Architecture M.Arch

Master in Architecture

(GIS)

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Module Detail

Subject Name M.Arch – Architecture

Paper Name GIS

Module Name/Title Data Structures - 6

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3. 2. Development Team

Role Name

National Coordinator

Subject Coordinator Dr. Monsingh D. Devadas Paper Coordinator Dr. Pratheep Moses Content Writer/Author (CW) Dr. S. Vijaysagar Content Reviewer (CR) Dr. Pratheep Moses Language Editor (LE)

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DATA STRUCTURES IN GIS – RASTER AND VECTOR DATA STRUCTURES

Introduction

Geographical data are referenced to locations on the earth’s surface by using a standard system of coordinates. Geographical data are often recognized and described in terms of well- established geographical ‘objects’, or phenomena. The topological and spatial aspects of geographical data processing is entirely different from other modern data handling in that the geographical data are complicated by the fact that they must include information about position, possible topological connections, and attributes of the objects recorded. All topological data can be reduced to three basic topological concepts – the point, the line, and the area. Every geographical phenomenon can thus be represented by a point, a line or as an area along with a label saying what it is.

Database structures: data organization in the computer

Before understanding how the graphical data is stored in a computer, we must understand the ways in which data can be organized in general. The essential features of any data storage system are that they should be able to allow data to be accessed and cross-referenced quickly. As there is no accepted ‘method’ to achieve the above objective that lot of labour that goes into database management systems, which are the computer programs that control data input, output, storage, and retrieval from a digital database.

Files and data access

Simple lists

The simplest form of database is a simple list of all the items. In this case, it is easy to add data to such a list but retrieval of data is inefficient. For a list containing n items, it takes an average (n+1) / 2 search operations to find the item you want. It is obvious, that a step is required to order or structure the data and to provide a key to that structure in order to speed up data retrieval.

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Simple or ordered sequential files will have only a key attribute data to retrieve the data, but in geographical information, the individual items (pixels, points, lines or areas) will not only have a key attribute such as an identification number or a name, but will also carry information about the associated attributes. For example, we have an ordered list of soil profiles that has been structured by soil series name, but we would like to retrieve information about soil depth, drainage, pH, texture or erosion. In such cases, indexed files are the best because access to the original data file can be speeded up in two ways. If the data items in the files themselves provide the main order of the file, then the files are known as direct files. The location of items in the main file can also be specified according to topic, which is given in a second file, known as an inverted file. In the direct file, the record for each item contains sufficient information for the search to jump over unnecessary items. Index files permit rapid access to databases. Unfortunately, they have inherent problems when used with files which records are continually being added or deleted such as often happens with interactive mapping systems. Addition or deletion of a record in a direct file that both the files and its index must be modified. When new records are written to a file accessed by an inverted file index, the new record does not have to be placed at a special position; it can be simply added to the end of the file, but the index must be updated. A disadvantage of indexed files is that very often data can only be accessed via the key contained in the indexed files; other information may only be retrievable using sequential search methods.

Database structures

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A database consists of data in many files. In order to be able to access data from one or more files easily, it is necessary to have some kind of structure or organization. Three main kinds of database structure are commonly recognized, termed hierarchical, network, and relational.

Hierarchical data structure

When the data have a parent/child or one-to-many relation, such as soil series within a soil family, or pixels within a region, hierarchical methods provide quick and convenient means of data access. Hierarchical systems assume that each part of the hierarchy can be reached using a key (a set of discriminating criteria) that fully describes the data structure.

Hierarchical systems assume that there is a good correlation between the key attributes (discriminating criteria) and the associated attributes that the items may possess.

Hierarchical systems have the advantage that they are easy to understand and they are easy to update and expand. Data access via the keys is easy for key attributes, but unfortunately is very difficult for associated attributes. Consequently, hierarchical systems are good for data retrieval if the structure of all possible queries can be known before hand. Further

disadvantages of hierarchical database structures are that large index files have to be

maintained and certain attribute values may have to be repeated many times, leading to data redundancy, which increase storage and access costs.

