Location intelligence

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Location intelligence (LI), or spatial intelligence, is the process of deriving meaningful insight from geospatial data relationships to solve a particular problem.[1] It involves layering multiple data sets spatially and/or chronologically, for easy reference on a map, and its applications span industries, categories and organizations.

Maps have been used to represent information throughout the ages, but what might be referenced as the first example of true location 'intelligence' was in London in 1854 when John Snow was able to debunk theories about the spread of cholera by overlaying a map of the area with the location of water pumps and was able to narrow the source to a single water pump. This layering of information over a map was able to identify relationships between different sets of geospatial data.

Location or geographical information system (GIS) tools enable spatial experts to collect, store, analyze and visualize data. Location intelligence experts can use a variety of spatial and business analytical tools to measure optimal locations for operating a business or providing a service. Location intelligence experts begin with defining the business ecosystem which has many interconnected economic influences. Such economic influences include but are not limited to culture, lifestyle, labor, healthcare, cost of living, crime, economic climate and education.

Further definitions[edit]

The term "location intelligence" is often used to describe the people, data and technology employed to geographically "map" information. These mapping applications can transform large amounts of data into color-coded visual representations that make it easy to see trends and generate meaningful intelligence. The creation of location intelligence is directed by domain knowledge, formal frameworks, and a focus on decision support. Location cuts across through everything i.e. devices, platforms, software and apps, and is one of the most important ingredient of understanding context in sync with social data, mobile data, user data, sensor data.

Location intelligence is also used to describe the integration of a geographical component into business intelligence processes and tools, often incorporating spatial database and spatial OLAP tools.

In 2012, Wayne Gearey from the real estate industry (JLL) offered the first applied course on location intelligence at the University of Texas at Dallas in which he defined location intelligence as the process for selecting the optimal location that will support workplace success and address a variety of business and financial objectives.[2][3]

Pitney Bowes MapInfo Corporation describes location intelligence as follows: "Spatial information, commonly known as "Location", relates to involving, or having the nature of where. Spatial is not constrained to a geographic location however most common business uses of spatial information deal with how spatial information is tied to a location on the earth. Miriam-Webster® defines Intelligence as "The ability to learn or understand, or the ability to apply knowledge to manipulate one`s environment." Combining these terms alludes to how you achieve an understanding of the spatial aspect of information and apply it to achieve a significant competitive advantage."[4]

Definition by ESRI is as follows: "Location Intelligence is defined as the capacity to organize and understand complex data through the use of geographic relationships. LI organizes business and geographically referenced data to reveal the relationship of location to people, events, transactions, facilities, and assets."[5]

Definition by Yankee Group within their White Paper "Location Intelligence in Retail Banking: "...a business management term that refers to spatial data visualization, contextualization and analytical capabilities applied to solve a business problem."[6]

Near defines location intelligence as the gamut of geo-spatial analytic models, algorithms, techniques and tools that harness location-indexed streaming and static data to provide requisite answers to questions around location such as the attributes of i) a place, ii) people visiting a place, and iii) products consumed at a place. Location intelligence platforms encapsulate these technologies, ingest relevant data sources, and provide it as data-as-a-service, or use it to power data-driven products.[7]

Instarea defines location intelligence as "Using machine learning algorithms to uncover trends that would be otherwise unknown using human based rules in a large location data set."[8]

Commercial applications[edit]

Location intelligence is used by a broad range of industries to improve overall business results. Applications include:

See also[edit]

References[edit]

  1. ^ Data Resources For Real Estate And Business Geography Analysis. Thrall,G.,I.,Ph.D.(2009)/Professor, Department of Geography, College of Liberal Arts And Sciences and Miller Center For Retail, College of Business, University of Florida
  2. ^ Avery, Lois (2015-05-20). "Why location intelligence is a 'game changer' for real estate". RealViews. Archived from the original on 2016-10-19. 
  3. ^ W.M. Gearey, N.M.Trodd, A. Fobes. "Utilizing Location Intelligence for the Placement of Corporate Services" (PDF). 
  4. ^ George Moon (c. 2008). "Location Intelligence – Meeting IT Expectation" (PDF). Pitney Bowes. Retrieved 2015-10-05. 
  5. ^ ESRI. "Using Location Intelligence to Maximize the Value of BI" (PDF). Retrieved 2015-10-05. 
  6. ^ Marcus Torchia (c. 2009). "Location Intelligence in Retail Banking" (PDF). Pitney Bowes. Retrieved 2015-10-05. 
  7. ^ "Location Intelligence: The Next Frontier". Mobile Marketing Association, Inc. May 3, 2016. 
  8. ^ "Market Trends: Ways CSPs Can Exploit Location Data". Gartner.com. Retrieved 2018-07-24.