What is Geospatial Data? Examples, Datasets and Providers

What Are the Key Components of Geospatial Data?

Geospatial data combines:

• Location information, like Earth coordinates.
• Attribute information, which includes object, event, or phenomenon characteristics.
• Temporal information, the time or duration in which the location and attributes exist.

The provided location can be classified as either:

• Static (short-term), like the equipment position or a temporary construction site.
• Dynamic, such as a moving vehicle or the spread of an infectious disease.

How is Geospatial Data Collected?

Geospatial data is collected from diverse sources like satellites, mobile devices, sensors, and public records. This data typically includes location-based information like human movement patterns, points of interest (POI), and physical or human-made features. Technologies such as GPS, cell towers, and WiFi signals are often used to gather this data, which is then anonymized and aggregated to ensure privacy.

What Are the Different Types of Geospatial Data?

Geospatial data include satellite imagery, GPS coordinates, digital maps, and aerial photographs. Here are the most relevant categories of geospatial data and their main applications with examples:

1. Point of Interest (POI) Data

Point of Interest (POI) data covers any physical places on Earth that people might want to visit. It includes details such as addresses, postal codes, phone numbers, and operating hours. This geospatial data helps improve map services and business evaluations. For example, retailers and consumer brands buy Point of Interest (POI) data to assess local market conditions and measure the potential opportunity for opening a store in a certain area.

2. Foot Traffic Data

Foot traffic data records how many people walk in certain places and tracks their movement patterns. It provides metrics on visitor counts, stay duration, and movement paths. This geospatial data helps businesses understand customer behavior and refine their operations and marketing strategies. For example, retail stores analyze foot traffic data to identify peak times and optimize their schedules.

3. Location Data

Location data shows the geographical location of devices or people. It includes latitude, longitude, and can also capture altitude and movement. This geospatial data is crucial for navigation, real-time tracking, and targeted advertising. For instance, delivery services use location data to plan and improve routes.

4. Map Data

Map data consists of geographic information used to make detailed maps, including boundaries, roads, landmarks, and terrain features. This geospatial data is crucial for urban planning, navigation, and spatial analysis. For example, city planners use map data to design transportation networks and manage infrastructure.

5. Satellite Data

Satellite data comes from satellites around Earth, providing images and environmental measurements. It covers weather patterns, land use, and natural events. This geospatial data supports weather forecasting, disaster management, and environmental studies. For instance, researchers use satellite data to monitor climate changes and track natural disasters.

6. Building Footprint Data

Building footprint data shows the shape and boundaries of structures on a map. This geospatial data is used in urban planning, infrastructure development, and disaster management. For example, city officials use building footprint data to evaluate urban density and plan public services.

7. Geographic Data

Geographic data includes details about Earth’s surface locations and features. It captures coordinates, topographical details, and natural landmarks. This geospatial data is key for environmental research, urban development, and risk management. For instance, environmentalists use geographic data to study animal habitats and migration routes.

8. Cell Tower Data

Cell tower data gives information about the locations and operations of cellular network towers. It tracks signal coverage, strength, and device connectivity patterns. This geospatial data is used to improve mobile network coverage and service quality. For instance, telecom providers analyze cell tower data to enhance coverage in specific regions.

9. GIS Data

GIS data combines different types of geographic information into a single system. It includes vector data (points, lines, polygons) and raster data (images, elevation grids). This geospatial data is essential for spatial analysis, urban planning, and environmental management. For example, planners use GIS data to design public transport systems and manage land use effectively.

What Are the Formats of Geospatial Data?

Geospatial data can have different formats and types depending on the source of the data. These are the most common types:

1. Vectors and Attributes

Vectors and attributes represent map objects using shapes such as points, lines, and polygons, each with descriptive information. Imagine a city map displaying points for restaurants, lines for streets, and polygons for parks and lakes. Each map element provides specifics, such as a restaurant’s name, street speed limits, or park amenities.

2. Raster and Satellite Images

Raster and satellite images present geospatial data as pixelated images, with each pixel representing a ground area. Satellite images offer high-resolution views of the Earth’s surface, revealing beaches, cliffs, forests, and structures like buildings and roads.

3. Point Clouds

Point clouds are collections of geospatial data points in three-dimensional space, typically captured using technologies like LiDAR. These points help create 3D models. Imagine scanning a historical monument in 3D, with each point capturing details like carvings and textures on its surface.

4. Census Data

Census data, collected through surveys, focuses on population dynamics in specific regions, providing insights into demographic changes and local amenities. Urban planning departments use geospatial data from census surveys to understand neighborhood population shifts.

5. Cellular Data

Cellular data includes geospatial information from devices, such as the locations where calls start or end, often pinpointed using GPS coordinates. Emergency services use this data to locate individuals dialing 911, enabling responders to quickly reach the scene and provide assistance.

