Introduction
This project is focusing on mapping healthcare facilities in New York City using Tableau. The idea generated from the willing to find how hard it is for citizens to seek for a healthcare help. For this lab on mapping, I chose to work with data on NYC healthcare centers, specifically a dataset from 2011 on Health and Hospitals patient care in New York City by council districts, which also contained data on the facility name, facility type, zipcode, borough, etc. Meanwhile, I sourced this dataset through NYC OpenData as the shape file. I was interested to create a visualization that demonstrated different facility types mapped in districts as well as counting how many types within the categories of location, residential and council.
Inspiration
As I was exploring the datasets on NYC Open Data, one file from Political and Administrative Districts caught my attention. The geographies represent the post-redistricting political boundary changes implemented by the NYC Board of Elections to reflect 2020 Census changes in population. A corresponding version of Geosupport Desktop Edition™ (Release 22A1) that reflects these political boundary changes is also available for download from the website.
When I look for other visualizations that use the same dataset, I encountered a report on HITConsultant which dive deep into the analysis of finding over a third of the U.S. population lives in a county where there is less than adequate access to pharmacies, primary care providers, hospitals, trauma centers, and/or low-cost health centers. (Resource) Inspired by the purpose of creating a health map that using released data from the City of New York Government , from which we can introduce a wide range of national datasets (e.g. census) and local health datasets to support a greater focus on evidence based planning and decision making.
Materials and Dataset
For this map dashboard in Tableau, I utilized a dataset called “NYC Health + Hospitals patient care locations – 2011” from City of New York Data.Gov, NYC Health + Hospitals is the largest municipal health care system in the country serving more than one million New Yorkers every year. From this dataset, the City’s public health care delivery system provides trauma, emergency, medical, mental health and substance abuse services across the five boroughs. This is a list of the public hospitals, skilled nursing facilities, and some of the community-based health centers that are part of the NYC Health + Hospitals system as of 2011.
I downloaded the dataset as CSV file, however after I tried to link the dataset file in Tableau, there’s some weird error with the value where the council district appears as Null instead of strings or number. I used OpenRefine to transform the file to google sheet then linked it in Tableau after I cleaned up and organized the data.
Methods and Process
1. Joining Sheets in Tableau
In order to mapping in Tableau, we need shape files and dataset files. By joining them through certain relationships, the information on a dataset file could be visualized on a map. Tableau also supports joining two spatial data sources using their spatial features (geography or geometry). You can only create spatial joins between points and polygons. For this project I’ll only use one shapefile on Council districts to see the distribution of the healthcare centers.(Figure 3)
For the first shape file “nyad”, there’s a Assemly district column and by connecting the column with the Council district column in the NYC health center dataset, it turned out the districts and the categorizes didn’t match. So I downloaded another shape file “nycc” with the same Council district which will map the data into matching categories.
2. Creating the Bottom Map
The base of the visualization is NYC City Council District(Clipped to Shoreline) with the Council District as categorized to dimension overlaid to top. And by dragging count of my dataset to the basemap, it’s obviously showcase the scale from 1 – 5 using gradient color. Green was selected for the district color based on general color association with the topic of healthcare center. I left the count as 0 area grey green to allow us to see the overlays in the next step more clearly. In the future development of this visualization, I will join borough shape file to make the districts presented in different scales.
3. Adding Layers and Creating Dot Map
Based on the category map, I’d like to visualize facility type of NYC in a visually straightforward way. Adding a layer of Facility Type upon the shape file achieved this goal and by adding in the coordinate information, it formed a dot map. Selecting the colors and opacities were crucial for the layers, which are meat to be viewed one layer at a time over the base map. Shades of red were used for the healthcare type because they are opposite to the green council district area on the color wheel. I choose colors that are generally associated with the variable that they represent, for example, red for acute care hospital, pink for child health center, purple for Diagnostic & treatment center and yellow for nursing home, all set to 80% opacity to create enough distinction. It was also important to consider the way the colors of dots would look overlaid on the shades of green in the base map to be able to recognize locations.
Moreover, I created a bar chart with the columns as borough, the rows as count of the facility type. I put the chart along with the map in my dashboard for a better interpretation of visualizing healthcare center distribution.
Results
From the council district map, we can tell from the color of the scale that there are more facility types in most of the districts in Manhattan, especially lower Manhattan and upper Manhattan. Also there are more healthcare facility types in Brooklyn, especially around Bedford, Crown Heights, Ocean Hill, New Lots, etc. There are fewer health care facility types on Staten Island and also on the east side of New York, as well as Bay Bridge in Brooklyn.
From the bar chart, we can tell among all of the facility types, the child health center occupies the highest propotion no matter in which borough. And Brooklyn owns the highest counts of facility types compared to other boroughs. It will generate more meaningful insights and information if connected with other map files and datasets such as race, salary, communities, or streets.
Reflection and Future Plans
For this project, I think the dashboards I created convey essential information and useful insights regarding to the healthcare services in NYC. Speaking of the conclusion of the process of this project, it’s important to double check that the joining relationship matches to each other and also if downloaded multiple shape files, it’s important to think through and select meaningful variables to visualize.
For the further development of this visualization, that would be interesting to add in other shape files such as borough, community board, streets and also would be meaningful to add in other dataset related to healthcare such as trauma center, etc. It’s worthwhile to think about how to create boundaries and form categories so as for a better view. For example, to overlay the borough map to community board or street map and think about the way to guide viewers’ attention to focus on a single variable as priority but also involve in other variables as comparison. Something else could based on this Healthcare Deserts, to investigate on a data level which connects other dataset to show more nuances and details from a more holistic scope.