The study adopted a mix methodological approach to address the various objectives. This approach helps optimize results for realism, reliability and validity (Ooms, 2016). The broad methods used includes literature review, case study and heuristic evaluation. To address the first objective which seeks to review the spatial and geospatial ecosystem of GCP, google cloud documentation was reviewed. Relevant literature was also reviewed in relation to Cloud GIS, Geographic data visualization. The second objective seeks to investigate the visualization dimensions of Google Cloud platform with emphasis on general and cartographic methods, parameters, dataset and implementation approaches. The case study methodology was adopted to realize this. The first case study was on the visualization of Covid-19 data and the second is on Department of Geoinformatics Web Analytics data whereas the last case study explored the visualization of world Population and Air Pollution Death data. The case studies were used to explore and demonstrate the data collection approaches, exploration and visualization in the context of spatial and cartographic dimensions. TFinally, expert evaluation was employed to complement the case studies. This also is to help address the third objective which seeks to evaluate, quantify and analyse the cartographic and spatial visualization and fourth objective to analyse the limitations and potentials of Google Cloud Platform for Geoinformatics and cartographic use. The expert evaluation adopted include SWOT analysis, non-technological aspect (Vondrakova, 2013) and heuristic analysis. All the expert methods were integrated and customised for the research goals.