this extensive bar chart because it was (a) the first (somewhat) successful data visualization, (b) it illustrated the breadth of art-related career paths in Philadelphia, and (c) it provided insight on the most common professions among Philadelphia artists. Each bar corresponds to a particular career path, and it is broken down into which geographic group professionals in that field live.

Center Cityy Kensington NE Philly NW Philly North Philly South Philly West Philly

Some Takeaways

Guess NE Philly and Kensington just aren't places to retire for Philadelphia artists -- Too expensive? Not enough of a creative scene? Most of the artist-retirees surveyed were located in NW Philly

The top three most common career paths are (surprise) (1) Artist, (2) Adjunct Professor, (3) Art Educator, (4) Unemployed, and (5) Retired

After that, however, is (6) Administrator

`35:T57b,html `The selection tool allows for greater exploration of the data, although the correlational strength of the visualizations is something to be considered. There are many other potential questions one could ask about the data, including "How do you find out about events?," "How long have you lived in Philly?," and "Pieces Sold, Last 3 Years (Centroid)." However, different variables are better suited for other kinds of graphs (line graphs, pie charts, circle graphs)

In the future, it would be interesting to examine the relationship between Queer/Trans identity and the number of pieces sold by an artist or the relationship between racial identity and annual income, holding factors such as age and education constant. However, these visualizations will provide a preliminary understanding of what the demographics of Philadelphia artists are like.

It would also improve users' understanding if the bar charts were reformatted so that the breakdown was clearer. Otherwise, it might not be clear to users how overwhelmingly White the art scene is in Philly: select any of the questions and use the Race or ethnic background criterion to see how much green takes up each bar graph. Therefore, if one is interested in looking at groups of color specifically, then they could filter out White populations to see a clearer breakdown of, say, the $20-$40K income bracket.

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Our motivation to assemble this information was and continues to be to clarify myths from truths about the greater Philly artist community. We received over 500 responses from the Philadelphia artist community.\"\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"phillydata = await FileAttachment(\"phillyartistcensus.csv\").text()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"philly = await d3.csvParse(phillydata)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"table(philly)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"philly.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"philly.length","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"image = FileAttachment(\"11.11.2020.JPG\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":407,"value":"md ` # Prototyping Notes `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":411,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"md ` # Geo View `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"md ` Illustration of sampled spread of Philadelphia artists throughout the city: `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":107,"value":"map = FileAttachment(\"map.png\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":464,"value":"md ` After converting the latitude and longitude values from a csv file to a topojson file, the [converter](https://mygeodata.cloud/converter/csv-to-topojson) I used also had the option of generating an image of the different regions: Center City, Kensington, Northeast Philly, Northwest Philly, North Philly, South Philly, and West Philly. Each geographic region is indicated by the red dot, and I attempted to recreate a more specific map with a legend (below) for visualization purposes. \n\nWith additional time and skill, I would like the scale factor tool to allow users to zoom in and out on the image and gain a better image of the city and where artists are more commonly located. \n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"md`### Scale Factor: ___${scale}___`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"viewof scale = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"vl\n .layer(\n vl\n .markGeoshape()\n .data(\n vl\n .topojson(\"https://cdn.jsdelivr.net/npm/us-atlas@3/counties-10m.json\")\n .feature(\"counties\")\n // .topojson(\"https://cdn.jsdelivr.net/npm/world-atlas@2/countries-110m.json\")\n // .feature(\"countries\")\n )\n .encode(\n vl.stroke().value(\"#fff\"),\n vl.color().value(\"#000\"),\n vl.fill().value(\"#ddd\")\n ),\n vl\n .markCircle()\n .data(philly)\n .encode(\n vl.longitude().fieldQ(\"Longitude\"),\n vl.latitude().fieldQ(\"Latitude\"),\n vl.color().fieldN(\"Geo group\"),\n vl\n .size()\n .fieldN(\"Geo group\")\n .scale({ range: [50, 1000] })\n )\n )\n .project(\n vl\n .projection()\n .type(\"orthographic\")\n .rotate([75, -40, 11])\n .scale(scale)\n )\n .width(.8 * width)\n .height(460)\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"md ` While I was able to load in the json file, I had to ask multiple questions along the way, some of which are still unanswered: \n\nQ: What exactly am I visualizing?

\nA: I am visualizing the latitude and longitude of each of the seven Geo groups, and adjusting the circles that appear so that the radii correspond with how many artists surveyed live in a particular Geo group. The larger the radius, the more artists surveyed that live there.

\n\nQ: How am I going to incorporate the scale tool?

\nA: I realized that the scale() function did what I wanted, which was adjusting how the final projection was rendered.

\n\nQ: How do I differentiate each point -- why are all the different circles piled on top of each other?

\nA: I am still not sure why. Is my scale too non-specific?

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":492,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":157,"value":"vl.markBar()\n .width(6000)\n .data(philly)\n .title(\"What is your current job title?\")\n .encode(\n vl.x().fieldN(\"What is your current job title?\"),\n vl.y().count(),\n vl.color().fieldN(\"Geo group\")\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"html` \"Calculated Age\"

A look at the distribution of Philly artists, where n > 500. The distribution of age is skewed to the right, with some older folks increasing the mean age. Most artists are in the mid-thirties and forties, and certain regions like West Philly and Kensington are made up of younger artists while North and NW Philly have a mix of all ages, from twenties to seventies. \n

Center Cityy Kensington NE Philly NW Philly North Philly\nSouth Philly\nWest Philly\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"//annotations\nvl.markBar()\n .height(500)\n .width(1000)\n .data(philly)\n .title(\"Calc Age\")\n .encode(\n vl.x().fieldQ(\"Calc Age\"),\n vl.y().count(),\n vl.color().fieldN(\"Geo group\")\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"md` # Selection `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":687,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"viewof question = select({\n title: \"What are you interested in learning more about?\",\n description: \"Note: there are other variables to investigate in the original data set\",\n options: [\"What is your individual annual income?\", \"Higher Education (Years)\",\"$ Job\",\"Avg Price (Adj Centroid)\"],\n value: select\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":717,"value":"viewof criteria = select({\n title: \"Which criterion to use?\",\n description: \"Note: these are not all of the options within the original data set\",\n options: [\"Geo group\", \"Race or ethnic background (according to US census)\",\"Queer/Trans (Y/N)\",\"To which gender identity do you most identify?\"],\n value: select\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"vl.markBar()\n .width(1000)\n .height(200)\n .data(philly)\n .title(question)\n .encode(\n vl.x().fieldN(question),\n vl.y().count(),\n vl.color().fieldN(criteria)\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"md ` # Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"import { table } from \"@tmcw/tables/2\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"import { select } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"d3 = require ('d3')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"vegalite = require(\"@observablehq/vega-lite@0.2\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"html `\n\n`\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"noticecss = html`\n\n`\n \n ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 32:"$31:body"