12/4/19

My research question was, what did visitors to the Vatican Pavilion at the 1964-1965 World’s Fair experience while walking through the pavilion. I also wanted to know about the physicality of the pavilion, how many people walked through the exhibit and what types of souvenirs were sold for what price.

The audience this visualization aims to serve are individuals who went to the fair and would like to relive the experience, individuals who do not know about aspects of the fair and would like to learn about specifics, and scholars interested in data visualizations of historical events and places.

The visualization is a story with three pages showing different aspects of the Vatican Pavilion. The first page gives a short run though of some of the aspects of pavilion, with the addition of an illustration of the pavilion during the fair and finally a map with points indicating the areas within each section. The points are colored to indicate what section they are in and when hovering over the point a short title and description inform of that specific exhibit. The second page includes two visualizations from data obtained through NYPL World’s Fair paperwork. The first visualization is data for two weeks of the fair during the first season. The fair attendance is represented in orange bars against Vatican Pavilion attendance in blue bars. The second visualization is the percentage of visitors of the fair who also went to the Vatican Pavilion for one week in mid-August.

My main focus of this visualization was to create a digital map of the pavilion from a paper map with clickable points to indicate the information regarding the exhibit. I based my color scheme around this map. I wanted to also include simple text in the visualization, so I added a few of the important numbers on the cover page, as well. In the second page, in addition to continuing with the color scheme from the map, I broke up this coloration to include colors of the fair, blue and orange, for the first graph. At first the bars of the first graph where squeezed together and where hard to differentiate from each other, so I changed the sizing of the bars and found that I like the thinner to thicker bar design to easily indicate each of the data sets used to indicate attendance. In the second visualization, I wanted to include another type of visualization, so I created a line graph to indicate percentages of people who went to the fair and also to the Vatican Pavilion. I decided to remove the X axis plane, but kept the guide bars to allow visitors of the site differentiate from each of the points. Also, in the tool tip, the percentage is indicated. For the final page, I had information regarding the different souvenirs sold at the pavilion and I wanted to try to express the many different items sold along with the different in price.

For the future I see this specific visualization based around one pavilion for all the pavilion of the fair. More work is needed to be done to ensure synchronicity, but this is a start.  

10/24/19

For my research question, I wanted to create a data set of my father’s stamp collection and visualize it. Although this is only a 15-year segment of the collection, I knew it would help give me a greater appreciation of the collection as a whole and from the visualization I could mold it with the technology used today, compared to what was around decades ago when my father was collecting. The audience here is first and foremost my family who would like to see what the collection contains. Any collection that contains more than a few hundred objects or images can be daunting to the observer trying to make sense of it without a ledger or gatekeeper. In this instance, my father was the gatekeeper and he knew the ins and outs of the collection due to his personal and meticulous creation and curation of the collection’s archive. After his passing this knowledge of the archive became lost and it has been a challenge to make sense of everything.

The visualization utilizes data I put together from my father’s collection dated from 1960-1975. The data set contains the individual stamp number or Scott number, year distributed, the city distributed from, perforation, denomination, count in the collection, and current valuation for 308 stamps. With this data set, I created an area chart showing the increase of the stamp denomination below the title. To the right of the rate increase graph I created a bar chart to indicate the number of stamps collected for that year. Additionally, when the bar is clicked on the tooltip indicates the total valuation for the year under its count. To the right of the bar graph is a pie graph with the percentage of a particular perforation size for all the stamps. Above the pie chart are the number of individual stamps and the total number of stamps in the collection from 1960 -1975. In the bottom lefthand corner is a map that shows the state where a particular stamp is distributed from. Higher density indicates that more than one stamp was distributed from there. To the right of the map is an image of the most valuable stamp in the collection overall. The information about this stamp are to the left and below it. In the bottom right I chose to include an image of a young Tim, possibly around the time his collection was starting to blossom, with the addition of information regarding the visualization.

