When healthcare is discussed, attention turns to hospitals, doctors, medicines and medical technology. Rarely does geography enter the conversation. Yet whether a patient survives an emergency often depends less on whether a facility exists than on whether they can reach it in time.
For geospatial and information management specialist Nancy Kinyua, this question has shaped much of her work across humanitarian response, Water, Sanitation and Hygiene (WASH) programmes and health systems research in Kenya. Working at the intersection of geospatial intelligence, data strategy and global health, Kinyua has focused on transforming complex datasets into practical evidence that supports better decision-making.
Afrospatial recently sat down with Nancy Kinyua to explore how geospatial intelligence is reshaping healthcare planning, why accessibility modelling matters more than straight-line distance, and what the future holds for AI-driven spatial health analytics.
Drawing on her experience and research, including work published in BMJ Global Health on travel time to emergency departments in Kenya, she explains why healthcare access is not only a medical issue but also a geographic one. A hospital may exist, but poor roads, difficult terrain, seasonal flooding or long travel times can still place care out of reach for many communities.
This is where geospatial intelligence is quietly transforming healthcare.
Far beyond producing maps, modern Geographic Information Systems (GIS) integrate population data, transport networks, terrain, land cover, environmental conditions and health infrastructure into a unified decision-making framework. Instead of asking, “Where are our hospitals?”, geospatial intelligence poses far more meaningful questions:
- Which communities remain underserved?
- How long does it actually take patients to reach emergency care?
- Where should governments invest next to achieve the greatest impact?
- Could upgrading roads and transport infrastructure save more lives than building another hospital?
These are no longer hypothetical questions. They are increasingly guiding how health systems are planned around the world.
“Distance is easy to measure, but access is experienced through time.” — Nancy Kinyua
Maps Don’t Save Lives. Better Decisions Do.
One of the biggest misconceptions about GIS is that its primary purpose is to create maps. In reality, maps are simply the means through which complex spatial relationships are communicated.
Healthcare is inherently geographic. People live in one location, travel through varying road networks, experience different environmental conditions and seek treatment elsewhere. These spatial relationships influence health outcomes long before a patient reaches a clinic.
The true value of geospatial intelligence lies in revealing patterns that might otherwise remain invisible.
A county may appear well served because it has numerous hospitals. However, spatial analysis can reveal that thousands of residents remain effectively isolated because rivers, poor roads, mountainous terrain or seasonal flooding make those facilities difficult to access.
A map therefore becomes much more than a visual product; it becomes evidence that can shape policy.
Distance Isn’t the Problem. Time Is.
For decades, healthcare accessibility has often been measured by the distance between people and health facilities. But distance tells only part of the story.
Two communities may both be 20 kilometres from an emergency department. One may reach it within 20 minutes via a paved road, while the other may require more than two hours because of poor infrastructure, difficult terrain or a lack of transport.
From a patient’s perspective, those two communities experience completely different healthcare systems. Travel time captures realities that straight-line distance cannot.
Once healthcare planners begin measuring access in terms of time rather than kilometres, entirely new policy options emerge. Improving roads, strengthening referral networks, deploying resources strategically or upgrading existing facilities may have a far greater impact than constructing new hospitals.
More Hospitals Don’t Always Mean Better Healthcare
One of the most surprising insights emerging from spatial healthcare research is that increasing the number of facilities does not automatically improve access.
Traditional health planning often focuses on counting infrastructure. Spatial analysis asks a different question: are those facilities located where people actually need them?
Two counties may have an identical number of emergency departments while delivering vastly different levels of healthcare accessibility. The difference lies in geography. Facilities clustered around urban centres can leave rural populations underserved, despite impressive statistics on paper.
This shift changes how governments evaluate healthcare investment. Instead of asking, “How many facilities do we have?”, policymakers begin asking, “How accessible are they?” That subtle change has enormous implications for resource allocation.
The Data Challenge Isn’t Collection. It’s Connection.
Kenya has made progress in collecting geospatial and health data. Population datasets continue to improve, road networks are expanding, health facility registries are growing, and Earth observation data has never been more accessible.
