Published: 20 March 2024
Contributors: Alexandra Jonker, Alice Gomstyn
Location intelligence is the method of gaining insights from geospatial data. These insights help identify patterns, make predictions and provide real-time understanding of location-specific questions.
Location intelligence is often visualized with maps, graphs, statistics and cartograms. The additional spatial context that these location intelligence data visualizations provide can help businesses, individuals and governments answer questions like:
- Based on foot traffic and traffic patterns, where is the optimal location to build a new storefront?
- Which communities are most vulnerable to the effects of climate change, such as sea level rise?
- How can we better visualize global supply chain operations to improve customer satisfaction and gain a competitive edge?
In this publication, we set out to provide you with guidelines to assist with your approach to ESG reporting.
Business intelligence (BI) is software that ingests data and presents it in user-friendly views to helps businesses gain actionable insights and make better decisions. It allows organizations to ask questions in plain language and get answers they can understand—relating to production, supply chains, customers or market trends.
BI tools connect to a wide variety of data, such as historical and current, third-party and in-house. Some of this data may be geospatial data used for gaining location intelligence insights and answering “where?” questions. Location intelligence software using spatial analysis is often an extension of or integrated into existing business intelligence mapping capabilities.
Geospatial data describes objects, events or other features on or near Earth’s surface at a given point or period in time. To be more specific, geospatial data combines location information (coordinates) and attribute information (characteristics of the object, event or phenomena) with temporal information (the time or lifespan at which the location and attributes exist). For example, whenever someone checks their smartphone to find a “brunch spot near me” they are relying on geospatial data to get an answer.
Geospatial datasets are large and can include demographic information from censuses as well as from technological data sources like satellites, LiDAR, cell phones and more. Geospatial data sets have two primary types of data: vector and raster. Raster data consists of grids or cells of pixels with spatial information associated with each cell, such as elevation or temperature. Vector data is the representation of a geospatial element through its x and y coordinates.
Location intelligence data comes from several technologies, like satellites, drones, LiDAR and sensors powered by the Internet of Things (IoT). In fact, the sharp increase in the number of IoT-powered smart devices in recent years has led to a massive amount of relevant geographic data for use in location intelligence platforms. The integration of artificial intelligence (AI) and scalable foundation models—machine learning (ML) models trained on broad data sets—is beginning to be implemented to help analyze this high volume of date.
However, at the core of location intelligence tools are geographic information systems (GIS). These are computer systems that capture, store, analyze, visualize and manage geospatial data. With GIS, users can create interactive queries, analyze spatial information, edit data, integrate maps and present results. Without GIS technology, geospatial data sets and their insights are easily overlooked due to volume and complexity. GIS display this data in digestible formats with recognizable patterns.
Organizations across industries use GIS-powered maps, dashboards, applications and analysis to improve resource management, asset management, environmental impact assessments, supply chain management and other activities. Esri is the industry leader in GIS software development and is responsible for developing many of the GIS methods and technologies used today, such as ArcGIS.
Some of the most critical challenges of today are correlated to location intelligence: the effects of climate change, geopolitical conflict, global pandemics and others. Location-based insights from geospatial data are vital to better understanding and addressing these challenges.
From a business perspective, location intelligence can provide a competitive advantage with three major benefits:
Location data analysis can provide a deeper understanding of processes, people and outcomes to better guide business strategy.
Location data can be gathered and updated in real time, empowering business decision makers with up-to-the-minute information.
Combining location data with traditional business data offers a holistic view of operations. This can help break down information and process silos.
Location data is used across industries, such as healthcare, real estate, financial services, farming and utilities. Here are just a few real-world use cases:
There are several use cases for location intelligence within supply chain management (SCM). For example, routing and workforce optimization. Using location data, businesses can optimize fleets and make drivers more efficient. Additionally, network optimization and site selection can help retailers decide where to build new warehouses that are closer to customers, reducing delivery lead times.
When location analytics are integrated with customer relationship management (CRM) software, they can help uncover consumer behavior patterns to improve marketing campaigns, segmentation strategy and customer experience. For example, marketing professionals can adjust pricing and messaging based on a customer’s location.
Assessing climate data and insights about vegetation growth using location intelligence can help utilities prevent disruptions to operations caused by damage to assets and infrastructure. This same data can also help identify energy consumption patterns, aiding in energy forecasting and informing proactive decision-making to help avoid outages.
The analysis of real-time spatial data can enable smart transportation in cities. Location intelligence uses traffic data to improve traffic flow, ease congestion and reduce emissions. Location intelligence also powers apps that display real-time public transit arrival updates and maintenance interruptions.
There are several use cases for location intelligence within the field of smart farming. For example, moisture data from soil condition sensors can be combined with weather data to provide smart watering recommendations and promote efficient water use. Location data from sensors attached to livestock can track activity, providing insights about behavior and land utilization.
Location intelligence allows for the monitoring of severe weather, flooding and air quality. For example, meteorologists use location intelligence to chart the path of tornadoes moving into an area. Similarly, location intelligence can help emergency response teams accurately predict and track the trajectory and growth of wildfires using satellite, census and wind data.
Businesses in this sector can use location intelligence for network planning and design, boundary identification and identifying new customer markets. For example, they can avoid costly mistakes like installing cell phone towers in suboptimal locations and instead identify locations closer to clients in a service area.
Factors relevant to climate change such as carbon emissions, coral reef conditions, sea temperatures and surface levels, forests and more can be monitored and analyzed using location intelligence. GIS technology can visualize risks and guide climate change mitigation decision making. Additionally, the ability to identify and predict weather conditions, climate risk and other location-related elements can help renewable energy developers determine the best location for infrastructure like wind turbines and solar panels.
Gain climate and weather insights to anticipate disruptive environmental conditions, proactively manage risk and build more sustainable operations.
Automate and unify integrated business planning across your organization. Turn AI-driven predictions into actions.
Simplify the capture, consolidation, management, analysis and reporting of your environmental, social and governance (ESG) data.
Geographic information systems (GIS) are computer systems that produce connected visualizations of geospatial data—that is, data spatially referenced to Earth.
Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs.
Learn more about the type, challenges and future of geospatial data, as well as how it reveals insights, patterns and trends.
See how LiDAR, a remote-sensing technology, uses laser beams to measure precise distances and movement in an environment in real time.
Read how geospatial data plays a key role in protecting wildlife, creating a healthier planet and a more resilient economy.
Read how GIS technology provides insights that are key to many industries and disciplines.