Project 1: Suitability Modeling for Locating Prenatal Clinics in Gainesville, FL
Course: GIS Models in Public Health
Instructor: Dr. Liang Mao
Semester: Spring 2020
Background:
The real-world problem to solve (background) This course integrated the principles of GIS in aims to address public health and healthcare issues, and the issue that I chose to delve into was prenatal care. Prenatal care is essential to the health of both mothers and newborns as women who do not receive this care are three times more likely to give birth to babies with lower birth weights. This serves as an issue since low birth weight is often associated with developmental problems and low immunity. Additionally, infants born to mothers who do not receive adequate care are five times more likely to die. With regular visits to doctors, it is possible to detect health problems earlier in the pregnancy and perform any suggested treatments, if curable. Thus, the aim of this project was to build a suitability model to evaluate the appropriateness of building prenatal clinics in Gainesville, Florida.
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Geographic Data:
The geographic data for this analysis was pulled from many sources, resulting in different contributing factors to be used for the geographic model. The six factors for this study were: population density, distance to toxic release inventory (TRI) sites, median income, distance to major roads, elevation, and land cover.
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Geographic Methods/Models:
To analyze the problem, a suitability model was built using ArcGIS. This allowed for an evaluation of the appropriateness of a location or an area for a specific purpose, determined through a combination of several factors. The weights for each factor are determined through a ranking method known as the weighted linear combination (WLC). The method called for data processing and cleaning, which some of it was conducted in Excel. Specific operations were performed in ArcGIS, such as rasterization, Euclidean distance, and reclassification. These necessary spatial operations then allowed for the Weighted Sum operation, which helped in developing the prenatal clinic suitability map using WLC. Figure 1 shows a flow diagram of specific spatial operations for each data source to result in the suitability model.
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Key Results:
The resulting outputs indicated that around 64.3% of the existing prenatal care clinics fell under the High/Very High suitability categories from the model, which validates the strength of the model. Figure 2 shows the comparison of the existing prenatal clinics in the city with the results from the suitability model. Figure 3 shows the frequency graph of the range of suitability scores, which shows that the majority of the existing clinics fall under the ‘High’ score range.
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Conclusion:
Through the use of suitability modeling, it was possible to locate potentials for building new prenatal clinics to better serve women in Gainesville, Florida seeking care during, and even after, their pregnancy. Further implications of the study suggest the need for considering other factors to incorporate in the model and to also apply this model in areas with a greater need, such as the country Sudan. However, one of the major limitations is that there were some gaps in the data, which serves as problematic since those spaces in the city were not able to be analyzed.
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Key Figures:
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Figure 1: Flow diagram of the suitability model
Figure 2: Comparing pre-existing prenatal care clinics with the model outcome
