Geospatial Analysis

Business Challenge

The explosion of big data, and the general adoption of IoT technology and cloud computing has called for faster, better and more intuitive integration with geospatial analytics. In 2017, Geospatial analytics market reached 38.65 billion . It is estimated to grow at an annual rate of 18.2%, and by 2027, the entire geo analytics market will reach 174.65 billion.

It’s no surprise 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 going through the digital transformation, it has been a real challenge to provide a unified solution to both online transaction processing (OLTP) and online analytical processing (OLAP) workloads simultaneously to satisfy their core business requirements, and adding real-time geospatial capability with seamless integration to existing and future applications, with minimal redesign of data schema and model.

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Traditional Solutions Are Missing the Mark

Using traditional RDBMS for geospatial analytics has its drawbacks: first of all, the data model is not designed for complex location-based analytics questions in real time; secondly, the geo analytics may rely on third-party index; 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. Because of these challenges, enterprises may not have taken geo analytics into the core business logic previously, hence losing crucial insights from the location data. Graph databases, on the other hand, model our 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.

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Deep Link Geospatial Analytics

TigerGraph’s massively parallel processing or MPP architecture empowers enterprises and governments to expand their solutions, adding the location dimension(s) to the use cases. While previous generation graph databases struggle to provide analytics and insights that require more than 3 hops or steps, 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 throughout during weekdays and on weekends. Popular commercial locations such as stores or restaurants visited is also considered. Traditional marketing campaign solutions lack the capability to combine customer demographics as well as browsing, search and purchase history with their mobility profile and deliver targeted offers based on the real-time location of the customer.
TigerGraph based marketing campaign solution 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 GAP 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 4 to 6+ hops going across multiple connected entities to find the location-based product or store recommendation. Traditional geospatial solutions built on the relational databases may struggle to integrate location data due to the rigid schema and require computationally intensive database joins, rendering deep link analysis infeasible.

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Real-Time Geospatial Analytics

Geospatial applications require real-time location insights. 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 geodata is collected continuously. TigerGraph’s highly performant and scalable engine 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.

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Geospatial Analytics for the Internet of Things (IoT)

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. With TigerGraph, you can process signals coming in from all of your IoT sensors, actuators, switches and routers, map those based on their locations and perform complex calculations in real- time to make decisions. A great application of this is a faster than real-time energy management system which combines geotagged IoT data to balance supply and demand.

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Analyze Geospatial Data in Real-time with TigerGraph