INTRODUCTION TO SPATIAL INTRODUCTION TO SPATIAL ANALYSISANALYSIS

Four fundamental functions of GIS fall under the manipulation and analysis component (Martin, 1991):

1. Reclassification operations

2. Overlay operations

3. Distance and connectivity measurements

4. Neighbourhood characterisation

Will be discussed in the next chapter.

1. Reclassification operations

• transform the attribute information associated with a single map coverage. E.g.

• allow the “cause-and-effect” of certain spatial factors be evaluated. E.g.

* population densities classified into classes such

as 'sparsely populated' or 'overcrowded' etc.)

* soil types and farmland values

* generalising land use pattern

MAP

Example: the darker the more dense the state population

Re-classification Analysis - Association Between Land Value and Soil Types

URBANRESIDENTIAL

URBANINDUSTRIAL

RURALFOREST

RURALAGRICULTURE

URBAN

RURAL

ORIGINAL CLASSIFICATION GENERALIZED CLASSIFICATION

classification provides new classification provides new patterns/relationshipspatterns/relationships

Classification (cont.)

Classification:• Land parcels for housing are classed into

• single storey terrace, • double storey terrace and • bungalow.

Generalisation:• single storey terrace, double storey terrace

and bungalow are generalised under housing lots

2. Overlay operations

• involve the combination of two or more maps according to boolean conditions and may result in the delineation of new boundaries of housing market

NbrMean of residuals - 500m.shp-0.74 - -0.57 (greatest overestimation)

-0.57 - -0.35-0.35 - 00

0 - 0.350.35 - 0.570.57 - 0.78 (greatest underestimation)

No Data

Glasgow City Council - UKBORDERS.shp

Motorway12km.shp

4 0 4 Miles

N

EW

S

Ordnance Survey Crown Copyright. All rights reserved

An overlay of three layers of data

3. Distance and connectivity measurements

• include both simple measures of inter-point distance and more complex operations such as the construction of zones of increasing transport cost away from specified locations.

• Distance measurement can be used to calculate straight line and network distance.

• Includes perimeter and area measurements…

MEASUREMENTMEASUREMENT

DISTANCEDISTANCE

PARAMETERPARAMETER

AREA/SIZEAREA/SIZE

A

B

CD

X Y5 KM

A- B = 20 = 40%

B- C = 20 = 40%

C - D= 10 = 20%

2

10 km

DISTANCE (STRAIGHT LINE) MEASUREMENT

A- B: Alor Setar - Kuala Lumpur

360 KM

B- C: Kuala Lumpur - Kuantan

270 KM

TOTAL: 630 KM

1470.998

1765.63529.69

42.423.6

51.75

177.006

99.928

96.084

97.883

95.026

96.215

95.231

173.255

AREA MEASUREMENT

4. Neighbourhood characterisation

• involves ascribing values to location according

to characteristics of the surrounding region.

• Such operations may involve both summary and mean measures of a variable.

• This can be used to examine positive and negative spatial autocorrelation house price hedonic models.

Mean selling price (£ per sq km) .shp20500 - 4593945939 - 7137771377 - 9681696816 - 122255122255 - 147694147694 - 173132173132 - 198571198571 - 224010224010 - 249449No Data

Glasgow City Council - UKBORDERS.shpMotorway12km.shp

4 0 4 Miles

N

EW

S

Ordnance Survey Crown Copyright. All rights reserved

Neighbourhood analysis of mean selling prices within certain distance of a house

• Anselin (1998) proposes that GIS functions can be classified as follows

– Selection– Manipulation– Exploration– Confirmation

Other View of GIS Functions

GIS functions• Selection: involves boolean queries and spatial

sampling. This seems similar to the overlay operations function.

• Manipulation: may be based on attribute data, map data, or integration of both, simultaneously. This means analysing data in an integrated manner where various data as available in the database can be combined in an analysis.

• Exploration: for investigation of spatial structure and involves description and visualisation. This is relevant to spatial autocorrelation analysis of hedonic models using geo-statistical method

• Confirmation: for modelling spatial association and/or autocorrelation. This is also more relevant to spatial autocorrelation analysis using geostatistical method.

DATA RECALLDATA RECALL

• can be invoked on spatial and attribute

components

• involves selective search

• no new objects created

• examples:

* lots owned by foreigners

* lots along the substation buffer

LOTS OWNED BY FOREIGNERS

CLASSIFICATION AND GENERALISATIONCLASSIFICATION AND GENERALISATION

• classification - identify a set of characteristics to group together objects.

• in a vector system, classification involves addition of objects characteristics.

• in a raster system, classification involves converting or coding cell values.

• classification examples: Land parcels for housing are classed into single storey terras, double storey terras and bungalow.

