Unlocking Location Insights: The Comprehensive Guide to Geospatial Data

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No matter the industry — from brick and mortar retail to software development — data is king. It powers innovation, drives decision-making, and is often a primary competitive edge. That’s why it’s no surprise Big Data, AI, and IoT are constant buzzwords. But one specific type of data is making a significant impact yet remains somewhat under the radar: Geospatial Data. 

Put simply, geospatial data is any data characterized by location information. From Points of Interest, to GPS data, or even migration data or footfall data, all of these are considered geospatial data.

Whether you're a retail analyst, a data scientist, or somewhere in between, geospatial data is not only relevant to modern business decision making, it can transform how business is done.

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What is Geospatial Data?

As mentioned above, geospatial data is data that contains or references location as a primary data point. This can include natural or assigned regional borders, geographic or constructed features, specific coordinates, route information and more. It can be data about locations only or it can be connected to other types of data such as demographic data or property and facility data.

Geospatial data is often broken into two primary types, vector data and raster data.

Vector Data

Vector data is what often first comes to mind when discussing geospatial data as it’s similar to what you see if you open a navigation app. 

Geospatial vector data is a collection of points, which are specific locations or coordinates, that can be connected by lines, such as a road, or sectioned off into regions by polygons, like cities, counties, or countries.

Raster Data

Raster data presents geospatial information as individual cells or pixels. The clearest example of this is aerial or satellite imagery of the earth’s surface. These are often used as base layer maps before adding additional data over it.

That said, geospatial raster data isn’t limited to actual photographs. Cells in raster mapping can represent various conditions like temperature, elevation, or even land use or zoning.

No matter the type of data, geospatial data is collected using various methods such as GPS (Global Positioning System), satellite imagery, and sensors. It can also be scanned or extracted from maps, reports, and historical archives.

Industries that Use Geospatial Data

As the importance of data grows in the digital age, the applications for geospatial data are vast and constantly evolving, regardless of the industry. In fact, several industries already make heavy use of geospatial data in their business intelligence efforts.

Geospatial Data in Retail

As geospatial data has become more accurate and accessible, retail companies have developed strong processes surrounding these types of data. Footfall traffic helps with everything from site selection to marketing and even product placement in-store.

Site Selection: 

By analyzing geospatial data, retail companies can draw insights based on factors like population density, proximity to competitors, accessibility, and how people move through specific areas to find the best place to build a new location — or to see why existing locations may be underperforming.

Customer Demographic Analysis: 

Retailers combine geospatial data with demographic and customer data to profile neighborhoods and other regions — helping them better understand the needs and preferences of potential and existing customers. This allows for better marketing campaigns, location-based advertising, and improved product offerings and partnerships.

Geospatial Data in Real Estate

Both commercial and residential real estate development benefits from geospatial data. Analyzing this data reveals migration trends, accessibility, and behavioral trends that can inform everything from choosing where to build to how to market existing properties.

Property Valuation

Accurate location data, as well as insights and trends gleaned from that data, can paint a clearer picture of a property’s worth and attractiveness to investors and predict how that may change in the future.

Risk Assessment

By helping identify flood zones or areas prone to other natural disasters, as well as revealing migration trends, geospatial data aids in risk assessment, insurance pricing, and investment planning

Geospatial Data in Transportation and Logistics

Logistics companies and shipping carriers are leveraging geospatial data to transform the supply chain into a smart, agile system that can better meet the needs of customers.

Fleet Logistics

Geospatial data has helped manage and track fleets — resulting in less downtime for maintenance and fewer detention charges while trucks idly wait for their turn to load or unload.

Route Optimization and Traffic Analysis

Route optimization using geospatial data is driving efficiency across the supply chain, resulting in lowered costs and increased flexibility across manufacturing and retail.

Geospatial Data in Financial Services

The financial services industry is finding ways to use geospatial data across their offerings — using it for risk mitigation, security, and marketing.

Personalized Offers

By analyzing geospatial data from their customers, financial services companies are able to offer services and promotions that meet their customers right where they are — whether they’re making a big, life-changing purchase or are simply spending their time at specific businesses.

Account Security

Historical and real-time geospatial data helps financial institutions assess when a customer’s accounts are being used fraudulently and lock or unlock accounts quickly — providing a better customer experience while limiting loss.

Risk Mitigation

Location data is helping lenders better understand the risks associated with real estate purchases and credit lines — driving smarter investments and lending decisions.

Geospatial Data in Healthcare

The healthcare industry is another sector that is making incredible use of geospatial data — helping serve their customers better and respond faster to healthcare trends and disease outbreaks.

