📊 Spatial Modelling for Data Scientists
Table of Contents
This course is the revised version of the course spatial analysis which I delivered in 2019/20. We have added new content, datasets and computational environment. I summarised these changes in this tweet thread
This module aims to:
- Build upon the more general research training delivered via companion modules on Data Collection and Data Analysis, which have an aspatial focus;
- Highlight the spatial dimension of key social issues;
- Explain the specific challenges faced when attempting to analyse spatial data;
- Introduce a range of analytical techniques and approaches suitable for spatial data analysis; and,
- Enhance practical skills in using software packages to implement spatial analytical tools.
Module Learning Outcomes
You will learn how to:
- Identify key sources of spatial data and resources for spatial analysis and modelling
- Explain the advantahes of taking the hierarchical structure of spatial data into account for data analysis
- Apply a range of computer-based techniques for spatial data analysis, including mapping, correlation, kernel density estimation, regression, multilevel modelling, geographically weighted regression, spatial interaction modelling and spatial econometrics
- Apply appropriate analytical approaches to tackle key methodological challenges often found in spatial analysis, such as spatial autocorrelation, spatial heterogeneity, the ecological fallacy
- Select appropriate analytical tools for the analysis of specific types of spatial data to address emerging societal issues
Week 5: Assignment Clinic
Week 11: Assignment Clinic
The final module mark is composed of the two computational essays. Together they are designed to cover the materials introduced in the entirety of content covered during the semester. A computational essay is an essay whose narrative is supported by code and computational results that are included in the essay itself. Each teaching week, you will be required to address a set of questions relating to the module content covered in that week, and to use the material that you will produce for this purpose to build your computational essay.
Assignment 1 (50%) refer to the set of questions at the end of Chapters 4, 5 and 6. You are required to use your responses to build your computational essay. Each chapter provides more specific guidance of the tasks and discussion that you are required to consider in your assignment.
Assignment 2 (50%) refer to the set of questions at the end of Chapters 7, 8, 9 and 10. You are required to use your responses to build your computational essay. Each chapter provides more specific guidance of the tasks and discussion that you are required to consider in your assignment.
To reproduce the code in the book, you need the most recent version of R and packages. These can be installed following the instructions provided in our R installation guide.
Formal assessment of two computational essays. Written assignment-specific feedback will be provided within three working weeks of the submission deadline. Comments will offer an understanding of the mark awarded and identify areas which can be considered for improvement in future assignments.
Verbal face-to-face feedback. Immediate face-to-face feedback will be provided during lecture, discussion and clinic sessions in interaction with staff. This will take place in all live sessions during the semester.
Online forum. Asynchronous written feedback will be provided via an online forum maintained by the module lead. Students are encouraged to contribute by asking and answering questions relating to the module content. Staff will monitor the forum Monday to Friday 9am-5pm, but it will be open to students to make contributions at all times.