Information poverty is an ongoing research topic at UNICEF, which aims to estimate an average Kilobyte of information a child would access daily. Since every child is directly or indirectly attached to a school, then it means that once a school is found, children are found also. Given this situation it is paramount to map every school, and collect important information such as location, names, address etc.
Crowd-sourcing is a fresh and innovative way of gathering knowledge and information from the people, public or masses using a variety of platforms/tools using the internet which serves as a link to bring different people from different locations to a particular project. This approach leverages on the flexibility of the internet and platform to disintegrate large projects into bits or micro-task that can be completed by volunteers within a short time. Although conventional methods of mapping are still in use today, but these would prove challenging in terms of cost, timing, and large-scale project such as this (School Mapping). Crowd-source mapping alternatively offers a solution that maximizes cost, time, and scale of the project without compromising data quality.
A mapathon event was organised to identify and map school buildings in Tarauni LGA in Kano state. Volunteers from Ahmadu Bello University were invited to participate in contributing data and make edits using the OpenStreetMap platform. A total of 13 mappers participated, old and new to the OSM platform and were further divided to mappers and validators.
Duties:
Mappers: the mapper’s basic functions were to identify buildings that are schools and digitize the building footprints of the school properly.
Validators: this category ensures that all building footprints are digitized properly and also validate the job done by the mappers using various data sources.
The event commenced with a presentation on the project overview, with highlights on the project background, challenges of school mapping, and its solution using crowd-source mapping. The event advances with an OpenStreetMap training session, during this phase participants (both old and new) were (re)trained on how to map on using the OSM platform. The training requires that each participant signed up on the OSM Platform and followed the provided methodology outlined in the pilot proposal to map out school buildings.
In addition to the training, image interpretation skills were taught to recognize and identify school buildings on the satellite imagery. The necessary interpretation skills outline was as follows:
Shape and size of the building: School building shape and size as seen from the imagery were different from regular residential structures in the area, they compromised of slim and elongated type of building due the nature of each classroom block on a row of building. Some buildings also had L-shaped structures while others U-shaped and I-shaped structures.
Patterns: School building patterns gave another insight to determine its location. Most buildings with various Classroom blocks were a little distant form each other, though some were closely parked within a building compound. The few other attributes were the presence of a football field in schools, these was bit of a challenges because not all schools had a field.
The mapathon started around 12 pm, each mapper was given field-papers which acted as a tile boundary for each mapper. All mappers were required to paste the link of every selected school building that was mapped to the Google spreadsheet. These links where used by the validators to verify each school’s location, the accuracy of the attribute as well as the geometry.
The event came to an end after 5 hours of mapping. The final outcome of 52 schools were mapped after data validation was made using both Google maps and INEC Polling stations data. These school data comprise more of public owned schools than private owned. The Privately-owned building structures have great similar structures with residential buildings, this would be difficult to identify from satellite images.
Data validation was done on the mapped data, of a total of 52 schools. 10 schools were validated using data from polling stations within the study area, 3 schools from google, and 39 schools from the mapathon. Its worthy of note that names of the schools were taken from existing data source during the validation process.
Finally, this mapathon and the data generated will serve as a based data to conduct further research, and product testing vis-à-vis information poverty project. Importantly, this data can also serve as benchmark on which other data can be tested or verified.
Olatubosun Adebayo is the Vice President of ABU Geomappers at Ahmadu Bello University in Zaria, Nigeria.