Location Data vs Geospatial Data: What’s the Difference and Why It Matters

Introduction

Location data and GoSpatial data are different from each other. However, they are often used interchangeably because they both describe where places, businesses, and assets exist in the world. If the right data is not used for research and analysis, businesses may make wrong decisions and draw poor insights. Understanding the difference between location data and geospatial data matters for analytics, business decisions, and AI to improve reliability, save time or resources, and guide better choices. In this blog post, you will learn about location and geospatial data, their use cases, and how to choose the right data for your business.

What Is Location Data?

Location data refers to where places, businesses, and assets exist. This data is business-friendly, cleaned, structured, and ready for use. Location data helps businesses make informed decisions. By scraping this data, your business can focus on places instead of complex spatial modeling.

Examples of Location Data

• POI Data: This includes landmarks and attractions.
• Business Locations: These business locations are malls, shops, offices, and more.
• Store Addresses: These are street addresses and coordinates.
• Operating Hours: This refers to a location’s opening or closing hours.
• Categories: These categories include health, retail, dining, etc.
• Attributes: These attributes include parking, Wi-Fi, ATM, and more.

Common Uses of Location Data

Market Analysis

Location data provides customer insights that help track foot traffic effectively. It helps you conduct competitor analysis and map nearby rivals.

Site Selection

Location information is important for your business because it helps you understand customer demands and identify high-traffic zones. It provides demographics to match the target audience profile. 

Competitive Mapping

Identifying competitors’ locations helps businesses pinpoint rival stores and understand market saturation. It empowers the development of strategic plans to find the underserved zones. Location data is used for potential risk assessment and to evaluate competitive threats. 

Expansion Planning

Location data reveals places with high competitor presence, helping businesses avoid oversaturated areas. It highlights areas that provide transport and reach coverage.

What Is Geospatial Data?

Geospatial data represents spatial relationships and geometry. Such data are often used in GIS systems in mapping locations to visualize spatial relationships. The most common geographic attributes include distance, shape, technology, boundary, etc. 

Examples of Geospatial Data

• Polygons: This includes city boundaries, trade areas, etc.
• Lines: Examples include roads, railways, and pipelines.
• Rasters: It refers to satellite imagery and heatmaps
• Elevation & Terrain Data: This data includes the steepness of land and mountain peak heights.

Common Uses of Geospatial Data

Mapping & Visualization

Geospatial data helps you with urban planning, enabling the design of city layouts. It is used for environmental studies, monitoring land and ecosystems.

Urban Planning

Spatial data is ideal for performing land use analysis and allocating zones wisely. It seamlessly discovers the infrastructure for planning roads and utilities.   

Environmental Analysis

By identifying utility placement, businesses can know about pipelines and cables. With geographic information, you can easily find risk zones and avoid flood-prone areas. 

Infrastructure Planning

Geodata assists planners in predicting patterns of urban expansion. It enables them to proactively monitor assets and repairs. 

Key Differences Between Location Data and Geospatial Data

Aspect	Location Data	Geospatial Data
Focus	It basically focuses on places and businesses.	It focuses on spatial relationships.
Data Type	Location data provides structured records.	Geospatial data provides coordinates, shapes, and layers.
Complexity	The complexity of collecting location data is low to medium.	The complexity of collecting geospatial data is medium to high.
Tools Used	Some key tools used to collect location data are geospatial analytics platforms and BI tools.	Geographic information is collected using GIS software.
Business Use	It is used for developing a smart strategy and making decisions.	Businesses use it for spatial modeling.
Examples	Store lists and POI.	Imagery, maps, and polygons.

Why Businesses Often Need Location Data More Than Geospatial Data

Businesses often need location data more than geographic information because of the following reasons:

Most business questions focus on locations rather than complex spatial geometry. Here, customer demand depends on location trends. Businesses generally incorporate location information to focus on accessibility, like proximity, roads, transport, and more.

Decision makers need to know where competitors are. They usually find places with similar businesses or high competition.  Business executives and managers are not interested in drawing polygons on a digital map.    

Location data is in a structured format. It is easy to visualize compared to geospatial data. This data can be transformed into charts, heatmaps, and dashboards for analytics and BI tools. 

When Geospatial Data Becomes Necessary

You have to use geospatial data when dealing with the following aspects:

Trade Area Modeling

Geodata is useful in identifying customer location and mapping shopper distribution. It provides you with competitor sites to assess rival proximity. Geospatial empowers businesses in forecasting sales to predict store potential. 

