Understanding Geospatial Data: Types, Applications, and Importance

Every village vaccination drive, every flood relief plan, and every smart city blueprint shares a common backbone today: geospatial data. It tells us not just what is happening, but precisely where it is happening on the surface of the Earth. For researchers in population and family health studies, this combination of location and information has quietly become one of the most powerful tools for understanding human lives, resources, and risks. Let’s break down what geospatial data really is, the forms it takes, and why integrating it with other information transforms how decisions get made.

What is geospatial data? 🔗

Geospatial data is any information that describes objects, events, or phenomena tied to a specific location on, above, or below the Earth’s surface. According to a working definition by TechTarget, spatial data numerically represents a physical object in a geographic coordinate system, most commonly using latitude and longitude. So when a primary health centre is recorded with the coordinates 22.5726° N and 88.3639° E, that record is geospatial. When a satellite captures the flood extent across the Brahmaputra basin, that too is geospatial.

To understand this concept fully, it helps to distinguish between two complementary categories of data that geographic information systems work with: spatial data and non-spatial data.

Spatial vs non-spatial data 🔗

Spatial data contains a locational component. It tells you where something is. This information is multidimensional, meaning it carries position (latitude, longitude, sometimes elevation), shape, size, and orientation. Non-spatial data, often called attribute data, describes the characteristics of an object without referring to its location. As explained by Brooklyn College Library’s GIS guide, attributes can be nominal (a unique identifier like an Aadhaar number), ordinal (ranked categories), or interval and ratio data with measurable values.

Consider a single village in Malda district. Its spatial data includes the polygon boundary of the village and the GPS coordinates of its sub-centre. The non-spatial data tied to that same village would include its population count, literacy rate, number of households below the poverty line, and immunisation coverage. Both kinds of data describe the same place, but one answers “where” and the other answers “what.” As FME by Safe Software explains, non-spatial data tells you the “what” instead of the “where,” and the real value comes from connecting the two.

Types of spatial data: raster and vector 🔗

Geographic information systems represent the world digitally through two main spatial data models: raster and vector. Each has its own logic, strengths, and ideal use cases.

Raster data 🔗

Raster data is built from a grid of cells, or pixels, arranged in rows and columns. Each cell holds a single value that represents a measurable variable at that location, such as elevation, temperature, vegetation health, or land cover type. The size of the real-world area each cell represents is called spatial resolution, and as Geography Realm notes, raster data is most commonly found in remotely sensed data, shaded relief, topographic data, satellite imagery, and aerial imagery.

Rasters are well suited to representing phenomena that change continuously across a landscape. ArcGIS Pro documentation explains that elevation values measured from the Earth’s surface are a common application of surface maps, but other values, such as rainfall, temperature, salinity, magnetic properties, and density of materials, can also define surfaces that can be spatially analysed.

Some everyday examples of raster data include:

• Satellite imagery from sources like ISRO’s Cartosat, Resourcesat, and Oceansat satellites, or NASA’s Landsat programme.
• Digital Elevation Models (DEMs) that capture terrain height.
• Land cover and land use maps categorising forest, urban, water, and agricultural zones.
• Climate raster layers showing temperature, rainfall, or humidity patterns.

Vector data 🔗

Vector data represents geographic features through three geometric primitives: points, lines, and polygons. Each primitive is defined by a set of x, y coordinates anchored in a spatial reference system. According to Atlas’s GIS guide, points represent features too small to be depicted as lines or polygons, like a water well or a location marker; lines (or polylines) depict features with length but no area, such as rivers and roads; and polygons represent areas like lakes, park boundaries, or land use zones.

Common examples include:

• Points: GPS-derived coordinates of health sub-centres, ASHA worker households, or tube wells.
• Lines: Road networks under PMGSY, river systems, electricity transmission corridors, or railway lines.
• Polygons: District boundaries, agricultural plot maps, ward boundaries in a municipal corporation, or forest reserves.

Vector data is typically stored in formats like Shapefiles (.shp), GeoJSON, and KML, and it pairs neatly with non-spatial attribute tables that describe each feature.

How raster and vector complement each other 🔗

Neither model is inherently superior. Vector data excels at discrete features with sharp boundaries, while raster data handles continuous phenomena and analytical surfaces. Modern projects routinely combine them. A study of urban expansion in Pune, for example, might use multi-date raster satellite imagery to detect built-up growth and then overlay vector ward boundaries to quantify how much each administrative unit has grown.

How geospatial data is collected 🔗

Geospatial data flows in from several sources. Remote sensing satellites continuously capture imagery of the Earth’s surface; the Global Positioning System (GPS) provides precise point coordinates collected by field surveyors with handheld devices or smartphones; drones and Unmanned Aerial Vehicles produce high-resolution aerial imagery for smaller areas; and ground-based sensors monitor variables like air quality, water flow, and soil moisture. Historic paper maps and census records also feed into digital systems through scanning and georeferencing.

