MSc Geographical Information Science and Earth Observation Applicant Page

Optional

Learning will focus on:

1. understand the critical role visualization plays in exploratory geospatial data analysis and machine learning
2. knowledge of spatial analysis technique and the conditions under which they can be applied
3. have the capacity to source and manage large amounts of different sorts of spatial data
4. demonstrate critical reflection when using spatial data and techniques
5. understand the importance of graphical design when trying to communicate with difference audiences

Optional

Learning will focus on:

1. data modelling and deconstructing real-world problems into models using appropriate tools (data literacy)
2. apply the principles of algorithm development and be familiar with a range of algorithms used to manipulate and analyse spatial data
3. create Structured Query Language (SQL) code for the manipulation of objects within a relational database management system
4. open-source and cloud-computing tools within geospatial analysis to perform analysis
5. code library that implements a geospatial analysis workflow to handle large and varied datasets

Optional

Learning will focus on:

1. characteristics of geospatial health data and methods for disease mapping
2. spatial data types, issues with spatial analysis and be familiar with a variety of methods of spatial inferential analysis in health research

Optional

Learning will focus on:

1. advanced understanding of EO approaches with a focus on field-based, UAV, airborne and space-based data used across the industry
2. knowledge and practical skills in a range of advanced analytical EO techniques
3. develop reasoned arguments, firmly grounded in the available literature
4. responsibility for independent learning

Optional

Learning will focus on:

1. understand the critical role visualization plays in exploratory geospatial data analysis and machine learning
2. knowledge of spatial analysis technique and the conditions under which they can be applied
3. have the capacity to source and manage large amounts of different sorts of spatial data
4. demonstrate critical reflection when using spatial data and techniques
5. understand the importance of graphical design when trying to communicate with difference audiences

Optional

Learning will focus on:

1. data modelling and deconstructing real-world problems into models using appropriate tools (data literacy)
2. apply the principles of algorithm development and be familiar with a range of algorithms used to manipulate and analyse spatial data
3. create Structured Query Language (SQL) code for the manipulation of objects within a relational database management system
4. open-source and cloud-computing tools within geospatial analysis to perform analysis
5. code library that implements a geospatial analysis workflow to handle large and varied datasets

Optional

Learning will focus on:

1. characteristics of geospatial health data and methods for disease mapping
2. spatial data types, issues with spatial analysis and be familiar with a variety of methods of spatial inferential analysis in health research

Optional

Learning will focus on:

1. learning focus how active and passive sensing data can be used to support disasters response and risk mitigation. This includes:
   1. learning about the driving mechanisms of disasters
   2. how earth observations data can be used to support emergency response to disasters
   3. mitigating risks in the future

The future of Geographic Information Science and Earth Observation is highly promising, and expected to expand significantly with advancements in technology, increased data availability, and a growing demand for real-time, spatially accurate information. Here are just some of the reasons why:

Integration of artificial intelligence and machine learning

AI and ML are being increasingly integrated into GIS and EO technologies to enhance data analysis capabilities. 

Improved sensor technologies

Rapid and continued improvements in the sensors used for EO, including higher resolution cameras, hyperspectral imagers, and advanced radar systems. These improvements will enable more detailed and accurate data collection, facilitating better monitoring and management of natural resources and urban environments.

Advancements in UAV and drone technology

Unmanned Aerial Vehicles (UAVs) and drones provide a flexible and cost-effective means of collecting high-resolution spatial data. Their use is expected to expand in areas like agriculture, disaster management, and environmental monitoring, with drones being equipped with more advanced sensors and AI capabilities for real-time data processing.

Big data and cloud computing

As the volume of data generated by sensors and satellites grows exponentially, cloud computing will become crucial for storing, processing, and analysing this data. 

Open data initiatives

Open data policies, allowing more users access to valuable geographic and observational data, will foster innovation and collaboration across different fields and between different countries.

Increased use in policy and decision-making

As the precision and reliability of GIS and EO data improve, their use in policy-making and strategic decision processes across sectors (such as environmental management, urban planning, and public health) will become more prevalent.

Sustainable development and climate change

GIS and EO will play a critical role in addressing global challenges like climate change and sustainable development. By providing detailed spatial insights, these technologies can help in mitigation planning, monitoring of climate change effects, and management of natural resources.