Network systems

In hierarchical systems, travel within the database is restricted to the paths up and down the taxonomic pathways. In many situations much more rapid linkage is required, particularly in data structures for graphics data where adjacent items in a map or figure need to be linked together even though the actual data about their coordinates may be written in very different parts of the data base.

Network systems fulfill this need.

Consider the following figure a which consists of a simple map of two polygons. The figure shows the map as it appears to the human brain; that is the two polygons are defined by a set of lines, one of which is common to both. The lines, in turn are defined by coordinate pairs, with each coordinate pair common to two lines. Clearly, a hierarchical data structure for the map would result in a clumsy representation involving much redundancy (figure (b e)). Each coordinate pair would have to be repeated twice, and coordinates 3 and 4 have to be repeated four times because line c has to be repeated twice. The structure is not only wasteful of space; it is clumsy, for if an operation were made to give polygons I and II the same name there is no easy way to suppress the display of line c, which would become unnecessary. These problems are avoided by the compact network structure shown in figure c in which each line and each coordinate need appear only once. With

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this structure it is a simple matter to suppress the printing of line c whenever it is referenced by polygons having the same.

(a) 2 b 3 e

5 a I c II f

Map M 4 g 6

1 d

b) 2 b 3 3 e

5

a I c c II f Polygons I and II 4 4 g 6

1 d

c) M

I II

a b c d e f g

1 2 3 4 5 6

Network linkages

d) M

I II

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a b c d e f g h Hierarchical data structure

1 2 2 3 3 4 4 1 3 4 3 5 5 6 6 4

Network systems are very useful when the relations or linkages can be specified beforehand.

They avoid data redundancy and make good use of available data. The disadvantages are that the database is enlarged by the overhead quite of the pointers, which in complex systems can become quite a substantial part of the database. These pointers must be updated/maintained every time a change is made to the database and the building and maintenance of pointer structures can be a considerable overhead for the database system.

Relational database structures

The relational database structures in its simplest form stores no pointers and has no hierarchy. Instead, the data are stored in simple records, known as tuples, containing an ordered set of attribute values that are grouped together in two-dimensional tables, known as relations. Each table or relation is usually a separate file. The pointer structures in network models and the keys in hierarchical structures are replaced by data redundancy in the form of identification codes that are used as unique keys to identify the records in each file as shown in the figure.

Map

M I II

Polygon

I a b c d

II c e f g

Lines

I a 1 2

I b 2 3

I c 3 4

II d 4 1

II e 3 5

II f 5 6

II g 6 4

II c 4 3

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A relational data structure for the map M.

Data are extracted from a relational database through a procedure in which the user defines the relation that is appropriate for the query. This relation is not necessarily already present in the existing files, so the controlling program uses the methods of relational algebra to construct the new tables.

Relational databases have the great advantage that their structure is very flexible and can meet the demands of all queries that can be formulated using the rules of Boolean logic and of mathematical operations. They allow different kinds of data to be searched, combined, and compared. Addition or removal of data is easy too, because this just involves adding or removing a tuple. The disadvantage of relational databases is that many of the operations involve sequential searches through the files to find the right data to satisfy the specified relations. This can involve a considerable amount of time with large databases, even on fast computers. Consequently commercial relational database systems have to be very skillfully designed in order to support the search capabilities with reasonable speed, which is why they are so expensive.

Perceived structures and computer representations of geographical data

The human eye is highly efficient at recognizing shapes and forms, but the computer needs to be instructed exactly how spatial patterns should be handled and displayed. Essentially there are two contrasting, but complementary ways of representing spatial data in the computer that we shall refer to as explicit and implicit ways of describing spatial entities.

The implicit representation requires fewer numbers, implying few storage spaces, to store the information about the chair (the vector representation uses 11 XY pairs and 14 connecting pointers and the raster representation uses 60 cells). Second, aesthetically more pleasing than the raster image - to produce an equivalent resolution the raster image would need to be based on a 0.5mm grid, thereby requiring 470 XY pairs. Third, the connectivity information allows directed spatial searches to be made over the chair. On the other hand, if the shape or size of the chair has to be changed, this can be done much quicker and easier in the raster representation than in the vector. In a raster representation data update merely involves deleting certain values and writing in new ones. In vector representation, not only must the coordinates be updated but the connectivity must also be rebuilt. we see that there are at least two fundamental ways of representing topological data which can, be summarized as follows:

Raster representation set of cells located by coordinates; each cell is independently addressed with the value of an attribute.

