Information can’t make decisions for you, but having access to relevant information can be very helpful. However, decisions are often complex, and while single piece of information might be useful, often multiple sources of information must be combined to generate the answers needed.  Having a good understanding of how information has been created, combined and analyzed is important to give a decision maker more confidence in the choices they are making.

Location – knowing where things are – is a great way to combine and analyze all the data that is now becoming available (e.g. via open government data; sensor streams; the internet of things; mobile phones; crowdsourcing) to decision makers and turn it into useful information. Integrated location data is used in decision making in areas ranging from asset management to archeology, from construction engineering to cadastral systems, from architecture and design to zoo management.   Managing this combined data centrally– in a database – allows it to be shared securely, and ensures that all users of the data have access to the latest information, in real time, again increasing confidence.

This module delves under the hood of data, databases and data management, and shows you how to create and integrate spatial (location) and other data in a database (e.g. PostgreSQL, Oracle) and then run SQL queries to exploit the power of this integration to generate information to solve real world problems. We focus in particular on relational databases (SQL), exploring: data and database design; data creation and update; basic data analytics; spatial data analytics (metric and topological analysis); integrating analytics to provide information to assist with decision making. An end-to-end case study helps to develop in an in-depth understanding of the full data lifecycle and of demonstrates how data underpins decision making in a real-world context. Advanced topics (depending on time) may include: evaluating evidence for decision making, improving database performance, NoSQL, how to share data over the internet via GeoServer, GeoBIM integration, Digital Twins.

Learning Outcomes

Understand the data lifecycle from creation to destruction (via in class examples)
	Understand the power of a relational database – and of location – to integrate data from disparate sources and manage and share that data centrally (via in class examples, lab exercises and assignments)
	Understand how to model data and spatial data for storage in a relational database (via in class examples and a design exercise forming part of an assignment)
	Be able to write SQL to create and modify spatial data in a relational database (via in class examples SQL script creation exercises and for assignments)
	Be able to write SQL to undertake spatial and non-spatial data analysis (via in class examples, SQL script creation in lab exercises and for assignments)
	Be able to demonstrate how spatially-enabled relational databases can be used in practice - from identifying the decisions to be made, through designing the database, capturing the data and providing information for those decisions, in the context of real-world applications (via in class examples and assignments)
	Be aware of emerging topics in this area