Inclusivity is in vogue with regards to the Manhattan’s fashion scene. There are a lot of all-get to mold encounters that anybody can take advantage of, no velvet rope or influencer status required – all things considered, New York is one of the world’s fashion capitals. All you need is the privilege intel on where to go to sharpen your style IQ. My guide is a mix of those retail information and monetary impression in Manhattan. It will be valuable for New York tourist when they are searching for a retail treatment alongside some fun certainties of this fashion capital.
Google maps: geo-data of retail spaces
Microsoft Excel: effectively and quickly help me clean all data.
Tableau: build graphs, very functional and easy to use.
Carto: build maps, very functional and easy to use.
For the data of this project,I firstly google all high fashion and fast fashion brands in New York. I searched all of these brands on Google Maps and pinned all the retail spaces in “My Places.” Then I exported this data on Excel Sheet. NYCEDC helped me find the revenue data of the Fashion Industry. There is bifurcation given about revenue generated by different sectors of the industry. NYU Furman Center helped me find data about the income of the Manhattan neighborhoods.
Because I extracted the data of google, I had to clean the data and only keep relevant information. This process I had to do for the high end fashion brands and fast fashion brands. Then after I uploaded the shapefile in Carto, then I had to edit the file in Carto as my area limit was only neighborhoods in manhattan. After cleaning shape file I had to manually enter the average income in all the neighborhoods.
Build map in Carto:
Firstly I uploaded the shapefile on Carto and edited it as mentioned above. Then I coloured this mapped according to the value in income from ascending to descending order. Then I uploaded the data about retail areas. As this data had Iongitude and latitude into the points exactly fell into places. Here I have given separate colour for the fast fashion & High Fashion brands to understand visual density on map. Then I added Subway lines to the map to give extra Information to my tourist users about the connectivity of these retail spaces.
Build graphs in Tableau:
I made graphs on Tableau using the data from I inferred from NYCEDC. It was very simple data and easy to portray.
With in excess of 900 design organizations headquartered in the Big Apple, the city’s style industry utilizes 180,000 individuals — that is 6 percent of the city’s workforce, as per the NYCEDC. It’s sheltered to state the city is in fact the style capital of the world.
Fashion is far beyond style; it’s enormous business that makes an immense range of good paying jobs. Fashion is probably the biggest business in the city, delivering $887 million in financial movement. Perfect style encapsulates New York, however it additionally gives New Yorkers something to do. The financial advantages to the city and its inhabitants incorporate $18 billion dollars in retail deals every year, more than $72 billion dollars in discount deals every year and $8 billion dollars in assembling deals, as indicated by the NYCEDC.
My map’s intended interest group are on the whole individuals identified with New York, so I pick one client who lived here and is in fashion industry and another is potential NYC tourist and a fashion enthusiast. I solicit their impression from New York’s style, additionally examine my contemplation with them. Here are some feature data I get from them.
User 1: This user is an employee in Fashion Industry. They thought this data visualization was a nice effort but the revenue information could have been compared to other industries in Manhattan.
User 2 : This user is potential tourists in New York and is an architect with an interest in fashion. They really liked to see the dominance of high fashion in new york but would have liked it more if the visualization was more interactive.
Here are a few contemplations about the subsequent stage: make it intuitive. I need it to be progressively useful, so perhaps users could hop to Google Map by clicking one destination. Some information is accessible however unquestionably enormous and difficult to clean.
From this whole process of final project I realised that not every time data is about inferring information but showing direct facts. I went through a lot of ups and downs in topic. But I knew what I wanted to show as the end result. As I stated previously in my lab reports of carto, I wanted to start the process of data visualization from the data. It was really hard but really a huge learning process.