GIS data software: clarity and intelligence for modern decisions
Learn how GIS data powers risk modelling, planning, and analysis. Nearmap delivers GIS data collection, management, and intelligence at scale.
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The world is built on data. But not all data is created equal. To understand the real world and act with confidence, organisations need GIS data — geographic information system data that connects every number, map, and decision to a location on Earth.
From insurance risk modelling to infrastructure planning, GIS data powers the systems that shape how we build, protect, and manage our communities. It transforms raw inputs into insight. It reduces risk, improves accuracy, and accelerates action.
This guide explores GIS data in detail — what it is, how it’s created, why it matters, and how organisations across industries use it to move faster and smarter.
What is GIS data?
GIS data is information tied to a geographic location. It describes features of the Earth — both natural and artificially made — and stores them in digital form so analysts can analyse, map, and visualise them.
Unlike simple maps, GIS data goes deeper. It doesn’t just show where things are; it explains what they are, how they relate to each other, and how they change over time. A GIS data model might represent roads, rivers, buildings, vegetation, elevation, or risk zones.
When integrated into a GIS map data system, these layers of information become the foundation for modern decision-making.
How to create GIS data
Several approaches create GIS data. Manual digitising remains a classic method, converting paper maps into digital layers. Surveying techniques like GNSS or total stations provide exact coordinates. Remote sensing — through satellites, aerial imagery, or LiDAR — delivers large-scale GIS spatial data quickly.
Field collection apps extend capabilities by allowing teams to capture photos, attributes, and GPS coordinates on mobile devices. Many organisations also integrate existing land records, environmental reports, and infrastructure data, layering them into modern GIS data management platforms.
Types of GIS data
GIS data fall into two broad categories. The first is spatial data, which describes the geometry of features: points, lines, and polygons that represent real-world objects. The second is attribute data, which provides descriptive details. For example, a boundary (spatial) may link to zoning codes, year built, or ownership details (attribute).
A real estate GIS data layer, for instance, can combine parcels, structural features, and zoning rules. When analysed together, they reveal insights impossible to extract from spreadsheets alone.
What are the two main GIS data models?
There are two principal models in GIS. Vector models use points, lines, and polygons to represent discrete features like roads or property boundaries. Raster models use grids or pixels, ideal for continuous surfaces such as elevation, land cover, or temperature.
Each model has its strengths, and many projects use both together. Raster data provides context, while vector data delivers precision.
What is a GIS data structure?
The structure of GIS data ensures that spatial features link seamlessly with descriptive attributes. Typically, this involves a relational format where a geometry table connects to an attribute table. Metadata sits alongside, documenting source, accuracy, and update cycles.
Modern GIS data management solutions build on this structure, storing data in scalable geodatabases or cloud systems. This allows fast queries, multi-user access, and streamlined integration with enterprise tools.
Which software is used for GIS data?
Organisations use GIS data software to collect, manage, analyse, and visualise data. The best platforms support both raster and vector GIS data types, handle diverse formats, and integrate with other systems.
Today’s software is increasingly cloud-based, enabling collaboration across teams and geographies. From risk modelling in insurance to environmental monitoring, software platforms turn GIS data into clear, usable intelligence.
How to analyse GIS data
Analysis makes GIS powerful. By overlaying layers, organisations can identify relationships — such as storm paths crossing insured properties. Proximity analysis can measure the distance between utilities and high-risk areas. Network analysis models traffic or supply chain flows.
Spatial statistics take this further, identifying clusters, trends, and anomalies. With AI embedded in GIS data analysis, organisations are now forecasting risk and predicting change, not just documenting it.
How is GIS data collected?
Collection depends on the balance between scale and accuracy. Survey crews gather pinpoint data on the ground. Remote sensing platforms capture vast areas at once, delivering imagery and elevation models. Sensors and IoT networks now stream live data into GIS data services, such as weather, traffic, or air quality feeds.
Mobile applications make collection more agile. Teams can document features on-site, adding photos, coordinates, and notes in real time. Together, these methods create dynamic datasets that reflect reality as it changes.
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Raster vs vector in GIS data
Raster and vector approaches define how data is stored and used. Raster is continuous and grid-based, perfect for images and elevation models. Vector is discrete and geometry-based, best for parcels, roads, and structures.
Think of raster as the backdrop — the landscape. Vector provides the details — the precise features. Together, they create a complete picture.
