Uncovering Medical Service Deserts in Urban Centers

This project focuses on mapping hospital facilities in New York City using ArcGIS. The idea stemmed from personal experiences of individuals facing difficulties in emergencies, prompting a desire to determine whether citizens in New York City have adequate access to medical services. The primary mapping utilizes data on healthcare facilities in New York City, specifically the population density for the year 2020, hospital locations, and population data collected by NTA( Neighborhood Tabulation Areas as created by the NYC Department of City Planning using whole census tracts).The analysis aims to provide insights into how many individuals a hospital should accommodate based on the population.

The dataset includes information such as population, census tracts, and borough. This data was obtained as a shapefile through NYC OpenData. Ultimately, the goal is to create a visualization project that identifies healthcare deserts in urban areas and offers insights into where establishing hospitals would be most beneficial.

When cardiac arrest lasts for more than 5 minutes without immediate cardiopulmonary resuscitation (CPR) intervention, there is a high likelihood of brain damage. If cardiac arrest persists for more than 8 minutes, the risk of death significantly increases.

This case study explores the distribution of healthcare facilities and mental health services in New York The city (NYC) aims to assess how effectively its residents have access to healthcare. The research makes use of 2011 datasets on the patient care locations of NYC Health + Hospitals, the Mental Health Service Finder data, and the population of NYC broken down by borough. Maps, charts, and dashboards are used to show the analysis, which focuses on the distribution of population in council districts and boroughs, both overall and per capita.

Three datasets—patient care locations, mental health services, and NYC population data—as well as two shapefiles are used in this research. Tools such as OpenRefine and R are used to tackle challenges related to data transformation and cleansing. Using shapefiles to join geographical data guarantees a thorough depiction of healthcare distributions among boroughs and council districts. Maps of distribution, bubble charts, bar charts, and tree charts are some of the Tableau visualization options. To improve visual clarity, different colors have been deliberately chosen to symbolize different kinds of healthcare facilities and mental health services. The report provides a thorough overview of healthcare resources in NYC by taking into account both overall distribution and per capita distribution. In-depth input is obtained from user testing with two participants. Although the participants have positive impressions, they also recommend improvements, such as a clearer distinction between the total and per capita distribution images and a better integration of council district maps with bar charts. Title changes, more dashboards for council district allocation, pie chart implementation, and using gray for zero counts on maps are among the recommendations. The visualizations show differences in the distribution of healthcare in New York City, with higher facility types observed in Manhattan and Brooklyn. Staten Island is exceptional in terms of resources per capita for health centers, even though overall resources are less. Participants commend the data interpretation and good use of color, but they also make improvements suggestions for improved user comprehension. Future enhancements will center on title modifications, more dashboards, the addition of pie charts for facility type ratios, and a way to have zero counts on maps appear in gray. With these improvements, the visualization should become more approachable and educational, facilitating evidence-based planning and decision-making around healthcare access in New York City.

-A Heuristic Approach to Lower Out-Of-Hospital Cardiac Arrests (OHCA) Mortality Rate Using Drones

-Mapping Healthcare Deserts: 80% of the Country Lacks Adequate Access to Healthcare

In the initial stages of our ArcGIS Story Map exploration, we laid the foundation with a straightforward border map of New York City, providing an essential geographic context for our investigation. Subsequently, our attention turned to a detailed examination of the city's population distribution using census tracts. Leveraging census data, we crafted an informative map that visually communicates population density across different areas of the city. This population map serves as a pivotal starting point for our analysis, offering a nuanced perspective on the demographic landscape within New York City. As we delve deeper into our methodology and results, these foundational maps become integral components in unraveling the intricacies of medical deserts and healthcare accessibility in the urban fabric.

1. Data Cleaning: Our methodology commenced with a thorough cleaning of the dataset, ensuring the accuracy and reliability of the information. Therefore, We removed non-emergency hospital data, such as mental health care, from the dataset.
2. Spatial Context: The initial map provided a plain border representation of NYC, establishing a geographical context for our subsequent analyses.

The story map unfolds progressing from foundational maps to zoomable representations. An in-depth exploration of spatial context, land use characteristics, and healthcare accessibility challenges in various areas is being offered. The maps reveal the nuanced dynamics of medical deserts across diverse neighborhoods in NYC.

Moving into a more in-depth analysis, we explored population data using a join method. This technique seamlessly integrated the count of hospitals in each zipcode, enriching the dataset. The introduction of a fresh variable, population density, provides a nuanced measure. It illustrates the population in each zipcode relative to the number of hospitals, offering a nuanced understanding of the intricate relationship between population distribution and healthcare facility accessibility at the zipcode level.

Impact on External Information and Unverified Data

Due to being situated along the boundaries with other regions, we were unable to examine the external information of the area we investigated. If there are hospitals along the adjacent boundaries, this area may not be a true hospital desert. The portions at the boundaries of the region contain unverified data, so they may not qualify as part of the hospital desert.

1 mile Buffer

A buffer was created based on symptoms such as sudden cardiac arrest, where the golden time is very short, using a round-trip distance of 4 minutes as a reference. At a speed of 50 miles per hour, traveling for 2 minutes covers approximately 1.67 miles. Considering urban traffic conditions, 1 mile was deemed an appropriate distance. However, this is a very approximate criterion, so further investigation is considered necessary in this regard.

Considering the population distribution, hospital desert analysis, and Hospital Population Capacity Allocation from the two analyses, we were able to derive the final results as follows.

Please check the locations designated as candidates by physically inspecting where they are situated. Check to confirm if they are in open areas.