NYC Restaurant Grades and Income

Project Description

 

This project was submitted as a final project for Professor Sula's Information Visualization class, with the purpose of answering the research question:

"Do lower income neighborhoods have restaurants with lower grades?"

This question was actually asked in a previous lab in the course where I attempted to use Carto to examine the relationship between NYC Restaurant Grades and Income, however, I was unable to identify any apparent trends with that map.

For the final project, I created a series of dashboards using Tableau Public to further answer this question. To do so, I used data on NYC restaurant grades, which I downloaded from NYC's open data portal as well as income information per census tract from American Community Surveys. These 

dashboards are comprised of a series of charts and maps which ultimately did not reveal any clearly defined trends between income and restaurant grades. However, it did reveal a that most restaurants have a grade of "A" regardless of income of the area. Through some further research on the grading process, I found that restaurants are initially inspected, given an initial grade, and then re-inspected until they either receive an A or are forced to close which explains why most restaurants have an A grade. 

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Method

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The datasets that I used to make my visualizations are from NYC Open Data and American Community Surveys websites. However, since I previously cleaned and used these datasets in my mapping lab, I simply exported them from Carto. Initially after exporting them I felt that I could simply upload the datafiles in Tableau and layer them on top of each other to create an interactive map, but this was not the case. Unfortunately, Tableau does not have the capability to handle multiple layers of geospatial data. To combat this issue, I worked with both my professor and class GA to find solutions for this issue. We attempted to use a SQL join in Carto to combine the tables into one but due to the size of the dataset (over 190,000+ records), this join did not work. Finally, we found a solution which involved using the intersection function on QGIS. This function runs an algorithm that clips a vector layer using the polygons of an additional polygon layer. This process took about fifteen minutes or so, and returned a single dataset that included all of the restaurant’s information and income data in one dataset. Then, I uploaded it to OpenRefine and cleaned the data once again by dropping any record that had missing information in any column. The next step was to actually create the visualizations using Tableau Public. After creating these five dashboards, I conducted two moderated in-person user interviews to reveal any usability issues and validate any design decisions. After my interviews I made some revisions based on the findings which improved the overall usability of the dashboard.

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My Role

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I was responsible for all aspects of this project.

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Learning Outcome Achieved 

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Communication - Students can formulate reasonable interpretations of data and share them effectively through visual and narrative means.

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Technology - Students can choose and employ appropriate tools for data collection, storage, manipulation, analysis, visualization, dissemination, and preservation, as relevant to goals, tasks, and users.

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Rationale

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Communication - The goal of this project was to ultimately to identify and examine any trends that exist between NYC restaurant grades and income and by using Tableau Public, I believe that I was able to effectively communicate my findings. I felt that the topic of this project would spark interest for a large audience so I purposely opted to create simple, easy to understand data-visualizations to make this project accessible to all users. The bar charts, stacked bar charts, and maps effectively visualize the massive amounts of data from this project, in a meaningful, digestible, and efficient way. Additionally, being able to create multiple dashboards that users can scroll between allow users to look at different cuts of the data. For example, some dashboards focused more visualizing information relating to borough and income whereas some dashboards focused more on cuisine type and borough or income.

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Technology - To fully complete this project, it required the use of a number of technologies - Excel, OpenRefine, Tableau Public, Carto, and QGIS. Each technology that was used was carefully selected and appropriately used. The first main technologies were both Excel and OpenRefine which were used for data storage, data cleaning, and data transformation, which accounted for a large part of this project. Tableau Public was the second main technology used, which was used for creation of all the visualizations. However, one the problems that I ran into with Tableau was that it incapable of handling multiple geospatial files. Similar to a previous lab which used Carto, I thought that I would be able to simply layer multiple files but that was not the case. To address this issue, I figured that I could use SQL joins to merge these files but due the size of the files this did not work. After some additional research and discussions with my professor and class GA, we determined that QGIS would be an appropriate software to use to address this issue. Using QGIS, I was able to join these datafile so that they would be in a format that would be compatible with Tableau Public.