Spatial Data Analysis An Introduction for GIS users

by Lloyd, Christopher

• ISBN13: 9780199554324
• ISBN10: 0199554323
• Format: Paperback
• Copyright: 2010-02-01
• Publisher: Oxford University Press

Summary

What is the shortest route between one point and another in a road network? Where is the incidence of disease the highest? How does rainfall correlate with altitude? How does the concentration of a pollutant vary in space, and where do high concentrations correlate with densely populated areas? Geographical or spatial data play a vital role in many parts of daily life. We are dependent on information about where things are located and about the attributes of those things, either directly, as in the use of a map for navigating around a city, or indirectly, where we use resources like water or gas. Spatial Data Analysis: An Introduction for GIS Users introduces students to key principles about spatial data, the methods used to explore such data, and the kinds of problems that can be tackled using widely available analytical tools. Taking a gradual, systematic approach, the text opens with coverage of core concepts; these ideas are illustrated and reinforced with careful explanations, numerous worked examples, and case studies throughout the book. Accessible to students who are new to the field, Spatial Data Analysis focuses on education rather than simple training; it not only shows students how to apply data analysis tools but also demonstrates how those tools work. A Companion Website provides resources for both students and instructors.

Author Biography

Chris Lloyd is a Lecturer in Geography (GIS) in the School of Geography, Archaeology, and Paleoecology at Queen's University, Belfast.

Table of Contents

Introduction

Spatial data analysis

Purpose of the book

Key concepts

Structure of the book

Further reading

Key concepts 1: GISystems

Introduction

Data and data models

Raster data

Vector data

Topology

Databases

Database management

Referencing systems and projections

Geocoding

Spatial data collection

Secondary sources

Remote sensing

Ground survey

Sources of data error

Visualising spatial data

Querying data

Boolean logic

Summary

Further reading

Key concepts 2: statistics

Introduction

Univariate statistics

Multivariate statistics

Inferential statistics

Statistics and spatial data

Summary

Further reading

Key concepts 3: spatial data analysis

Introduction

Distances

Measuring lengths and perimeters

Length of vector features

Measuring areas

Areas of polygons

Distances from objects: buffers

Vector buffers

Raster proximity

Spatial dependence and spatial autocorrelation

Moving windows: basic statistics in sub-regions

Geographical weights

Spatial scale

The ecological fallacy and the modifiable areal unit problem (MAUP)

Merging polygons

Uncertainty in spatial data analysis

Geographic data mining

Summary

Further reading

Combining data layers

Introduction

Multiple features: overlays

Line intersection

Point in polygon

Overlay operators

'Cookie cutter' operations: erase and clip

Applications and problems

Multicriteria decision analysis

Case study

Summary

Further reading

Network analysis

Introduction

Networks

Network connectivity

Summaries of network characteristics

Identifying shortest paths

Location-allocation problems

Other problems and approaches

Case study

Summary

Further reading

Exploring spatial point patterns

Introduction

Basic measures

Exploring spatial variations in point intensity

Quadrats

Kernel estimation

Distance based measures

Nearest neighbour methods

K function

Applications and other issues

Case study

Summary

Further reading

Exploring spatial patterning in data values

Introduction

Spatial autocorrelation

Local statistics

Local univariate measures

Local spatial autocorrelation

Regression and correlation

Spatial regression

Moving window regression (MWR)

Geographically weighted regression (GWR)

Other approaches

Case studies

Spatial autocorrelation analysis

GWR

Summary

Further reading

Spatial interpolation

Introduction

Spatial interpolation

Triangulated irregular networks

Regression for prediction

Inverse distance weighting

Thin plate splines

Ordinary kriging

Variogram

Kriging

Other approaches and other issues

Areal interpolation

Case studies

Variogram estimation

Spatial interpolation

Summary

Further reading

Analysis of grids and surfaces

Introduction

Map algebra

Image processing

Spatial filters

Derivatives of altitude

Other products derived from surfaces

Case study

Summary

Further reading

Summary

Review of key concepts

Approaches

Other issues

Problems

Where next?

Summary and conclusions

References

Matrix multiplication

Ordinary kriging system

Problems and solutions

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