Ironically, one of the biggest challenges is no longer collecting data. It is integrating it.
Each dataset is developed for a different purpose, maintained by different organisations, updated on different schedules and stored using different standards. Population estimates may be current while road data is outdated. Health facilities may exist but have inaccurate coordinates. Environmental datasets may use entirely different spatial resolutions.
The real work of geospatial intelligence therefore lies in building trust across multiple datasets rather than analysing each one in isolation. This growing need places data governance, interoperability, metadata standards and transparent methodologies at the centre of modern GIS practice.
As artificial intelligence becomes increasingly integrated into spatial analysis, these foundations become even more important. Sophisticated algorithms cannot compensate for fragmented or poor-quality data.
AI Is Already Changing Public Health
Artificial intelligence is often presented as the future of healthcare. In reality, geospatial AI has been supporting decision-making for years.
Machine learning models already assist in predicting crop yields, identifying flood-prone regions and analysing environmental change. Healthcare is now beginning to benefit from similar capabilities.
By combining climate information, population movement, disease surveillance, environmental conditions and health service availability, predictive models can identify areas where health risks are likely to emerge before outbreaks occur.
Yet technology alone is never enough. An early-warning system has little value if governments lack the funding, staff or operational procedures needed to respond. The real success of AI lies not in producing predictions but in enabling action.
From Technical Analysis to Better Decisions
Perhaps the greatest evolution in geospatial practice is philosophical rather than technical.
Early GIS projects often focused on producing increasingly sophisticated analyses. Today’s leading practitioners begin with a different question: what decision needs to be made?
This seemingly simple shift changes everything. Rather than producing dozens of impressive maps, geospatial professionals increasingly work backwards from operational decisions, whether identifying locations for new health facilities, prioritising ambulance deployment or determining which communities require immediate intervention.
Success is no longer measured by the complexity of the model. It is measured by whether services improve, budgets change or resources reach the people who need them most.
Why Communication Matters as Much as Analysis
Healthcare planning brings together professionals who speak very different technical languages. GIS specialists discuss accessibility surfaces. Epidemiologists analyse disease burden. Clinicians focus on patient outcomes. Policymakers think in terms of budgets, regulations and implementation timelines.
The role of geospatial professionals increasingly extends beyond analysis to translation. Instead of overwhelming stakeholders with technical detail, effective spatial analysts frame every discussion around one central question: what decision will this evidence help us make?
That shared focus allows professionals from different disciplines to collaborate around common objectives rather than isolated technical perspectives.
The Future Is Spatial
Healthcare is steadily moving beyond reporting what has already happened towards anticipating what might happen next.
Real-time Earth observation, mobile data, AI, environmental monitoring and routine health information systems are converging to create a new generation of healthcare intelligence.
Imagine satellite imagery identifying environmental conditions that favour disease transmission. Imagine mobility data revealing changing healthcare demand. Imagine predictive models highlighting communities likely to require emergency services weeks before traditional indicators detect a problem.
The technology already exists to support many of these applications. The remaining challenge is sustaining the people, institutions and governance structures needed to keep these systems operational.
A Seat at the Planning Table
Geospatial intelligence has earned recognition within healthcare through disease surveillance, immunisation planning, emergency preparedness and facility mapping. Yet, in many cases, GIS still enters the conversation after major planning decisions have already been made. That needs to change.
Healthcare planning is fundamentally a spatial challenge. “Healthcare planning is a set of choices under uncertainty,” Kinyua says. “Those are spatial questions before they are administrative ones.”
Every decision about where to build a facility, upgrade a road, deploy an ambulance or allocate limited resources has a geographic dimension. As climate change, rapid urbanisation and population growth place increasing pressure on health systems, historical utilisation data alone will no longer be enough.
The future belongs to health systems that integrate geospatial intelligence from the very beginning, not as an afterthought but as a core planning capability.
Because, in the end, healthcare is not only about treating illness. It is about ensuring that every person, regardless of where they live, has a fair opportunity to reach care when it matters most. And that begins with understanding the geography of health.
