Gong’s current research focuses on API-driven geospatial analysis methods that support near real-time and on-demand data access.
Maynooth University PhD researcher Chao Gong has spent more than 10 years as an expert in the field of geospatial data analysis, with a track record of surveying and mapping work at the national level in China.
While working in China, Gong was involved in managing a large geospatial database and supporting infrastructure, land use and environmental projects.
Currently, he is pursuing a PhD in geographic information systems (GIS) and remote sensing at Maynooth, while working as a GIS consultant at Quarry Consulting, based in Co Mayo.
His most recent work has focused on automating spatial analysis workflows and integrating real-time and on-demand data access into GIS applications.
“Over time, my research has evolved from traditional GIS processing to more powerful, API-driven methods that aim to improve efficiency and bridge the gap between academic research and real-world applications,” he says.
What inspired you to become a researcher?
I was first introduced to GIS during my undergraduate studies, and was immediately fascinated by how spatial data can be used to understand and interpret the real world.
However, a more defining moment came later in my professional career. I was involved in projects where large volumes of geospatial data had to be processed manually, often requiring significant time and effort before any meaningful analysis could begin.
I remember thinking that the real challenge was not just analyzing the data, but accessing and managing it properly. That realization stayed with me and was transformative. It made me realize that improving the way we work with spatial data can have as much impact as the analysis itself.
It was then that I became interested in exploring new methods, which eventually led me to research.
Can you tell us about the research you are currently working on?
My current research focuses on developing API-driven geospatial analysis methods that support near real-time and on-demand data access.
I developed a QGIS-based tool that integrates spatial data with live web services such as WFS and ArcGIS APIs. This allows users to perform close-up analysis without relying on downloading the complete dataset in advance, which is a common limitation in traditional GIS workflows.
The tool was used in collaboration with Quarry Consulting to support environmental analysis activities in real-world industry situations.
By combining techniques such as system integration, localization and caching, the system improves both efficiency and effectiveness while maintaining analytical reliability.
This work reflects a broader shift in GIS towards highly scalable, flexible and data-friendly workflows.
In your opinion, why is your research important?
Traditional geospatial analysis often relies on downloading and processing datasets, which can be time-consuming and inefficient, especially as data volumes continue to grow.
My research addresses this by enabling on-demand access to a relevant subset of geographic data through APIs, allowing users to find the information they need when they need it.
This approach can greatly reduce data transmission and processing overhead, making spatial analysis efficient and accessible. It is especially important in situations where timely and reliable information is important, such as environmental monitoring, planning and infrastructure development.
What commercial applications do you foresee for your research?
This research has great potential in many fields, including environmental compliance and monitoring, infrastructure planning and site selection, engineering and construction projects and site risk assessment.
By enabling faster and better access to relevant geographic data, organizations can improve decision-making while reducing operational costs and technical barriers.
What are some of the biggest challenges you face as a researcher in your field?
One of the biggest challenges is balancing real-time data access and analytics reliability. Ensuring that results remain robust while working with dynamic and distributed data sources is not always straightforward.
Another challenge lies in integrating various data sources, which often differ in format, quality and coordination systems.
Additionally, translating research results into practical tools that can be well accepted by industry requires bridging the gap between technological innovation and real-world applicability.
Are there any common misconceptions about this area of research?
A common misconception is that access to more data automatically leads to better analysis.
Although large data sets can be valuable, they do not lead to better results if they are inappropriate or poorly managed. In many real-world situations, the biggest challenge is identifying and accessing the right data at the right time, rather than processing all available data.
A typical workflow often involves downloading complete data sets, even when only a small part is needed. My research focuses on addressing this inefficiency by enabling API-based, on-demand access to a relevant subset of data, allowing analysis to be targeted and efficient.
In this sense, it is not just about having more data, but about having better access to important data.
What are some of the research areas you would like to see addressed in the coming years?
I would like to see more research in areas such as real-time geospatial data processing, integration between cloud-based GIS and desktop systems and the combination of GIS and AI and machine learning for more automated analysis.
In particular, developing simple and user-friendly GIS systems that can be easily distributed in all research and industrial areas will be an important direction for the future.
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