• classification provides new patterns/relationships• generalisation: single storey terrace, double storey

terrace and bungalow are generalised under housing lots

Housing Age

Legend

Before 1900

1901-1930

1931-1950

1951 to 1999

Map showing classification of buildings according to age

URBANRESIDENTIAL

URBANINDUSTRIAL

RURALFOREST

RURALAGRICULTURE

URBAN

RURAL

ORIGINAL CLASSIFICATION GENERALIZED CLASSIFICATION

... ...

• vector data

– converting attribute values for polygon, line and point

• raster data

– converting attribute values of group cell

MEASUREMENT

• measurement functions includes

distance, parameter and area

• example: land parcels larger than 5

hectares

• example: shortest distance from KLCC to

Pudu bas station

MEASUREMENTMEASUREMENT

DISTANCEDISTANCE

PARAMETERPARAMETER

AREA/SIZEAREA/SIZE

A

B

CD

X Y5 KM

A- B = 20 = 40%

B- C = 20 = 40%

C - D= 10 = 20%

2

10 km

DISTANCE MEASUREMENT

A- B: Alor Setar - Kuala Lumpur

360 KM

B- C: Kuala Lumpur - Kuantan

270 KM

TOTAL: 630 KM

1470.998

1765.63529.69

42.423.6

51.75

177.006

99.928

96.084

97.883

95.026

96.215

95.231

173.255

AREA MEASUREMENT

... MEASUREMENT... MEASUREMENT

• vector data

– area and parameter is obtained from

coordinates of the polygon nodes

– distance is derived from coordinates

of starting/ending nodes

– is more accurate than raster data

SEARCHINGSEARCHING

• determine values against target object according to a neighbourhood characteristic

• three parameters need to be identified– targets– neighbourhood around the targets– applied neighbourhood function for resultant

values• example: total of households within 1 km of proposed

shopping mall– target-shopping mall– neighborhood-in the radius of 1 km– function-total residential units

... SPATIAL SEARCH... SPATIAL SEARCH

• operated as additional points in polygon, line in polygon and polygon in polygon

• vector data

– point, line or polygon analysed with neighbourhood polygon using coordinate nodes

– involves complex calculation with overlapping and out-of-boundary neighbourhood

• raster data

– perform as overlay operations

NEIGHBOURHOODNEIGHBOURHOOD

• represents ‘distance’ between map features

• ‘distance’ unit can be in measurement units or other units like travelling time, noise level, visibility distance etc.

• requires 4 parameters

– target location - schools, highways, etc.

– ‘distance’ units - meter, dB, ppm, etc.

– function for calculation on distance, perimeter, travel time

– location to be analysed

... NEIGHBOURHOOD... NEIGHBOURHOOD

• used to generate buffer zones

• example: a 2km zone along a proposed transmission line alignment; zones exceeds 50dB around the airport

• neighbourhood is most often complex and involves data from various layers. For example, more than 50dB from noisy roads AND more than 1km from factories AND 15 minutes walking time AND ...

R Buffering a Point

eg. All area within one mile of a city

Buffering a Line

eg. All areas within 100 meters of a road

Buffering an Area

eg. All areas within 500 meters of a wetlands area.

Buffering

OVERLAYOVERLAY

• Involves two or more data layers

• Produces new layers

• Two types of overlay operation

– arithmetic overlay

– logical overlay

• Arithmetic overlay involves mathematics operation such as addition, subtraction, multiplication, etc.

MAP ALGEBRA (MULTIPLICATION)MAP ALGEBRA (MULTIPLICATION)OVERLAY BY MULTIPLICATION

DISTRICT

1 2

3 4X

CROP AREA

1

B B=

1 2

3 4

OVERLAY BY MAXIMUM VALUE

3 3 4

0 1 0

2 4 6

4 2 25 5 54 1 1

4 3 45 5 54 4 6

RAINFALL : RAINFALL: RAINFALL:

1980 1981 1980 - 1981

+ =

OVERLAY

... OVERLAY... OVERLAY

• vector data are sometimes more efficient than raster data if data are not dense.

– vector data - operation based on the selected data only

– raster data - operation on all cells - even null values

Soil Type

+

Crops Production(ton/ha)

Overlay Result

GIS Technology: Relationship between Land use and Crop Productivity

Overlay Analysis

Jalan Raja Alang

Jalan Abdul Aziz

Jala

n D

atuk

Mal

ikJalan Hamzah

Jalan Raja Uda

Noise Zone Map

Sewerage pond

Sewerage pond

Jalan Raja Alang

Jalan Abdul Aziz

Jala

n D

atuk

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ikJalan Hamzah

Jalan Raja Uda

Area Map For Areas Outside Sewerage Services

Jalan Raja Alang

Jalan Abdul Aziz

Jalan Hamzah

Jalan Raja Uda

Pan MalaysianPlastic

Jala

n D

atuk

Mal

ik

Industrial Buffer Zone Map