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 a professional in the geospatial data analysis area, with a background in national-level surveying and mapping work in China.

During his time working in China, Gong was involved in managing large-scale geospatial databases and supporting infrastructure, land use and environmental projects.

Currently, he is pursuing a PhD in geographic information system (GIS) and remote sensing at Maynooth, while also working as a GIS specialist with Quarry Consulting, based out of Co Mayo.

His most recent work focuses on automating spatial analysis workflows and integrating real-time and on-demand data access into GIS systems.

“Over time, my research has evolved from traditional GIS processing towards more dynamic, API-driven approaches 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 I was immediately fascinated by how spatial data could be used to understand and interpret the real world.

However, a more defining moment came later during my professional work. 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 only analysing data, but accessing and managing it efficiently. That realisation stayed with me and became a turning point. It made me see that improving how we work with spatial data could have just as much impact as the analysis itself.

That was when I became interested in exploring new approaches, which ultimately led me towards research.

Can you tell us about the research you’re 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 have developed a QGIS-based tool that integrates local spatial data with live web services such as WFS and ArcGIS APIs. This allows users to perform proximity analysis without relying on downloading complete datasets in advance, which is a common limitation in traditional GIS workflows.

The tool has been applied in collaboration with Quarry Consulting to support environmental analysis tasks in real-world industry contexts.

By incorporating techniques such as coordinate system standardisation, spatial indexing and caching, the system improves both efficiency and performance while maintaining analytical reliability.

This work reflects a broader shift in GIS towards more scalable, flexible and data-efficient workflows.

In your opinion, why is your research important?

Traditional geospatial analysis often relies on downloading and processing entire datasets, which can be time-consuming and inefficient, particularly as data volumes continue to grow.

My research addresses this by enabling on-demand access to a relevant subset of spatial data through APIs, allowing users to retrieve the information they need when they need it.

This approach can significantly reduce data transfer and processing overhead, making spatial analysis more efficient and accessible. It is particularly relevant in contexts where timely and reliable information is critical, such as environmental monitoring, planning and infrastructure development.

What commercial applications do you foresee for your research?

This research has strong potential across several sectors, including environmental compliance and monitoring, infrastructure planning and site selection, engineering and construction projects and spatial risk assessment.

By enabling faster and more efficient access to relevant spatial data, organisations 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 main challenges is balancing near real-time data access with analytical reliability. Ensuring that results remain robust while working with dynamic and distributed data sources is not always straightforward.

Another challenge lies in integrating heterogeneous data sources, which often differ in format, quality and coordinate systems.

Additionally, translating research outputs into practical tools that can be effectively adopted by industry requires bridging the gap between technical innovation and real-world usability.

Are there any common misconceptions about this area of research?

A common misconception is that having access to more data automatically leads to better analysis.

While larger datasets can be valuable, they do not necessarily result in better outcomes if they are not relevant or efficiently managed. In many real-world scenarios, the key challenge is identifying and accessing the right data at the right time, rather than processing all available data.

Traditional workflows often involve downloading complete datasets, even when only a small portion is required. My research focuses on addressing this inefficiency by enabling API-based, on-demand access to a relevant subset of data, allowing analysis to be more targeted and efficient.

In this sense, it is not simply about having more data, but about having better access to the data that matters.

What are some of the areas of research you’d like to see tackled in the years ahead?

I would like to see further research in areas such as near real-time geospatial data processing, integration between cloud-based GIS and desktop systems and the combination of GIS with AI and machine learning for more automated analysis.

In particular, developing scalable and user-friendly GIS systems that can be easily deployed across both research and industry environments will be an important direction for the future.

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