Shadyside Talks

Made by Shruti Srikar · UNLISTED (SHOWN IN POOLS)

Continuing our journey to better understand how the people of Pittsburgh perceive and respond to their neighborhood, this project has expanded its viewpoint from one location to three. We now look at not just Shadyside, but also the rest of the 'University area' by including Oakland and Squirrel Hill to our study. (For context, visit 'Shadyside Talks' in the pool 'Urban Caricatures')

Created: October 5th, 2017

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Intention

What kind of relationship does Pittsburgh have with its surroundings? While this may be a loaded question with empirical loopholes, its worth noticing that it is human nature to actively go out and change our surroundings only when something passes a personal threshold level of tolerance. While the threshold itself might vary person to person- it does exist. This threshold determines the action we take. The intention is to better understand this region I call my home here in the United States and find its social irritants. What made Shadyside Talk? What makes this University area talk?

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Collection

The Data source continued to be the 311 Complaints file sourced from WPRDC:

https://data.wprdc.org/dataset/311-data

What changed however was the the way in which the analysis was conducted. For instance, I found that while SQL increased the possibilities of use of data in analysis on Carto, I am not well versed enough (yet!) to be able to use it effectively. Hence I found data cleaning to be easier in Microsoft Excel. For instance, to better understand the time of day when 311 requests were made, it seemed easier to apply the 'IF' constraint on Excel than SQL.

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Analysis

The university area is very interesting in that these are predominantly residential areas with a smattering of commerce that has burgeoned due to the university students who come from all over the world and call it home. The project parameter being to analyse a given data via a 3X3 matrix that tells story, the 311 data was perfect to understand its unique narrative. In this case, the idea was to find out if there were any patterns in the issues of the University Area. Hence I asked the three locations: Shadyside, Squirrel Hill & Oakland, three questions:

1. Which Departments do you think will solve the problem?
2. What media do you find most convenient to use/are aware of?
3. At what time of day do you seem to have/find most of your problems?

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Product

The results were as predictable as they were surprising. While on the one hand, most complaints were predictably made in the morning/midday, the number of complaints made from university campuses was almost as robust as that of the residential locations. What was even more fascinating was the clusters that were noticed in the Shadyside data were consistent in that of Squirrel Hill and Oakland as well. The physical distribution of these clusters in more or less uniform and seems to act as a centroid of population density. This made me think that either these clusters are either people from apartments who end up acting as conservators of the neighborhood or there is a glitch in the data set. 

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  1. Which Departments do you think will solve the problem?  

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  2. What media do you find most convenient to use/are aware of?  

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  3. At what time of day do you seem to have/find most of your problems?  

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Data Windows