C

C C

C C C

C C C C

C C C C

C C C C

C C

C C

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C C C C C

C C C C C C

C C C C C C C

C C C C C C

C C C C C

C C C

C C C

C C

C A chair in (a) raster or grid cell and (b) vector format.

Vector representation - three main geographical entities, points, lines and areas; points are similar to cells, except they do not cover areas; lines and areas are sets of interconnected coordinates that can be linked to given attributes. Note that there is no necessary or unique connection between the raster or vector structure of the geographical database and the raster or vector structure of the devices used to display the data, although this is very often the case.

Raster data structures

The simplest raster data structures consist of an array of grid cells (sometimes termed pixels or picture elements). A row and column number references each grid cell and it contains a number representing the type or value of the attribute being mapped. In raster structures a point is represented by a single grid cell, a line by a number of neighbouring cells. This type of data structure is easy to handle in the computer, because of the ease with which arrays of rows and columns can be stored, manipulated, and displayed. This data structure also means that the two- dimensional surface upon which the geographical data are represented is not continuous, but quantized, which can have an important effect on the estimation of lengths and areas when grid cell sizes are large with respect to the features being represented. For example, the figure shows that the (Euclidean) distance between a and c could be 7 or 4 units depending on whether one counts cell edges or whole cells that must be traversed. The area of fig. 2.7(a) is 6 units2; the area of fig. 2.7 (b)is 7 units2.

Raster representation assumes that the geographical space can be treated as though it were a flat Cartesian surface. Each pixel of grid cell is then associated with a square parcel of land. The resolution, or scale of the raster data is then the relation between the cell size in the database and the size of the cell on the ground.

c 4/7 c

5 3 3 a 4 b a 4 b

Raster coding can affect estimates of distance and area because of the quantizing effect of the cells.

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Because each cell in a two-dimensional array can only hold one number, different geographical attributes must be represented by separate sets of Cartesian arrays known as 'overlays'.

z

y

x

Fig. 2.9 Three-dimensional arrays used for coding map overlays in faster database structures.

In its simplest form, the overlay concept is realized in raster data structures by stacking two- dimensional arrays. This results in a three-dimensional structure as shown in the above figure. The overlay concept is essentially equivalent to the 'picture function' in digital image processing, a 'data plane' in remote sensing or 'image-based' storage and it is fundamental to most raster image processing.

The different types of raster data structures are:

Run-length code Scan order

Prime order (Boustrophedon) Morton order

PI order Quadtree

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1 2 3 4 5 6 7 8 9

O O O O O O O O O

O O O O O O O O O

O O O V V O B B B

O O V V V B B B B

O O V V V B B B B

O O O O O O O O O

O O O O O O O O O

O O O O O O O O O

1) RUN LENGTH CODE

In this method the overall storage area is very less, as we omit the 'O's and it is coded according to rows and columns. Ex: A (4,4) dimension requires 16 bytes, but the data will be stored only in 11 bytes. The coding should always be from left to right and from top to bottom order, further a table created would contain the details of those cells which has an attribute value.

O O O O

O V V B

V V B B

B B V V

Row no. Column Start Column end Attribute

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2 3 3 4 4

2 4 1 3 1 3

3 4 2 4 2 4

V B V B B V

2) SCAN ORDER

This is similar to run length coding, but differs in that it usually starts from left hand corner (0,0) but it is not a rule.

1B, 3A, 3A, 1B, 3B, 1A, 2A, 2B.

(OR)

1B, 6A, 4B, 3A, 2B.

