Overview
The Nigerian railway network, designed and run by the Federal Government of Nigeria (FGN), is a 4.3K+ km track network that cuts across each geopolitical zone of the country.
While these transport networks serve millions of Nigerians’ passenger and freight needs, it is worth investigating how much of the Nigerian population has convenient access to railway transportation in Nigeria.
The following analysis will serve as an insight for the Nigerian Railway Commission (NRC) when planning a transit-oriented development.
Dataset Description
The Nigerian State boundary: provided by DIVAGIS
Nigerian Population data: A grided raster file provided by WorldPop. The data was produced by the 2020 population census/projection-based estimates. Relevant building footprints were provided by Ecopia.AI and Maxar Technologies
The Railway station location: Provided by OpenStreetMap. Its database was queried directly using QGIS.
Data Wrangling
The population dataset shows a spatial distribution of the population across the country. Each cluster shown on the map represents a population of 1.28 to 9,491.77 people.
The railway stations dataset includes columns to show if the identified station is a monorail, subway, light rail or regular railway.
A quick visualization of all three data sets reveals that most metro stations are located at the existing population clusters
Data Cleaning
A scan of the railway station's attributes table shows some of the recorded facilities are recreational monorail in Calabar.
The data can be filtered using the attribute table by writing an expression in the filter pane. However, in the monorail column, as shown in the image, regular commercial stations are recorded as “Null”.
To convert the null data to retrievable data, the following expression is written in the field calculator;
If(“monorail” is NULL, 0, “monorail”
This replaces all null fields with “0”.
A simple expression can be written in the query builder to exclude all monorails;
“monorail” = ‘0’
Reprojection
Performing buffer analysis on the acquired data from OpenStreetMap requires reprojection from its original coordinate reference system, ESPG:4326 WGS84, to a CRS that works with a Metric scale for its units, in this case, EPSG:3857 - WGS 84 / Pseudo-Mercator.
Data Analysis
Question: How much of the Nigerian population has convenient access to available railway stations?
Convenience is relative. In this analysis, it is defined as residents living within a 10 km radius of a railway station.
Create a 10 km buffer zone for each station
Perform zonal statistics on the population layer with the buffered zone as an overlay.
Create Buffer Zone
Using the reprojected railway station layer, create a buffer using the Vector geometry tool in the processing toolbox.
Reproject the buffered layer to the original CRS to ensure analysis is performed with layers in the same coordinate system.
Zonal Statistics
The population data from WorldPop records the estimated number of people within the 1oo sq. m. in its pixel value. To determine the estimated population, a sum of the values in the single band layer is required.
In the raster analysis tool on the processing toolbox, open the zonal statistics algorithm. This process overlays the available data in the raster file to match the region on the input layer.
This should be repeated while setting the input layer (layer to be overlayed) as the boundary layer and the buffered stations’ layer.
Using the information tool (also visible on the attribute table), a click on the boundary population and the buffer stations layers shows that of the estimated 206,016,803.59085977, only 35,078,320.27690625 people reside within 10 km. of a railway station.
Conclusion
From the analysis, only 17% of the Nigerian population lives within reasonable convenience of a railway station.
While a working 4k+ km. network is an impressive development, it is worth noting that the addition of a 277 km line connecting Ajaokuta to Warri is the first line constructed since the 1920s.
More railway networks need to be built and stations established to capture population hotspots.