Before creating my own visualizations, I gathered some visualization examples for design inspirations. The first one is a typical Carto dashboard which includes a map and some widgets by the side to add more description and detail about the dataset. The second one is from a Carto analysis tutorial about creating travel\/distance buffers. The third one is a visualization of NYC subway line, the color scheme (black background) contrast the subway line very well.<\/p>\n
![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2018\/04\/Design-Examples.jpg?resize=840%2C164\")
(Design Inspirations)<\/p><\/div>\n
Categorizing and Labeling the Retailers<\/strong><\/h2>\nWhen starting the project, I first imported the dataset of Lower Manhattan Retailers<\/a>. Then I labeled each retailer with their name, address, primary and secondary categories. Therefore, the audience of the map can click each retailer spots to view the detail information.<\/p>\nIn order to better contrast retailer location, I rendered a flat and dark basemap so the audience can focus on the key information. I also added three widgets to display the total number of retailers in Lower Manhattan (703) and the bar charts of primary and secondary retailer categories.<\/p>\n
From the bar chart, we can see \u201cshopping\u201d (212), \u201ccasual eating & takeout\u201d (211) and \u201cpersonal and professional services\u201d (199) are the top three primary categories of the retailers. Within \u201cpersonal and professional services\u201d, \u201cbanks and check cashing\u201d (39) , \u201chair salon & barber shop\u201d (27) and \u201cnewsstand\u201d (24) are the most common types of business. In terms of food, \u201cDeli\u201d (38) and \u201cFast Food\u201d (28) are the most common types of retailers. It matches the needs of two major target customers in Lower Manhattan: worker and tourist.<\/p>\n
![\"\"](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2018\/04\/NYC-Lower-Manhattan-Retailers.png?resize=840%2C473\")
<\/a><\/p>\nImport Dataset of Subway Lines and Stations<\/strong><\/h2>\nTo understand how the retailer locations are related to the subway system, I further imported the dataset of NYC subway lines<\/a> and station<\/a>. Each subway station is labeled with the name on the map, and the subway line is labeled with a pop-up window. The subway lines are colored based on the lines, while some lines are categorized by the system as \u201cothers\u201d, so they are displayed with the same color.<\/p>\n![\"NYC](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2018\/04\/NYC-Subway-Line-and-Station-with-lower-manhattan-retailers.png?resize=840%2C546\")
<\/a><\/p>\nThe result shows the retailer location is highly related to the subway lines and stations. Many retailers are overlapping the subway line, and there are more retailers near subway stations than those not, especially the shopping business.<\/p>\n
![\"NYC](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2018\/04\/NYC-Subway-Line-and-Station-with-lower-manhattan-retailers1.png?resize=840%2C570\")
<\/a><\/p>\nCreate Travel Buffers (50m) around Subway Stations<\/strong><\/h2>\nCarto has many useful analysis tools which can add more insights to the dataset. \u201cCreate Travel or Distance Buffers\u201d is one of the great tools which creates buffers of distance in meters, kilometers, or miles, so the audience can have a clearer understanding about how one location is related to others. For this project, I created buffers of 50 meters for each subway stations. I found \u201cFulton St\u201d station has the most retailers around within 50m, followed by \u201cPark PI\u201d, \u201cRector St\u201d and \u201cWall St\u201d.<\/p>\n
![\"NYC](\"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2018\/04\/NYC-Lower-Manhattan-Retailers-Subway-Station-Travel-Distance.png?resize=840%2C512\")
<\/a><\/p>\nFuture Directions<\/strong><\/h2>\nBy going forward, I would like to conduct more analysis for the retailer category. In the map widget, Carto only shows the top 5 categories and categorizes the remaining data in \u201cothers\u201d, which is not very accurate and satisfying. I want to see the ratio of all categories and look for what kind of business might be missing there.<\/p>\n
I also look forward to adding more dataset to provide more context and comparison for the current data. Here are some datasets I\u2019m very interested in including: First, the divide of sub-districts of Lower Manhattan (e.g. Lower Broadway, WTC\/Tribeca South, Fulton, Financial District, etc.); Second, the median household income in each neighborhood; Third, the data of office building in Lower Manhattan.<\/p>\n
Besides dataset, Carto itself is a powerful tool for mapping with many features that I haven\u2019t explored, especially the analysis tool. It will be worthwhile to learn more about Carto and apply as many functions as possible that work for the dataset I use. For example, Carto has a very cool feature which can animate maps with time data<\/a>. If I were able to find the dataset of the established time of the retailers, I can use Carto to create a map which animates how the retailers were built as time goes.<\/p>\nGIS visualization reveals and exams how geographic relations affect the world around us, which is the most straightforward way to display geographic data. When creating the visualization, it is important to consider how to help the audience relate the data with its context and compare in different ways. Choosing right color scheme and proper size of elements are also important to deliver the messages to the audience clearly. It is fine to explore different features of the software when creating the visualization, but having some goals in mind is a more efficient way to build a meaningful visualization.<\/p>\n
