Geospatial Data: What It Is and Why Your Business Needs It

In today’s data-driven world, location data has become a crucial asset for staying competitive. From optimizing marketing campaigns to improving site selection, the ability to understand and utilize geospatial data can transform the way your business operates. Businesses across numerous industries use location information to enhance decision-making, predict customer behavior, and streamline operations.

What is geospatial data? In simple terms, it refers to information linked to a specific place on the surface of the earth. This data can consist of points of interest (POI), which provide geographic coordinates such as latitude and longitude, along with additional attributes and details on a given place. If you want to access valuable insights like POI data, providers like dataplor offer comprehensive location data to help you gain a competitive edge.

A study revealed that the geospatial data industry is growing rapidly, with the global GIS market projected to reach $14.5 billion by 2025.

Source: Markets and Markets

In this article, we’ll review what geospatial data is, how spatial analysis works, and why this data is critical for business. Whether you’re looking to optimize site selection, improve real-time marketing strategies, or enhance customer engagement, geospatial insights are the key to success.

What is Geospatial Data?

Geospatial data refers to information tied to a specific location on the earth’s surface. This data comes in various formats and is crucial for gaining insights about locations. Understanding the spatial relationships between different places allows you to make more informed decisions.

By linking geographic locations to real-world data points, geospatial data helps you visualize trends effectively with the help of data visualization tools.

Key Components:

1) Spatial Data:

This component represents the physical location of a place, defined by precise geographic coordinates such as latitude and longitude. These coordinates enable the accurate pinpointing of locations on Earth, making spatial data essential for location analysis.

For example, point clouds, polygons, and vector data are common spatial elements used to define the boundaries of a geographic area, whether it’s a business, landmark, or other point of interest. Additionally, raster data represents information in a grid format with pixels, often used in satellite imagery for analysis. 

2) Attribute Data:

While spatial data answers the “where?” question, attribute data provides additional details by addressing the “what?” of a location. This includes important information such as the type of business, address, and operating hours.

For businesses utilizing Point-of-Interest (POI) data from providers like dataplor, attribute data offers essential context for making informed decisions about site selection and location optimization. Here are some examples of how geospatial data is used across various industries.

Examples of Geospatial Data:

Geospatial data is widely used across industries to support operations and strategy. Here are a few examples:

• Point-of-Interest (POI) data: This type of geospatial data is essential if you’re aiming to gather insights about locations such as stores, restaurants, offices, and more. For instance, dataplor’s comprehensive POI data allows you to analyze the demographics and real-time updates of business locations, helping you optimize your operations.
• Satellite Imagery: Used for a range of applications, from urban planning to environmental monitoring, satellite imagery gives you a bird’s eye view of large geographic areas. It is often processed using GIS data and tools like ArcGIS for detailed analysis.

What is Spatial Analysis?

Spatial analysis is the process of examining, interpreting, and extracting actionable insights from geospatial data. By leveraging location data and advanced algorithms, you can uncover hidden patterns, trends, and relationships that may not be apparent in traditional datasets. 

This approach allows you to make more informed decisions based on spatial relationships, enhancing everything from market analysis to customer targeting.

How does it work?

Spatial analysis leverages geospatial data to assess the relationships between various geographic points. By utilizing Geographic Information Systems (GIS) software such as ArcGIS and other analytical tools, you can perform detailed examinations of locations, analyze spatial relationships, and extract valuable insights to support decision-making.

Whether you’re examining land usage patterns or assessing the proximity of competitors, spatial analysis serves as a powerful tool for optimizing your business operations. Data collected through APIs and data sources like ESRI can support these analyses, providing the insights needed to support your strategies.

Here are some examples of spatial analysis techniques:

1. Hot Spot Analysis:

This technique allows you to identify areas with a high concentration of specific types of geospatial data. For example, if you’re looking to find optimal locations for new stores or distribution centers, you can use hot spot analysis to determine which geographic location offers the most promising opportunities.

Many businesses, like Walmart, use big data combined with geospatial information to identify these patterns.

2. Proximity Analysis:

This method evaluates the distance between different POIs, helping you understand your location in relation to your competitors. You can, for example, use proximity analysis to assess how close your store is to your suppliers or potential partners, ensuring that its location is strategically advantageous. 

Why is Geospatial Data Important for Businesses?

As businesses increasingly rely on data-driven strategies, geospatial data has become a vital resource for gaining a competitive edge. Recognizing how physical locations and their surrounding environments impact your business operations can result in more informed decision-making and better resource allocation. 

Whether you’re refining your real estate strategy or optimizing supply chain logistics, geospatial data plays an essential role in today’s data management and decision-making landscape.

Make Data-Driven Decisions

In today’s competitive business landscape, making decisions based on reliable, actionable data is essential for success. Geospatial data empowers businesses to make data-driven decisions by providing valuable location insights that enhance strategic planning, resource allocation, and operational efficiency.

For example, geospatial data analysis can assess potential sites for new retail or office locations by evaluating nearby infrastructure, competitor presence, and customer demographics. By analyzing both spatial and attribute data, businesses can optimize site selection, reduce risks, and maximize return on investment.

In industries like commercial real estate, integrating geospatial technology enables professionals to make informed decisions in investment research, risk assessment, and market and competitor analysis. These decisions are supported by precise geographic data, ensuring a comprehensive understanding of the operational landscape.

With tools like GIS software, you can visualize geographic data, making it easier to interpret complex data models and make informed decisions in real-time. Research shows that geospatial technology is widely used in urban planning and resource management to support smarter decision-making.

Improved Market Analysis and Site Selection

By utilizing POI data, Starbucks has refined its store placement strategy by targeting urban and suburban areas with high customer potential. Through geospatial data analysis, they were able to pinpoint areas with the greatest opportunity and gain a deeper understanding of their customers. Enriching their data ultimately helped them expand to over 60 countries with approximately 19,767 company-operated and licensed locations.

By targeting high-traffic and high-visibility areas, Starbucks has optimized its global reach and solidified its dominance in the coffeehouse industry. It currently holds a 36.7% market share in the U.S. coffeehouse market, depicting the effectiveness of its geospatial data-driven site selection strategy.

Enhancing Property Insurance With Geospatial Data

Tensorflight, a leader in property analytics for the insurance sector, partnered with dataplor to improve the accuracy of data used in property insurance assessments. By leveraging our POI data, they were able to improve geocoding accuracy, which allowed them to generate more accurate building replacement cost estimates and better classify building occupancy types.

These advancements have empowered Tensorflight to provide more reliable and actionable data to their insurance clients, transforming how real estate and property insurance decisions are made.

Unlock the Power of Location Data with dataplor

In today’s competitive landscape, leveraging geospatial data and spatial analysis empowers you to make informed decisions, improve operational efficiency, and enhance customer engagement. Whether optimizing market strategies, refining site selection, or tailoring your marketing campaigns, geospatial data helps you understand the key geographic elements that drive success.

With dataplor’s Point-of-Interest (POI) data, you can gain the insights needed to make smarter location-based decisions. Learn how dataplor’s comprehensive data helped companies like Wolt and FLO® to transform their business. Request a sample today and experience the powerful benefits firsthand.

Unlock the power of location intelligence and position your business for success with our advanced geospatial data solutions.