More and more companies and organizations are using research based on big data to make decisions and solve problems. In the corporate world, this research is known as business intelligence, or BI. Part of a company’s BI strategy can include data on where (and when) things are located and events happen. This is called location intelligence, or LI.
So exactly what is location intelligence? How does it relate to business intelligence, and how is it different? What sectors inside and outside of the corporate world use it, and for what purposes? And where can one get the kinds of data needed for location intelligence? In answering these questions, this guide will give a general overview of location intelligence through the following sections:
We’ll start off with a location intelligence definition so you can understand what the term means with a little more clarity.
Location intelligence (LI) refers to using geospatial data to understand how to perform a certain task or solve a specific problem. This is typically done by overlaying geospatial data on a map to study relationships between locations, or how a location’s attributes change over a period of time.
The first recorded utilization of location intelligence techniques was in London, England during the mid-19th century. A physician named John Snow was able to use geospatial data to trace and minimize the impact of a cholera outbreak in one of the city’s districts. He did so by mapping out areas of the district where infections had occurred, and then comparing them against a map of the district’s water supply points. In doing so, he was able to pinpoint and disable the specific water pump where the outbreak had originated. Since then, LI has played an increasingly integral role in businesses, governments, and academia – and become much more technical.
The difference between location and business intelligence is in what kind of data is used and what the resulting insights are applied to. Business intelligence involves the integration and analysis of several different kinds of data. However, the information gained from this analysis is used for a specific purpose: to make decisions that improve a business’s operations.
Location intelligence, meanwhile, uses a specific class of data: geospatial data. That includes things like information about points of interest, building footprints, footfall patterns, weather systems, and road traffic. However, it is not used only to solve problems and improve processes for businesses; it has several other use cases as well.
In short, you can have location-based business intelligence, but not all business intelligence is location-based.
As we just mentioned, businesses can make use of location intelligence, but they’re not the only organizations that do so. Here’s a quick list of sectors that use location intelligence, and why:
As we demonstrated in the previous section, many different types of businesses and organizations use location intelligence. Now, we’ll take a more in-depth look at some specific location intelligence applications.
Plotting geospatial data on a map is one of the cornerstone applications in location intelligence. It allows for visualizing potential relationships between sets and attributes of geospatial data. This is something that can be very difficult without actually mapping out where (and sometimes when) in the real world the data corresponds to.
Location intelligence plays a big role in helping governments plan out how municipal land is to be used, as well as many other facets of urban life. Artificial intelligence and location-based services allow local authorities to leverage geospatial data to design more efficient communities through understanding who constituents are, where they go, and what they need. That includes things like increasing accessibility to critical facilities, reducing traffic congestion, better managing waste collection and energy consumption, and deploying security personnel more efficiently to keep citizens safe.
Another thing city planners have to work out is how to design utility infrastructure for inhabited areas. Again, they need to use location intelligence to look at factors such as where terrain allows systems to be built, as well as any natural or artificial obstacles they may encounter. They also need to look at what areas tend to be busiest, so they can build critical hubs in areas where they achieve maximum coverage with minimal hardware.
Telecom companies need to leverage location intelligence for similar considerations when planning their networks. They can also use it to determine the price of additional infrastructure (e.g. WiFi hotspots) for people or businesses, depending on the traffic the surrounding area gets. People and businesses themselves can use location intelligence to decide how to manage their network availability and bandwidth based on how busy their sites get (and when).
When a government has designated land for residential or commercial use, real estate companies have to decide which parcels are worth investing in. To do this, they need to look at geospatial factors such as the physical features of the property, what the local environment is like, how accessible the property is, and how busy the nearby area gets. These can all affect things like the costs associated with the property, as well as how much a development project on the property will sell for.
These factors can also affect how the property is marketed. A commercial plot close to foot traffic and other points of interest may be advertised for its accessibility to customers, while a residential plot may be advertised as being conveniently close to essential services. Conversely, a residential plot away from busy areas could be marketed as a quiet retreat from urban hustle and bustle. So real estate investors need to use location intelligence analytics to decide which parcels of land will provide the best return on their investment, and how best to get that return.
Two fundamental pieces of retail location intelligence are trade area analysis and site selection. Trade area analysis consists of a large-scale survey of business opportunities in a given geographic space. It examines things such as how likely people there will become customers based on their demographics and lifestyles, and how well competitors already serve a business’s market niche in the area.
Site selection, similar to in real estate, involves a closer examination of the advantages and disadvantages of specific properties when deciding where to build a store. Is a location accessible? How efficient are supply chain routes? How much foot traffic does the surrounding area typically get? How much of that foot traffic is likely to visit and buy the store’s products, based on their demographics? How close are other nearby stores, and are they competitors, complements, or neither? These are all questions related to location intelligence that a business should ask before settling on a spot.
