Leisure for All

Published in

Vikas Yadav

5 min readJan 27, 2017

“Leisure should be for everyone but is it equal for all ?”

“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.

Duration: 4 Weeks

Team: Meric Dagli, Vikas Yadav

Role: Data Research, Graphic Design, Invision Prototype

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

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. Pay close attention to patterns that emerge.

INITIAL RESEARCH

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.

“ 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)

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