Geolocation data is an extremely valuable and informative element of modern analytics data. While organizing a traditional data analysis does not allow for spatial context, direct location data adds a layer to event behavior, pop. Also, the location where things happened —and importantly, the sequence of those events. The event can originate from a mobile app or an IoT device, and location data helps explain where these events occur. Not only does location data provide helpful context, but it also helps us understand decision-making and enables decisions based on entities in question, which in turn inform market, service use, and availability. Furthermore, it enables data to be analyzed spatially, allowing insights to be gained from static datasets.
In this blog, we will explore the specific uses of geolocations (how they accompany data across the data life cycle) from collection to processing, analysis, and visualization for various purposes across different industries.
What Is Geolocation Data and Why It Matters in Analytics?
Geolocation data provides the world location of human beings and objects, typically in the form of coordinates (latitude and longitude). Such data is produced by various technologies, especially smartphones, GPS receivers, and other Internet of Things (IoT) sensors. Geolocation data may include additional context, such as altitude, speed, and time of day. Geolocation data can be available to analysts in various ways, such as through active geolocation in apps or passive geolocation in the background on devices.
When it comes to analytics, geolocation adds an important spatial dimension to datasets whose context primarily centers around time-based events. In analytics, it is essential to understand not only when something happened, but also where it happened. Geolocation makes it generally easier to identify location-based patterns and develop location-aware decisions based on time-delimited actions or movements, such as consumer choices or the movement of assets in a logistics value chain.
What Are The Types of Geolocation Data?
How data is collected and used is critical to the feasibility and effectiveness of geolocation analysis. There are several methods for collecting geolocation data. Each method has various levels of accuracy, availability, and applications.
GPS
Global Positioning Systems (GPS) are the most precise method for collecting geolocation data. It provides near-real-time high-resolution coordinates, along with other helpful information. GPS supports location services in smartphones, smartwatches, vehicle navigation systems, and fleet and logistics tracking.
IP Address Geolocation
IP address geolocation is not as accurate as GPS, especially when dealing with mobile networks; however, it can still be effective for geolocation purposes, such as desktop devices, web content personalization, and region-based analytics.
Wi-Fi / Cell Triangulation
Wi-Fi and cell triangulation work particularly well indoors or in dense urban environments where GPS signals may be compromised. This approach estimates the location of a device based on proximity to Wi-Fi access points and cell towers.
Geotagged Content
Smartphones, cameras, and other technologies include geolocation capabilities. Many platforms will also automatically geotag content, such as photographs and videos, when it is shared via social media or other applications. This type of ‘passive’ geolocation can help understand travel behaviours and media-sharing behaviours.
IoT and Sensor Devices
Many types of devices, such as container ships, drones, autonomous vehicles, and smartwatches, will automatically report their locations. These types of devices typically report their GPS data in real-time (via GPS), but they can also be geolocated using IP geolocation or Wi-Fi triangulation (among other means). The value of accurate geolocation information from IoT and sensor devices can be immense, particularly for industries such as logistics, agriculture, and healthcare.
Each of these types of data has its use cases. While each will undoubtedly be helpful in its way, geoanalysts often obtain more accurate and reliable spatial analysis, particularly with spatial data, when using various sources of location data.
Why Use Geolocation Data in Data Analysis?
Geolocation data can enhance data analytics by introducing a locational aspect to temporal information, thereby adding a level of analysis to spatial data. Geolocation Intelligence offers the capability to uncover more contemporary insights regarding location-based trends, patterns, and behaviors within a dataset that can often be obscured in non-location-based datasets.
Geolocation as part of a delivery mechanism can drive value in business applications that are useful for tactical promotional marketing, customer segmentation, and site selection. For example, retailers can analyze walking consumer pedestrian footfall averages to understand how customers typically navigate specific store layouts.
Marketers may choose to target marketing goods or services through geofencing when a consumer enters their designated geographic area.
Logistics also offers geolocation capabilities and can provide route optimization, tracking, and supply chain management efficiencies. Urban planning can utilize geolocation analysis to identify gaps more effectively, as well as points of congestion and underserved areas within our infrastructure.
Insurance, along with supply chain analyses, provides support for risk modeling and premium calculations that are optimized through geolocation. Healthcare can leverage geolocation with available datasets for tracking the epidemiology of illnesses.
Geolocation is used exclusively for the spatial data, which can also be associated with demographic, behavioral data, and similar datasets. The geolocation data will also be utilized in future predictive analytics, enabling organizations to leverage the data to support structured decision-making processes with a greater focus on management reporting.
Geolocation data adds a spatial intelligence layer to raw data, providing organizations with a more informed and localized decision-making capacity.
What Are The Methods of Collecting Geolocation Data?
The appropriate data collection method depends on the specific use case you are aiming for, balancing the attributes of accuracy, scale, and privacy. Some data collection methods include:
APIs
APIs, such as Google Maps or HERE, and other third-party providers can translate input, including IP addresses or place names, into geographic coordinates. APIs are great for providing real-time tracking and providing additional layers to datasets.
SDKs
Mobile SDKs from providers like Mapbox, Esri, or Foursquare can be integrated into the app (with user consent) to collect spatial data, as well as associated context/attributes, such as speed, direction, or nearby features.
Manual Input
Users can also provide their data (through forms or surveys). Although this is less accurate, there may be instances where approximate data is sufficient and real-time tracking is not essential.