Identify Underserved Areas

Migration data, demographics, and footfall data aid healthcare providers and municipalities in identifying areas and ways the community is underserved — helping find ways to better meet those needs.

Resource Optimization

Whether it’s physical inventory or ensuring the right specialists are on-hand, geospatial data is helping healthcare providers provide better care, more efficiently.

Disease Mapping

Understanding how a disease has, or is currently spreading gives healthcare providers the information they need to plan for community responses and allocate resources where they’re most needed.

Geospatial Data in Environmental Management

Broad, integrated geospatial datasets are helping environmental managers and policymakers better understand climate data and trends, as well as interconnected causal relationships.

Deforestation Tracking

Mapping and tracking terrain features and satellite imagery overtime helps better assess the health of a region and understand the impact and natural response to deforestation.

Monitoring Air and Water Quality

Understanding historical conditions and monitoring changes over time — as well as predictive modeling — can help with finding and responding to threats to air and water quality quickly, or possibly even before they occur.

Conservation Optimization

By comparing data across regions and better understanding trends, conservation efforts can be optimized to have a greater impact and prioritized to ensure the biggest needs are getting appropriate resourcing.

Just the Beginning

These examples are just some of the more wide-spread uses in prominent industries. The applications and optimizations from geospatial data are practically endless, making it a cornerstone of intelligent decision-making in the modern world. As technology advances, the significance and utility of geospatial data will continue to grow exponentially — for companies of all sizes.

Organizations of All Sizes Use Geospatial Data

While it’s easy to think of ways that global enterprises can make use of geospatial data, the reality is that nearly every organization can afford and benefit from location intelligence.

Geospatial Data for Enterprises

Folded into a larger business intelligence effort, geospatial data as used in location intelligence helps global companies respond better to regional differences, create process efficiencies across their organizations, and plan location and talent growth to best meet their needs.

Geospatial Data for Small and Medium Businesses (SMB)

From finding and targeting your ideal audience with just the right ads at the right time, to delivering products quickly, to building a customer experience that delights, even the smallest companies can leverage geospatial data easily and with clearly-attributable success.

Geospatial Data for Government Organizations

Governments of all sizes — including federal agencies, councils, and everything in between — can make use of geospatial data to shape policy and meet their constituents' needs. Urban planning, disaster response management, infrastructure development, education, and community healthcare all benefit from robust location intelligence.

Geospatial Data for Nonprofits and Research Institutions

Geospatial data can help nonprofits shape programs by identifying areas of increased need or potential donors. Research institutes can conduct deeper, more insightful research or more easily target specific subjects or regions for study. 

Benefits of Geospatial Data

As discussed earlier, data is paramount in modern business, and geospatial data is no different. Whether it’s adding net new information or serving as a new lens to evaluate existing data through, geospatial can benefit every organization.

Improved Decision Making

Geospatial data adds critical context when deriving insights from your existing data. Whether showing the spike in sales correlated with specific product placement or how climate is impacting foot traffic, geospatial data helps reveal patterns and new understanding.

Enhanced Customer Experience

By leveraging location data and the insights it provides, companies are better able to personalize their products and marketing or promotional offers to both existing and potential customers. This gives businesses a chance to delight their audience and create an experience that builds lasting customer relationships.

Operational Efficiency 

Monitoring geospatial data throughout the supply chain — both historical and real-time — means companies are able to optimize processes, as well as resource allocation and utilization. This not only creates efficiencies and cuts costs, it also allows businesses to become more agile and responsive to changes in markets or customer needs.

Risk Mitigation

Location-based factors can reveal existing and potential risks, allowing businesses to mitigate those risks better. Whether it’s migration data, like demographics shifting out of an area, or emerging trends in foot traffic to specific locations, identifying risks earlier leads to better outcomes.

Innovation Opportunities

Geospatial data recontextualizes business intelligence — driving savvy companies to develop new business models, find unique product gaps in the market, and offer services that better meet the needs of their customers.

Challenges and Ethical Considerations of Using Geospatial Data

While the potential benefits are enormous, there are also challenges and ethical considerations to keep in mind when utilizing geospatial data.

Data Privacy and Ethical Concerns

Geospatial data is often connected to personal identifiable information (PII) or other sensitive data. Because of this it is critical that geospatial data is sourced, stored, and used ethically and in compliance with applicable regulations. This helps protect customers’ privacy and rights and can help shield companies in the case of a data breach or leak.

Technological Requirements

Properly handling and analyzing large geospatial datasets requires specific technologies and infrastructure. This includes everything from maintaining security and privacy to having the tools to infer insights without manually combing through the information. 

