Struktur Data

Spasial2012

Struktur DataMengapa penting?

Memahami keuntungan dan kerugian penggunaan suatu struktur data pada analisis

Suatu analisis umumnya dikaitkan pada suatu struktur data tertentu

Analisis boolean -> vektor

Model spasial -> lattice

Analisis Spasial MEMBUTUHKAN Data Spasial

1.Location information—a map

2.An attribute dataset: e.g population, rainfall

3.Links between the locations and the attributes

4.Spatial proximity information

Knowledge about relative spatial location

Topological information

Topology -- pengetahuan tentang posisi spasial relatifTopography -- bentuk permukaan tanah, khususnya elevasi

Berry’s Geographic Matrix

locationAttributes or variables

Variable 1 Variable 2 … Variable P

areal unit 1

areal unit 2...

areal unit n

locationAttributes or variables

Population Income … Variable P

areal unit 1

areal unit 2...

areal unit n

locationAttributes or Variables

Population Income … Variable P

HenanShanxi

.

.

.

areal unit n

time

geographicassociations

geographicdistribution geographic

fact

Berry, B.J.L 1964 Approaches to regional analysis: A synthesis . Annals of the Association of American Geographers, 54, pp. 2-11

2010

1990

2000

Admin_Name Admin_TypeCode_GB GMI_ADMINArea_km2Area_mi2 Area_prcnt_CHArea_prcnt_AllPop2008 PopDenKM2_03Anhui Province 340000 ANH 139400 53800 1.44 1.44 61350000 463.5Beijing City 110000 BJN 16808 6490 0.17 0.17 22000000 1309Chongqing City CQG 82300 31800 0.85 0.85 31442300 379Fujian Province 350000 FUJ 121400 46900 1.26 1.26 36040000 289.2Fujian, ROC ROC PNG 182.66 70.51 0.00 91261Gansu Province 620000 GAN 454000 175300 4.71 4.70 26281200 57.7Guangdong Province 440000 GND 177900 68700 1.84 1.84 95440000 467Guangxi Province_AR 450000 GNG 236700 91400 2.45 2.45 48160000 207Guizhou Province 520000 GUI 176100 68000 1.82 1.82 37927300 222Hainan Province 460000 HAI 33920 13100 0.35 0.35 8540000 241Hebei Province 130000 HEB 187700 72500 1.94 1.94 69888200 363Heilongjiang Province 230000 HLN 460000 177600 4.77 4.76 38253900 83Henan Province 410000 HEN 167000 64500 1.73 1.73 94290000 582Hong Kong SAR HKG 1104 422 0.011 0.01 7003700 6380Hubei Province 420000 HUB 185900 71800 1.93 1.92 57110000 324Hunan Province 430000 HUN 211800 81800 2.19 2.19 63800000 316Inner MongoliaProvince_AR 150000 NMN 1183000 456800 12.28 12.24 24137300 20.2Jiangsu Province 320000 JNS 102600 39600 1.06 1.06 76773000 724Jiangxi Province 360000 JNG 166900 64400 1.73 1.73 44000000 257Jilin Province 220000 JIL 187400 72400 1.94 1.94 27340000 145

1. Continuous (surface) data

2. Polygon (lattice) data

3. Point data

4. Network data

4 Tipe Data Spasial

1: Continuous (Surface) Data

Spatially continuous data attributes exist everywhere

There are an infinite number locations

But, attributes are usually only measured at a few locations There is a sample of point

measurements

e.g. precipitation, elevation

A surface is used to represent continuous data

2: Polygon (lattice) Data

Polygons completely covering the area*

Attributes exist and are measured at each location

Area can be:

• irregular (e.g. US state or China province boundaries)

• regular (e.g. remote sensing images in raster format)

*Polygons completely covering an area are called a lattice

3: Point Data

Point pattern The locations are the focus

In many cases, there is no attribute involved

4: Network Data

Attributes may measure the network itself (the roads)

Objects on the network (cars)

We often treat network objects as point data, which can cause serious errors

Crimes occur at addresses on networks, but we often treat them as points

Data Spasial Yang Akan Dipelajari ?

