The popularity of Craigslist is, in part, due to relaxed information requirements. This quality imposed by the platform made it tricky to create a clean dataset. \u00a0Posters only need to input enough information to place their message in the correct borough\/city and category (\u2018Missed Connections\u2019 in this instance). \u00a0There are options to include a specific location, age, body type and other attributes, but these are not mandatory. \u2018Specific location\u2019 is almost always used to note neighborhood, while a secondary source of geographical information exists in the body of the message. This is where the setting of the sighting would be mentioned, such as subway, cafe, theater, etc.<\/p>\n
The data points chosen for review were age, neighborhood, borough, post type, gender, place and subway line. \u00a0The last category was chosen to further define the most highly noted place for sightings. \u00a0After connecting this formatted dataset, the tools featured by Tableau created rich analysis and interactive visualizations. Drag and drop options were provided to review information by column and row.<\/p>\n
Visualizations<\/strong><\/p>\n This\u00a0NYC Missed Connections story was told through four visualizations divided by \u2018Types of Post\u2019, \u2018Age\u2019, \u2018Location\u2019, and \u2018Subway Lines\u2019. \u00a0For the first, posts in Missed Connections appear divided between three message types: date (someone you saw and want to reconnect with), love\/hate Letter (an undesignated note about general feelings toward a relationship), or a solicitation (requesting professional or personal services). For the second, age is reviewed among posters. The third reviews which city sites are hotspots for sightings. This influenced the final visualization of subway lines, which goes off of the most popular place noted for a missed connection.<\/p>\n Types of Posts<\/em>:<\/p>\n An area chart shapes the number of posts by type to review trends by gender. Immediately it becomes clear how many more posts are made by men (both m4m and m4w). There is also an inverse trend between m4m and w4w, the latter almost solely posting hate and love letters while the former exists in the other two categories.<\/p>\n Age<\/em>:<\/p>\n A line chart was appropriate for visualizing the average age of posters. Color divides the posts by interest and gender, while horizontal lines provide information about the wide range in age of users \u2013 between 96 and 19 years old. The dominating age group is 25-29 but early 30s come in at a close second.\u00a0 Our most diverse interest group is m4w.<\/p>\n Location<\/em>:<\/p>\n Location was an opportunity to effectively use a bar chart. \u00a0There were a variety of spots named for sightings and viewing how these differed (or remained the same) based on interest groups, created a dynamic visualization. \u00a0Interest groups and location mentions were in descending order by number of posts to display frequency of occurrences by group. \u00a0Across the board, the subway is the number one hotspot for checkouts with the street coming in at second. The option to view location posts by day is provided through a filter to uncover whether trends exist by time of the week.<\/p>\n \u00a0Subway Line:<\/em><\/p>\n So many posts list the subway as the place of their sighting that it seemed worthwhile to review which platforms are noted more frequently. While there are a few popular lines with a similar average rate of mention, the L beats out all of them with nearly twice as many notes. This is displayed using packed bubbles grouped and sized by the sum of their records with color showing details about the subway lines. Posts which note meeting on the subway but fail to mention a line were excluded.<\/p>\n View Interactive\u00a0Story and Dataset:<\/p>\n![\"age\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infoshow\/wp-content\/uploads\/sites\/2\/2015\/03\/age.jpg\")
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