I chose to utilize the graphs included in my visualization for simplicity. I knew the major aspect of my visualization was going to be a map of the places of distribution. Within this map, I wanted to include most of my data information within the tooltip setting for a particular point. The colors chosen are based on the coloration of the stamp included in the visualization – the 3¢ Statue of Liberty stamp from the 3¢ & 8¢ Fifth International Philatelic Exhibition Souvenir Sheet. The stamp is a purple hue and I wanted to utilize this color scheme not just throughout and within the visualizations but with some of the font, as well. In addition to the map, I wanted to utilize two or three other graphs. After I noticed the denomination of the stamp increase through the 15 years I wanted to create an area graph to show this increase. For the pie chart, I noticed that one of the data sets I was continually adding to my spreadsheet was the perforation of the stamp. For my copy and pasting to not go to wasted time, I wanted to somehow visualize this information. For this, I created a percentage for the size of perforations in the collection for this time period. In regards to the bar graph used, I wanted to try and showcase how many stamps my father collected. Due to not being an immediate answer and kind of just showing counts for each year, I knew I wanted to show a couple, to a few, numbers as simple text, as well. I do this for the total amount of stamps in the collection, the total number of individual stamps and the valuation for the most valuable stamp. Another new technique I wanted to use that I haven’t done before was toadd an image. In this case, I added two. I liked the personality of the dashboard in the Understanding Data chapter of Yau’s Data Points: Visualization That Means Something and wanted to emulate with a personal image of my father as a kid and a photograph of my father’s stamp.

Next steps for this research is to continue to build my data set. The data set is working, so that is comforting and I would like to add to the data to expand on this visualization of my father’s collection. I would like to know if this section of my father’s collection is the most valuable or is it more valuable at the tail end of the collection. Also, is there a correlation between age and count of stamps. I would imagine that the older my father became the more stamps he would be able to buy during that time frame – does the data agree? I look forward to finding out.

9/26/19

For my research question I wanted to utilize 311 service requests for New York City and see where the location of the most public urination violations took place in the city took place from August 19^th, 2017 to August 19^th, 2019. No one should have to interact or be confronted with someone urinating in public, whether child or adult, and I wanted to inform the public to where these complaints happened. The visualization of the data is intended to shine a light on the dark corners of the city to identify where the highest density of these violations take place.

The intended audience for this visualization is from public service employees of the city, who could enact change, to the common public, who would like to know of these occurrences for their safety. An addition to the city that could be enacted are, of course, more public bathrooms.

The dashboard visualization showcases numerous visualizations to inform the viewer of public urination in New York City for the past two years. I first start with the title that immediately describes the subject with a sub-title with where the data came from. All the points and bars on the visualization are interactable and inform the viewer of further information. The most information are in the points on the map. The legend on the right is to help inform the density of the map. With the highest density being in Manhattan, which is indicated in the bar graph. The line graph is a timeline with points indicating number of complaints for the given month. The description informs the viewer why the visualization was created and who it was created by.

For the dashboard visualization I started with the creation of the map to show heat density of occurrences in an area. The initial coloration utilizes a blue gradient and I wanted to use a gradient that matched my subject matter. So, I made my color gradient orange – gold to match the urine color chart used in the medical field. From the coloration of the map, I created a tooltip list that described by visualization and what I wanted it to show. The title of the tooltip is “Complaint: Urinating in Public” in bold, with the location type, incident address, borough, created date and closed date below. Due to having difficulty creating a legend from the density map, I created an additional map to showcase a legend on the dashboard. The legend shows the density coloration of number of records for a location from one to ten. For the line map, I utilized the month of the created violation for the columns and number of records for the row. To create continuity throughout I adhered to my color variants from the map, based off the urine color chart. I made the line the lighter shade and the points the darker shade. I decided to show mark labels to indicate where the highs and lows were over the course of the three years. For the bar graph, I utilized the same colors as the line graph to showcase the number of urinating in public violations for each borough through August 19. 2017 to August 19, 2019. My next focus for the dashboard was the title and sub-title. For the title I went with, “Recent Violations of Urinating in Public for NYC”. With a sentence underneath with a description of the data utilized to make the visualizations in the dashboard. The last aspect of the visualization was the creation of the descriptive note on the bottom right of the dashboard. I wanted to inform the common public about the visualizations background and who created it.  

Next steps for this project would be to show it to my local congressional representative to see if it would be of any importance to them and if there is a case for public bathrooms in the higher density locations. More complicated next steps would be to make a 3-D map with the clustering of boroughs and the time of day in different colors. Improvements to the visualization could be to break up the bar graph into day vs. night complaints in addition to being broken up by borough.