introduction to spatial analysis. four fundamental functions of gis fall under the manipulation and...
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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
Mal
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