Obtain Insights From Geospatial Data With TigerGraph
Global Geospatial Analytics Market in 2017
CAGR of the Geospatial Analytics market to 2026
Percentage of C-level executives planning to invest in location data
Geospatial Analysis Is Becoming a Must-Have for Many Organizations
The popularity of cloud computing. the proliferation of big data and the increasing footprint of the Internet of Things is mandating faster and more intuitive integration with geospatial analytics. The geospatial analytics market reached 38.65 billion in 2017 and is estimated to grow at an annual rate of 18.2% through 2027.
It’s no surprise, therefore, that over 78% of C-level executives are planning to invest in location data next year since the location data is used for finding new markets and revenue sources, delivering timely and tailored marketing campaigns and detecting fraudulent activities in real-time. For many enterprises it has been a real challenge to provide a unified solution to both online transaction processing and online analytical processing workloads simultaneously to satisfy their core business requirements, and adding real-time geospatial capability with seamless integration to existing and future applications, with the minimal redesign of data schema and models.
Legacy Systems Are Insufficient for Geospatial Analysis
Relational databases are poorly suited to geospatial analytics for several reasons: first of all, the data model is not designed for complex location-based analytics questions in real time; secondly, the geospatial analytics may rely on third-party index; and, in addition, there are no easy SQL queries to answer these complex location focused questions. Traditional geospatial analytics applications built on relational databases were not designed to address this challenge. Due to these challenges, many organizations have taken geospatial analytics into their core business intelligence modeling and are missing crucial insights as a result.
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Database for Geospatial Analysis?
Uncover Insights From Geospatial Data Using Deep Link Analytics
Graph databases provide the capabilities that organizations need for geospatial analytics because they model the surrounding environment as interconnected entities and explore the relationship between people and things. Geolocation data is an intuitive addition to graph data modeling, which is naturally designed to model the absolute and relative position of the entities.
TigerGraph empowers organizations to expand their solutions, adding the location dimension(s) to the use cases. TigerGraph’s geospatial analytics can easily traverse 10 or more steps to deliver new insights based on the location data.
Consider a mobile offer or recommendation system that factors in the customer movement data, where customers are categorized into different mobility patterns based on their movement during weekdays and weekends. Popular commercial locations such as stores or restaurants are also considered.
TigerGraph combines the transactional data from sales and service channels containing browsing, search and purchase history, with the location history data indicating the most popular commercial locations such as stores and restaurants for the customer. All of this information is combined with the real-time location of the customer and a tailored mobile offer (such as 30% off at a store in downtown San Francisco or Tokyo) is computed for real-time delivery to opt-in customers.
In order to detect these hidden relationships among location, time, product, transaction and customer data, TigerGraph executes a deep link query with 6+ hops going across multiple connected entities to find the location-based product or store recommendation.
Get Insights From Geospatial Data Using Real-Time Analytics
Imagine a ride-hailing company which tries to identify vehicle supply and demand mismatch, to predict and resolve long passenger wait time and reduce vehicle idling time in a busy metropolitan area. Real-time geo insights from TIgerGraph can identify these issues and recommend actions to resolve them. A large amount of geospatial data is collected continuously. TigerGraph can analyze and detect the movement patterns of many people and vehicles at the same time and respond in real-time to the demands from millions of customers moving across a dense Metropolitan area such as New York, London, and Tokyo.
Uncover Insights From Geospatial Analysis of Internet of Things Data
Geospatial analytics is particularly relevant for the Internet of Things as all the resources (sensors, switches, routers) in the network are connected and there is a location tag associated with the collected information. TigerGraph enables you to process signals coming in from all of your sensors, actuators, switches and routers, map those based on their locations and perform complex calculations in real-time to make decisions.
FAQ
Geospatial analysis is the process of analyzing location-based data to understand how people, assets, devices, events, and services move and interact across physical space. It is critical because location patterns can reveal customer behavior, operational risk, demand changes, service gaps, and business opportunities that are often hidden in traditional analytics.
Graph databases improve geospatial analysis by modeling locations, customers, devices, sensors, vehicles, stores, transactions, and events as connected data. Unlike relational databases that require complex joins across separate tables, graph databases can traverse relationships in real time to reveal movement patterns, proximity, dependencies, and location-based opportunities across multiple connected entities.
TigerGraph’s geospatial analysis solution supports real-time, deep link analytics across massive volumes of connected location data. It can analyze multi-hop relationships across customers, products, transactions, sensors, vehicles, routers, locations, and time-based events to uncover hidden patterns, improve recommendations, optimize operations, and support faster location-aware decisions.
Yes, TigerGraph can support real-time location-based recommendations by combining customer behavior, transaction history, product interest, mobility patterns, and current location context. This helps organizations deliver more relevant offers, services, and recommendations based on where customers are, where they have been, and what nearby opportunities are most likely to matter.
TigerGraph supports AI and machine learning for geospatial analysis by generating graph-based features from connected location data, such as mobility patterns, proximity scores, route relationships, demand clusters, asset dependencies, and location-based influence. These features help models improve recommendations, forecasting, anomaly detection, routing, and operational decision-making across complex geospatial networks.
The main challenges include massive location data volumes, fragmented systems, fast-changing movement patterns, complex proximity calculations, and limited visibility into how location, time, behavior, and infrastructure are connected. Traditional systems often struggle with deep location-aware analysis at speed. A graph database addresses these challenges by analyzing connected geospatial data directly and at scale.
Real-time graph analytics helps organizations understand how location-based events affect connected people, assets, services, and infrastructure. Instead of analyzing location data in isolation, teams can detect demand changes, supply gaps, movement patterns, routing issues, and service disruptions as they happen, enabling faster response and better operational decisions.
