Courses :: Module, DATABASES

	Overview
	Conceptual spatial data modeling; Why is working with spatial data different to other types of data and information? Structured Query Language (SQL); relational database models for spatial data; spatial data structures; spatial data integrity; spatial data manipulation; spatial algorithms used in Geographic Information Systems (GIS); spatial analysis techniques including statistical approaches for spatially-informed decision making; spatial data visualisation; spatial data formats (PostgreSQL PostGIS database, GeoJSON, ESRI Shapefiles, CSV); alternative database models for geospatial data. Using NoSQL to store and manage spatial data (Key-value stores, document databases, Wide-column stores) Unstructured geospatial data sources (from social media, from APIs, etc).

Overview

Conceptual spatial data modeling; Why is working with spatial data different to other types of data and information?
Structured Query Language (SQL);
relational database models for spatial data;
spatial data structures; spatial data integrity; spatial data manipulation;
spatial algorithms used in Geographic Information Systems (GIS);
spatial analysis techniques including statistical approaches for spatially-informed decision making;
spatial data visualisation;
spatial data formats (PostgreSQL PostGIS database, GeoJSON, ESRI Shapefiles, CSV);
alternative database models for geospatial data. Using NoSQL to store and manage spatial data (Key-value stores, document databases, Wide-column stores)
Unstructured geospatial data sources (from social media, from APIs, etc).

	Learning Outcomes
	On successful completion of the module, students should be able to: Design and implement spatial databases using standard models and spatial database management systems. Query spatial databases using standard query tools and languages Use Geographical Information Systems (GIS) to analyse and visualise spatial data Create interfaces to view and customise, interact with spatial data Design and implement spatial indices for efficient searching of data Perform spatial analysis on large spatial databases and datasets Ensure reliability, security, integrity and privacy in spatial databases Design and implement spatial databases using NoSQL model approaches such as key-value stores, document databases, etc Understand the challenges and most effective approaches to working with large databases of unstructured spatial data.

Learning Outcomes

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? No

Autumn Supplementals/Resits

	Timetable
	This timetable is not currently available, please contact the Computer Science Department.

Timetable

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DATABASES