A Manifesto for Pursuing Geoinformatics
I pursue Geoinformatics as a long-term intellectual path: to understand the Earth geographically, to model it computationally, and to transform evidence into human-centered decisions for a more sustainable future.
The path I choose
I choose Geoinformatics not because it is the safest path, but because it is the field in which the two strongest parts of my experience can operate as one: more than two decades of geographic thinking and years of close engagement in an Engineering Technology environment where GIS, BIM, computing, modeling, and spatial information systems intersect.
Many people are asked to choose between disciplines. I have come to believe that my strongest position is precisely at the intersection. Geography explains where, why there, at what scale, and through which relationships. Computing makes those questions operational through algorithms, data structures, simulation, programming, and artificial intelligence.
For me, this is not a compromise between Geography and Computer Science. It is a deliberate synthesis: geographic theory guides the questions; computational methods expand what can be measured, simulated, predicted, and explained.
My research philosophy
Geography
Geography provides the conceptual foundation: spatial distribution, scale, place, landscape, regional differentiation, interaction, and the geographic whole.
Computing
Computer science and engineering provide the instruments: programming, databases, simulation, spatial algorithms, web systems, machine learning, and AI.
Environment · Economy · Society
The research objects are coupled natural and socio-economic systems: climate, resources, livelihoods, cities, infrastructure, energy, communities, and territorial change.
People
People remain at the center. Models are valuable when they help explain behavior, reduce uncertainty, improve choices, and support fairer and more resilient transitions.
Why Geoinformatics matters
Modern challenges such as climate change, resource management, livelihood transition, urban transformation, renewable-energy development, and resilient infrastructure cannot be understood from one side alone. They require the ability to connect spatial evidence with process, behavior, technology, and policy.
That means being able to work across GIS and remote sensing, spatial statistics, databases, web technologies, social and environmental data, simulation, and AI — while still understanding the geographical meaning behind every result. The technical toolkit I continue to deepen includes C/C++, JavaScript, Python, R, Go, and Rust, together with GIS, BIM, WebGIS, spatial analysis, agent-based modeling, and modern AI methods.
I do not want to become a geographer who stops at description, nor a technologist who can build systems without understanding the spatial processes they represent. I aim to operate as a bridge between geographic reasoning and computational engineering.
The pipeline I pursue
The pipeline is designed to move from observation to explanation, from explanation to simulation, and from simulation to decision. Geography defines the system. Geoinformatics makes the system computable. AI extends the system’s capacity to learn and reason.
Human-centered sustainable transformation
My long-term research purpose is not technology for its own sake. It is to use spatial intelligence to support transitions that improve both human well-being and environmental resilience.
Net Zero 2050
Develop spatial evidence, models, and decision tools that can contribute to climate-neutral development pathways and Net Zero 2050 goals.
Livelihood transition
Understand how households, communities, and regions adapt to environmental and economic change, and simulate viable transition pathways.
Renewable energy
Use geospatial suitability, infrastructure analysis, scenario modeling, and socio-economic evidence to support renewable-energy development.
Circular economy
Model flows of resources, waste, infrastructure, and behavior to support circular systems that reduce pressure on climate and ecosystems.
The niche I intend to build
I therefore remain firmly committed to Geoinformatics. I do not see value in abandoning it for either pure Geography or pure information technology. Doing so would separate capabilities that are most useful when combined.
Instead, I intend to deepen a focused niche in intelligent spatial systems, particularly:
- Simulation: agent-based modeling, cellular automata, system dynamics, landscape and socio-environmental dynamics.
- GeoAI: spatial machine learning, graph learning, prediction, optimization, and interpretable geographic AI.
- GeoNLP: geographic language understanding, place-aware corpora, spatial ontologies, knowledge graphs, and retrieval-augmented reasoning.
- Spatial agents: AI agents that can reason with maps, query spatial databases, operate geospatial tools, compare scenarios, and explain decisions.
- Human behavior & urban systems: GIS-integrated ABM, social-network analysis, social media, urban analytics, and spatial decision support.
Declaration
I choose a path in which Geography gives meaning, computing gives capability, environment–economy–society define the research system, and people remain the center of every decision. I will continue building at this intersection with patience, technical depth, and a complete love of Geography — turning spatial knowledge into simulations, intelligent tools, and practical pathways toward sustainable development.