Nigerian-Hospitals-Project

Overview

This project provides a detailed analysis of the distribution of hospitals across Nigeria. It includes visualisation of hospital locations, calculation of geographical distances, and insights into healthcare access across the different states and regions of the country. The project aims to assist in identifying areas with healthcare needs and promote data-driven decision-making for healthcare infrastructural development.

Table of Contents

1. Project Description
2. My Motivation
3. Features
4. Technologies Used
5. Data Source
6. Key Insights
7. Structure
8. Images
9. Contact

Project Description

In this project, I performed a geo-spatial analysis of healthcare facilities across Nigeria. The main objective was to visualize the distribution of hospitals, compare the number of hospitals to population density, and explore potential gaps in healthcare access. You can explore the Tableau Dashboard for this project here

My Motivation

Why I built it:

Healthcare is a critical sector, especially in developing nations like Nigeria. I was motivated to build this project to better understand the distribution of healthcare infrastructure across the country and identify areas where more healthcare services might be needed. The project helps identify regions with limited access to hospitals and provides insights that can assist policymakers in planning future healthcare infrastructure in Nigeria.

What I learnt:

Working on this project taught me valuable lessons in handling geo-spatial data, as well as large data. I improved my skills in using Python libraries like Geopandas, and gained a deeper understanding of the healthcare challenges in Nigeria.

Features

• Geo-spatial visualizations:

  Interactive maps that display hospitals across Nigeria, categorized by state and type.

• Hospital Clustering:

  Insights into the concentration of hospitals and areas that are underserved.

• Metrics:

  Information like average hospitals, distance ratios, trends in hospital naming.

Technologies Used

• Pandas: For data access, manipulation and handling.
• Geopandas: For geo-spatial data manipulation.
• Folium: For interactive map visualization.
• Tableau: For creating the dashboard and respective visualizations.
• Jupyter Notebook: For code execution.

Data Source

The data for this project is sourced from: The Humanitarian Data Exchange platform, which was provided by the Nigeria Federal Ministry of Health.

Key Insights

1. Hospital Distribution:

• 42,063 hospitals/clinics exist in Nigeria, with 96% operational.
• Each state has an average of 1,000 hospitals, with Lagos State leading at 2,372 hospitals, and Bayelsa trailing with only 320 hospitals.

2. Hospital Establishment Trends:

• More hospitals were established between 1999-2001.
• A notable decline in hospital construction has occurred since 2020.

3. Public vs. Private Facilities:

• 74% of hospitals are public, indicating strong government involvement in healthcare.
• 85% are primary facilities, with fewer secondary (14%) and tertiary facilities (<1%).

4. Facility Levels:

• Hospitals are categorized into primary, secondary, and tertiary, based on the complexity of cases they handle.
• Tertiary facilities are mostly found in the North-West zone, with Kaduna State having the highest number.

5. Regional Variations:

• The North-West geopolitical zone has the highest number of hospitals (8,557), while the South-South zone has the lowest (5,017).
• Nasarawa State leads in the number of unlicensed and unregistered hospitals, followed by Benue, Kaduna, Sokoto, and Cross River.

6. Travel Distances Between Facilities:

• On average, the distance between facilities ranges from 0 to 850 kilometers. Traveling at 45 km/h, it takes approximately:
• 1 hour 47 minutes to drive from a primary to a secondary facility.
• 1 hour 20 minutes to drive from a secondary to a tertiary facility.

Structure

1. The assets folder contains the dashboard images for this Readme file.
2. The data folder contains the source and modified data files used in this project.
3. The file dashboard contains the link to the final tableau report.
4. The folder scripts houses two python files and one folder.

• The first file data_cleaning contains all the steps used to process and clean the data.
• The second file contains additional visualizations used to explore the data which were not included in the final report.
• The nested folder distance_calc_trials contains the various methods used to arrive at a most optimised calculation of geographical distances.

4. The file map.html contains a folium map which renders to the tableau dashboard.

Images

Contact

For any inquiries, feedback, or issues, please reach out to me via disephdumigoni@gmail.com, with the subject "Nigerian Hospitals Project".