Using Python as a GIS and using datasets of choice, identify 5 specific lower-super-output areas of Liverpool for
investment opportunities.Grade: 83% [moderator's comments are attached to the document]
1. Targeting Areas - Cobain
Schofield
by Cobain Schofield
FILE
TIME SUBMITTED 18-DEC-2015 07:43PM
SUBMISSION ID 50884147
WORD COUNT 3562
CHARACTER COUNT 22186
TARGETING_AREAS_-_COBAIN_SCHOFIELD_200923027.HTML
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14. FINAL GRADE
83/100
Targeting Areas - Cobain Schofield
GRADEMARK REPORT
GENERAL COMMENTS
Instructor
This is a very good answer that demonstrates
the student has very advanced technical skills
and has gone beyond the basic requirements,
obtaining new datasets and producing a novel
analysis.
A higher mark could be obtained by using more
techniques presented in class and incorporating
them explicitly and at the core of the decision
process to f ind the target areas.
PAGE 1
Comment 1
Great use of plenty of additional datasets to those used in class
PAGE 2
Comment 2
Documentation is very good.
Comment 3
"For" loops beyond the required level taught in class.
Comment 4
Low-level access to core classes in geopandas
PAGE 3
Comment 5
Integration of analysis and f urther literature. An academic ref erence would have been even
better.
Comment 6
This is really how many catchments the centroid f alls within, but it's a sensible simplif ication.
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Comment 7
15. Complex and advanced layered map.
PAGE 6
Comment 8
Great external ref erence
Comment 9
Smart approach, going beyond levels.
PAGE 7
Comment 10
This is a sensible assumption but it should have made clear in the text because it is an
arbitrary cutof f . There's nothing theoretical or conceptual that says that you need to
disregard the 75% of less popular areas and not, f or example, the 70% or the 80%. This
decisions need to be made clear and explicit in the text.
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Comment 11
This does not show up
PAGE 9
Comment 12
This denotes something is not quite right. Maybe missing data?
Comment 13
You could have exploited the geo-demographic classif ication f urther.
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