Topic and Rationale

As an urban designer, one of the heavily emphasized methods of understanding a neighborhood or a territorial entity is usually through the data that is readily available. Primarily the population, the demographics and the income of the region are some of the statistics that grab the attention. This data set is defined by boundaries and numbers, a method that clearly separates and does not include how the space feels and looks. Furthermore, what is hidden from that dataset is how a person feels. As Jan Gehl mentions that qualitative values are also equally important to understand urban life and public space.

Perceptions of a space that one ties to a place or a neighborhood is also an essential component in this study. Although quantitative datasets have a clearly defined boundary of neighborhoods, people have a less faint idea of the extent of such intangible lines. Cores that express a stronger sense of place are clearly identifiable but as one moves closer to the edge of a neighborhood, the extent becomes blurrier and this affects how people feel as well. Investigating the subjective realm to understand a place and the different picture it portrays is the overall goal of the study.

Data Collection

The study is based more on the subjective responses of the surveyor and thus the qualitative dataset would be more descriptive. Geo-location is crucial to locate the attributes to a place and thus for this purpose, an app called Reporter will be used as it allows the investigator to prepare surveys and will record data such as the geo-location, sound levels, and weather.

The data collection method employed here would be obtained directly from the surveyor and the sensors on the phone. The dataset generated can be exported as a csv file or a .json file-format that can be used with Carto or ArcGIS among other tools.

This study will need to be based on an unobtrusive form of data collection so that the people in the space or the space itself is unaffected by anything. For example, behavior of the people might change if it becomes evident that I’m counting them frequently at different spots.

A method that can be explored and that would prove to be beneficial is to crowd-source data. This would allow me to gather information from different regions within a short-period of time or would definitely encompass a diverse range of actors and subsequently their emotions and perceptions. However, things that I need to consider is whether people would be willing to buy an app for such a study or not. It would also entail a considerable amount of time to define and refine the questions. Once the survey goes live, it would be near impossible to make changes to questions or the options for it. The answers would also differ on the time the surveyor spends in the place. A person walking or on a bike could have a different perception of a place than one that's in a car. It cannot be guaranteed as well that prior experiences in a place will cloud the judgement of the surveyor. Among others, the different ways to answer would be the most challenging task to tackle. Therefore, some of the questions have a predefined list of options to choose from whereas some are left to the user to decide. An increase in the scale and volume of the data would mean that a considerable amount of time needs to be spent to clean it up as well.

Data Exploration

A couple of iterations of data-collection was performed before finalizing on a set of questions and options that could be useful. Some of the questions that have been included in the final survey are-

a. How big does the space feel like?

b. Are the people engaged in the place?

c. What does the place look like?

d. What neighborhood are you in?

e. What do you feel?

f. Does the place feel safe?

Some of the relationships I want to see after the completion of the study includes how people associate the perception of a space to the way they feel. Questions such as how big a space feel like might lead to answers such as big or small and then co-relating it with answers such as bright or colorful or dull as an attribute of how it looks could help understand the reason for a certain emotion. The number of people seen on the streets and whether there were activities on the street could also help determine how safe people feel. However, there is still a lot to improve on. The iterations for the survey entailed researching what values can be assigned to describe a space as well as emotions.

I’ve based the emotions used for the survey on a list I came across on Forbes.com. With the app, I am able to ask question and generate visualizations for it instantly as well. However, with the multiple choices answers or open-ended questions, the options are concatenated in the same cell separated by a comma and this makes it difficult to query. This is particularly the case with two of the questions –

a. How big does the space feel like?

b. What does the place look like?

c. What do you feel?

A place described as ‘Barren’ also had other adjectives such as ‘Open’ or ‘Dull’ or ‘Wide’. It was tough to query relations based on individual words in the cell as the entry acted as a string. For the initial investigation into the relations that can be made, I separated the comma separated values within a cell in a table into multiple columns and used the first value to co-relate with other headings. However, this presents only a partial picture as the dataset consists of multiple values to answer a question.

A project by Danial Goddemeyer, Mortiz Stefaner, Dominikus Baur and Lev Manovich called “On Broadway” is interesting to see as it explores Broadway in NY with a set of “image-centric interface, where numbers play only a secondary role, and no maps are used.” It uses images taken along the Broadway as well as Instagram posts, twitter, foursquare data along with taxi pickups and drop-off and economic indicators to paint the picture of the modern city as seen on Fig 1.

The outcome of the study is quite fascinating. They find that the study portrays two completely different cities along the street with much affluent and social-media active groups towards the Financial District and lesser up North of 110^thstreet.

Similarly, it also demonstrates the behavior of people who are affluent and those who are not.

An important aspect of the project is that it does not focus on the neighborhoods for data representation but on a major street that runs through the city. As I’ve mentioned earlier, usually data is viewed in terms of neighborhoods or some other geo-political boundary, this cross-section of the city is unique and is a different way to view the city. Therefore, an idea could be to not let the perceptions of a neighborhood drive the study but let that be a result of the investigation.

