Defining spatial data and using it to analyze competitors, identify locations for new stores, and understand demographic trends in a given area.
What is the most useful spatial data for a large business?
Spatial data includes vector, raster, and attribute data. Vector data uses lines, points, and polygons to represent an object or area’s spatial features. A point can represent a storage facility, supplier’s headquarters, etc. The distance between franchise stores, roads, and rivers can be represented as lines.
Attribute data is additional information about the spatial features. For example, a point that represents a warehouse in vector data may have attribute data such as a name, how much inventory is stored there, etc.
How GIS mapping tools can help a business
Businesses use spatial data to analyze competitors, identify potential locations for new stores, and understand demographic trends in a given area. By grasping the spatial relationships between different points in a supply chain, they can shorten routes and reduce expenses.
To use spatial data, you must import it into GIS mapping software. Most GIS platforms support GeoTIFFs for raster data and shapefiles for vector data.
You can visualize the spatial data as maps. GIS software lets users overlay multiple data layers (roads, rivers) on a single map and customize their appearance. They can create zones around a specific line, point, or polygon (to find out what buildings there are within 500 feet of a store, for example). This is also useful for finding the shortest distance between points.
Spatial analytics: a game-changer for franchises
Retail giants strategically place outlets in popular shopping areas as a result of location-based insight. Location intelligence is achieved by visualizing and analyzing geospatial data. You can gain rich insights by overlaying foot traffic, weather data, demographics, and other location-based data onto maps. This intelligence allows businesses to understand where their customers are.
Foot traffic analysis is key to retailers’ profitability. US retail and dining foot traffic consistently increased between June 2023 and May 2024. In 2024, physical retail is expected to account for over 83% of retail sales in the country, equivalent to $6.2 trillion. Franchises such as IKEA and Bloomingdale’s are investing in smaller-format stores to attract foot traffic in locations where consumers work and live.
Location intelligence only starts with simple demographic mapping. It extends to advanced algorithms that can forecast future buying behavior. It can aid in analyzing customer behavior, planning store locations, and predicting local demand. Retailers can understand the impact of campaigns and promotions, plan their marketing campaigns, and compare their performance to that of their competitors.
Benefits for real estate companies
Leveraging spatial data can also benefit real estate companies. It can provide valuable insights into real estate value based on proximity to facilities, local school quality, etc.
Successful real estate firms map area demographics to inform their investment decisions. For example, Florida has the most people aged 65 and over of all states. Seniors make up almost 20% of the Sunshine State’s population. This knowledge helps companies identify the types of properties seniors prefer and ensure new developments align with their needs.
Analyzing geofencing data to extract insight
Finally, businesses process and analyze geofencing data to extract actionable insights. 58% of retail companies currently use geofencing, and this percentage is expected to grow each year until 2030. More than half of marketers consider bars and restaurants prime locations for driving foot traffic, and 47% of consumers are likely to shop from a business that offers promotions when they are in the area.
FAQ
How do I know if my business is ready for location intelligence?
It’s ready if you need to optimize performance, understand your customers better, or improve decision-making in the context of foot traffic.
What are the most common mistakes when using spatial data?
Common mistakes include using poor-quality data, not setting clear goals, and not keeping up with technological advancements.
