UNIVERSITY COLLEGE LONDON
Department of Geomatic Engineering
Gower Street, London WC1E 6BT
Programme(s): MSc/PgDip Geographic Information Science
Academic Year: 2010/2011
Module Title:
Geographic Information Science Data Analysis
Module Code: tbc
Credit Points:
7.5
Term: 1 (weeks 8-12)
Brief Description:
This course will provide an introduction to spatial analysis including: measurement, representation and
statistics; spatial processes; point data - distribution, randomness & clustering, densities, ESDA and
prediction; lines and linear data; areas and spatial autocorrelation; interpolation, prediction and
geostatistics; accuracy, error and quality.
Aims & Learning Outcomes:
The aim of this module is to equip students with an understanding of the principles underlying the
analysis of spatial data. By the end of the unit, the student should:





have an good understanding of the principles underlying the analysis of spatial data in general
and spatial statistics in particular
be able to use GIS for generating and visualising summary statistics
be able to examine, analyse and simulate a range of spatial patterns and processes, notably
point, line and area-based (mapped) patterns
have a good understanding of the nature of spatial fields and their structure
appreciate the many different sources of uncertainty in spatial data and spatial processing and
how to address such issues in analysis and research.
Although primarily a lecture-based course, there will be a number of different learning approaches
used, including in-Lecture practicals and Microprojects (brief presentations by students). Students will
be expected to access library and web resources and to download and run free software from the
departmental cluster and the Internet. Lecture notes and a range of electronic documentation, data
and software will be made available from the course homepage
Module Coordinator:
Dr Mike de Smith
Pre- and Co- Requisites:
Pre-requisite(s) for this module:
Co-requisite(s) for this module:


Analytical Methods
None
mike@desmith.com
Teaching Outlines:
Week:
AM/PM:
7
AM
PM
AM
8
PM
AM
9
PM
AM
10
PM
Content:
----Basic Concepts: Measurement,
representation and statistics
Introduction to module structure, aims and
objectives; Introduce/review core concepts
of measurement and statistics as a precursor
to studying spatial statistics and spatial
analysis methods, including descriptive
statistics and univariate distributions, simple
regression
Introduction to Spatial Processes and
Spatial Analysis
Introduction to formal methods and tools of
spatial analysis: pattern, process,
dimensionality, MAUP, spatial statistics and
GIS toolsets, brushing and linking;
classification and mapping; microprojects
programme
Point data: Distribution, randomness &
clustering
Examine measures of spatial centrality and
dispersion; simple indices and tests for
randomness; quadrat and NN methods,
Ripley’s K
Point data: Densities, ESDA and
prediction
Introduction to density estimation, hotspot
analysis, kernels and ESDA, control and
covariate data; point/region duality; temporal
analysis
Lines and linear data
Microproject: MP1a
Obtain a better understanding of notion of
‘line’, network and associated
representation and analysis issues and
methods, including alternative conceptual
models (mathematical, statistical, fractal..),
metrics and measurement, and additional
properties of linear forms
Network analysis;
Microprojects: MP1b
Collections of linear forms as networks;
representation as graphs, trees and circuits;
simple optimisation problems and solutions
Duration:
Staff:
Room Type: (i.e.
Lecture/Practical)
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
Week:
AM/PM:
AM
11
PM
AM
12
PM
Content:
Areas and Spatial Autocorrelation
Microproject: MP2a
Obtain a basic understanding of nonstationary processes, anisotropy and
autocorrelation analysis: area analysis and
landscape metrics; joins counts; Moran’s I
and Geary C
Interpolation, prediction & Geostatistics 1;
Microprojects: MP2b
Obtain an understanding of fields and
creation/estimation of (continuous) fields from
point and other datasets - deterministic
procedures (e.g. IDW and splines);
discussion of surface attributes (e.g. gradient,
aspect, roughness…)
Interpolation, prediction & Geostatistics 2;
Microproject: MP3a
Obtain an understanding of fields and
creation/estimation of (continuous) fields from
point and other datasets - statistical
procedures (e.g. simple regression, GWR,
and kriging)
Accuracy, Error and Quality
Microprojects: MP3b
Obtain an appreciation of the types and
sources of error; examination of how to deal
with uncertainty; followed by a review of the
Analysis module and role of analysis
Duration:
Staff:
Room Type: (i.e.
Lecture/Practical)
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom/GE
Computer Cluster
3 Hours
MdS
GE Classroom
Marking: Will be by coursework submission. Coursework will be set in week 9 and must be completed
by end of week 12.