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2019/20 Undergraduate Module Catalogue

GEOG2150 Social and Spatial Data Analysis with GIS

10 creditsClass Size: 200

Module manager: Alexis Comber
Email: a.comber@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

Module replaces

This module replaces and augments the GIS content in GEOG2025 Service Analysis and Planning and the Social Data Analysis in GEOG2561/2761/2762 Research Methods, both of which are also being discontinued and replaced with new modules.

This module is not approved as a discovery module

Module summary

This module is designed to give human geography students advanced training in social data statistics, spatial data analysis and the theory behind Geographical Information Systems (GIS). A combination of weekly lectures and computer practicals introduce students to advanced data collection, analytics and mapping techniques. The module blends new digital, mobile and spatial technologies with core numerical skills and conceptual understandings of the representation of space. Students will develop advanced skills in collecting, summarising, and manipulating data as well as exploring spatial data relationships. Three general topics will be covered: 1) statistical analysis of data and spatial data (regression, classification, tests of statistical significance, predictive models); 2) the principles of spatial analysis (spatial overlays, creating surfaces and heat maps, suitability analyses); 3) an introduction to advanced spatial analysis (spatially varying coefficient modelling; ecological fallacy / MAUP; accessibility analyses). Along the way students are introduced to the potential uses and applications of spatial data from policing to planning with a focus on site and location suitability analyses. Students are also given advanced training in spatial data collection, overlay analysis, sieve mapping, cluster analysis and classification, heatmaps, spatial trends, data projections, network analyses, digital elevation models, and regression. Students will acquire a number of advanced skills in how to perform basic data manipulations, summaries and mapping.

Objectives

The overall aims of this module are to:

- provide students with advanced training in social data statistics, spatial data analysis and the theory behind Geographical Information Systems (GIS)

- enable students to develop advanced skills in collecting, summarising, and manipulating data as well as exploring spatial data relationship

- develop core GIS, mapping and statistics skills in GIS and related theoretical approaches

- introduce students to the potential uses and applications of spatial data with a focus on site and location suitability analyses

Learning outcomes
At the end of this module students will be able:

1. to obtain data and spatial data from data portals: census data, census areas; Geo-demographic data, OS data (roads urban areas, etc) from EDINA, DIGIMAP, CDRC
2. to develop and apply simple distance measures (buffers, isodistance, isochrone) for geographic features and use these for accessibility / site selection analyses
3. to be able to apply and understand measures of association for correspondence tables (with Chi-square)
4. to undertake spatial queries through overlays of spatial data
5. to understand the basics of regression (independent and dependent variables, t-values and p-values, mapping outliers)
6. to quantify existing and derive new relationships between spatial data attributes and to use these create small area classifications and indices
7. to understand the principles and relative advantages of different approaches for generating surfaces from data points: heat mapping, interpolation, inverse distance weighting, kriging

Skills outcomes
The proposed module will be built around the learning and teaching of explicit core QAA sub-specific skills:

- spatial awareness and observation
- abstraction and synthesis of information
- numeracy and statistical literacy
- preparing effective maps, diagrams and visualisations
- primary data generation, collection and recording, and the use of secondary data sets (both quantitative and qualitative)
- analysis and problem-solving through quantitative and qualitative methods
- conducting fieldwork and field data collection
- employing a variety of social survey methods (for example questionnaire surveys and structured interviews)
- methods for the collection and analysis of spatial and environmental information (for example: GIS, remote sensing, statistical and mathematical modelling)
- taking responsibility for learning and reflection upon that learning
- recognising the moral, ethical and safety issues involved in all aspects of geographical enquiry.

It will also deliver the following general QAA knowledge and understanding skills:

- the concept of spatial variation
- a critical awareness of the significance of spatial and temporal scale
- ability to use critically a systems framework to conceptualise patterns, processes, interactions and change in the physical world
- knowledge and critical understanding of the diverse manners of representation
- principles of research design
- numeric skills
- geolocated data and geospatial technologies
- geographical knowledge and understanding
- field skills


Syllabus

The syllabus will cover the following kinds of themes:

- Key concepts in spatial data analysis: why space is special
- Spatial overlays: set theory, intersect-union, overlay and integration
- Obtaining spatial data: sources, formats and portals
- Predictions and models using regression
- Classification, Geodemographics and Indices
- Mapping techniques: surface-from-point, Kernal Density Estimation and spatial clusters
- How to not lie with maps
- Network analyses, Isodistances, travel times, accessibility, supply and demand
- Suitability, visibility and location analyses
- Advanced spatial analysis and spatial autocorrelation

Teaching methods

Delivery typeNumberLength hoursStudent hours
Supervision21.002.00
Lectures22.002.00
Lecture101.0010.00
Practical102.0020.00
Private study hours166.00
Total Contact hours34.00
Total hours (100hr per 10 credits)200.00

Private study

Private Study and Independent Learning - Detail private study and independent learning outside formal classes as a guide to students about what is expected from them for the module
Students will use their private study time to reinforce their own learning by devoting:
- c. 50 hours to additional reading to enhance their understanding of themes introduced in lectures and practicals;
- c. 40 hours to preparation for practicals;
- c. 44 hours to reading and other preparation for the two assignments

Opportunities for Formative Feedback

The interactive lectures will be supported by an app (eg https://www.polleverywhere.com) or a web-based tool (eg https://www.mentimeter.com) to collect live feedback from students about their understanding of the topics in the lecture sessions.
Each practical is formative and will have self-test questions with answers against which students can test their understanding and data manipulation skills: students will be able to compare their “results” and understanding with model answers. Staff will be able to monitor performance in practicals.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
ReportAccessibility analysis (2000 words equiv)100.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019

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