Sensor Data
Many IoT devices and wearables collect data from GPS, accelerometers, or gyro sensors to provide spatial and motion data that can be useful in logistics, healthcare, and other applications.
Public Data Sets
Spatial data is implicit in public and open datasets from government sources and other contributors. This data may also integrate data sets on weather, road, and traffic conditions, as well as demographics that support baseline models or provide context for different data sets.
The accepted data collection method will depend on factors such as accuracy, scale, compliance, and technological capability.
How to Add Geolocation Data to Business Intelligence Workflows?
Geolocation data can be a significant enhancement to Business Intelligence (BI) by incorporating spatial relationships into KPIs and metrics. Geolocation is vital to organizations because geolocation data helps them understand from WHERE the results originate, allowing them to make the most informed decisions.
Location-based dashboards
Tools like Tableau, Power BI, and Looker enable organizations to locate KPIs on a map, identifying areas of SWOT analysis that can be further explored. When supportive location-based tools overlay performance data on a map, they assist organizations. Identify underserved areas or regions of excellence based on key performance indicators (KPIs).
Territory and segmenting markets
Organizations can later divide customers based on geo-demographic features such as proximity, density, or regions. This more responsive and informed data further allows organizations to design smarter sales territories and targeted marketing tactics.
Regional operational KPIs
Organizations can use KPIs to track regional operational metrics, such as on-time delivery results by proximity, delivery personnel turnover by district or region, and customer churn per location. These methods enable organizations to identify fuzzy performance indicators and help local leaders manage more manageable local or regional performance.
Integrating internal systems
Internal systems (i.e., CRM, ERP, and logistics management) combined with geolocation can also allow organizations to emulate systematic automated factors that would make sense of data on routes, assets, and addresses.
Integrating with BI will enable organizations to take geolocation planning to another level, and utilize the spatial attributes to drive their analysis, creating relevant location-aware strategies that drive meaningful insights for actionable BI decisions for their organization.
Use Cases Across Different Industries
Geolocation data provides significant value through insights to improve efficiency, personalization, and business growth.
Retail
Geolocation insights offer retailers critical information for location analysis, foot traffic, and identifying new store locations, which can aid in personalizing promotions. For example, loyalty applications can send a customer an offer or promote an item when the loyalty user enters the geofenced area. This method increases consumer engagement and better sales outcomes.
Transportation and Logistics
Real-time tracking enables you to plan routes, schedule maintenance, and set delivery times with greater accuracy. Logistics players, such as FedEx, UPS, and Uber, have made these types of insights the basis for improving their operations.
Healthcare
Geolocation can aid in monitoring mobility practices, logistics management in hospital settings, and enhancing patient care. It gained more importance during the COVID-19 pandemic, particularly in the monitoring of outbreaks and quarantines.
Real Estate
Geospatial layers, such as school districts, crime statistics, and transportation access, are significant influencers on property value. Now, parts of housing platforms will show some score for location to match property scores with buyer preferences.
Agriculture
Precision farming utilizes GPS-equipped machinery (connected to cloud servers) for zoning fields, automated irrigation, yield mapping of crop productivity, and sustainability-related assessments.
Finance and Insurance
Banks and insurance companies (mainly insurance) have been using location data for things like estimating credit risk, fraud detection, premiums (implying environmental risk) based on location (for example, whether weather or crime location can factor into risk assessment).
Location Intelligence is invaluable across industries, not only in a historical sense of identifying where things happened, but it also provides situational context intelligence.
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What Is The Future of Geolocation Analytics?
Geolocation analytics are advancing at an unprecedented pace, due to new technology and shifts in user expectations.
- Real-time analytics enabled by 5 G and edge computing would allow businesses to respond to events as they happen, easily detecting fraud or traffic congestion in real-time.
- Artificial Intelligence and Machine Learning will provide richer predictive analytics of customer behavior, traffic patterns, and operational forecasts.
- Augmented & Virtual Reality Applications will rely on geolocation data to create immersive experiences for customers at retail establishments, game developers, urban planners, and tourism activities.
- Indoor Positioning will transition geolocation analytics indoors by enabling location tracking through technologies such as BLE and LiDAR. These technologies will be utilized by retailers for marketing purposes and by emergency response teams for navigation and wayfinding.
- Privacy-preserving technologies, such as federated learning, will enable organizations to gain insights from geolocation and location-driven experiences without violating customer privacy.
These exciting innovations will continue to position geolocation and its related analytical methodologies as an essential part of any future analytics ecosystem.
Conclusion
Geolocation data is a resource beyond a technical resource; it is a strategic indicator for business intelligence and innovation. From optimizing marketing opportunities to reducing risk and improving public services, location intelligence will provide information that better enables context-aware decision-making.
Converting geospatial data into actionable insights relies on advanced tools, proper methodologies, and the right partners. Location Intelligence plays a clear role, through LocationsCloud, provides enterprise-ready, accurate, and privacy-compliant geolocation datasets for various business opportunities, including recommendation engines and business intelligence (BI) dashboards.
As mobile devices, IoT applications, and smart cities become increasingly ubiquitous, the role of location-based analytics will continue to grow. Organizations that implement geolocation intelligence and utilize reliable platforms, such as LocationsCloud, will be better positioned to gain an early advantage in a data-driven, location-aware world.
