Mapping of Pharmacies in Bandar Lampung Municipality: Nearest Neighbor Analysis and Overlay Approach

https://doi.org/10.22146/jmpf.83082

Nurma Suri(1*), Resmi Zakiah Oktarlina(2), Dwi Aulia Ramdini(3), Dedy Miswar(4), M. Bobby Rahman(5)

(1) Department of Pharmacy, Faculty of Medicine, University of Lampung
(2) Department of Pharmacy, Faculty of Medicine, University of Lampung
(3) Department of Pharmacy, Faculty of Medicine, University of Lampung
(4) Department of Geography Education, University of Lampung
(5) Department of Urban and Regional Planning, Institut Teknologi Sumatera
(*) Corresponding Author

Abstract


Background: Pharmacies should improve people's quality of life by providing pharmaceutical services. In this case, the distribution and accessibility of pharmacy facilities play an essential role.

Objectives: This research aims to provide an overview of the distribution of pharmacies in Bandar Lampung municipality and analyze their distribution and accessibility.

Methods: Non-experimental research with descriptive methods was conducted using spatial analysis. All population pharmacies in Bandar Lampung were selected as a sample, and their location was pointed using the Garmin Global Positioning System (GPS). Arc-Gis software was used for data analysis. A nearest-neighbor analysis was conducted to get a distribution pattern, and the pharmacies' density was calculated to obtain accessibility data. The overlapping approach was carried out to determine factors related to pharmacy distribution patterns.

Results: Bandar Lampung municipality has 268 pharmacies spread across 20 districts. This research shows that the average density of pharmacies per 10,000 residents is 2.26. The highest pharmacy accessibility is in Sukarame, with a ratio of 4.07, and the lowest is in Bumi Waras, with a ratio of 0.62. The distribution of pharmacies is a clustered pattern (T value 0.55) and centralizes on collector roads, residential urban land use, and health facilities.

Conclusion: The distribution of pharmacies is odd. They are distributed in a cluster pattern.


Keywords


Global Positioning System; Nearest Neighbor Analysis; Pharmacy; Spatial Analysis

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DOI: https://doi.org/10.22146/jmpf.83082

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