MSc Geographical Information Science

Year of entry: 2024

Course unit details:
Data Acquisition for GI Scientists

Course unit fact file
Unit code GEOG62411
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? No


The unit will consist of two introductory lectures followed by a 5-day (including travel) residential field course to the Ennerdale Valley in the Western Lake District.

Staff and students will be met by partners working at Forestry England and Wild Ennerdale National Nature Reserve in order that students can learn about the background of the landscape and the ongoing research collaborations with course leaders that employ GIS modelling and analysis, setting the scene for the field work activities that will take place. Students will engage in group work and learn how to use a range of instruments, equipment and techniques for the acquisition of both quantitative and qualitative spatial data, including: real-time kinematic global navigational satellite systems (RTK-GNSS), camera drones, environmental sensor networks and participatory GIS software.

Students will build and deploy sensors and associated data communication hardware using standard ‘open hardware’ platforms such as Arduino and radio communications protocols such as long-range wide area networks (LoRaWAN). Using the resulting sensor network, students will learn to georeference their data and employ spatial interpolation approaches in order to inform a self-directed project on a choice of physical, management or cultural process relevant to this landscape (e.g., nature recovery, forestry, eco-tourism). Students will learn advanced cartographic techniques to produce high-quality and informative cartographic visualisations based upon these data.

Students will learn how emergent technology might be used to facilitate data collection at higher spatial and temporal resolutions, in order to gain deeper insight into medium and large scale environmental and geographical phenomena. This will be facilitated through the use of RTK-GNSS and camera drones to examine land cover patterns in the Ennerdale valley, which are changing rapidly due to the ‘Wild Ennerdale’ rewilding project, which cannot always adequately be captured using satellite-based remote sensing data. Students will learn about the operation of a camera drone (theoretical, regulatory and practical), the deployment of ground control points using a RTK-GNSS, the planning and execution of a data collection flight using a camera drone, and the processing of the imagery into a GIS dataset. Students then have the opportunity to undertake a landcover classification using GIS software, which will allow them to compare the results with the equivalent satellite data in order to better understand the benefits and limitations of each approach.

Students will learn to apply Public Participatory GIS (PPGIS) techniques for the collection of public opinion relating to environmental and conservation-based issues. They will learn to construct suitable spatial and aspatial questions relating to the Lake District in order to inform a self-directed project relating to cultural issues affecting the wider iLake District landscape such as cultural landscapes, tourism, conservation and rewilding. Students will then deploy their survey amongst tourists in the Lake District at locations such as Keswick and Grasmere to collect information that can be processed into spatial data, which will be used to support the production of an essay relating to their PPGIS project. Throughout all the above projects, students will have the opportunity to experience, explore and apply fundamental GI Science concepts that apply widely across the rest of the course, including bias, scale, generalisation, accuracy, precision and geodesy


The unit aims to:

Equip students with essential field data collection skills, including land surveying, sensor deployment, ground truthing, geo-referencing and Participatory GIS. These skills are key approaches to data collection that are highly relevant to more applied aspects of the wider programme and are not possible to learn effectively in a classroom environment.

The unit will provide students with essential knowledge on data acquisition, processing and visualisation, as well as essential field techniques that are involved in the production of spatial data and derived products.

Teaching and learning methods

Classes will be delivered as a mix of lecture, practical demonstrations and field work. The course material will be delivered via Blackboard and physical materials in the field.

Knowledge and understanding

Knowledge of the characteristics of location-based data, including how data can made spatial and the challenges that this poses

Understanding of the techniques used to capture, process and analyse both quantitative and qualitative location-based data

Knowledge on the application of a range of sensors, communication networks and remote data capture technology for environmental research

An understanding of the complex cultural and ecological context of the Ennerdale Valley and the Wider Lake District landscape

Knowledge relating to the regulation and operation of camera drones in various settings

Intellectual skills

The ability to account for fundamental GI Science concepts, including bias, scale, generalisation, accuracy, precision and geodesy

Spatial data handling and analysis

Participatory GIS Survey Design

Practical skills

The development, design and deployment of sensor networks

The planning and operation of a camera drone flight, as well as the subsequent data processing and georeferencing

Land cover classification using semi-supervised and machine-learning methods.

