Introduction

Through this project we will explore how accessible are New York City Art Galleries from Subway Stations which is the most popular form of public transportation used by New Yorkers.

Art galleries are crucial because they are integral to the arts. Without them, the works that the artists imagined and made real would not easily get into the hands of the spectators. It could then be said that galleries are the communicating vessels between creators and the public. For many of us they are places for enjoying artistic pieces and the possibility of acquiring them. For plenty of us, it is a place where we can unwind, relax, rejuvinate and broaden our horizons. New York City is known to be a loaction where arts and culture is celebrated.

In New York City, the subway system has played an integral role in taking New Yorkers across the city in minutes. Living a life without these metallic trains almost seems next to impossible.

Through intercative visualizations, the idea behind this project is to help and make readers aware about the accesibility of New York City Art Galleries through subways. Proximity here is defined by the users. Their experiences were taken into account and visualizations were made accordingly.

Rationale

UX Research

Qualitative Research

The approach towards qualitative research for this particular project was a bit different. I narrowed down the interview to 2 people who would later on add on to my persona building. They were selected based on the survey responses I received when I sent out the survey forms. Qualitative research was done in order to gain a better understanding for the following:

1. Why am I creating these visualizations?
2. For whom will I be creating these visualizations?
3. Who will be using it?
4. Who will benefit from it?

The interviews were conducted to know their views about NYC Art Galleries that are accessible from subway stations and whether or not the audience might be interested in a visualization that depicts the same. The challenge was to gauge what the users want and what they will be looking for.

Quantative Research

In order to begin with this project, I needed to know my target audience, their needs, wants and pain points. Hence, survey forms were sent out to validate the same, verifying the challenges whether the users are willing to gain more information or not . I wanted to validate whether or not the creating a visualization about accessible art galleries is a solution that people are looking for. I prepared a questionnaire asking about their visitation frequency to art galleries , their mode of transportation etc. The purpose was to understand the user’s thinking better.

Creating Visualizations

After collecting user data, I had a sense as to how to proceed further. The visulizations I needed to create depended on the data and responses I received from my qualitative as well as quantitative research. By now, I knew why am I creating, what am I creating. The how would be the tool I would use to create the visualizations. I opted for Tableau. Building upon my previous work from Lab 4, I went about by layering spatial data and creating maps with the help of shape files.

The Process

UX Research – Survey Responses

Quantitative data provides crucial objective information that helps us evaluate what design solutions, implementations or chnages may be necessary. It is helpful in collecting statistical and numerical data to draw generalized conclusions about user’s attitudes and behaviors.

The questionnaire was sent out to users who are art enthuisasts, frequent museum goers coupled with a few other parameters such as if they live in or around New York City, use subways as their mode of transportation and what do they think is the ideal walking distance. The questionarrie was prepared via google forms and the responses were recorded that are as follows:

UX Research-User Personas

Here, qualitative data, provides us with important subjective information. Moreover, effectively translating qualitative research insights ensures an improved user experience. From the pool of partcipants, two interviews were conducted to get to know the user, their gain and pain points, difficulties faced, the kind of experience they had and how are they looking to improve it. Consequently, two personas were created which gave insights into our user’s experience.

While creating the personas, the focus was on:

1. Where do the interests of our users lie ? Are they interested in visiting NYC Art Galleries?
2. How does visiting Art Galleries tie into their daily routine ?
3. How does being new to the city interfere with the interest ?
4. What is their purpose in visiting these galleries?
5. How much dependency is there on subways as a mode of transportation to access these galleries?

Persona 1

Persona 2

Methodology

1. Finding A Data Set

In order to work in Tableau, create a visualization and understand where the art galleries in the city lie, an appropriate data set was needed. Amongst various data sources that were provided, I found the data about NYC Art Galleries on NYC Open Data . As, I browsed through the data sets available on NYC Open Data, art galleries in New York City interested me and thus I chose to work with this data set. Being one of the most popular cities in the world, I was intrigued to see how many different art galleries can there be.

The data was available in multiple formats however, since a map had to be created, the data set was downloaded as a spatial file in the format of a shape file. Since our first set of data points only depict the art galleies in New York City, a second data set had to be used in order to find the proximity of these art galleries from subway stations. Hence, another data set was required that depicted the subway station points. Accordingly, the data set was found on NYC Open Data about subway stations points in New York City.

The data set about subway stations was used as a spatial data in the format of a shapefile in order to create maps and layers.