6. Hand-Drawn Images

Hand-drawn images, crafted using computer-aided design (CAD) tools, offer detailed blueprints and layouts of structures like buildings. An architect might produce a CAD sketch for an office complex, detailing each floor’s layout, including wall positioning, doorways, windows, and utility placements.

7. Social Media Data

Social media data comes from user-generated content on platforms like X, Facebook, and Instagram. At a public event like a music festival, experts might analyze social media posts to gauge the event’s popularity and identify any issues that need attention.

What is Geospatial Data Analysis?

Geospatial data analysis combines this data with time and map visualization, allowing experts to spot changes over time and forecast possible future trends and patterns. Here are the trends that have radically changed geospatial data analysis:

• Cloud-based platforms are making geospatial analysis more accessible and scalable, offering the computational power and storage needed to handle large datasets without significant infrastructure investments.
• Big data analysis of geospatial data is opening up new possibilities for real-time analysis and decision-making, enabling complex analyses of huge datasets and providing insights at unprecedented scales.
• AI and machine learning are enhancing geospatial analysis capabilities by automating complex tasks, improving predictive modeling, and generating deeper insights from geospatial data.
• The Internet of Things (IoT) is producing vast amounts of geospatial data from connected devices, which, when integrated with geospatial analysis, can provide real-time insights for smart cities, environmental monitoring, and more.

What Factors Determine the Quality of Geospatial Data?

The quality of geospatial data depends on its accuracy, how current it is, and how well it matches real-world events like foot traffic or visits to locations. Good data has a high match rate with real-world activities. We recommend asking providers how they validate their data. It’s a good idea to choose data that has been cross-referenced with multiple sources to ensure you’re getting reliable insights.

How Accurate is Geospatial Data?

Accuracy in geospatial data is often described as “horizontal accuracy”—how close the data points are to actual locations. You can expect accuracy levels to be as high as 90% in many datasets. For use cases like retail site selection or urban planning, we suggest opting for high-resolution data (up to 19 meters) to get the most precise insights possible.

Is Geospatial Data Updated in Real-Time?

Yes, depending on the provider. Some datasets offer real-time or near real-time updates, while others may be updated daily, weekly, or monthly. We recommend considering how timely your data needs to be for your specific project. For fast-moving sectors like retail or event planning, real-time updates can give you a competitive edge.

How is Geospatial Data Secured?

Security is a big concern when it comes to geospatial data. Typically, it’s anonymized and aggregated, so personal privacy is protected. Providers use encryption and other protocols to ensure data security. When evaluating data providers, we suggest asking about their security measures and checking that they comply with privacy regulations like GDPR.

How is Geospatial Data Delivered?

Geospatial data is typically delivered in easy-to-use formats like .csv, .json, or through APIs. This gives you flexibility to integrate the data with your existing systems. Delivery methods often include cloud storage options like S3 buckets or real-time API access. If you’re not sure which format works best for your setup, we recommend starting with .csv for ease of use, then scaling to API integrations if you need real-time data.

How Much Does Geospatial Data Cost?

Pricing can vary depending on what you’re looking for: coverage area, data volume, and update frequency all play a part. Data can be purchased as a one-off or through a subscription model (monthly, yearly), and some providers offer usage-based pricing for API access. Our platform offers free samples so you can test the data before committing. We recommend taking advantage of those samples to make sure the data meets your needs.

What Geographic Areas Are Covered by Geospatial Data?

Geospatial data generally offers global coverage, with datasets available for regions across North America, Europe, Asia, Africa, and South America. Some datasets cover more than 200 countries. If you’re focusing on specific regions, be sure to check the coverage details. We recommend going for providers that offer detailed, region-specific data for the areas you’re targeting.

Can Geospatial Data Be Integrated With Other Tools?

Absolutely! Geospatial data is typically designed to integrate with most Geographic Information System (GIS) tools and platforms. This means you can enhance your existing datasets by layering on additional geospatial insights. Integration is usually smooth through APIs or standard formats like .csv. We suggest confirming the compatibility with your current tools before making a purchase to ensure seamless integration.

Can I Request Custom Geospatial Data?

Yes, you can! If you have unique requirements, Datarade offers flexibility in requesting custom geospatial data. Pricing can be tailored to fit your specific needs, so we recommend reaching out to data providers to discuss options that suit your project.

What Types of Geospatial Data Are Available on Datarade?

Datarade offers a wide range of geospatial data types, including raster maps, vector graphics, geocoded imagery, orthoimagery, and geo-referenced audio and video recordings. These datasets cover attributes like coordinates, elevation, land cover, shapes and boundaries, and more. If you’re not sure where to start, we suggest beginning with general location data and scaling up as your needs grow.