This is why Unacast offers different ways of accessing data based on your organization's capabilities: device-level data for the most technically sophisticated companies; aggregated datasets for those looking for easy-to-ingest data; and a visualization platform for companies in need of easily accessible insights.

Data Integration

Geospatial data can absolutely help drive better decision making on its own. But to uncover the types of comprehensive insights that can transform your business, it’s vital to integrate the location data with other data sources such as customer information, marketing metrics, and sales figures.

Skills and Expertise

Like any kind of data analysis, geospatial data requires specific skills and expertise to manage and interpret. Without this expertise the data can either be useless or lead to incorrect conclusions and poor decision making.

Many companies these days mitigate these challenges by leveraging a third party platform and expertise. Unacast helps companies of all sizes ethically source, effectively manage, and expertly analyze geospatial data.

Real-world Success with Geospatial Data

Optimizing Brick and Mortar Strategy

A nation-wide retail brand faced the challenge of expanding into new markets after announcing plans for aggressive growth. By leveraging Unacast’s migration trends data they identified growing communities at the right income levels for their stores. Once identified, they used footfall data for their competitor’s locations in those areas to find gaps and opportunities that were right for their expansion strategy. 

Increasing Return-on-Assets for Commercial Real Estate Investments

The real estate and private markets division of a global private investment bank needed highly accurate foot traffic data to maximize earnings from commercial real estate investments. Realizing that dynamic geospatial data would give greater context to existing data, they leveraged Unacast Migration Patterns to identify and prioritize markets of interest and the types of investments that would be successful — all driving them towards their revenue goals.

Driving Insurance Profitability

A U.S.-based Fortune 500 property and casualty insurer needed additional datasets to assess risk in the underwriting process. By accurately assessing how many people visited commercial properties, when they visited, and how long they stayed, the insurer was able to better assess risk through the use of custom metrics. This increased efficiency powers their ability to effectively optimize their pricing strategy for profitability.

Building Better Parks

Researchers at the University of Chicago were able to pair smartphone GPS data from park visitors with existing park data to better evaluate how the spaces were used. These insights were used to optimize public transit in the area, inform maintenance and security operations, and help guide park expansions and development.

Tools and Technologies for Working with Geospatial Data

How an organization intends to leverage geospatial data will dictate the tools and technologies they ultimately need to use. Here are a few of the technologies that are useful to look at when evaluating a specific use case.


ArcGIS provides capabilities for analyzing geospatial data on both local computers or servers as well as through a cloud-based product. Contextual tools to visualize and analyze data help reveal new insights and collaboration tools enable sharing of maps, apps, dashboards and reports.


An open-source alternative to ArcGIS, QGIS provides similar functionality without the benefit of a cloud-based product. Because there is not a software-as-a-service offering, QGIS can be more difficult to manage, but the open-source nature of the project means that a robust community exists to support and develop the product. There are also third-party commercial support partners available.


Unacast is the industry’s most accurate geospatial data and location intelligence provider. By integrating multiple types of geospatial data and leveraging cutting-edge machine learning, we are able to provide deeper insights that drive better business decision making. Customers utilize a variety of offerings from Unacast: 1) Foot traffic data; 2) Cross-visitation data; 3) Migration trends data; 4) Trade area data; 5) Demographic data; and 6) Unacast Insights, our AI based location intelligence platform that turns data into immediate insights.

The Future of Geospatial Data

The importance of geospatial data is likely to continue growing, thanks in part to advances in technology and a greater appreciation for data-driven decision-making.

Technological Advancements in Geospatial Data Use

Artificial Intelligence (AI)

Machine learning algorithms can sift through vast amounts of geospatial data to detect patterns and make predictions that would be nearly impossible for humans to identify manually.

Unacast also utilizes generative AI in its location intelligence platform to provide instant summaries and quick insights from its geospatial data.

Internet of Things (IoT)

IoT devices, ranging from smart cars to industrial sensors, can collect an array of geospatial data in real-time, allowing for more dynamic and responsive decision-making.

Big Data Analytics

As the volume of geospatial data expands, big data analytics tools will become increasingly essential for processing and drawing actionable insights from this information.

Virtual Reality (VR) and Augmented Reality (AR): 

These technologies can bring geospatial data to life, allowing for immersive experiences that could revolutionize sectors like real estate, tourism, and education.

Sustainability and Climate Change: 

Geospatial data will play a critical role in monitoring and mitigating the effects of climate change, as well as in planning for sustainable development.


Geospatial data, once the purview of cartographers and urban planners, has evolved into a critical asset for businesses across industries. From helping retailers to identify the most lucrative locations for new stores to creating more efficient supply chains for manufacturing, the applications are as varied as they are impactful. 

