Introduction to Geographic Data Science

• Credit value: 30 credits at Level 7
• Convenor: Dr Shino Shiode
• Tutors: Dr Shino Shiode, Dr Roberto Murcio
• Assessment: two pieces of practical coursework: (a) construction of maps and simple analysis of 3000 words (50%) and (b) collection and handling data from different sources and analyse it using different spatial tools (50%)

In this module we introduce you to the newly emerging subject of geographic data science. We first explore how to digitalise and map events and phenomena that happen in the real world using Geographic Information Systems (GIS) - a computer system for handling spatially referenced data. We will then identify different types of spatial data and discuss how they can be used in solving a wide range of problems using geoprocessing methods. Examples range from facility locations (e.g. finding the best location for an urban/retail facility solving multiple spatial and non-spatial conditions) to environmental issues (e.g. mapping and estimating damages by pollution and natural disasters) and more. Finally, by working on various case studies, you will gain hands-on experience in capturing, editing, mapping, analysing, sharing and presenting spatial data, essential skillsets for independently solving data science problems.

The second part of the module focuses on Big Data management and modern geospatial data formats. You will learn how to work with different data types in GIS software and how to represent these data on a geospatial database. In addition, you will be introduced to the concept of relational databases, entity-relationship modelling, and spatial databases suitable for representing geographical data.

Each lecture will be accompanied by a practical session designed around specific real-world applications (including but not limited to urban, social, environment, retail, health and diseases) to help acquire practical techniques for data handling and solving geographical problems under various settings.

Indicative syllabus

• What is geospatial data?
• Representing geography in a digital form
• Digital map production
• Spatial data models
• Vector- and raster-based geoprocessing analysis and spatial queries
• Georeferencing and coordinate systems
• Working with modern geospatial data formats - Big Data
• Relational databases and Structured Query Language (SQL)
• Spatial databases
• Geospatial applications examples

By the end of this module, you will be able to:

• describe theoretical concepts for representing various forms of spatial data
• understand cartographic principles and presentation of geographical information in the mapped form, and produce digital maps using specialist GIS software
• use relevant GIS software to carry out various forms of (geoprocessing) analyses and queries
• work successfully across a range of geographical data formats and standards
• collect and integrate geographic information from different sources, like official census, social media and infrastructure data
• plan effectively and organise various data processing techniques through a GIS project and complete the project on time
• use and design relational databases using conceptual modelling
• use and design spatial databases to handle large volumes of geospatial information
• formulate spatial and non-spatial queries on database management systems
• deploy theoretical concepts for the representation of spatial data in practical scenarios.