Collecting data about something is the kind of activity you would love to do without too much effort on your side. Location data collection has always been very costly, but things are changing.
Technological progress has brought us a wide range of small, cost-effective, and powerful devices, which are location capable and can carry a wide range of sensors.
Just think about what you have in your pocket: a powerful computer which is capable to know its location, in every moment. Your smartphone can actually do a lot with location data, as it is able to manipulate data and show you the results in real time (that’s why I’m developing carto.app :-).
Take your smartphone and shrink it down. You are making your pocket device way smaller and more energy efficient. The desiderata are a small device, which is location aware with enough computational power and connectivity to do everything you need for sensing and surveying.
Nowadays, you just need to deploy IoT sensors to collect data using (not so) simple infrastructures. What you get is a truck load of data you could use to create analysis and extract insights, maybe using artificial intelligence, machine learning and business intelligence applications.
Location data is the way to extract value insights from data.
What is location intelligence?
Location or spatial intelligence (LI) is a branch of business intelligence (BI). It is a process which derives insights from geospatial data to solve a specific problem. It works processing multiple data sets with spatial and alphanumeric relationships, taking also time into account. Location intelligence’s applications are very wide and span industries, private and public organizations. Geographical information system (GIS) tools empower experts to survey, store, process and visualise geospatial data.
Let’s make an example.
You have a phenomenon and you need to find the origin. Every instance of the phenomena has its location, but data seems uncorrelated. How can you extract some insights in order to know more about the phenomena? Location intelligence suggests to start overlaying geospatial data, for instance roads, buildings, rivers, and to try to understand if there is some kind of correlation. For example, smelling reports alone seems uncorrelated but if you add an industrial sites overlay it may indicate that some chemistry companies in the site is not managing waste correctly.
The business impact of Location Intelligence
Location intelligence organizes business and geographically referenced data to reveal the relationship of location to people, events, transactions, facilities, and assets.
It can also be seen as the application of spatial data visualization, contextualization and analytical capabilities to a specific business problem, in order to solve it or to achieve a significant competitive advantage.
During this year, or at most in the next few years, many new business sectors will benefit from spatial intelligence. Let’s try to forecast three of them.
I think this will be the sector that will benefit more from Location Intelligence in the near future. We are moving towards the ecologic transition and we need to cut on carbon emissions. In order to do that we need to place a lot of renewable energy sources on the field.
Where to put those solar and wind farms we need?
Location intelligence will help the sector to obtain better answers to that question. A lot of things may should be considered: is there enough open space? Will that trigger a NIMBY effect? Is there an electric substation near the chosen place or must be built? Is it windy enough but not too much? how many hours of sun a day will be there? it is better to build offshore or onshore? And I could go on forever…
Healthcare is becoming a commodity with more and more devices which are constantly analyzing our bodies. We are collecting health data like never before, and this could be really useful (also frightening if data is misused) in some LI applications. Let’s think about at the fastest route to bring support to an injured person or to make rounds themselves faster, at the possibility to have indoor positioning and to guide users inside the hospital, at the opportunity to tag and connect medical furniture in order to always be sure of their status. GIS is applicable and it should be applied. Moving into the future, data collected from the past can be used to feed an Artificial Intelligence and to foresee which resources are needed for a specific medical procedure, or to give insights based on the history of the patient.
Sales and retail
This is quite a classic in Location Intelligence. One of the first use case of LI is in fact the process of choosing the right place for a store. It would take into account the type of shop, the local demography, the traffic condition, the population kind and many other aspects, like the weather. Future advances into sales related LI may include automatic POS, which knows what a specific customer will buy during the year, and communicates with stock for replenishment but also can bring insights on what to buy in that specific time of the year. LI could also help to optimize carbon neutrality, picking the nearest producer for that specific amount of goods.