Point data(point pattern analysis: clustering and dispersion)

Polygon data* (polygon analysis: spatial autocorrelation and spatial regression)

Continuous data*

(Surface analysis: interpolation, trend surface analysis and kriging)

1: Analyzing Point Patserns (clusterirg and dispersion)2: Analyzing Polygons  (Spatial Autocorrelation and Spatial Regression models)3Surface analysis: nterpolation, trend surface analysis and kriging)

Mengkonversi dari satu jenis data

yang lain - sangat umum dalam

analisis spasial

Konversi Titik ke Data Kontinu :interpolasi

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Interpolasi

Menemukan nilai atribut di lokasi di mana ada data tidak ada, menggunakan lokasi dengan nilai data yang dikenal

Dasar :

• Value at known location

• Distance from known location

Metode

• Inverse Distance Weighting

• Kriging

Simple linear interpolation

Unknown

Known

Converting point ke polygons :using Thiessen polygons

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Thiessen or Proximity Polgons(also called Dirichlet or Voronoi Polygons)

Polygons created from a point layer

Each point has a polygon (and each polygon has one point)

lokasi manapun di dalam poligon lebih dekat ke titik tertutup daripada titik lain

Ruang dibagi ‘semerata' mungkin antara poligon

A

Thiessen or Proximity Polygons

How to create Thiessen Polygons

1. Connect point to its nearest (closest) neighbor

2. Draw perpendicular line at midpoint

3. Repeat for other points

4. Thiessen polygons

Converting polygon to point data using: Centroids

Centroid—the balancing point for a polygon

digunakan untuk menerapkan analisis titik pola data poligon

Using a polygon to represent a set of points: Convex Hull

polygon cembung terkecil dapat berisi satu set poin

• no concave angles pointing inward

A rubber band wrapped around a set of points

“kebalikan ” dari centroid

Convex hull often used to create the boundary of a study area

• a “buffer” zone often added

• Digunakan dalam analisis titik pola untuk memecahkan masalah batas

• Called a “guard zone”

No!

Models for Spatial Data:Raster and Vector

two alternative methods for representing spatial data

Entitas terkait dengan dimensi data Data titik (Point) : dimensi 0 (lokasi saja)

Data garis (Line) : dimensi 1 (lokasi dan panjang)

Data area (Poligon) : dimensi 2 (lokasi, panjang, lebar)

Data volume : dimensi 3 (lokasi, volume)

Kenampakan yang diletakkan pada DEM – dimensi 2.5

Adakalanya suatu objek dapat mempunyai sifat lebih dari satu dimensi tergantung skala data

2.5-D

Data Vektor

Titik

Garis

Poligon

node / vertex

Data

Vekt

or

Keuntungan: Efisien tempat

Scaling dapat dilakukan dengan ketajaman batas yang dipertahankan

Kelemahan: Kompleksitas

penyimpanan dan pengambilan data -> tidak cocok untuk pemodelan spasial murni

Data Lattice/Raster/Grid

Atribut :1: rumah2: sungai3: pohon4: rumput

Data

Rast

er

Buruk untuk menggambarkan data

geometrik atau kartografis

Data cenderung besar karena wilayah

yang ‘tidak’ perlu juga harus diisi

Mudah untuk proses pengoperasian,

umum dipakai dalam simulasi atau

pemodelan Kompatibel dengan data citra satelit

Cocok untuk data yang bersifat

kontinyu seperti tanah, elevasi,

temperatur, curah hujan dan erosi

tanah Posisi sel yang statis,

direpresentasikan sebagai matriks

(baris-kolom) dalam proses komputasi.

Umumnya bahasa pemrograman

mudah menangani variabel yang

bersifat array (matriks)

0 1 2 3 4 5 6 7 8 90 R T1 R T2 H R3 R4 R R5 R6 R T T H7 R T T8 R9 R

Real World

Vector RepresentationRaster Representation

Concept of Vector and Raster

line

polygon

point

27Briggs Henan University 2012

house

river

trees

Raster model

corn

wheat

fruit

clov

er

fruit

0 1 2 3 4 5 6 7 8 90123456789

1 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 52 2 2 2 2 2 2 3 3 32 2 2 2 2 2 2 3 3 32 2 2 2 2 2 2 3 3 32 2 4 4 2 2 2 3 3 32 2 4 4 2 2 2 3 3 3

Land use (or soil type)

186

21

Each cell (pixel) has a value between 0 and 255 (8 bits)