AGRICULTURE MAP

A A B B

B B B A

A A A B

B A A A

(0,0)

3) BOUSTROPHEDON -PRIME ORDER TECHNIQUE (As an ox ploughs) 2A, 3B, 1A, 2B, 2A, 1B, 1A

This is different from the above, in that instead of coding from L to R each time, we can use a pattern similar to the ox plough pattern.

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A B A

B B A

A B B

A A B

1. MORTON ORDER - WIDELY USED

Instead of labels A and B, we can also use a number which is called the Morton number.

The disadvantage is that this can only be applied for a square grid map.

A A B B

B B B A

A A A B

B A A A

For the above image number is 1B, 6A, 3B, 2A, 1B, 1A, 2B.

5) Pi order - used by Hilbert otherwise known as PI order

10 11 14

15

8 9 12 13

2 3 6 7

0 1 4 5

10 11 14 15

8 9 12 13

2 3 6 7

0 1 4 5

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ii. Morton technique for quad tree for numbering and retrieving iii. Single numbering technique used by Samet

Quad - one in four - 1/4 - dividing into 4 equal parts - Tree - arrangement of these in a tree like fashion.

1) DATA COMPRESSION

A scene size of 512 * 512 is divided into quadrants and in each quadrant of 256 * 256, the average of 4 pixels will be taken and replaced into another level and similarly it goes on to 128, 64, …… in a pyramid or tree like structure.

LEVEL 1 512 X 512 LEVEL II 256 X 256

A B

C D

A B

C D

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LEVEL III 128 X 128 LEVEL IV 64 X 64

The advantage is that the search time is reduced to 1/4 (one fourth). The search starts at the lowest level for a range of fonts, and if found it goes to the next higher level ……… and so on, but the disadvantage is that the storage area is needed more as we have to store similar data at different levels.

CHAIN CODE: Directions (Cardinal)

The base denotes the direction and the power denotes the number of pixels. The starting point 'A' may have to be defined say A (3,2), which can be any point.

A (3,1) – 0¹ 1¹ 0¹ 1² 2¹ 3¹ 2¹ 3¹

√ √ √

√ √ √ √

A √ √ ^{W (3)}

N (0)

E (1) A B

C D

A B

C D

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B and C are square blocks D, E, F are single pixels

√ √

√ √ √

Α Β √ √ √

D √ √ √

F C

A1, B4, C4, E1, F1

For A, B,C,D,E,F we have to define the row and column. The advantage is, you need not mention in name of the block every time.

II) MORTON TECHNIQUE: Similar technique as seen earlier

The aim is to Locate the Well

1) Using quad tree Morton technique, first divide it into 4 quadrants and represent the quadrants as

0, 1, 2, 3 as per Morton order.

1 2 3 4 5 6 7 8

O O O O O O O O

O O O O O O O O

O O O O O O O O

O O O O O O O O

O O W O O O O O

O O O O O O O O

O O O0 O O O O O

S (2)

1 2 3

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O O O O O O O O

O - Open space W - well

2) Divide the '0' 3) Consider the '03' quadrant, into quadrant, divide four parts into four quadrants

II. Proceed further until the object is reached by dividing in quad form.

III. Stop dividing further and the code for object 'w' is 033. In the system it will be stored as (w).

To display the well from the system is, to describe the Morton quad form.

Vector data structures for geographical entities

The vector representation of an object is an attempt to represent the object as exactly as possible. The coordinate space is assumed to be continuous, not quantized as with the raster space, allowing all positions, lengths, and dimensions to be defined precisely. In fact this is not exactly possible because of the length of a computer word on the exact representation of a coordinate and because all vector display devices have a basic step size, albeit very much smaller than the resolution of most raster devices. Besides the assumption of mathematically exact coordinates, vector methods of data storage use implicit relations that allow complex data to be stored in a minimum of space. There is no single, preferred method, however. This section explains a range of vector structures used in geographical information systems for the storage of points, lines, and areas.