2. Using Tableau

2.1 Importing Data Sets

The downloaded data set which was available in the form of a spatial file is supported by Tableau and can be directly accessed. In order to map the two data sets as layers, they had to be combined. In this case, the two data sets were joined using the join function in Tableau.

To combine data from multiple tables ; NYC Art Galleries as well as Subway Stations, a full outer join was used in this case so that the result is a table that contains all values from both data sets. As it is a spatial data, geometry of both the table was used to intersect the tables.

2.2 Creating Visualizations

Once the data sets were imported and combined, the visulaizations were created by layering the data points. However, given any topic, I like to start with my visualizations by breaking it down and building upon it. Hence before a final map was created with all the radial points, I created a visualization of subway station points in New York City. This is to give the audience an imagery and build upon the final visualization one step at a time.

Design Choice: To depict this, I went with the colour blue which is MTA’s logo colour. The stations have been labelled on the map and the font used for that is Helvetica as it used by the MTA.

Data points of subway stations were listed and geometry along with latitude and longitude were loaded on the sheet.

Similarly, a visualization was created enlisting the New York City Art Galleries. I wanted that the audience should be able to visualize both the data sets before it is combined into one. Data points of New York City Art Galleries were listed and geometry along with latitude and longitude were loaded on the sheet.

Design Choice: To depict this, I chose the colour Aqua. The reason being that during my interviews, I asked the users what their purpose to visit art galleries were? What do they associate visiting these galleries with and the most common answer was peacefulness. Hence,I chose the colour that reflects tranquility and peace of mind. The font is consistent with Helvetica.

Since the two visualizations above give the readers an idea what the two data sets look like, the next step in the process was to combine the two tables in order to understand the how accessible are New York City Art Galleries from the subway stations. This was done by using the buffer function.

Buffers are boundaries around a point, that can be used to spatially aggregate data. They create a boundary around a point-location using a spatial function. With this boundary, you can see your area of interest or use it to join another geospatial dataset together.

Findings

To further proceed with the given task, the use of buffer functions helped in making the following visualizations:

In order to create the visualization seen above, a calculated filed had to be generated which would specify the radial points. The calculatef field is mentioned below. For, ease of understanding , a distance of 1000 ft was used.

However, the distance mentioned will vary depending upon what our survey responses were, what our users thought would be an ideal walking distance. From, the responses mentioned above, our pool of participants, 70.6% opted for 5-7 and 23.5% chose 10-15 minutes as an ideal walking distance from an art gallery to a subway station hence calling it accessible. According to a research article that I came across, The 5-minute walk , it states that “Walkable neighborhoods are typically characterized by having a range of community services within 5-10 minutes (400−800 metres/0.25-0.5 miles) walking distance of residential areas which residents may access on foot.”

Hence, putting together our survey responses as well as the takeaways from the article, new buffer calculations were made by changing the distance parameter giving rise to the following visualizations:

Even if we take 5-7 minute walking distance as an ideal time, there are still many art galleries which reside outside the radial points. To eleminate and filter that, new calculations were made to the orginal data source:

Inner join was selected so as to eliminate the the extra data points which do not fall under the radius measure.

After eliminating and filtering the pints outside 1320 ft or 0.25 miles, the visualization is as follows:

However, this map restricted to only art galleries within the range of 0.25 miles. To further include interactivity and give flexibility to the user, a parameter was created which incorporates both 70.6% who opted for 5-7 and 23.5% who chose 10-15 minutes as an ideal walking distance from an art gallery to a subway station.

Recommendations

The project initially was challenging for me. I knew I wanted to build upon my previous work from Lab but did not know exactly how to go about it. What really helped me was breaking down the data sets and working on it step by step.

The user research I conducted was at the begining of the project unlike I thought I would do when I first started on this task. However, as I proceeded further, I did not know have a sense of direction and the UX research helped me with exactly that. I knew why I was creating, how I was creating and what I was creating.

The data sets I used personally also resonanted with me as being new to the city, it is helpful if there are easier ways to navigate an unfamiliar and unknown place. This is also why I was interested in this topic.

With respect to further improving this project, user testing can be conducted in order to understand how the audience is interacting with the visualizations. Is it intuitive enough or does it need further refining? This woud also give us insight as to whether or not the approach that was carried out is succesful or not.

Secondly, I would recommend using filtering by either zip codes or boroughs of New York in order to narrow down the data points and understand which area is the most dense and hence focus on that.