Introduction to Spatial Analysis

GEOG73001 Introduction to Spatial Analysis 15 Semester 1 Postgraduate Taught Level 7 Matthew Tomkins Unit coordinator Geography 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 This unit introduces the principles, approaches and applications of spatial analysis, focusing on developing a deep understanding of how we study and reveal patterns, relationships, and trends in spatial data.Students will be introduced to the core data structures used to represent the world in GIS to show how these formats shape the types of questions we can ask of geographic information. Alongside experience in ArcGIS Pro, a widely used geographic information system, students will develop understanding of cutting-edge techniques in spatial analysis, and will apply these to a range of environmental, social and ecological datasets.Alongside practical skills in data handling, analysis, and visualisation, the unit aims to equip students with an advanced understanding of the concepts and methods that underpin spatial thinking, enabling students to engage critically with the results of spatial analyses, and to apply this approach in real-world contexts. This unit introduces the principles, approaches and applications of spatial analysis, focusing on developing a deep understanding of how we study and reveal patterns, relationships, and trends in spatial data.Students will be introduced to the core data structures used to represent the world in GIS to show how these formats shape the types of questions we can ask of geographic information. Alongside experience in ArcGIS Pro, a widely used geographic information system, students will develop understanding of cutting-edge techniques in spatial analysis, and will apply these to a range of environmental, social and ecological datasets.Alongside practical skills in data handling, analysis, and visualisation, the unit aims to equip students with an advanced understanding of the concepts and methods that underpin spatial thinking, enabling students to engage critically with the results of spatial analyses, and to apply this approach in real-world contexts. This unit aims to:<ul><li>Introduce students to the principles and methods of spatial data analysis, with a view to developing deep understanding of how we understand and model spatial patterns and relationships.</li><li>Equip students with the knowledge and skills to identify and utilise appropriate spatial analyses, to critically assess outputs, and to communicate results to academic and non-academic audiences.</li><li>Equip students with practical skills in ArcGIS Pro, and experience working with a range of environmental, social and ecological datasets.   </li></ul> By taking this unit, students will gain practical skills and experience in ArcGIS Pro. This outcome is essential for student employability, given the wide utilisation of this software in both industry and research.Students will also gain valuable experience working with raster and vector data and utilising these for spatial data analysis. In addition, students will develop understanding of the key underlying principles and techniques, which will allow students to apply their knowledge to new datasets and research themes. Moreover, by developing deep understanding of the techniques themselves, rather than a superficial knowledge of how to run the analyses in a particular software, students will be better equipped to understand and critically engage with the results and be able to communicate these to a range of audiences.    <ul><li>Describe and explain the fundamental principles and approaches used in spatial analysis.</li><li>Recognise potential applications of spatial analysis to environmental, social and ecological datasets.   </li></ul> <ul><li>Identify appropriate spatial analysis techniques for different datasets and research questions. </li><li>Interpret and critically assess the results of spatial analysis techniques.</li></ul> <ul><li>Operate industry-standard GIS software when managing and analysing spatial data.</li><li>Create data visualisations and cartographic outputs. </li></ul> <ul><li>Communicate the results of spatial analysis for academic and non-academic audiences. </li><li>Demonstrate proficiency in data handling and management.</li></ul> Typical syllabus (indicative curriculum content):Block 1: Representing the World P1.       Working with raster and vector dataBlock 2: Spatial patterns P2.       Spatial weights      P3.       Global and Local Spatial autocorrelation   P4.       Spatial correlation    P5.       Point Patterns−Process ModellingBlock 3: Spatial modelling  P6.       Spatial interpolation P7.       Spatial regression P8.       Geographically Weighted RegressionBlock 4: Locations and Flows P9.       Location allocation P10.       Spatial interaction modelling   The module will be delivered through ten one-hour lectures, followed by ten two-hour computer practicals.All materials will be delivered synchronously but with asynchronous access to materials and practical instructions.Links to all resources are made available through the course Canvas page. 0 Other 40% 3 Report 60% Assessment task 1 Short-answer question and answer format, with maps and deliverables. 1000 words. 40% weighting.Assessment task 2 Report format, with map deliverables. 1500 words. 60% weighting. Written feedback, through Canvas, 15 working days after submission. MSc Env.Montrng, Modlng & Reco MSc Env.Montrng, Modlng & Reco PGDT Taught Component Optional MSc Env.Montrng, Modlng & Reco MSc Env.Montrng, Modlng & Reco PGDT Taught Component Optional MSc GIS MSc GIS PGDT Taught Component Mandatory MSc Green Infrastructure MSc Green Infrastructure PGDT Taught Component Optional MSc Green Infrastructure MSc Green Infrastructure PT PGDT Taught Component Optional N Anselin, L., 1995. Local indicators of spatial association—LISA. Geographical analysis, 27(2), pp.93-115.Comber, A., Brunsdon, C., Charlton, M., Dong, G., Harris, R., Lu, B., Lü, Y., Murakami, D., Nakaya, T., Wang, Y. and Harris, P., 2023. A route map for successful applications of geographically weighted regression. Geographical Analysis, 55(1), pp.155-178.Crampton, J.W. and Krygier, J., 2005. An introduction to critical cartography. ACME: An International Journal for Critical Geographies, 4(1), pp.11-33.Dennett, A. and Wilson, A., 2013. A multilevel spatial interaction modelling framework for estimating interregional migration in Europe. Environment and Planning A, 45(6), pp.1491-1507.Fotheringham, A.S., Yang, W. and Kang, W., 2017. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), pp.1247-1265.Font-Casaseca, N. and Rodó-Zárate, M., 2024. From the margins of Geographical Information Systems: Limitations, challenges, and proposals. Progress in Human Geography, 48(4), pp.421-436.Getis, A. and Ord, J.K., 1992. The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3), pp.189-206.Moran, P.A., 1950. Notes on continuous stochastic phenomena. Biometrika, 37(1/2), pp.17-23.Openshaw, S., 1984. The modifiable areal unit problem. Concepts and techniques in modern geography.Wolf, L.J., Fox, S., Harris, R., Johnston, R., Jones, K., Manley, D., Tranos, E. and Wang, W.W., 2021. Quantitative geography III: Future challenges and challenging futures. Progress in Human Geography, 45(3), pp.596-608. Lectures 10 Practical classes & workshops 20 120 Materials will be provided online in advance of sessions in accessible formats and all lectures will be recorded. Students can engage with discussion in class and online via Canvas forums.Assessment instructions, criteria and rubrics will be clearly communicated in advance of assessments deadline via Canvas.Individualised written feedback is provided for all students for both assessments, and formative feedback will be provided in class, via the discussion board, and via Canvas quizzes. Assessments are spaced through the semester to give students time to act on feedback.Essential software used as part of this unit will be available in the relevant computer clusters or via the AppsAnywhere portal.