Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Spatial Modelling for Data Scientists
Code ENVS453
Coordinator Dr FR Rowe
Geography and Planning
F.Rowe-Gonzalez@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ Second Semester 15

Aims

Build upon the more general research training delivered via companion modules on Data Collection and Data Analysis, both of which have an aspatial focus;      
Highlight a number of key socialissues that have a spatial dimension;
Explain the specific challenges facedwhen attempting to analyse spatial data;
Introduce a range of analyticaltechniques and approaches suitable for the analysis of spatial data;  
Enhancepractical skills in using software packages to implement a wide range of spatial analytical tools.


Learning Outcomes

(LO1) Identify some key sources of spatial data andresources of spatial analysis and modelling tools

(LO2) Explain the advantages of taking spatial structure intoaccount when analysing spatial data

(LO3) Apply a range of computer-based techniques for theanalysis of spatial data, including mapping, correlation, kernel densityestimation, regression, multi-level models, geographically-weighted regression,spatial interaction models and spatial econometrics

(LO4) Select appropriate analytical tools for analysingspecific spatial data sets to address emerging social issues facing the society

(S1) Problem solving skills

(S2) Numeracy

(S3) IT skills


Syllabus

 

Syllabus Week Topic Staff;
Point pattern analysis
Kernel density estimation
Spatial econometrics
Multilevel modelling: random intercepts
Multilevel modelling: random slopes
Spatial Interaction Modelling
Geographically Weighted Regression
Spatio-temporal modelling


Teaching and Learning Strategies

The teaching method for the module will be a mix of e-lectures and on-campus computer-based practicals. The combination of lectures and hands-on practicals is designed to enhance students' understanding and application of general spatial analysis and modelling approaches.

Teaching Method 1 - E-Lectures
Description: Weekly lectures will be asynchronous and be available via VLE. They will cover descriptions of spatial analysis and modelling approaches, motivations of the development of these methodologies and selection of suitable spatial analysis strategy for specific spatial data.
Attendance Recorded: No

Teaching Method 2 - Weekly 2-hrs computer-based practicals will be on-campus and guide students through computer based spatial data manipulation, modelling build and interpreting model results.
Attendance Recorded: Yes
Notes: 2hrs x 12 weeks


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 22

          22
Timetable (if known)              
Private Study 128
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Assigment 2 assesses teaching content from Weeks 7 to 11. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (W  2000 word project    50       
Assigment 1 assesses teaching content from Weeks 1 to 5. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (Wh  2000 word project    50       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.