Who uses GIS data and how they apply it?
GIS data powers decisions wherever location matters. Insurers use it to model hail, wind, and flood exposure, and to compare storm tracks against portfolios to estimate potential losses. Governments rely on it for zoning, land-use planning, emergency management, and disaster response, including mapping evacuations in real time. Utilities leverage GIS to monitor networks, pipelines, and vegetation with spatial precision.
Engineers tap elevation and slope data to design infrastructure safely, while environmental groups track wetlands, forests, deforestation, erosion, and habitat loss. Across industries, GIS data turns raw geography into insight—enabling quicker, smarter, and safer actions.
Common GIS data formats
The industry relies on a handful of widely accepted formats. Shapefiles remain a workhorse for vector data, though modern alternatives like GeoJSON and File Geodatabases are more efficient. KML/KMZ is popular for web and Google Earth integrations. On the raster side, GeoTIFF and IMG dominate.
Choosing the correct format depends on project requirements — speed, compatibility, and scalability.
Components of GIS data
A complete GIS dataset includes several layers. At its core are spatial references and geometries, defining where and what features are. Attributes add the descriptive context. Metadata ensures understanding of the data’s origin, accuracy, and limits.
This combination allows GIS projects to deliver reliable, transparent insights. Without metadata or attributes, maps become pictures rather than intelligence.
How to use GIS data
GIS data supports countless workflows. Insurers overlay flood zones with portfolios to calculate exposure. Utilities plan upgrades by mapping asset conditions. Governments validate land use and zoning compliance.
Even in claims, GIS provides clarity: when a storm hits, analysts can cross-check damage zones with insured properties for fast, evidence-based payouts. GIS data collection ensures these systems stay accurate and current.
Benefits and advantages of GIS data
GIS data delivers clarity and confidence. Decisions are grounded in evidence, not assumptions, while efficiency improves as accurate information replaces manual inspections and guesswork. At scale, organisations gain portfolio-wide insight, and transparency builds trust with regulators, customers, and other stakeholders.
Strategically, GIS creates a competitive edge by helping teams move from reactive to proactive. They can anticipate risks, model scenarios, and align departments around a single, authoritative dataset. Because GIS data integrates with enterprise platforms, its value flows across underwriting, compliance, claims, planning, and beyond—reducing claims costs for insurers, sharpening underwriting accuracy, and supporting smarter planning and resource allocation for governments.
Limitations of GIS data
Even the best systems face challenges. Data quality depends on inputs — outdated or low-resolution data can skew results. Integrating GIS with older systems can take time. Advanced GIS data models require expertise to interpret correctly.
Costs are also a factor. Collection, processing, and storage all demand resources. But for most organisations, the long-term savings far outweigh the investment.
Frequently asked questions
Get answers fastWhat is the purpose of GIS data?
To connect information to location, enabling organisations to analyse, visualise, and act with confidence.
Who needs GIS data?
Insurers, government agencies, utilities, infrastructure managers, and environmental organisations all rely on GIS to manage risk and assets.
Which is the best GIS data platform?
The best platform supports multiple GIS data types, integrates with enterprise workflows, and provides scalable management and analysis.
How can I extract GIS data?
Extraction depends on the source — through APIs, direct exports, or database queries. Most systems allow export into shapefile, GeoJSON, or CSV formats.
How to convert GIS data to Excel?
Most GIS data software allows exporting attribute tables as CSV or Excel files, enabling integration with business reporting tools.
Is GIS data real-time?
Increasingly, yes. IoT devices, remote sensors, and streaming services now deliver live GIS data for weather, traffic, and utilities.
What’s the difference between GIS data and GIS maps?
GIS data is raw information. A map is a visual product created from that data.
Why GIS data matters
The future belongs to organisations that see clearly. GIS data provides the lens. It turns raw inputs into intelligence, guiding underwriting, claims, planning, and resource management.
For insurers, GIS data is the foundation of accurate risk modelling and claims validation. For governments and enterprises, it’s the key to resilience and growth.
GIS data is not just information — it’s actionable intelligence.
Ready to put GIS data to work? Nearmap gives you the images, analytics, and intelligence needed to turn location data into actionable insights that help you beat the competition.
Connect with Nearmap today — and unlock the full potential of GIS data for insurance, infrastructure, and beyond.