Geo Data Processing Using Graph Databases

Geo data is information about geographic locations that is stored in a format and be analyzed in a way to help organizations’ activities. Most business has such as customer addresses, sales territories and physical map presences. Many organizations are trying to take advantage of their geographic information by incorporating location analysis and intelligence into their applications or services. Generally, this allows organizations to make better decisions, respond to customers more effectively, and reduce operational costs – increasing ROI and creating competitive advantage.

Traditionally, to store and analyze geo data, many organizations have used spatial databases, which are mostly based on relational databases. A spatial database is a database that is designed and optimized to store and query data that represents objects defined in a geometric space. These traditional databases allow representing simple geometric objects such as points, lines and polygons, while some of them offer extra features such as 3D objects processing, topological coverages, and linear networks. Since these database systems use indices to quickly look up values and the way that most databases index data is not optimal for spatial queries, spatial databases use a spatial index to speed up database operations. This means that conventional relational databases are not designed to support geo data in their natural format, and, instead, try to store geo data in their tabular format and find data through spatial index, which are not naturally designed for relational database from the ground. It causes counter-intuitive data domain modeling and comparably slow performance.

Geography is a natural data domain for graphs and graph databases. Graph data model is composed of nodes (data points) and relationships (lines between points), and objects such as locations, geometries and topologies could simply be drawn on graph databases like Neo4j. With their ability of expressing geo data intuitive way, graph databases could be used from calculating routes between locations in an abstract network such as a road or rail network, airspace network, or logistical network to spatial operations such as find all points of interest in a bounded area, find the center of a region, and calculate the intersection between two or more regions. Below picture shows a simple example of the Global Post parcel network. The network comprises parcel centers, which are connected to delivery bases, each of which cover several delivery areas. These delivery areas, in turn, are subdivided into delivery segments covering many delivery units. In graph data model, each location can be expressed as nodes and delivery routes between locations can be expressed as relationships.

To find the shortest delivery route, below graph query could be used to analyze geo data in the domain. As you can see, unlike conventional databases with several steps and algorithms to analyze geo data, simple one-page script (in this case, graph query language, cypher, was used) can return a result that applications or services need.

Conventional databases have been the power-horse of most software applications and continue to be so today. They handle structured data exceedingly well, and they are one of the best tools for storing and handling data for the right use case and the right architecture. However, today’s user requirements and applications such as geo data processing (or geo information system) are requiring more intuitive modeling and structure flexibility to handle massively incoming unstructured data. As recent IT environment getting more complex, it is not becoming an option but mandatory to use a right tool for a right job. It is time to set your constraint data free from tabular format and travel your data freely with graph databases.