The chapter gives a summary of all the results of the work in addressing the research objectives. It begins by expounding on the spatial and geospatial ecosystem of google cloud platform and then further explored the visualization dimensions leveraging on the google data studio tool. Expert evaluation of key functionalities of google cloud platform was discussed and also bringing to light potential strength and limitations of the platform. Finally, a design workflow for spatial data visualization was explored. There are varied tools and services offered by google to support geospatial data visualization but the study settled on google data studio. This choice is informed by its free and ease of use making it a choice for more people. Another reason for this choice is the transitions of geospatial visualization and analysis to user centric approach where significant technical requirement will not stonewall data usage and integration into business decision. When it comes to making profound decision making, both speed and accuracy are critical components. This also falls in line with the GeoAI moonshot of have making on intelligent decision based on data. Another deal breaker was the easy reporting component of data studio using dashboards. Dashboards are very pivotal for generating insights and presenting them on a single platform. The ability to interplay maps, diagrams and other attribute information allows for a strategic, analytical and operational use.

Spatial and Geospatial Ecosystem of Google Cloud Platforms

The first objective of the work is to review the spatial and geospatial ecosystem of google cloud platform. An exploratory review was conducted involving google product and documentation review. General literacy review on cloud computing paradigm in the context of cloud GIS was also explored. This analysis was supplemented with case studies. Broadly, google cloud services can be categorized under computing and hosting, storage, database service, networking, big data service and machine learning. The study realized that google data ecosystem supports spatial data integration and processing. They operate on a distributed physical and virtual system across the globe called zones. The considerations for the review include, data connection and integration, data analysis or exploration and data visualization and generation of insights. Since the focus on the general ecosystem, a handful products and associated service was used for this review and the choice of google data studio is discussed earlier. Overall, the entire ecosystems support interconnected data integration, scalability and ease of use, thereby reducing the technical barriers to entry as with other geospatial products.

Visualization Dimension of Google Cloud, methods, parameters and implementation approaches

There are numerous ways to integrate data into google cloud’s data studio platform. The platform allows for data integration through specially developed connectors and also via customized connectors. This allows for a seamless data ingestion based on users preferred choice. Spatial data can be integrated via BigQuery and accessed directly on the cloud storage. Also, users can add the data directly from their computer or create their own connectors. The first case study explored covid 19 visualization using google data studio. Spatial data for the study was download from the Johns Hopkins University github in CSV data format. The data was then integrated into google data studio using the file upload option. However, the second case study- UPOL department of Geoinformatics Web analytics integrated data directly via google analytics connectors. For the case study 3, population data was integrated directly from google sheets whereas global air pollution data was added as a CSV. More importantly, the data were integrated for visualization. As the another goal of the research is to ascertain the spatial visualization of google cloud platforms, various general and cartographic methods were tested. The study found through the various case studies the application of various visualization strategies to generate insight from data. In the first case study on covid- 19 Dashboard, an exploratory data analysis was conducted on the data. This involved feature selections and testing out various visualization methods. The study ascertained that both charts and maps can be harmonized to provide more insight. The first case study adopted the proportional symbol map visualized the death and confirmed cases in of global covid-19 using color and size visual variables. Also, being able to employ various cartographic methods like the choropleth maps and proportional symbol maps as well as charts, Google data studio allows for a synergistic understanding of observed phenomena. The case study also demonstrated the ability to use basic map design features like cartographic scale, color, typography layout and visual hierarchy.


The Case study 2 adopted a choropleth map in visualizing the sessions and page views of the Department of Geoinformatics website. Using line chats, a session trend analysis was determined for the last 28 days. Also, Tree maps, were used to augment the countries with more interaction with the platform. It was realized that, users can customize map interfaces according to critical design principles like proximity, alignments, balance, contrast, repetition and white space among others. The case study 3 explores the visualization of world population and share of pollution death data. The extra goal of this case is to test out the design capabilities of google data studio. A 3D impression was created to visualize the share of global air pollution. Infographics were tested to create a visually appealing representation of the phenomena. In all, google data studio allows for the visualization of key cartographic and basic visualization methods and allows for integration of key deign principles in visualization.



Evaluation of Cartographic and spatial Visualization

Whilst the case studies offer a comprehensive overview of the spatial visualization potentials of google data studio and google cloud platforms in general, other evaluation approaches were tested to complement the case studies. For an effective evaluation, a design framework was developed. The framework has four components including Geospatial data source, map design considerations, Cartographic method visualization and evaluations. The study revealed that google data studio integrates numerous data types and formats. As demonstrated by the case studies, both raw data from a computer or data in a cloud can be integrated for visualization. For the map design considerations, it is possible to work with scale, visual variables and symbolizations including color, size among others. It also offers the opportunity to incorporate typography, customized layout and visual hierarchy. For cartographic and general method visualization, only simple methods like choropleth maps, proportional symbol maps and diagrams can be applied. As the time of this report, it is not possible to implement more advanced visualizations methods like cartograms.

Limitations and Potentials of Google cloud platform

The limitation of google cloud platform just like most GIS cloud is the limitation of the methods and approaches that can be used. As evident by the various case studies, only basic cartographic and general methods like choropleth maps, proportional symbol maps. It is also not possible as at the of this report not able to perform complex analytical geospatial operation. However, a key potential is the ability to tell stories with data through generating insights. Also, it is possible to worked in a collaborative manner among teams and allows for control of user functionalities.

Workflow for spatial data connection and visualization

Working with google data studio requires registration and creating an account. The major stages include data connectors, data transformation, data visualization and evaluation. Data integration can be done either via google or community connectors after which exploratory data analysis process then performing actual visualization. Here, controls and interactions can be integrated. The final stage is evaluation in terms of user, cartographical, methodological and general visualization.