This data layer shows 311 Service Requests in the 30 days(from 15th May 2019 to 15th June 2019) for requests for the removal of a dead or badly damaged tree from public space. DDOT’s Urban Forestry Administration (UFA) removes trees that are dead, diseased, or unsafe. The arborist inspects the tree, and then the arborist will determine what measures the authority should take regarding the tree in question. In the event of an emergency, such as broken or hanging limbs, or a tree is down blocking the flow of traffic or obscuring the view of a stop sign or other traffic sign, the DDOT’s Urban Forestry Division will respond to mitigate the issue.
Inspiration & References
I was inspired by various sources from the internet to come up with this visualization.
I saw a similarity between my dataset and the dataset used in this visualization. This visualization helped me with how I can categorize the data into different fields. The animation in this visualization also informs the viewer with valuable information.
I took a lot of inspiration from this chapter of the readings. It helped me decide the layout of the map and also choose the appropriate colors for the final visualization.
Dataworld.com provided the dataset for tree removal, which I have used in this visualization. You can find the tree removal data set here! Since the dataset was already in geospatial format, it allowed me to import the dataset to Carto directly. With the Carto software, I have explored various options for my visualization and selected the best-suited options for my outcome.
The visuals for this visualization attempts to tell a story through colors. I primarily focused on colors to showcase the visualization. The red color in this visualization represents – Issue registered, whereas the green color represents – Issue resolved.
1 – Issue registered vs Issue resolved
In this visualization, I attempt to represent the full picture of the dataset at one glance. This visualization informs the viewers with all the needful information recorded in the period of 15th May to 15th June. With this information, viewers can come to know about the number of issues registered and the number of issues resolved. By looking at the visualization, we can tell that not all the problems filed are fixed. Viewers can also understand the density of calls within a particular area. The contrast between red and green helps to distinguish between two different pieces of information.
2 – Breakdown of Issue registered and Issue resolved
With the breakdown of this visualization, viewers can get an in-depth look at each information. This visualization tells the viewer about each day’s activity for the 311 calls. Putting this information side by side, viewers can compare the number of issues registered and the number of issues resolved. With this visualization, the viewer can understand the density of calls and also the consistency of fixing the problem.
The final result for this dataset provides an overall and breakdown of visualization. Viewers can view this visualization to get information about Tree removal calls in the neighborhood of DC. It also helps the viewers to understand the relation between a problem registered and the fixing of it. This visualization can inform users in several ways, which can be beneficial for the general audience.
With this visualization, I explored the boundaries of mapping and learned a lot about cartography from the class readings. Geospatial visualization has a lot of potentials if the dataset is vast. Even though my data set was appropriate, but it is limited to just 30 days of recorded data. This limitation restricted me from exploring other options in Carto. I wished to merge the breakdown visualization into one outcome, unlike the one I have done here. Compared to Tableau, Cart has a lot of options to explore and produce results distinctly. The dataset used in this visualization can also be made vast by expanding its boundaries to different states. With this, it can inform its viewers about how efficiently the problems are resolved within a region. It is also helpful for government authorities to check how a respective state is doing in regards to handling these 311 calls. Not only this, but they can also hand out contracts to different organizations if a particular organization is not efficient enough with their work. This visualization can not only be limited to tree removal but also many other issues faced by the citizens. With this exercise, I understood how to use a geospatial dataset to provide appropriate and needful information.
- Murphy, E., Mapping 311 fireworks complaints in Brooklyn 2020 “https://studentwork.prattsi.org/infovis/visualization/looking-at-311-fireworks-complaints-in-brooklyn-in-2020/“
- Visualizing geospatial data “https://clauswilke.com/dataviz/geospatial-data.html“
- Tree Removal in Last 30 Days “https://data.world/dcopendata/53b4afcee29a470e858d0c60bd70b35b-28“
- Tree Removal in Last 30 Days – Visualization “https://opendata.dc.gov/datasets/53b4afcee29a470e858d0c60bd70b35b_28?selectedAttribute=RESOLUTIONDATE“