Due to COVID-19, New Yorkers have been required to stay at home since late March and it has been nothing less than arduous, both mentally and physically. These confined times have reacquainted folks with nature and public spaces as an escape from the harsh everyday. However the diversity in public data about our public spaces (i.e. parks, etc.) shows these spaces can be measured in various ways, especially related to environmental consciousness and socioeconomic factors. Depending on the park or space, these factors aren’t necessarily equal in proportionality to their neighborhood’s size, financial funding for long-term development, and physical size. These often overlooked measurements are impactful to the space’s experience for visitors and, importantly, the community surrounding it.
In a historical sense, New York City has committed itself to environmental and legislative efforts in recent years to mobilize one of the largest climate solutions in a major city. All these efforts are combined into the The Climate Mobilization Act, 2019: Local Laws 92, 94, 96, 97—a cluster of laws that incentivize “green” behaviors from the city’s residents, but also establishes goals and environmental standards for the city to be held accountable for its progress towards a green future. The progress and impacts of these laws have been debated, given the controversial new businesses and regulations considered for neighborhoods affected by climate change the most.
Through this project, I want to provide a visual and spatial experience of a public park, Prospect Park, in contrast to other surrounding public spaces. Using new data measurements available through NYC Open Data, the audience will be able to see the unseen differences that influence the environmental and socioeconomic standings of the park or other public spaces. Hopefully these visualizations will spark discussion about the impact of specific factors on public parks / spaces and interrogate how we experience our natural, shared community spaces.
Since this project would utilize different datasets and visualization tools, it was split into two phases: spatial visualization via Carto and quantitative visualization via Tableau. There were process similarities between both phases, but their process looked slightly different and it seemed better to separate for organizational reasons.
Spatial Visualization via Carto
A. Choose associated datasets to illustrate the chosen narrative.
For the spatial visualization, these specific datasets were chosen for these specific reasons:
Open Space (Parks): Planimetric basemap polygon layer containing open space features, such as courts, tracks, etc. including parks.
Forever Wild Preserves and Natural Areas: The Forever Wild dataset identifies areas in NYC Parks with the most significant natural habitat. Many of these areas have management practices in place to help preserve this habitat.
B. Clean the dataset to manipulate into the tool’s parameters and chosen data relationships that should be highlighted.
Carto’s technology is dependent on geographic data points and the following datasets had these values, so I completed a clean-up of the datasets’ formatting by separating values into separate columns and removing any additional details (i.e. spaces, special characters). This allowed the viewer to see each dataset’s geographic locations in relation to each other and flow through the chart with visual ease.
C. Experiment with design aesthetics to clearly and interactively communicate the data relationships.
Since there are multiple layers of spatial data due to the three datasets, each one needed an individual design aesthetic to be easily identified by the audience. The base layer was the Open Spaces (Parks) dataset and, rather than use color or other textures, the data’s spatial dimensions were outlined in a solid, dark brown color to emphasize the separation as well as the physical size of each space. The next layer was the Forever Wild Preserves and Natural Areas dataset and these areas are illuminated by a highlighted green color to accentuate the environmental consciousness of this dataset.
D. Publish the spatial visualization on Carto.
Quantitative Visualization via Tableau
A. Choose associated datasets to illustrate the chosen narrative.
For these visualizations, these specific datasets were chosen for these specific reasons:
Parks Closure Status Due to COVID-19: Playgrounds: In response to the COVID-19 pandemic, NYC Parks temporarily closed several amenities, including Playgrounds. This data collection contains the status of each Playground, and is subject to change. Although the data feed is refreshed daily, it may not reflect current conditions.
Air Quality: Dataset contains information on New York City air quality surveillance data. Air pollution is one of the most important environmental threats to urban populations and while all people are exposed, pollutant emissions, levels of exposure, and population vulnerability vary across neighborhoods. Exposures to common air pollutants have been linked to respiratory and cardiovascular diseases, cancers, and premature deaths. These indicators provide a perspective across time and NYC geographies to better characterize air quality and health in NYC.
B. Experiment with design aesthetics to clearly and interactively communicate the data relationships.
Since the previous datasets were focused on spatial relationships, the comparisons for these visualizations are focused on measurements per borough or neighborhood. The viewer can interact by filtering or scrolling through all the data points or categorical variables. Additionally green was the primary color palette, while shape and form were the main ways to differentiate each comparison from each other.
C. Publish the spatial visualization on Tableau.
Click Here for Final Design for Air Quality: https://public.tableau.com/views/AirQuaityCountperNeighborhood/Dashboard1?:language=en&:display_count=y&publish=yes&:origin=viz_share_link
Click Here for Final Design of Playground Closures: https://public.tableau.com/views/PlaygroundClosures/Dashboard1?:language=en&:display_count=y&publish=yes&:origin=viz_share_link
Inspired by the Bauhaus design movement, the visualizations’ colors, formatting, and functionalities would be built on three principles:
- Less is more.
