leisureforall.jpg

Leisure for All

Duration: 4 Weeks

Team: Meric Dagli, Vikas Yadav

Role: Data Research, Graphic Design, Invision Prototype

Tools Used: Autocad, Illustrator, InVision, After-Effects

"Leisure for All" is an interactive data-visualization which utilizes Yelp's open dataset to visualize universal accessibility of restaurants in Pittsburgh. Visualization can also be used as a tool for decision-making if going out to eat.

Detailed process blog here.

#datavisualization #dataanalysis #UI/UX #communicationstrategy #InVision #YELPdataset #Googledatastudio

 


Duration: 4 Weeks

Team: Meric Dagli, Vikas Yadav

MY Role: Data Research, Graphic Design, Invision Prototype

Tools Used: Autocad, Illustrator, InVision, After-Effects

#datavisualization   #dataanalysis   #UI/UX   #communicationstrategy   #InVision   #YELPdataset   #Googledatastudio


"Leisure for All" is an interactive data-visualization which utilizes Yelp's open dataset to visualize universal accessibility of restaurants in Pittsburgh. Visualization can also be used as a tool for decision-making if going out to eat.
Detailed process blog here.


PROMPT

Dive in the process of conceiving and crafting, visual, aural and temporal representations of data that communicates information in way that is useful, usable and desirable.


INITIAL RESEARCH


DEFINING TERRITORY

First challenge of this project was to search for a relevant dataset which can express our intentions of mapping equality in leisure. Dataset should be rich enough to express multiple factors which can collectively express an overarching concern for equality in leisure. Max Neef's classification to fundamental human needs guided us to next points of exploration.

Max-Neef's "fundamental human needs"

" Food is the most common type of Leisure people engage into."

After searching for a couple of days, we decided on using Yelp's open dataset which they provided publicly as part of their "Dataset Challenge". Provided dataset covered all American restaurants. It contained data of over 86k businesses and over 566k business attributes like hours of operation, parking availability, ambience, etc. From this point onwards we decided to focus on restaurants of Pittsburgh and attributes related to universal accessibility.

PURGING DATASET

We used Google Data Studio to purge and better understand out data before delving in the process of creating appropriate representations. Our expectation was to understand and establish a purpose for presenting the information.


GENERATIVE PHASE


The most important question at this point was to identify factors that are key to a restaurant's accessibility. After purging our dataset from data studio, we organized and mapped significant attributes/categories of data based on classroom exercises and internal discussions with faculty members. Besides accessibility, these attributes lay foundation for restaurants overall popularity. 

Individually mapped categories of data, defined by TYPE, SCALE and RANGE that it can be mapped on.

CRAFTING A SCRIPT FOR VISUALIZATION

Before working on the visual language, we worked on a script of how we want to architect the entire visualization, breaking down to individual stages. A well crafted script can guide us to the final form and address crucial question like: Where will this visualization live? What platform is most appropriate: mobile or web?


REFINEMENT PHASE


We took help from Nathan Yau's book "Data Points" to design a visually cohesive design language. Book provided a better understanding of using distinctive visual cues. It suggests that strategic data overlay and less taxing visual language is delightful and sometimes could reveal hidden information.

By the end of script, it was clear that visualization had to live on the web platform, through an interactive visualization which should be advertised on yelp's main website.

STAGE 1: OVERALL ACCESSBILITY

Entrance screen is an overall mapping of Pittsburgh's restaurants, neighborhood-wise. Dataset: Wheelchair access Visual Representation: THIN Strokes (Restaurants with no wheelchair access) / THICK Strokes (Restaurants with wheelchair access)

Entrance screen is an overall mapping of Pittsburgh's restaurants, neighborhood-wise.

Dataset: Wheelchair access

Visual Representation: THIN Strokes (Restaurants with no wheelchair access) / THICK Strokes (Restaurants with wheelchair access)

STAGE 2 : ATTRIBUTES CONTRIBUTING TO ACCESSIBILITY

Each neighborhood can be accessed in detail with individual restaurants. The UI is mostly composed of classical toggle button type interaction. Here, East Liberty has been laid out.

Dataset : Names of restaurants coupled with an interactive UI

Visual Representation : Same language from Stage 1 has been used to represent individual restaurants

Cuisine toggle activates a dropdown menu which enlists all cuisines available in the neighborhood. For  assistance we have marked each cuisine using color with respective flag of the country of cuisine origin.

Dataset : Cuisine

Visual Representation : Color, flag of country of origin

 

Dataset : Price Range

Visual Representation : Length of stroke representing individual restaurant

 

Dataset : Ratings Visual Representation : White dots  

Dataset : Ratings

Visual Representation : White dots

 

Dataset : Free wifi and Alcohol

Visual Representation : Literal symbol for wifi, Haze for alcohol (overlaid on stroke of respective restaurant)

 

Dataset : Good for Groups

Visual Representation : Distance between "Ratings" dots

 

Dataset : Good for Kids

Visual Representation : Size of "Ratings" dots

 

Dataset : Home Delivery Visual Representation : Symbol for home  

Dataset : Home Delivery

Visual Representation : Symbol for home

 

We also designed isolation mode where people can see all details in isolation for each restaurant.

If you like a particular restaurant, clicking on it will take you to Yelp's main website for contact details.


INVISION PROTOTYPE

Here