Explore this in-depth guide to spatial analytics strategies, examples, and technologies to learn how to leverage geospatial analysis in your business.
Spatial analysis is the collection, display and manipulation of location-based data—or geodata—such as street addresses, zip codes, satellite images and GPS coordinates. Also referred to as geospatial analytics or geoanalytics, it helps you uncover location-based insights by overlaying maps with layers of location and business data—such as sales figures or transit times—so you can visualize, analyze, and get a more complete view of your data.
Imagine opening up a treasure map for your business that shows you where new business opportunities, cost savings, and partnerships are or could be. In business terminology, this treasure map is the “geo-enablement” of the digital enterprise.
CIO.com1
Analyzing location-specific performance in businesses with distributed physical locations like retail, manufacturing, and banking is well established. But as the number of connected devices rises, so does the amount of geodata and the opportunity to gain new insights. Visual mapping that includes location data from satellites, smartphones and wearable devices, vehicles and even shipping cargo can strengthen your existing analytics and dashboard reporting by providing new perspectives for better decision-making.
Growth in the spatial analytics space is driven by the Internet of Things (IoT), artificial intelligence (AI), big data analytics, cloud data warehousing, and the integration of geospatial information systems (GIS) with business intelligence (BI) and analytics platforms.
The geospatial analytics market is projected to grow from $40.65 billion in 2018 to $86.32 billion by 2023.
Markets and Markets2
Location analytics provides insights on the “where” aspects of your business, and can help you determine anything from the best location to open a new retail store or bank branch, to predicting the most efficient transportation routes. It also allows you to see changes over time, such as how consumer spending rises or falls each season, or how weather fluctuations affect delivery times. And, because geospatial analytics make this data available through visual mapping, you can more easily explore and identify patterns and gain insights that you might have missed from data presented in tables or columns.
By uncovering ways you can use spatial analysis to help increase revenue, cut costs, and improve your core competencies, you can ultimately increase ROI and grow your competitive edge.
Geographic location can increase the value of a wide range of business data. More than just visualizing data points on a map, geospatial analysis tools help you analyze and aggregate location and other business data to solve challenges and meet business goals. To maximize the value of your geodata, it’s important that your data analytics tools incorporate these geospatial analysis capabilities:
With on-the-fly location lookup, you can automatically populate and update dashboards and maps with data about specific places and areas for use in spatial analysis. Search by city, county, zip code, place names or airport codes. For example, if you want to analyze police activity in a specific region, you can quickly include that data on your map without knowing any geographic coordinates.
Clustering lets you view the geographic density of points or events, showing groupings of high or low values that stand out in your data. For example, by analyzing how many people live within a 15-minute drive time from existing stores, a retailer can determine the best possible locations for a new shop.
Colors, lines and shapes can help you more easily make sense of your data. When analyzing customer buying behavior, for instance, you can use colors to represent the shops and zip codes in which people are buying, as well as shapes to differentiate between shop locations and customer data.
Geospatial analysis tools allow you to perform visual mapping, which helps you view and analyze multiple data sets by displaying them on maps in different layers. The layers are overlaid on background maps and can include area, bubble, chart, geodata, heatmap and line layers. Data for background maps and layers can come from both internal and external sources, such as CAD files from traffic or weather warning systems.
Your analytics solution should allow you to select any type and combination of data on any layer, whether you select from a bar graph, or directly on the map using lasso selections or pan and zoom. For example, you could combine road geographies with traffic conditions to analyze traffic flow in a certain area, or overlay real-time weather with weather warnings data to understand how those patterns affect airport traffic in a particular region.
Geospatial data analytics can be used to uncover powerful location-based insights through a wide range of industry-specific applications. Here are a few common examples:
Retailers use geospatial analysis and visual mapping to help them understand the reach and performance of their stores and competitors, uncover customer behavior and buying patterns, analyze drive times, and predict sales and revenue potential for new store sites.
Learn how retailers can use geospatial analysis to make better decisions for store expansion.
Try Store Site Selection and Performance Demo
By supplementing customer shipment data with external data like alerts from the National Weather Services (NWS), transportation providers can more proactively address delivery delays, and provide customers with a higher level of service.
Learn how Oddysey Logistics uses location-based data for better risk management.
Geospatial analytics can help banks address and prevent customer churn by providing insights into customer and competitor locations relative to bank branches, as well as providing recommendations for optimizing customer routes to branches.
Discover how banks use geodata to analyze performance, branch closures and competitors.
Explore more industry examples with these analytics demo applications
Location data comes from a variety of sources— both internal and external—and is often stored in proprietary formats across different systems, making it hard to access and use. If your business faces this challenge, modern data integration solutions will help you standardize and filter your data to make it analytics-ready.
Different goals and priorities across business functions can result in data silos. To break down silos in your own organization, first consider whether or not the data you collect should be restricted to your team for security reasons. If not, find out how you can extend the value of that data by sharing it with other teams for further analysis.
Managing massive amounts of geodata requires a level of computing power that many organizations don’t have. Cloud-based solutions can help you store, manage and process data in real time at scale. You should also have the option of storing map servers on premise for even more secure, faster access to visual mapping information.
Bring in information from external sources so you can see the bigger picture and get a thorough understanding of what’s happening with your customers, your environment and the industry.
Make it easy for people across the business to access insights and take action. While map visualization tools are a start, it’s the ability to easily explore the data and perform analysis that empowers users to solve business problems.
A best-in-class, self-service business intelligence architecture is just one way Qlik Sense® sets the benchmark for next-generation data analytics technologies. Our one-of-a-kind associative analytics engine and sophisticated AI empowers people at all skill levels to freely explore data, make bigger discoveries and uncover bolder insights that can’t be obtained using other analytics tools. With Qlik, you can support nearly any use case and massively scale users and data, empowering everyone in your organization to make better decisions every day.
Spatial analysis means finding location-oriented insights by overlapping layers of geographic and business data onto maps. This allows you to visualize, analyze, and get a more complete view of your data to solve complex location-based problems.
Geospatial data can be very helpful in analyzing a wide range of use cases such as improving logistics planning and transportation for fuel and time saving or selecting store locations for optimal customer traffic.
Geospatial big data refers to spatial analysis using location-based or geographic data sets that stretch or exceed the capacity of most current computing platforms. Geospatial data represents a large and growing portion of big data.
1. “Zenus, Matthew, et al. “How Geospatial Analytics Can Give Your Business a Competitive Edge.” CIO.com, 2 Mar. 2017
2. “Geospatial Analytics Market by Component, Type, Application, Vertical, and Region – Global Forecast to 2023.” Markets and Markets, April 2018