View and play around with the visualization result here<\/a><\/strong><\/h3>\nReferences:<\/strong>
\n1. NYC Open Data-Lower Manhattan Retailers (2017, September 8). Retrieved from https:\/\/data.cityofnewyork.us\/Business\/Lower-Manhattan-Retailers\/cw88-qpsr<\/a><\/p>\n2. NYC Open Data-Subway Stations. Retrieved from https:\/\/data.cityofnewyork.us\/Transportation\/Subway-Stations\/arq3-7z49<\/a><\/p>\n3. NYC Open Data-Subway Lines. Retrieved from https:\/\/data.cityofnewyork.us\/Transportation\/Subway-Lines\/3qz8-muuu<\/a><\/p>\n4. First Design Example: https:\/\/libraries.mit.edu\/news\/online-intro-carto-2\/25850\/<\/a><\/p>\n5. Second Design Example: https:\/\/www.youtube.com\/watch?v=S_kNe24cO18<\/a><\/p>\n
In order to better contrast retailer location, I rendered a flat and dark basemap so the audience can focus on the key information. I also added three widgets to display the total number of retailers in Lower Manhattan (703) and the bar charts of primary and secondary retailer categories.<\/p>\n
From the bar chart, we can see \u201cshopping\u201d (212), \u201ccasual eating & takeout\u201d (211) and \u201cpersonal and professional services\u201d (199) are the top three primary categories of the retailers. Within \u201cpersonal and professional services\u201d, \u201cbanks and check cashing\u201d (39) , \u201chair salon & barber shop\u201d (27) and \u201cnewsstand\u201d (24) are the most common types of business. In terms of food, \u201cDeli\u201d (38) and \u201cFast Food\u201d (28) are the most common types of retailers. It matches the needs of two major target customers in Lower Manhattan: worker and tourist.<\/p>\n
To understand how the retailer locations are related to the subway system, I further imported the dataset of NYC subway lines<\/a> and station<\/a>. Each subway station is labeled with the name on the map, and the subway line is labeled with a pop-up window. The subway lines are colored based on the lines, while some lines are categorized by the system as \u201cothers\u201d, so they are displayed with the same color.<\/p>\n The result shows the retailer location is highly related to the subway lines and stations. Many retailers are overlapping the subway line, and there are more retailers near subway stations than those not, especially the shopping business.<\/p>\n Carto has many useful analysis tools which can add more insights to the dataset. \u201cCreate Travel or Distance Buffers\u201d is one of the great tools which creates buffers of distance in meters, kilometers, or miles, so the audience can have a clearer understanding about how one location is related to others. For this project, I created buffers of 50 meters for each subway stations. I found \u201cFulton St\u201d station has the most retailers around within 50m, followed by \u201cPark PI\u201d, \u201cRector St\u201d and \u201cWall St\u201d.<\/p>\n By going forward, I would like to conduct more analysis for the retailer category. In the map widget, Carto only shows the top 5 categories and categorizes the remaining data in \u201cothers\u201d, which is not very accurate and satisfying. I want to see the ratio of all categories and look for what kind of business might be missing there.<\/p>\n I also look forward to adding more dataset to provide more context and comparison for the current data. Here are some datasets I\u2019m very interested in including: First, the divide of sub-districts of Lower Manhattan (e.g. Lower Broadway, WTC\/Tribeca South, Fulton, Financial District, etc.); Second, the median household income in each neighborhood; Third, the data of office building in Lower Manhattan.<\/p>\n Besides dataset, Carto itself is a powerful tool for mapping with many features that I haven\u2019t explored, especially the analysis tool. It will be worthwhile to learn more about Carto and apply as many functions as possible that work for the dataset I use. For example, Carto has a very cool feature which can animate maps with time data<\/a>. If I were able to find the dataset of the established time of the retailers, I can use Carto to create a map which animates how the retailers were built as time goes.<\/p>\n GIS visualization reveals and exams how geographic relations affect the world around us, which is the most straightforward way to display geographic data. When creating the visualization, it is important to consider how to help the audience relate the data with its context and compare in different ways. Choosing right color scheme and proper size of elements are also important to deliver the messages to the audience clearly. It is fine to explore different features of the software when creating the visualization, but having some goals in mind is a more efficient way to build a meaningful visualization.<\/p>\n References:<\/strong> 2. NYC Open Data-Subway Stations. Retrieved from https:\/\/data.cityofnewyork.us\/Transportation\/Subway-Stations\/arq3-7z49<\/a><\/p>\n 3. NYC Open Data-Subway Lines. Retrieved from https:\/\/data.cityofnewyork.us\/Transportation\/Subway-Lines\/3qz8-muuu<\/a><\/p>\n 4. First Design Example: https:\/\/libraries.mit.edu\/news\/online-intro-carto-2\/25850\/<\/a><\/p>\n 5. Second Design Example: https:\/\/www.youtube.com\/watch?v=S_kNe24cO18<\/a><\/p>\n<\/a><\/p>\nImport Dataset of Subway Lines and Stations<\/strong><\/h2>\n
<\/a><\/p>\n<\/a><\/p>\nCreate Travel Buffers (50m) around Subway Stations<\/strong><\/h2>\n
<\/a><\/p>\nFuture Directions<\/strong><\/h2>\n
View and play around with the visualization result here<\/a><\/strong><\/h3>\n
\n1. NYC Open Data-Lower Manhattan Retailers (2017, September 8). Retrieved from https:\/\/data.cityofnewyork.us\/Business\/Lower-Manhattan-Retailers\/cw88-qpsr<\/a><\/p>\n