Point entities

Point entities can be considered to embrace all geographical and graphical entities that are positioned by a single XY coordinate pair. Besides the XY coordinates, other data must be stored to indicate what kind of 'point' it is, and the other information associated with it. For example, a 'point' could be a symbol unrelated to any other information. The data record would have to include information about the symbol and the display size and orientation of the symbol. If the 'point' were a text entity, the data record would have to include information about the text characters to be

2' 3' 0' 1'

02' 03'

00' 01'

032'

033 '

030'

031 '

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displayed, the text font (style), the justification (right, left, centre), the scale, and the orientation, as well as ways of associating other non-graphic attributes with the 'point'.

Line entities

Line entities can be defined as all linear features built up of a straight line segments made up of two or more coordinates. The simplest line requires the storage of a begin point and an end point (two XY coordinate pairs) plus a possible record indicating the display symbol to be used. For example, the display symbol parameter could be used to call up solid or dashed lines on the display device even though all the segments of the dashed display had not been stored in the database.

An 'arc', a 'chain' or a 'string' is a set of n XY coordinates describing a continuous complex line. The shorter the line segments, and the larger the number of XY coordinate pairs, the closer the chain will approximate a complex curve. Data storage space can be saved at the expense of

processing time by storing a number that indicates that the display driver routines should fit a mathematical interpolation function to the stored coordinates when the line data are sent to the display device. As with 'points' and simple lines, chains can be stored with data records indicating the type of display line symbol to be used.

Networks

Simple lines and chains carry no inherent spatial information about connectivity such as might be required for drainage network analysis or for road and transport sites. To achieve a line network that can be traced by the computer from line to line it is necessary to build 'pointers' into the data structure. The pointer structure is often built up with the help of nodes. The following figure illustrates the sort of data structures that would be necessary to establish connectivity between all branches of a stream network. Besides carrying pointers to the chains, the nodes would probably also carry data records indicating the angle at which each chain joins the node, thereby fully defining the topology of the network. This simple linkage structure incorporates some data redundancy a total of (n X chains + 1) times, where n is the number of chains joining a node.

C15 C12 N7 C14 N6 C13

C11 C8 C9 N5 C10 N4

C6 C7 N3

C5 N - Intersecting Nodes C3 N2 C - Chains (poly lines) C2 C4

N1 C1

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Vector data structure of line networks using node to carry the connectivity information.

Area entities

Areas of polygons (sometimes called 'regions') can be represented in various ways in a vector database. Because most kinds of thematic mapping used in geographical information systems have to do with polygons, the way in which these entities can be represented and manipulated has received considerable attention.

The aim of a polygon data structure is to be able to describe the topological properties of areas (that is their shapes, neighbours, and hierarchy) in such a way that the associated properties of these basic spatial building blocks can be displayed and manipulated as thematic map data. Before describing the ways in which a polygon data structure can be constructed it would be as well to state the requirements of polygon networks that geographical data impose.

First, each component polygon (or region) on a map will have a unique shape, perimeter, and area. There is no single standard basic unit as is the case in raster systems. Even for the most regular or regularly laid out American street plan it will be unwise to assume that all or even some of the blocks have exactly the same shape and size is clearly most unlikely. Second, geographical analyses require that the data structure be able to record the neighbours of each polygon in the same way that the stream network required connectivity. Third, polygons on thematic maps are not all at the same level islands occur in lakes that are themselves on large islands, and so on.

VECTOR DATA STRUCTURES I Point entity

A Well, a landmark, a lamp post etc, elevation of a point.

POINT

- Unique identifier [point type, serial number]

- x,y co-ordinates (to represent spatial position)

- non graphic attributed - 1)used for display II) like a quantity (pH, porosity)

Unique Identifier:

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101 102 103 104

a) Single point - symbol, well lamppost etc.

b) Text - Point with a label

c) Node - intersection /junction point /terminal point used for network Analysis.

ii) Serial no. - plot nos., a road network.

