Toxic Data Infrastructures: Emission and Ridesharing

Description

My work is centered around the formation of civic data about vulnerable communities, primarily focused on the practices of categorizing and classifying transportation and pollution data in in Southern California. My work is committed to furthering research on civic infrastructures and human-computer-interaction by revealing the complex data economy among emerging transportation infrastructures, such as Uber and Lyft, and its social consequences. I am particularly interested in how information infrastructures, while making certain data visible, selectively renders others opaque. The invisibility of the link between transportation problems in Southern California and related risks, ranging from air pollution to governance, creates a kind of grey politics that is especially harmful to marginalized communities in the area.  The images I propose attempts to capture this link through various data-driven work including; 1) visualizing data sets accessible from OEHAA, 2) found images, and 3) and a short analysis of the visualizations.

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Found Image: Toxic Over-Time

Caption:

This is a triptych that includes a set of three photographs of downtown Los Angeles. Each of these photos were taken by different photographers, John Malmin, Fitzgerald Whitney, and Robert Durell, in 1955, 1973, and 1990, respectively. These years correspond to important events:

1955 Air Pollution Control Act (the first U.S. federal legislation that pertained to air pollution)

1970 Clean Air Act

1990 Clean Air Act Amendments (important ones)

These photographs attest to the ways in which ‘smog’ has continuously been perceived as a social problem in Los Angeles for half a century. They all focus on the significance of smog as a risk and the visual experience that a smog hovering over a city’s center can produce.

Design Statement:

  • Historicity

  • Infrastructure

I organized this triptych in order to provide a sense of narrative, from left to right, without being accompanied by text. The increase in the density of buildings and the change in the color scheme of photographs enables the audience to intuitively understand the flow in time. The triptych, hence, demonstrates the historicity of air pollution as an ethnographic subject. This is different from capturing moments in time where smog was present in Los Angeles. It conveys to the audience that air pollution was a persistent social problem that perhaps had its own trajectories of development.

These photographs are also interesting in that they show images of infrastructures that blend with the smog, creating the overall cityscape. For instance, the vehicles in the right-background of the the photograph in the far-left (1955) demonstrates how automobiles have historically been the main source of transport in Los Angeles, as opposed to public transportation, contributing to the city’s smog problem.

Created Image: Risk in Colors

Caption:

This is a data visualization of the CalEnviroScreen 3.0 Data released by OEHHA (Office of Environmental Health Hazard Assessment) in 2017. California Environment Screen is a California Communities Environmental Health Screening Tool that identifies California communities by census tract that are disproportionately burdened by, and vulnerable to, multiple sources of pollution. This data visualization utilizes longitude, latitude, CES score percentiles, and disadvantage community identification data. The height of the polygons represent the CES score percentile (the low the percentage the less there are environmental hazards) and the color of the polygons represent whether the neighborhood is predominantly identified as a disadvantaged community or not. The data visualization is a web-based application that utilized the Mapbox token as its base map. The layering was done through implementing Deck.gl.

Design Statement:

  • Risk and Vulnerability in Scale

I created this data visualization in order to provide a pollution visualization schema that focuses on the idea of ‘scale.’ Conventional data visualizations, especially geospatial visualizations, that deliver information about air pollution in Los Angeles tend to quantify emission data without pointing to how such hazards might affect neighborhoods quite differently. These visualizations convey correct information about air pollution (that pollution is higher in areas where major freeways pass by, etc) but without denoting the potential scale of its effects. For instance, the height of these polygons are not drastically different, which means that the CES score percentile themselves among these communities are not drastically different. While this means that environment hazard, including air pollution, is significant in all areas of Los Angeles, it does not guarantee that the effects of hazards are felt similarly across different neighborhoods as well. In order to represent this problematic, I decided to differentiate the colors of the CES score percentile according to whether the neighborhood is a disadvantaged community or not.

Found Image: Uber's Clean Air Act

Caption:

This is a promotional image of Uber’s introduction of clean air fee London. Following other ride-hailing services like Lyft, who announced that they will be going Carbon Neutral in April this year, uber decided to charge extra per mile for driver’s who are driving electric cars. While this is not exactly the same as Lyft’s more direct efforts to cut emissions, including “the reduction of emissions in the automotive manufacturing process, renewable energy programs, forestry projects, and the capture of emissions from landfills,” (Zimmer) Uber is advertising themselves as an eco-friendly corporation by creating tangible promotional objects such as the car-covered-in-grass.

Design Statement:

  • Industry

I chose this promotional image in order to demonstrate the ride-hailing industry’s response to public accusations of the industry’s contribution to carbon emissions. Ride-hailing services like Uber and Lyft are especially popular and can create lasting infrastructural impacts in cities like Los Angeles, where public transportation is scarce. By creating pop-up installations such as this, Uber is promoting itself as an environmentally-conscious corporation. Whether these efforts actually amount up to what they allegedly claim to be should be interrogated by academics and activists.

Created Image: Risk in Colors (Revised)

Caption:

This is a data visualization of the CalEnviroScreen 3.0 Data released by OEHHA (Office of Environmental Health Hazard Assessment) in 2017. California Environment Screen is a California Communities Environmental Health Screening Tool that identifies California communities by census tract that are disproportionately burdened by, and vulnerable to, multiple sources of pollution. This data visualization utilizes longitude, latitude, CES score percentiles, and disadvantage community identification data. The height of the polygons represent the CES score percentile (the low the percentage the less there are environmental hazards) and the color of the polygons represent whether the neighborhood is predominantly identified as a disadvantaged community or not. The data visualization is a web-based application that utilized the Mapbox token as its base map. The layering was done through implementing Deck.gl.

Design Statement:

  • Risk and Vulnerability in Scale

I created this data visualization in order to provide a pollution visualization schema that focuses on the idea of ‘scale.’ Conventional data visualizations, especially geospatial visualizations, that deliver information about air pollution in Los Angeles tend to quantify emission data without pointing to how such hazards might affect neighborhoods quite differently. These visualizations convey correct information about air pollution (that pollution is higher in areas where major freeways pass by, etc) but without denoting the potential scale of its effects. For instance, the height of these polygons are not drastically different, which means that the CES score percentile themselves among these communities are not drastically different. While this means that environment hazard, including air pollution, is significant in all areas of Los Angeles, it does not guarantee that the effects of hazards are felt similarly across different neighborhoods as well. In order to represent this problematic, I decided to differentiate the colors of the CES score percentile according to whether the neighborhood is a disadvantaged community or not.

SOURCE

Yoo, Chae. 2018. “Created Image: Risk in Colors.” In Toxic Correspondence, created by Chaeyoon Yoo. In Visualizing Toxic Subjects Digital Exhibit, curated by James Adams and Kim Fortun. The Center for Ethnography. March.

License

All rights reserved.

Contributors

Created date

November 26, 2018