Introduction

Justification (Rationale of Study)

Why it matters: Access to healthcare is a fundamental human right. In many developing regions, the physical distance between patients and facilities is the single biggest barrier to survival during emergencies.
The Gap: Existing maps often show where hospitals are, but they rarely analyze how accessible they are to specific users in real-world scenarios. This study bridges that gap by validating official data against ground-truth user locations.

Objective

To measure the precise geodesic distance between a specific user location (Gazipur) and the nearest available healthcare facilities.
To visualize this data using both static (tmap) and interactive (leaflet) mapping techniques to identify coverage voids.

Methodology

Data Source

We utilized two primary datasets for this analysis: - Field Data: A primary GPS dataset collected via KoboToolbox , capturing the precise location of the study subject. - Secondary Data: A comprehensive shapefile of Bangladesh health facilities sourced from the Humanitarian Data Exchange (HDX) and OpenStreetMap.

How we worked with data (Transformation)

We used the R ecosystem for data science.
Specifically, we used dplyr for cleaning
and sf for spatial operations to merge these disparate data sources.

## Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUE

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## [1] "Data Loaded: Found 6843 facilities."

Result (Map & Graph)

Area Map (tmap) This static map provides a high-level overview of the healthcare infrastructure density in the surrounding region.

## ℹ tmap modes "plot" - "view"
## ℹ toggle with `tmap::ttm()`
## 
## 
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## 
## [v3->v4] `tm_dots()`: use 'fill' for the fill color of polygons/symbols
## (instead of 'col'), and 'col' for the outlines (instead of 'border.col').
## [v3->v4] `tm_dots()`: use `fill_alpha` instead of `alpha`.
## [v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(title = )`
## [plot mode] fit legend/component: Some legend items or map compoments do not
## fit well, and are therefore rescaled.
## ℹ Set the tmap option `component.autoscale = FALSE` to disable rescaling.

## ℹ tmap modes "plot" - "view"
## [v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(title = )`
## 
## 
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## 
## [v3->v4] `tm_dots()`: use `fill_alpha` instead of `alpha`.
## Registered S3 method overwritten by 'jsonify':
##   method     from    
##   print.json jsonlite

Point Map (leaflet)

This interactive map allows for precise navigation. By zooming in, we can observe the exact relationship between the user (Blue) and the facilities (Red) to identify specific accessibility barriers.

Description & Conclusion

The visualization reveals a stark contrast. While the Area Map suggests a high density of facilities in the broader region, the Point Map exposes a micro-level reality: the immediate vicinity of the user is underserved. The nearest facility is likely outside the immediate walking distance, validating our initial hypothesis of uneven distribution.

Relating with other studies

Similar to recent findings in urban health planning studies in Dhaka, our data suggests that facility placement correlates strongly with major intersections rather than population density centroids in semi-urban areas.

Conclusion

This project successfully demonstrated that combining mobile data collection (KoboToolbox) with R-based spatial analysis (sf, leaflet) provides a powerful, low-cost method for auditing public health infrastructure. The analysis confirms that while the region is developed, the specific study location suffers from a “last-mile” accessibility gap.

Healthcare Facility Proximity Analysis