Predicting the Where with Spatial Intelligence

Learn where to open a new store, where to distribute supplies, where to add resources – and more

Often where an event happens can be just as important if not more important than the event itself.  In fact, an event’s location or geographic relationship to other events can significantly affect the results or implications of many performance indicators.

For example, the consequences of an underperforming asset in an urban location may be very different from one in a rural location when it comes to the effects on customers, employees and financial performance. Moreover, outcome-based measures such as the revenue generated by a retail store are strongly influenced by location because location typically dictates trade area demographics, proximity to competition, transportation facilities, associated customer travel times, and other cultural and physical landscape feature relationships.

Location matters
Location is very much an integral part of knowledge management. Issues, events, problems and opportunities all occur in places (the where) and occupy space (the extent). Knowledge of events, business transactions or government polices and the subsequent quantification of performance or impact is of little value without knowing where things occur. Likewise, being able to predict where things will occur provides tremendous strategic value in achieving desired future outcomes.

So why does location matter? Consider the following:

• Organizations do business somewhere.Their employees and customers come from somewhere.
• Inputs must be received from and outputs (products and services) must be delivered to somewhere.
• Economic and financial performance, public safety and domestic security, and environmental and social conditions all are inherently geographic whether localized, regional or global.
• Issues and activities that occur in a particular place rarely occur in a vacuum but affect surrounding people, economic and natural ecosystems, institutions, and communities.

For more than 25 years, geographic information system (GIS) technology has provided automated methods for collecting, organizing, analyzing and visualizing the locational aspects of business information. More recently, the integration of GIS from the Environmental Systems Research Institute (ESRI) with SAS business and predictive intelligence technology has provided the opportunity to implement true spatial intelligence systems.

What is spatial intelligence?
At the highest level, spatial intelligence adds a geographic or locational dimension to business and predictive intelligence systems. It provides both the means for map-based visualization of business information as well as the application of spatial analytics to further discover, analyze and understand the locational relationships within and between data features.

Spatial analytics and geographics is the science of location, adjacency and direction between physical and cultural features on the landscape. There are many examples of how spatial analytics can be applied. These include:

Proximity analysis – How many customers are within a specified distance of a retail store?

Network analysis – What is the shortest route? What is the quickest route, taking into account impediments such as traffic lights and patterns?

Buffer analysis – Show me all customers within 200 yards of Main Street between the 1000 and 5000 blocks.

Cluster analysis – How many and where in a given area are our customers who also use competitive products and have an income above $150,000?

By coupling GIS with SAS technology, these questions can become predictive. Where will our competition site a new facility? How much local market share might we lose? What would be the effect on net revenue and market share if we site a new facility directly across the street? Two blocks down? How much can we expect to spend on fuel based on route optimization? How will customers be affected by that optimization?

Spatial intelligence and the SAS® Enterprise Intelligence Platform
The SAS Enterprise Intelligence Platform provides a solid foundation for achieving spatial intelligence through the merger and integration of its business and predictive intelligence capabilities with GIS.

SAS and GIS are indeed complementary technologies. Both are integrative. Both source, organize and use key business data in support of decision making. Both are analytical. Both visually depict complex ideas and results. Especially when integrated, SAS and GIS can be very strategic, transforming transactional business data into powerful intelligence that optimizes decisions in context. That is, not only knowing whether, but where to act or not act. As such, SAS and GIS can help refine policies as well as strategy development and execution by focusing effort and resources in the most compelling locations.

The following are applied examples of spatial intelligence through integrated use of the SAS Enterprise Intelligence Platform and GIS:

Data integration in support of domestic security preparedness and response. A state department of agriculture and consumer services uses spatial analytics as part of data integration efforts. Proximity analysis was embedded as a business rule within a SAS ETL process to determine whether inconsistent names of facilities contained in different regulatory systems were in fact the same or different organizations. This helps the department understand who and where their employees and customers are in order to prepare for and recover from man-made and natural disasters.

Organizing and storing data to manage the environment. A large urban city and growing county collaborated to build an environmental data management system (EDMS). This system integrates data from multiple regulatory and management systems to manage environmental assets in the field, such as streams, wells and other hydrological features. Using SAS and ESRI GIS technology, the EDMS reports on an array of performance and environmental outcomes based on the location of assets or groups of assets in a particular area. This system gives managers insight into the performance of specific assets and personnel and gives guidance on problem areas or problem assets.

Spatial analytics to identify optimal store locations. A FORTUNE 50® retailer uses spatial and traditional analytics to support market planning and analysis and site location. This system automates the capture of numerous internal and external data streams and encapsulates the company’s analytical models to determine the optimal location of stores. This system takes a variety of factors into consideration, including customer location and preferences and the presence and effect of competitors. It also provides predictions of revenue and other success metrics based on location alternatives. In addition to improving results, this system has reduced the decision-making process time frame, including various due diligence meetings, from more than six weeks to less than two. Another financial institution client has used spatial intelligence to site a branch facility. Within 30 days of opening the new branch, it handled more transactions than any of its other locations, including its corporate headquarters.

Advanced visualization to provide intelligence in context. Each of these spatial intelligence clients use map-based visualization to interpret circumstances and understand cause and effect based on geography. “Temperature” and “pin” maps can illustrate the location of and outcomes from analytical processes. Relationships and context not revealed by analytics can be seen in the map context.

As you can see, there are many, many uses for spatial intelligence. These include customer analysis, market analysis, site selection, risk analysis, territory management, facility management, property management, fixed and mobile asset management, supply chain management, logistics, customer relationship management and house holding, fraud detection, incident management, environmental management, and business continuity.

Spatial intelligence can enhance business and predictive intelligence by allowing issues to be viewed and analyzed in the context of a community or broader communities of interests. Behind the scenes, spatial intelligence can be used to source and improve data to serve strategic, not transactional, objectives.

Bio: William S. Holland is the CEO of GeoAnalytics Inc. He is an industry expert in the organizational, legal, economic, policy and administrative aspects of geographical information systems (GIS) implementation.

Spatial intelligence enhances the traditional BI dashboard by displaying location data as graphical map images.