One way a business can understand how its selected locations will perform is by gathering and using location-based market intelligence. For example, it can look at data for a store’s area to figure out which products or brands are popular in nearby stores. It may then choose to stock those products or brands, make them the focus of marketing materials or campaigns, or even rearrange a store to make what customers want most more accessible.
A business might also look at what other places people typically visit before or after visiting one of its stores. This may highlight complementary businesses that could be approached for cross-promotions. It may also point out competitors that customers are visiting to find certain inventory that a business’s own store doesn’t have.
Visit attribution is a form of location intelligence that combines footfall data with building footprint data. It is used to determine if a person actually entered the bounds of a location, rather than walking past it, around it, or into a neighboring location. Accurate building polygon data is critical for this, especially in buildings such as malls or airports that have multiple tenants in close proximity.
Visit attribution is usually used as location intelligence for retail stores. They use it to track how much of an area’s foot traffic is converting into store visits and purchases, especially if they are advertising nearby. They can also use it as a way to measure how much exposure their advertisements in specific locations are getting.
Another way a business can use location-based intelligence is to analyze the geospatial strategies of its competitors. For example, a business may observe that its competitors’ stores are more popular because they’re more accessible. They might have bigger parking lots to accommodate more cars, or they might be conveniently located close to stops for public transportation. Or a business might discover that two or more of its own stores are competing over the same customers because it takes nearby consumers similar amounts of time to travel to any of them.
A business can also look for potential opportunities that competitors’ geospatial strategies leave open. To illustrate, a competitor may select a site for a store based on accessibility to certain neighborhoods, because it’s targeting certain demographics that live there. If the business is targeting different demographics, it may be able to build a store in a prime location, even if it’s close to a potential competitor. This is because there is less risk that the store and the competitor will fight over the same customers.
One of the more unique location intelligence use cases is for the insurance industry. Weather patterns and terrain in an area can give clues as to how vulnerable a person or property is to nature-related damage. Footfall and traffic patterns can also indicate risk, as accidents are more likely to occur in areas where more people and vehicles are active. Co-tenancy is another factor, as being located next to certain risk-prone businesses or people can increase the likelihood of an accident.
Studying geospatial patterns can also help insurers and others quickly identify and respond to fraudulent claims and other transactions.
Companies like private equity firms and investment banks can use location analytics and business intelligence to help manage their assets. They are looking for indicators that a business or piece of real estate will produce returns with minimal risk. So they will want to look for some of the same geospatial information and patterns that real estate developers and other businesses can use to gauge and model performance.
For example, how accessible are store or land parcel locations, and how busy do they get (and when)? Do nearby consumer demographics match the target audience of a house or store? How many confirmable visits does a location get? What other points of interest are nearby, and could they be beneficial or detrimental to the location’s operations or traffic? This kind of business location intelligence may give clues to a company’s financial performance long before they release the official information.Where to get geospatial data for location intelligence
There’s a big thing we haven’t talked about yet in relation to these location intelligence examples: where to get the specific kinds of data you’ll need to get the insights you want. Different use cases may require different types of data, and not every provider offers the same kinds of data. With that in mind, here is a list of companies and organizations that can supply you with the types of data that power location intelligence:
Major data types: points of interest, building footprints, transaction
SafeGraph is the market leader in global POI data. Our Places and Geometry datasets have detailed attribution for points of interest and property information that includes accurate polygon geofences. This can all be used for various commercial use cases, and some non-commercial ones as well.
Also, be sure to check out our Spend dataset. It’s the first US consumer transaction dataset that’s based on where people spend money, to give context to when and how they spend it.
Major data types: point of interest, streets, imagery
Bing is Microsoft’s search engine. As part of that service, they offer a GIS function that provides imagery of most parts of the world, information on road networks, and details about points of interest. Many commercial applications may find this data handy.
Major data types: point of interest, property
CAP Locations has data on over 1.2 million stores in the US and Canada, including retail outlets as well as restaurants. That includes stores inside malls and other shopping centers; CAP Locations has data on over 40,000 such complexes, including about 20,000 complete building footprints of them. This is great information for most retail-based applications of location intelligence, as well as some for financial investment and insurance.
Major data types: point of interest, property, mobility, demographics, boundaries, environmental, streets
CARTO previously specialized in environmental data, but now it’s partnered with over 40 other companies to provide most types of geospatial data. It even has pre-mapped datasets designed for analyzing specific geographic attributes. In total, it has over 10,000 datasets for a variety of location intelligence needs.