The utilisation of spatial interpolation techniques

The use of surveying instruments and remote data capture technology, including camera drone and RTK-GNSS.

Basic electronics and network connectivity skills for the creation and deployment of data-logging sensors

Advanced skills in cartographic visualisation using QGIS

Transferable skills and personal qualities

Group work, particularly in the context of working effectively in the field

Planning and executing of data collection campaigns

Handling, processing and analysing both qualitative and quantitative spatial data

Problem solving

Assessment methods

1. Cartographic visualisation of collected field data (40%)

Single A3 page cartographic visualisation

Verbal feedback and written feedback through Turnitin 15 working days after submission of map layout

2. Written evaluation on a participatory GIS project on eco-tourism in the Lake District (60%

1500 words

Written feedback through Turnitin 15 working days after submission. 60%

Recommended reading

Crameri, F., Shephard, G. E., & Heron, P. J. (2020). The misuse of colour in science communication. Nature communications, 11(1), 1-10.

Denwood, T., Huck, J. J., & Lindley, S. (2022a). Participatory Mapping: a systematic review and open science framework for future research. Annals of the American Association of Geographers, 1-20.

Denwood, T., Huck, J. J., & Lindley, S. (2022b). Paper2GIS: improving accessibility without limiting analytical potential in Participatory Mapping. Journal of Geographical Systems, 1-21.

Denwood, T., Huck, J. J., & Lindley, S. (2022c). Effective PPGIS in spatial decision‐making: Reflecting participant priorities by illustrating the implications of their choices. Transactions in GIS, 26(2), 867-886.

Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment, 80(1), 185-201.

Gómez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55-72.

Graser, A., & Peterson, G. N. (2018). QGIS map design 2nd ed. Locate Press.

Hart, J. K., & Martinez, K. (2006). Environmental sensor networks: A revolution in the earth system science?. Earth-Science Reviews, 78(3-4), 177-191.

Harrower, M., & Brewer, C. A. (2003). ColorBrewer. org: an online tool for selecting colour schemes for maps. The Cartographic Journal, 40(1), 27-37.

Huck, J. J., Whyatt, J. D., & Coulton, P. (2014). Spraycan: A PPGIS for capturing imprecise notions of place. Applied Geography, 55, 229-237.

Huck, J., Whyatt, D., & Coulton, P. (2015). Visualizing patterns in spatially ambiguous point data. Journal of Spatial Information Science, 2015(10), 47-66.

Huck, J. J., Dunning, I., Lee, P., Lowe, T., Quek, E., Weerasinghe, S., & Wintie, D. (2017). Paper2GIS: A self-digitising, paper-based PPGIS. In Geocomp 2017: Proceedings of the 14th International Conference on Geocomputation.

Huck, J. J., Whyatt, J. D., Coulton, P., Davison, B., & Gradinar, A. (2017). Combining physiological, environmental and locational sensors for citizen-oriented health applications. Environmental monitoring and assessment, 189(3), 1-14.

Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173-189.

Martinez, K., Hart, J. K., & Ong, R. (2004). Environmental sensor networks. Computer, 37(8), 50-56.

Mitas, L., & Mitasova, H. (1999). Spatial interpolation. Geographical information systems: principles, techniques, management and applications, 1(2).

Phiri, D., & Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sensing, 9(9), 967.

QGIS (2022) Laying out the Maps. QGIS User Guide (Accessed 18/11/22)

Wang, Y., Huang, Y., & Song, C. (2019, October). A new smart sensing system using LoRaWAN for environmental monitoring. In 2019 Computing, Communications and IoT Applications (ComComAp) (pp. 347-351). IEEE.

Study hours

Scheduled activity hours
Fieldwork 35
Lectures 18
Independent study hours
Independent study 97

Teaching staff

Staff member Role
Matthew Dennis Unit coordinator
Jonathan Huck Unit coordinator

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