San Francisco – Emotion Map | Christian Nold

“Common everyday maps typically show static architecture and exclude the people who inhabit and create the place. The San Francisco Emotion Map attempts to remedy this by mapping the space of human perception and Experience.” (Nold, n.d.)

In this project, Christian Nold gathered data from 98 participants as they walked around the area using Nold’s custom Bio Mapping device. The participants were asked to walk upto an hour and their data was collected on their return.

They could then see the data collected as a series of high and low peaks of arousal. They also annotated their experience on the map as they described the state they were in or what affected their emotion or level of arousal.

One of the interesting things about this experiment to me is that studying the emotions experienced in a place will reveal a hidden layer to the city. The use of devices to measure the state of arousal was clever and then to annotate the sensation makes it more legible than to assign a value or a distinct emotion.

Data Analysis Method

I’ve been able to prepare datasets by walking along the following streets

a. Baum Boulevard

b. Centre Avenue

c. Murray Ave (Squirrel Hill)

d. Penn Ave (Strip District)

Due to the multi-choice nature of some of the questions, the dataset has multiple values or words in a cell separated by a comma and thus had to be cleaned so that I have a value to work with.

a. Uploading the raw data into Carto without separating the data within the column didn’t result in faulty descriptions but I was not able to co-relate places with exact emotions instead had to make do with a string of emotions. This led me to use Excel.

b. Convert these texts to columns so that I could at least work with a column and perform some analysis using Excel. The Text-to-Column option under Data in Excel is particularly useful for this purpose but the column needs to move to the end of the table as it will overwrite the subsequent columns.

c. Aspects of the study to look at

• Change in emotions along a street
• Emotions associated with neighborhoods
• Level of activity in different neighborhoods
• Emotions associated with the physical characteristic of a space.

For the analysis, making chord diagrams will reveal interesting relationships within the dataset. I intend to use Insights for ArcGIS for this purpose.

Analysis

Most of the spaces that I felt were not big or closer to a more human scale were present along Murray Ave and then on Penn Ave (Strip District) between 17^thSt and 22^ndStreet. Most of the big spaces looked more dull, empty, or car-centric whereas spaces of a smaller scale looked defined, pedestrian, colorful as well. Murray Ave and Penn Ave on the Strip District seemed more pedestrian friendly whereas few places of Baum Blvd on Bloomfield, East Liberty and the block between 25^thand 26^thstreet on Penn Ave looked more vehicle oriented. However, I couldn’t tell if the Strip District had ended when I reached the 29^thSt on Penn Ave. The space was not defined and is open and looks empty as well.

Co-Relating what I felt and the Neighborhood I thought I was in:

I felt indifferent or casual about Murray Ave and Penn Ave in the Strip District as I already expected what it would be like and it did seem like a busy commercial street but also felt anxious as I reached the bottom of Murray Ave. The change in the scale in the place was the primary cause.

Synthesis

Depending on the emotions listed on the dataset, I assigned each distinct emotion a color. The number of emotions (colors) felt at the time of record were then mixed and made into a gradient using Photoshop. The gradient was then applied as a gradient overlay on a straight brush stroke and a gaussian blur of 65 in value was applied to it.

Further Research

Crowd Sourced Data would definitely be a way forward to expand this study. It would require a few more rounds of iterations on revising the questionnaire and the options if available on the app or the survey. So far, I’ve not compared my dataset with any neighborhood boundaries or any demographic or economic indicators for Pittsburgh and thus could add them just like Lev Manovich did in his On Broadway project.

I will try to further investigate new measures to display such emotions whilst refraining the use of maps and data to do so. Adding more layers of information such as the timeline is something I will be looking into as the next steps. Styling in Carto is also something I haven’t explored yet so maybe looking at options for synthesis where the context is also represented could be avenue worth exploring.

As seen in Christian Nold’s Emotion Maps for San Francisco, annotations on the emotions felt proved to communicate the emotions well with the audience well and is something I will need to record in the future too.

Tools Used

- Reporter App

- Carto

- Microsoft Excel

- Insights for ArcGIS

- Trycolors.com

- Adobe Photoshop

- Adobe Illustrator

Dataset

https://drive.google.com/open?id=1Qmx4XNXOMOjJyI1MdcyJZVZetz6Boi8U

Works citied and refences:

Atlas of Emotions - http://atlasofemotions.org

Nold, Christian. Emotional Cartography – Technologies of the Self.

http://manovich.net/index.php/exhibitions/on-broadway

http://www.on-broadway.nyc

https://www.forbes.com/sites/brucelee/2017/09/09/here-are-the-27-different-human-emotions-according-to-a-study/#260562cc1335

Shannon Mattern – Maps as Media -

Yagmur Gokce – Emotion Maps - https://yagmurgokce.format.com/emotion-map