But effectively using geospatial data can quickly become complex or cumbersome if it’s not implemented correctly. It also comes with significant, but manageable ethical concerns around data privacy. But a look at how geospatial data is currently being used and technology trends make it clear that it’s no longer a nice-to-have, but is critical to how businesses of any since make smart decisions.

Whether you're a mom and pop shop connecting with your audience or a global company planning your new building projects, geospatial data can provide you with a fresh perspective and drive smarter decisions. The future is geospatial, and the time to embrace this potent form of data is now.

Glossary of Geospatial Data Terms

The complex and technical world of geospatial data and location intelligence comes with an incredible amount of specific terminology and jargon. If you’re just learning about it, it’s important to speak the lingo. Here are some of the major terms surrounding geospatial data, GIS, and location intelligence.

Address cleaning

Address cleaning is a process used to standardize address data to reduce errors and duplicates and to improve accuracy.

Address standardization

Address standardization is formatting addresses in a consistent format for storage and analysis purposes.


Adjacency refers to two objects that share the same boundary, common side, or vertex. It can also refer to the proximity of locations relative to one another.

Administrative area

An administrative area is a geographic region designated as having a specific level of governmental authority or use.


In geolocation, annotation is the addition of information or metadata to a location or map to provide additional context.

Base Map

A base map is a map that provides the foundational layer that other data is added to.

Bounding Box

A bounding box is a rectangular coordinate system used to segment a map. It consists of perpendicular coordinates called latitude and longitude. Together they can pinpoint specific locations on the map.


Cartography is the science of map creation. It involves determining how to represent the world or a part of it in ways that are accurate, informative, and visually appealing.


In geospatial data, a centroid is the geometric center point of a specific feature or region.

Coordinate Reference System (CRS)

A Coordinate Reference System (CRS) defines how to locate and display geographic data in maps and other visualizations.

Digital Elevation Model (DEM)

A Digital Elevation Model (DEM) is a 3D digital representation of the topography of the earth’s surface or a portion of it. It includes elevation, as well as slope, aspect, and other characteristics.

Distance matrix API

Distance Matrix API is a web service providing travel distance and time specific origins and destinations, based on the recommended route between them.

Dynamic maps

Dynamic maps are a mapping service that provides easily updatable map tiles that change as the underlying data changes.

False color imagery

False color imagery is a type of satellite image that uses colors to correspond to different wavelengths of light, such as infrared or ultraviolet, creating an enhanced image for analysis.


A geocoder is a software tool or service that performs geocoding, reverse geocoding, or both.


Geocoding, sometimes called forward geocoding, is the process of converting address data into geographic coordinates.

Geocoding accuracy

Geocoding accuracy is the degree to which the geographic coordinates produced by a geocoder match the actual location of places.


Geofencing is setting virtual boundaries or "geofences" around physical locations. These are often used to trigger promotions or services for users who cross the boundary.

Geographic Information System(GIS)

A geographic information system (GIS) is software that creates, manages, analyzes, and maps all types of data.

Geographical User Interface (GUI)

A Geographical User Interface (GUI) is a type of user interface that uses geographic data, maps, and satellite imagery, to create interactive data visualizations.


A location specified in longitude and latitude.


Geonames is an open source geographical database maintained by the Open Geospatial Consortium.


Georeferencing is the process of aligning geospatial data, such as coordinates, to a digital map.

Global Positioning System (GPS)

Global Positioning System (GPS) is a satellite-based navigation system that allows pinpointing specific locations on the earth’s surface by triangulating signals from multiple satellites.


Granularity refers to the degree of detail or accuracy of geospatial data.

Grid Layer

A grid layer is a map layer overlaid over a map. The grid is usually made up of a series of evenly spaced horizontal and vertical lines that divide the map into smaller, uniform sections.

Ground control points (GCPs)

Ground control points (GCPs) are known locations on the Earth's surface used to align satellite imagery during georeferencing.

Haversine formula

The Haversine formula calculates the distance between two points on a sphere using their coordinates.

Heat Maps

Heat maps use color-coded cells to visualize data. Heat maps are useful in uncovering patterns and trends in data.


An intersection is a point where two or more features intersect, such as roads, lines, polygons, or surfaces. Intersections help identify locations and define relationships between features, and are critical for spatial analysis.


Latitude coordinates define a point by its north to south position on the Earth's surface.


Leaflet is an open source JavaScript library for mobile-friendly interactive maps.


Longitude coordinates define a point by its east to west position on the Earth's surface.