In the Indian context, the Bhuvan geoportal developed by ISRO’s National Remote Sensing Centre is a central hub for satellite-derived geospatial data. The Open Geospatial Consortium notes that Bhuvan supports projects ranging from the Road Asset Management System with the National Highways Authority of India to monitoring schemes like MGNREGA and the Pradhan Mantri Awas Yojana through geo-tagged photographs of houses under construction.

Integrating spatial and non-spatial data for decision-making 🔗

The real magic of geospatial data appears when spatial layers are linked with non-spatial attributes. A polygon of a village is just an outline; pair it with population, age structure, child immunisation rates, and disease incidence, and suddenly the same polygon becomes a decision-making tool. This integration is what allows planners, public health officials, and researchers to convert raw data into actionable insight.

Public health and disease surveillance 🔗

Geospatial integration has become essential to epidemiology. A systematic review published in BMC Health Services Research highlights how GIS-based mapping helps in routine surveillance activities to understand the various spatial factors associated with emerging pandemic diseases such as COVID-19, providing an understanding of epidemiological linkages with potential risk factors for identifying the spread of diseases.

One striking example comes from eastern Uttar Pradesh, where a study published in the National Library of Medicine archive overlaid ten different GIS maps covering socio-economic aspects (population, child population, literacy, workforce participation), epidemiology indicators (annual parasitic index and slides examined) and geographical features (settlement, forest cover, water bodies, rainfall, humidity, temperature) to identify malarial hotspot villages. By combining where mosquitoes thrive with where vulnerable populations live, the team identified the precise pockets that needed targeted intervention.

Urban planning and infrastructure 🔗

The Atal Mission for Rejuvenation and Urban Transformation (AMRUT) made GIS-based master plan formulation a mandatory reform across 500 cities. According to the National Remote Sensing Centre, the Bhuvan-NUIS web application hosts the National Urban Information System database, and 2,200 town planning personnel across states and union territories have been trained on it for master plan formulation. The integration of cadastral vector layers, demographic attribute tables, and high-resolution raster imagery enables planners to decide where to add a hospital, route a sewer line, or zone for affordable housing.

Resource management and rural development 🔗

The Bhuvan Panchayat portal, an outcome of the Space-based Information Support for Decentralised Planning (SISDP) project, equips gram panchayats with thematic layers on land use, drainage, water bodies, and infrastructure. When village-level officials can see crop patterns layered on rainfall maps and groundwater data, they make smarter calls on watershed development, MGNREGA work allocation, and drought relief.

Disaster management 🔗

During cyclones, floods, and earthquakes, integrated geospatial data shortens response times. Satellite imagery captures the inundation extent (raster), road and bridge networks indicate accessibility (vector), and population and household data attached to wards (non-spatial) reveal exactly how many people are affected. The National Database for Emergency Management uses this kind of layered approach for risk assessment and mitigation.

Why this matters for population and family health research 🔗

For students and researchers in population studies, geospatial data offers a way to move past tabular averages. National Family Health Survey indicators, for instance, can be mapped at district or even sub-district level to reveal stark inequalities. Maternal mortality is not uniform across a state; child stunting clusters in particular blocks; access to a primary health centre depends not just on its existence but on the road that leads to it. Without spatial context, these patterns stay invisible. With it, interventions can be designed for the specific geographies that need them.

Challenges remain. Research published on PubMed Central notes that limited access to high-speed internet, advanced GIS software, and trained personnel are significant barriers in rural areas, hindering the use of GIS for real-time monitoring and analysis. The growth of open-source tools like QGIS and freely available datasets through Bhuvan and similar platforms is gradually narrowing this gap, but capacity building remains essential.

What do you think? 🔗

What do you think? If you were designing a study on maternal health in your home district, which two or three non-spatial attributes would you most want to layer onto a map of village boundaries, and why? And how might the choice between raster and vector data shape the kind of conclusions your study could draw?

References

1. https://www.techtarget.com/searchdatamanagement/definition/spatial-data
2. https://libguides.brooklyn.cuny.edu/gis_intro/geospatialdata
3. https://fme.safe.com/blog/2021/10/non-spatial-data-difference-fme/
4. https://www.geographyrealm.com/geodatabases-explored-vector-and-raster-data/
5. https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/introduction-to-raster-data.htm
6. https://atlas.co/blog/raster-vs-vector-data-the-ultimate-guide/
7. https://bhuvan.nrsc.gov.in/ngmaps
8. https://www.ogc.org/blog-article/bhuvan-transforming-indias-governance-with-geospatial-insights/
9. https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-024-11837-9
10. https://pmc.ncbi.nlm.nih.gov/articles/PMC4435919/
11. https://www.nrsc.gov.in/nrscnew/Apps_Urban_Apps.php
12. https://bhuvan-panchayat3.nrsc.gov.in/
13. https://pmc.ncbi.nlm.nih.gov/articles/PMC11580228/