Pittsburgh Mural and Graffiti Location Relationship in Selected Neighborhoods

Made by Lu Zhu

Our team out of Data Analytics course challenged ourselves to explore the location correlation of graffiti & murals and main streets in different Pittsburgh neighborhood, including Strip District, Downtown Culture District and Shadyside. All three neighborhoods have much different urban identities, such as dense urban core, urban commercial and neighborhood commercial district. With manual data collection and computational visualization, the data reflects certain location difference of data.

Created: September 22nd, 2017

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Intro of Project

The project is part of course anticipation to help us understand the process of data collection and visualization. It all started from brainstorming of team members on why and how in exploring urban mural and graffiti arts. Finally, we decided to explore the potential location correlation between mural & graffiti, two very different type of street art - one with more commission and the other as more arbitrary,  and main streets in selected neighborhoods, including Downtown Culture District, Strip District, and Shadyside.

These neighborhoods have much different neighborhood characteristics, Culture District is in much denser urban core, where has many commercial, social and cultural activities throughout the day; Strip District is a transitional urban commercial district rises from industrial remaining, where provided urban canvas for many artists because of large industrial building facades, and now it is a iconic shopping and eating place in Pittsburgh; Shadyside's Ellsworth is a neighborhood commercial district where has a wide mix of businesses, including art, dining and office space. Three distinct selections provides a good analysis sample for location correlation exploration purpose. 

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Methodology

The process of project includes mainly two parts -Manual Data Collection and Computational Data Visualization. Before we start collecting, the team developed many versions of data collection form and collect testing samples to see if the criteria would work for final data collection. After a few time of field testing and feedback from Professor Daragh Byrne, our team finalized the collection indexes to GPS location, type of street art, visual connection with pedestrians eye sight, street art's location in relation to main street, position of the work, and size of them, and lastly the condition of art work whether they have well maintenance.

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Data Tabulation
Graffiti & Mural Team - https://imgur.com/zu5c1uK
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After collecting all data, we used Google sheet and exif programs to organize the data, and manually clean and filter the dataset. With the clean dataset, we imported it into Carto to explore the location relationships. Please have a look of the interactive map below and see it for yourself.

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Pittsburgh Mural and Graffiti Location Distribution in Selected Neighborhoods
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Location of Data in Culture District
- https://imgur.com/wLOWEOb
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Commercial use mural type has much higher appearance in Culture District and visible for street level activities.

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Location of Data in Strip District
- https://imgur.com/JxgOJCr
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In Strip District, the map can tell a concentration of commercial use mural along main street - Penn Ave, and non commercial use mural are mainly located in the back or alley ways in the neighborhood.

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Location of Data in Shadyside(Ellsworth)
- https://imgur.com/JfYR8PK
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In neighborhood commercial district - Shadyside's Ellsworth Ave, it has a good mixture of street arts along main street.

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Position of Graffiti on 6th, Culture District
Lu Zhu - https://imgur.com/OnAVncX
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The property value in dense core is relatively high, building façade along main streets are mainly used for commercial purpose, so Culture District has limited number of Graffiti, and most of them has limited visual connection with street level pedestrian activities.

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Position of Mural on Penn, Culture District
Lu Zhu - https://imgur.com/u8lNsmf
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Commercial use of building façade in dense urban cores or urban business districts are usually highly visible for traffic and pedestrians.

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Distribution of Data and Density
Lu Zhu - https://imgur.com/1icoUgh
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Regarding the density of street arts, Strip District has highest, Ellsworth in Shadyside comes second and Culture District has lowest density of mural and graffiti.

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Location of Good Condition Street Arts in Culture District
Lu Zhu - https://imgur.com/lhHVjpd
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Location of Street Arts in Strip District
Lu Zhu - https://imgur.com/gUTxGiq
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Location of Street Art in Shadyside
Lu Zhu - https://imgur.com/Z1aj6zs
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Mural and graffiti in good conditions are mainly along main streets in Culture District and Ellsworth in Shadyside. However, in Strip District, they are well distributed in commercial district.

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Findings

Mural and graffiti have different correlation to main street regarding location on the streets and position of them, for example, Roosevelt Apartment on Penn which is commercial purpose mural on the wall. It has great visual connection for drivers and pedestrians to market its brand. However, graffiti are typically "hide" in the alley ways or on the back sides of buildings. Also, interestingly, Downtown Culture District has limited number of graffiti, it may resulted from regulation or rule.

Additionally, neighborhood identities may influence concentration of street arts in the neighborhoods. From the map, Strip District has much denser street arts along the investigation area - Penn Ave. Many of them were old commercial mural on the wall, which represents a historical industrial site identity. On the other hand, many new murals have been added along the main street, which shows a transition of neighborhood as well. 

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Personal Reflection

Data collection isn't just writing down the data records or taking a few pictures. It is a process of thinking and observation to form a story in order to tell a story why this is worth the data to do it. Additionally, from this project, even with limited dataset, we can still tell many things from dataset, I think it was because of distinct neighborhoods for comparison. There might be better combination of neighborhoods for analysis purpose to test the hypothesis - there is a correlation of location of different types of street art, however, it would need more time to test and expand the dataset in order to strengthen the conclusion. 

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Note: 

1. Although with many data collection testing, during the final data collection process, we still encountered many questions which would influence the data collection, such as clusters of art works, whether we should consider them separately.

2. The dataset in Strip District isn't completed due to unfinished dataset from a team member, which might increase biases of comparison among three neighborhoods.

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Our team out of Data Analytics course challenged ourselves to explore the location correlation of graffiti & murals and main streets in different Pittsburgh neighborhood, including Strip District, Downtown Culture District and Shadyside. All three neighborhoods have much different urban identities, such as dense urban core, urban commercial and neighborhood commercial district. With manual data collection and computational visualization, the data reflects certain location difference of data.