Most Bauhaus designs have crisp structures with minimal, yet intentional splashes of color. Since this project focused on a park as a public space, a limited color palette based on the color green seemed the best fit because it would highlight the environment-conscious data and create a sense of familiarity for the audience. Then the design’s structure would intentionally be simple with a limited grid system, which allows the colors and data to be more prominent.
- Establish a hierarchy through information architecture.
This project has so many layers, from the several datasets to the gradient green colors, and a hierarchy needed to be established for the audience to easily navigate the visualizations within this one space. From the beginning, I wanted labels and legends to be parsed throughout each visualization so the viewer could conceptualize each data value as well as connect the relationships in a comprehensible manner. Then I built the layers of spatial data in Carto, based on the visualization logic that I wanted the viewer to become familiar with. For example, the viewer needed to identify the open public spaces first and then they could understand how other components within this space fit. This would help the viewer connect relationships between the datasets and identify the overall narrative of this project.
- Experiment for functionality and interactivity.
While the hierarchy provided the foundation and the colors illustrated minimalism, the functionalities were meant for experimentation and interactivity from the audience. For example, the viewer can turn on or off different layers of spatial datasets, in case they want to focus on specific values or trends. Then in another case, the audience can hover over labels within the datasets to review key information that illuminate a quality of the dataset. The audience can playfully move through these data-heavy spaces and identify insights in a structured environment.
- User Research Feedback
Two users were selected to provide feedback on the visual and conceptual experiences of these visualizations together. The first user was an insider within the climate and environmental space whose daily interactions with climate and “green” data was a great advantage. They enjoyed the limited color scheme as well as the minimalistic designs in the Carto graph. However they requested more precise labeling and increasing the sizes of the Tableau graphs for visibility. The next user was an outsider to the current environmental conversations, but has a strong background in visual design. This user liked the interactivity with each visualization and noted that the scrolling feature was helpful when reviewing all the locations’ data. However they requested that the Air Quality visualizations be separated into two different dashboards for better visibility.
The spatial comparisons of open space size to forever wild preserves and natural areas is surprisingly low.
Through the Carto graph, the viewer will notice New York City has disbursements of open space through parks, but most of these open spaces have little to no forever wild preserves and natural areas within them. This is quite troublesome and shocking, especially in the context of Prospect Park, because it exposes the question of whether New Yorkers have a relationship to nature and, if so, will that become nonexistent in the future, based on spatial graph.
Higher levels of gas emissions in air quality were located in boroughs with less forever wild preserves and natural areas.
The Bronx, Brooklyn, and Manhattan had the highest concentrations of gas emissions in their air quality than Queens, even though they had a large amount of open spaces through parks. I can’t exactly explain why this phenomenon is happening, but it may be due to the growth of urbanization and housing / business constructions in these boroughs over the past ten years. More buildings and people have migrated to these areas for housing and jobs, which may have caused the increase in gas emissions.
Brooklyn, Queens, and Manhattan had the highest amounts of playground closures and reopenings, but it seems the children only have exposure to wild preserves and natural areas in the larger flagship parks, and these parks are concentrated in specific areas.
Upon viewing the count of playground closures and reopenings, the above three boroughs have the highest amounts in each category. And yet when you review these boroughs in the Carto graph, the wild preserves and natural areas seem to be largely associated with flagship parks, such as Prospect Park or Central Park. This is concerning because all the other open park spaces have little to no natural areas, which means a child may visit a playground and their chance of interacting with a natural area is slim. It raises the question about the child’s relationship to nature and whether this relationship will grow considering their minimal exposure to natural areas.
While interesting findings were found through these visualizations, there are few recommendations for the next steps or future iterations of a similar project.
- Find more consistent data related to neighborhood labeling and better data codes.
While all this data was valuable, each dataset had its own categorization system for neighborhoods and similarities could easily be found, but consistency could help the viewer create relationships between the different agencies’ datasets. If the viewer wants to take the analysis further, they would need a data code available because specific datasets had naming conventions that seemed unfamiliar at first glance; a data codebook would be helpful in clarifying some of these abbreviations.
- Try experimenting with ArcGIS for the spatial data.
It was recommended to experiment with ArcGIS for this final project, but the tight timeframe and low familiarity with the tool led to the decision to continue with Carto. However if more time and tutorials were available, ArcGIS would have been the primary mapping tool and the Tableau visualizations would have been incorporated into the spatial mapping structure.