Major data types: property, environmental
ClimateCheck has run historical US weather data through over 25 internationally-sanctioned climate change models. As a result, they can offer an assessment on any home in the US of how vulnerable it will be to weather-related damage over the next 30 years. That includes fires, storms, floods, droughts, and heat waves. So it’s a good resource for those in insurance or real estate investment.
Major data types: environmental, imagery
Like its name implies, CustomWeather sells a number of different datasets on meteorological patterns and imagery on current globe conditions. Among them are daily weather forecasts for over 8,500 locations worldwide, including monthly summaries and year-over-year comparisons of weather on specific days or during particular months.
It also has data on things like severe weather, air & sea travel, ski conditions, and wildfire danger. All of this is useful for conservationists and other environmentalists, of course. But it can also be applied in insurance, site selection, and decision-making for those operating (or investing) in businesses that may be impacted by inclement weather.
Major data types: point of interest, property, mobility, demographics, boundaries, environment, streets, imagery
Esri is one of the largest location intelligence platforms out there, thanks in part to its leading mapping software, ArcGIS. It also has data for sale from over 150 partner companies, including almost every type of geospatial data that you’d need for any kind of location intelligence.
Major data types: property, demographics, address
Those who want to use location intelligence for marketing in the US should pay Infutor a visit. Its demographics datasets cover the social and commercial activity of over a quarter of a billion Americans. It also has data on US property attributes, plus an index of over 360 million US address records that includes some places overlooked by official US government records.
Major data types: mobility
Locomizer has foot traffic data for points of interest in the UK, but also puts a unique twist on it. Its “brand affinity” dataset uses a type of artificial intelligence location intelligence that blends footfall patterns and mobile application use data to estimate how likely someone is to engage with a particular brand at a certain place and time. That could be shopping, eating, learning, enjoying entertainment, transporting somewhere, and many more things. This particular dataset is superb for retail analysis, consumer insights, and investment decision-making.
Major data types: mobility, boundaries, streets
Mapbox’s data-gathering is supported by a global community of over 500 million monthly active users. This lets the company offer datasets on over 20 billion daily mobility pings and over 30 billion road segments worldwide that are refreshed regularly for both real-time and historical traffic patterns. Mapbox also carries data on over 4 million administrative boundaries around the world. So it’s a provider to consider for applications ranging from urban and telecom planning to retail analysis and logistics.
Major data types: streets, imagery
Nexar provides a unique combination of street and imagery data. It has developed AI for location intelligence on roads, not only photographing what roads look like but also identifying street signs or hazards that may affect traffic and driver safety. This can have implications for insurance firms, retail logistics, and urban planners.
Major data types: property
Regrid’s property data encompasses over 150 million parcels of land in the US, accounting for over 3,000 counties and almost 99% of the country’s populated areas. These datasets are flexible in that they can be bought based on certain attribute clusters and on the areas they cover (county, state, or the entire US). So anyone looking to use them for location intelligence can limit the amount of data they buy based on specific areas or attributes they need.
Major data types: demographics
Spatial.ai enriches traditional demographics data for US census block groups with online traffic and activity data from nearby areas. This has allowed the company to create over 70 “geosocial profiles” of Americans based not only on who they are and where they live, but also what they do on the Internet. This data is great for those looking to use location intelligence in marketing.
Major data types: environmental
Tomorrow.io doesn’t just sell historical weather data for millions of locations worldwide. It also offers a coordination and monitoring platform that makes it easy for organizations to communicate and make decisions based on that data. So while insurers can use this weather data to assess risk, retail chains and other businesses can use the platform to minimize the degree to which weather will disrupt their operations.
Major data types: demographics, boundary
The US Census Bureau provides publicly-available data on US census block groups and the demographics of people who live in them. This is useful information for planning communities and the infrastructure that supports them. It can also be handy for making demographics-based decisions regarding retail operations and financial investments.
SafeGraph has taken data from the American Community Survey for 2016-2019 and cleaned it for a bulk download. This includes polygon-based boundaries of census block groups.
Major data types: address
One of the US Department of Transportation’s projects is a National Address Database. It’s a collection of over 65 million address records from across the country, as provided by state, local, and tribal governments. This is critical data for urban planning, along with some other site selection applications.
Major data types: property, mobility
Veraset’s “Movement” dataset contains aggregated footfall measurements around points of interest from 150 countries worldwide. This gives you a comprehensive view of global footfall patterns. Its “Visits” dataset adds building footprints for over 6 million US properties to this foot traffic data, allowing for easy visit attribution. This makes it ideal for retail and financial investment applications.
For those who have wondered “what does location intelligence do, and what does it look like?”, we hope this guide has provided you with a starting point. You should now know what location intelligence is, who uses it, what it can do, and where to get the data you need for it.
