HAPPY & HEALTHY?
Type: Cooperation with City Science Lab Hamburg, Teamwork of 6
Direction: Data Visualization, Web Design
Year: 10.2018 - 02.2019
Location: University of Applied Sciences Potsdam (Germany)
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Background: In cooperation with the City Science Lab Hamburg, we tried to finish a data visualization on Hamburg's social infrastructure. Based on the available data, we decided to define our research question on what characteristics of neighborhoods determine access to healthcare infrastructures (HCI). We asked ourselves what other factors besides location could be potential indicators for this question and came up with the neighborhood residents' income status. Based on the median income from 2016, which was around 1,600 euros per month, we have divided the Hamburg districts into three groups and verified our conjecture by analyzing the ratio of the number of residents in each group to the medical infrastructure.
Research Question
What characteristics of neighborhoods determine access to healthcare infrastructures?
Hypothesis - Status
The higher the status of an area, the greater the access to healthcare infrastructures.
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Status: Status = avg_income / neighborhood
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Access:
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Access_registered doctor = residents / registered doctor
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Access_general doctor = residents / general practitioners
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Access_HCI = residents / HCI
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Income Groups
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High income: > 48.000, - EUR per year
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Middle-high income: 32.000-48.000, -EUR per year
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Lower income: <32,000, -EUR per year
Available HCI Data
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Hospitals
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Pharmacies
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Dentists
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Registered doctor
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Physical therapists
Calculation & Analysis
Legal guideline for the number of inhabitants per general practitioner: max. 1.600 inhabitants. We used this benchmark to calculate the distribution of general practitioners, and registered doctors for each district:
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Underserved: Districts in which more than 1.600 inhabitants per doctor.
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Over-served: Districts with less than 800 inhabitants per doctor.
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Drastically over-served: Districts with less than 400 inhabitants per doctor.
In the second step, we calculated the ratio of residents per HCI. There are no uniform guidelines here, so we have defined a guideline value of 1,000 inhabitants per healthcare infrastructure.
Potential Falsities
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Data collection: The income data were not gathered in the same year as the HCI data.
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We adopted a “hard” perspective on HCI and disregarded “soft” HCI like parks and recreation.
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Implementation of the guideline-value of general practitioners for the registered doctor.
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Dividing access by neighborhoods is a simplification.
Finding
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There is a slight relationship between density and access to HCI. However, even in less dense neighborhoods, access is still below the threshold of 1000 residents / HCI.
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A relationship between status and access to HCI: there is sufficient access to HCI in lower-income areas, excluding access to ‘general practitioner.’ However, the greatest surpluses of HCI are concentrated in high-income areas.
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→ spatial realization of unequal access to healthcare
Data Visualization