Image

Vector Model

point (node): 0-dimensions

single x,y coordinate pair

zero area

tree, oil well, location for label

line (arc): 1-dimension

two connected x,y coordinates

road, stream

A network is simply 2 or more connected lines

polygon : 2-dimensions

four or more ordered and connected x,y coordinates

first and last x,y pairs are the same

encloses an area

county, lake

1

2

7 8

.x=7

Point: 7,2y=2

Line: 7,2 8,1

Polygon: 7,2 8,1 7,1 7,2

1

2

7 8

1

2

1

1

2

7 8

Using raster and vector models to represent surfaces

Representing Surfaces with raster and vector models –3 ways

Contour lines Lines of equal surface value

Good for maps but not computers!

Digital elevation model (raster) raster cells record surface value

TIN (vector) Triangulated Irregular Network (TIN)

triangle vertices (corners) record surface value

Contour (isolines) Lines for surface representation

Advantages Easy to understand (for most people!)

Circle = hill top (or basin)

Downhill > = ridge Uphill < = valley (lembah) Closer lines = steeper slope

(curam)

Disadvantages Not good for computer representation

Lines difficult to store in computer

Contour lines of constant elevation- also called isolines (iso = equal)

Raster for surface representation

Each cell in the raster records the height (elevation) of the surface

Raster cells(Contain elevation values)

Surface

105

110

115

120

Raster cells with elevation valueContour lines

satu set segitiga (tidak overlap) yang dibentuk dari titik teratur

preferably, points are located at “significant” locations, bottom of valleys, tops of ridges

Each corner of the triangle (vertex) has: x, y horizontal coordinates

z vertical coordinate measuring elevation.

Triangulated Irregular Network (TIN):

Vector surface representation

Point # X Y Z1 10 30 1602 25 30 1503 30 25 1404 15 20 130

etc

valley

ridge

vertex

1 2

4 3

5

Using raster and vector models to represent polygons

(and points and lines)

Representing Polygons (and points and lines)

with raster and vector models

Raster model not good not accurate

Also a big challenge for the vector model but much more accurate

the solution to this challenge resulted in the modern GIS system

0 1 2 3 4 5 6 7 8 90123456789

1 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 51 1 1 1 1 4 4 5 5 52 2 2 2 2 2 2 3 3 32 2 2 2 2 2 2 3 3 32 2 2 2 2 2 2 3 3 32 2 4 4 2 2 2 3 3 32 2 4 4 2 2 2 3 3 3X

Using Raster Model for points, lines and polygons

- not good...!

37

Polygon boundary not accurate

Line not accurate

Point located at cell center--even if its not

Point “lost” if two points in one cell

For points

For lines and polygons

Using vector model to represent points, lines and polygons:

Node/Arc/Polygon Topology

The relationships between all spatial elements (points, lines, and polygons) defined by four concepts:

Node-ARC relationship:

• specifies which points (nodes) are connected to form arcs (lines)

Arc-Arc relationship

• specifies which arcs are connected to form networks

Polygon-Arc relationship

• defines polygons (areas) by specifying which arcs form their boundary

From-To relationship on all arcs

• Every arc has a direction from a node to a node

• This allows

This establishes left side and right side of an arc (e.g. street)

Also polygon on the left and polygon on the right for

every side of the polygon

LeftRight

from

to

from to

New

!

Node TableNode ID Easting Northing

1 126.5 578.12 218.6 581.93 224.2 470.44 129.1 471.9

Node Feature Attribute TableNode ID Control Crosswalk ADA?

1 light yes yes2 stop no no3 yield no no4 none yes no

Arc TableArc ID From N To N L Poly R PolyI 4 1 A34II 1 2 A34III 2 3 A35 A34IV 3 4 A34 Polygon Feature AttributeTable

Polygon ID Owner AddressA34 J. Smith 500 BirchA35 R. White 200 Main

Polygon TablePolygon ID Arc ListA34 I, II, III, IVA35 III, VI, VII, XI

Arc Feature Attribute TableArc ID Length Condition Lanes NameI 106 good 4II 92 poor 4 BirchIII 111 fair 2IV 95 fair 2 Cherry

Birch

Cherry

I

II

III

IV

1

4 3

Node/Arc/ Polygon and Attribute DataExample of computer implementation

Spatial DataAttribute Data

A35SmithEstateA34

2

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