Over the past few years, a number of technologies notably Cloud Computing, Volunteered Geographic Information (VGI), Free and Open Source Software (FOSS), Internet of Things (IoT) and Linked Data have emerged. Such technologies have great potential in supporting wider adoption of SDIs. Coupled with maturing industry web services such as Web Map Service (WMS) and Web Feature Service (WFS), there could be no better time that African countries can to quicken development of their SDIs by adopting the new technologies.

This paper investigates the potential of emerging technologies that can support development of SDIs, through a simple geospatial application based on Google Cloud Services (GCS). In spite of the availability of cloud services such as Amazon Web Services Elastic Compute (AWS EC2) and Microsoft Azure Engine, we choose GCS as the development platform.

GCS is an attractive option for several reasons. First, it is a flexible and powerful cloud platform, providing services such as Google Compute Engine (GCE), Google Container Engine (GKE) and Google App Engine (GAE). Secondly, GCS is still relatively new and therefore little geospatial research has been carried out. More importantly, our application can take advantage of Google’s vast cloud infrastructure, including GAE (a PaaS cloud) and versatile authentication and authorisation framework. PaaS clouds can be used to extend SDIs by providing geoprocessing services based on tools such as Web Processing Service (WPS).

We specifically use GKE, IaaS cloud, to showcase several technology trends. The Kenya Certificate of Primary Education (KCPE) results of 2015, together with school mapping data of 2007, are used in the study. We obtain shapefiles of Kenya’s key administrative boundaries from the Independent and Electoral and Boundaries Commission (IEBC). Using PostgreSQL/PostGIS DBMS, we carry out several operations and spatial analysis typically common in SDIs.

The technologies and services showcased through our application include GCS, OGC Web Services, FOSS, Open Layers, Linux, Docker Containers and Kubernetes. We demonstrate the huge potential of new technologies in supporting development of SDIs. We further show that highly scalable geospatial services can be deployed in the cloud, greatly improving the reliability and performance of SDIs. The spatial analysis carried out may be of interest to practitioners in the education sector, who may adapt the system for their use.