Route Optimization

Geospatial data enables businesses to schedule deliveries and optimize routes. It enables you to deliver the product faster at lower fuel costs. By optimizing the route, businesses can maximize daily visits. 

Distance-Based Calculations

Geospatial data is utilized for accurately calculating travel distance. It offers drive-time zones to define catchment areas. Geodata empowers your retail business to efficiently plan delivery and optimize logistics routes.

Infrastructure & Zoning Analysis

Geodata assists in verifying land suitability for assessing soil and terrain. By identifying proper housing zones, you can locate a residential area. It works in a similar manner for commercial zoning to allocate a business district.       

Geospatial data is powerful for advanced spatial analysis. However, it is most beneficial when you want to know how location impacts outcomes, avoid extra complexity, and save on your analysis expenses.

How Location Data and Geospatial Data Work Together

We saw that location data provides context, and on the other hand, geospatial data provides spatial logic. A collaborative approach to implement both location data and geodata unlocks spatial patterns for analysis, guides planning and strategy, provides better visualization, and produces actionable results.

Let’s understand how location information and geospatial data work together with one example. In the city mapping project, the Dunkin is considered a POI location; the trade area polygon is a 10-min drive-time zone polygon. If we combine, it will define the customer attraction area.

Industry Use Cases Explained      

Retail & Franchise Expansion:

• Location data offers store coordinates that empower you to identify rival proximity. It is best for retail and franchise expansion and avoiding oversaturated areas.
• On the other hand, geodata delivers competitor POI data to pinpoint exact rival locations. With Trade area polygons, you can appropriately visualize overlapping zones.

Real Estate & Investment

• If we consider location data, you can have demographic data to match the population with property demand. It works well to forecast urban expansion impact.

• Aside from this, geospatial data helps investors compare properties by evaluating assets by location. These businesses can optimize their portfolio and balance assets across regions. 

Logistics & Mobility

• Location information empowers freight forwarding businesses to know supplier locations, reducing inbound transport costs. It helps identify locations near high-demand regions or logistics hubs.

• While geospatial data draws trade area polygons and defines service coverage zones for hubs. It empowers freight forwarding businesses to map the transport network by aligning hubs with ports and highways. 

Market Research & Analytics

• Marketing research firms can choose location information to map customer clusters and increase service area reach. An analyst can check the competitor density to cover distribution gaps.

• The same firms implement geographic information to detect hotspots and conduct comparative analysis. It allows market intelligence companies to map color intensity for contextual relationships.

Choosing the Right Data for Your Use Case

Choosing the right data is clearly based on your needs for business listings or spatial shapes. You need to consider whether you want maps & coordinates or single point information, spatial relationships, or an address or place name. You need to ensure whether the data is used in BI tools or GIS software. By considering these factors, you can utilize location data or geospatial data.

How LocationsCloud Supports Location Data Intelligence?

LocationsCloud is an agile POI & business location datasets provider. It has worked on location data scraping and enrichment for many years. The organization delivers data in API and other bulk delivery formats such as JSON and CSV. It offers accurate and reliable analytics-ready, B2B location datasets for your business that are easy to integrate with AI and BI models. 

Conclusion: Use the Right Data for the Right Decision

Location data and geospatial data serve different purposes. These concepts are different but overlap in practical applications. Every piece of geographical data is gathered from a single point. Geospatial data adds power when spatial modeling is required; therefore, to solve the problem, it is good to use both. Enterprises can leverage LocationsCloud to access clean, scalable location intelligence datasets for analytics, expansion planning, and market research. Are you looking for a reliable POI and location data for analytics or expansion? To request a sample location dataset, you can talk to a location intelligence expert today.

FAQs

Is location data the same as geospatial data?

Both are related but not the same. Location information describes single-point info, and geographic information provides spatial relationships.

Do I need GIS tools to use location data?

No. It is not necessary to use GIS tools for location data, especially for business analytics and simple point lookups. GIS tools are utilized when managing large datasets or analyzing spatial patterns.

What is POI data location or geospatial?

POI data is both location and geospatial. This is because POI data focuses on where the place is and how places relate spatially.

Can location data be converted into geospatial formats?

Yes, location data can be converted into geospatial formats. It is common practice to collect GPS points, street addresses, and place names.            

Which is better for site selection?

For site selection, geospatial data is good to use because it enables you to match spatial relationships, demographics & traffic flow, and competitor proximity analysis.

Does LocationsCloud provide location data via API?

Yes. With locationsCloud, you can receive your location data via API or in the format of your choice.