Magnetic Declination

Magnetic declination is the angle between magnetic north and true north at a particular location.

Map Matching API

Map Matching API is a web mapping service that correlates digital maps and the physical world.

Map Resolution

Map resolution is the level of detail and accuracy in a map. In other words, how finely the map is divided into smaller units, and how accurately it represents the real world.

Map theme

Map themes refer to the categories, styles or types of geospatial data represented on a map.

MapLibre JS

MapLibre JS is an open-source, user-friendly JavaScript library for building interactive maps. It allows creating customized maps with various features, including zooming, panning, and adding markers or pop-ups.

Multispectral imagery

Multispectral imagery is satellite imaging that captures different wavelengths of light.


Nadir is the point directly under a satellite as it orbits the Earth. This is the point on the Earth's surface that is closest to the satellite at any moment.


Node often refers to a point of interest on a map. It can be any point on a map that has a unique location, such as a building, a statue, or a tree.

Open Source Routing Machine (OSRM)

Open Source Routing Machine (OSRM) is an open-source routing engine that provides interactive route planning, turn-by-turn navigation, and network analysis.


OpenAddresses is a public data project that maintains a global, freely-available dataset of address-level geocoding.


OpenLayers is another open-source, user-friendly JavaScript library for building interactive maps. It allows creating customized maps with various features, including zooming, panning, and adding markers or pop-ups.

OpenStreetMap (OSM)

OpenStreetMap is a collaborative, open source global map database project.


Orthorectification is removing distortion caused by the curvature of the Earth from an image.


Parsing is the process of analyzing and interpreting data to understand its structure and meaning.


In maps, a pixel is the smallest unit of display. It represents a single point on the map and can represent specific natural or man-made features.

Point of Interest (POI)

Points of interest (POI) is a location that has a specific or particular purpose.

Polar orbit

A polar orbit is an orbit that takes a satellite over both of the Earth's poles during one complete orbit.


A polygon is a shape that defines a two-dimensional plane. In mapping it’s often used to denote specific regions.

Polygon overlay

A polygon overlay in GIS layers two or more datasets to create a new layer containing the overlapping areas.


A polyline in map applications displays a line connecting multiple points on the map, allowing users to visualize routes and paths.

Radiometric resolution

Radiometric resolution is the degree to which a satellite sensor can detect and distinguish between different levels of reflected or emitted radiation from the Earth's surface.

Raster Maps

Raster maps are digital maps made up of a grid of pixels. Each pixel contains a value that represents a specific feature or characteristic of the area it represents.


A renderer is a software component that converts geospatial data into a visual representation on a map.

Reverse Geocoding

Reverse geocoding is converting coordinates into a human-readable address or other descriptive information.

Route Optimization

Route optimization is using geospatial data to find the most efficient route to deliver goods or services from one point to another.

Satellite imagery

Satellite imagery is captured by satellites orbiting the Earth. The images can be used to create detailed maps, monitor changes in the environment, and track weather patterns.

Search radius

Search radius is the distance from a given starting point or coordinate included in a search.


Shapefiles are a type of file used in GIS to store and manage spatial data. They are typically used to represent geographical features such as rivers, roads, and buildings.

Spatial Analysis

Spatial analysis is used to find relationships and patterns in geospatial data to derive insight and solve problems.

Spatial reference system

A spatial reference system any scheme is used to define specific locations of objects on the Earth's surface like coordinate systems.

Spectral bands

Spectral bands are different wavelengths of light captured by a satellite sensor and can include visible light, and non-visible wavelengths, such as infrared and ultraviolet.

State Codes

In GIS, state codes denote geographic regions, including not just states, but provinces, countries, and more.

Static Maps

Static maps are built with pre-rendered map tiles that can be used to quickly render a map image but are not updatable to reflect changes in data.

Sun-synchronous orbit

A Sun-synchronous orbit links a satellite’s orbit with the sun’s position so that it can capture images with consistent lighting.

Swath width

Swath width is the distance covered by a satellite sensor in a single pass and is typically several kilometers wide.

Synthetic Aperture Radar (SAR)

Synthetic Aperture Radar (SAR) is a type of radar technology that uses microwave frequencies to create high-resolution images of the Earth's surface.


Topologically Integrated Geographic Encoding and Referencing (TIGER) is a geographic dataset created by the U.S. Census Bureau for use in GIS applications.


A tag is a keyword or label associated with a specific location or feature, used to refine or filter search results.


A timestamp is a numerical value used to indicate the exact date and time of an event or action.

Timezone API

Timezone API is a web service that provides reliable time zone data for any location relative to GMT.

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