Non-graphic attributes:

i. Symbol

a) Scale (size) b) Orientation ii. Text

a) Charnockite size (6mm,10mm etc) b) Font size (Roman, Greek etc) c) Orientation

d) Justification

iii Node

(a) No. of incoming & outgoing lines (b) Pointer

(c) Angle net node (some cases L, 1, 2)

II Line Entity (a) Segment (b) String

 Adyar

Mount road

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(d) Chain

i) Single segment - set of x, y co-ordinates (x1y1, x2,y2) ii)

iii) String - (x1y1, x2y2……….xnyn)

iv) Curve - Spline formula it will calculate the different position

v) Chain - It should be node; incoming and outgoing lines

III Network

Ex: Stream Network

1 is not a segment with link, since in a network all the lines are linked, with nodes such as a, b, c, d ………..

A Table is generated as shown below:

Table 1 Table-2 Table-3 or or

Link

Node

1 a

2 a

3 b

4 -

5 -

- - -

- - - -

Node Link

a 4 b 5

- -

- -

- -

Link Node

1 4

2 4

3 5

4 5

5 7

6 7

7 9

8 9

3 4

2 6

c 5 7 8 d 9

b

1 a

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III Area entity: The different structures under this category are i. Simple polygon structure

ii. Point dictionary structure iii. Edge dictionary structure iv. Arc node structure

v. DIME structure

vi. Relational data Base RDB) structure vii. Digital line graph (DIG) structure

viii. TIGER-topological integrated geographical reference and coding

(i) SIMPLE POLYGON STRUCTURE

Sliver over sheet

Gap -undershoot

In the above figure, we find three polygons viz., vegetation, soil and built-up land, consider digitizing the polygons form one point, start digitizing first polygon say vegetation and after completing it we can digitize the second polygon say soil, while digitizing soil you will find a common boundary which may have to be digitized again leading to an error. Similarly, when the third polygon is digitized the common boundary of the first two polygons is also digitized again.

The digitization of common boundary two or three times would lead to problems like gap and sliver.

(ii) POINT DICTIONARY TYPE:

In this technique, the common boundary is digitized only once, and a point dictionary is generated as shown below:

P1 P2

14 15

16 3 13

4

5

11 6

2

P 1 1

P 1

12

Built up Soil Ve

g I

III II

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Name of polygon Points

P1 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12

P2 5,13, 14, 15, 16, 17, 18, 19, 6, 5,

Though the digitizing is done only once, each polygon will have repetition of co-ordinates leading to redundancy of data.

iii) Edge dictionary type

P1 a b c d e f g h P2 I j k l m n o e d

I

J K

L N M

A

B C

D

E G F

H

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In this method instead of storing the digitized points we can store the edges A, B, C etc.

and each will be having a co-ordinate, and these co-ordinates will be stored in a co-ordinate table, and only the edges will be stored in the polygon table. However, a similar drawback of redundancy of will occur.

iv) ARC NODE STRUCTURE

In this method, three tables generated for the polygon shown above, first a Node table showing the number of the node, followed by the coordinates, and attribute data if any.

Later, an arc table is generated indicating the details of the nodes enclosing the arcs (lines), and finally a polygon table is generated detailing the arcs, area and perimeter.

I Node table (Table)

Number Co-ordinates Attribute

(Traffic signal) Sign bond Lamps Easting Northing

1 2 3 4

110.50 : : :

36.35 : : :

Yes No

: :

Yes No

: :

No Yes

: :

II Arc Table & Polygon Table

Name From

Nodes

To Nodes

Length Road Met.

No. of Lanes

Name Arcs Area Perimeter

I II III IV

4 1 2 3

1 2 3 4

38.07 : : :

Com A I II III IV 3567 All

lengths of arcs

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So three tables are generated-node, arc and polygon and most of the GIS packages use this structure. However, in ARC-INFO, we use a similar structure with a slight modification.

(v) DIME (Dual Independent Map Encoding (DIME)

The US Bureau of Census has developed this method. In this method only a node segment structure exist.

Table 1: Directory segments

Record # Zip code Polygon Record

1000 8695 572 10

Table II: Segment details

Name (Line) Nodes Polygon Address

Record

From To Left Right Left Right

Mount road Street 1

4 1

1 2

0 0

Block 1 Block 1

0 0

1-200

1-50 -

Table III: Node details

Node No. Easting Northing

1 2 3 4

108.52 - - -

87.65 - - - (vi) RELATIONAL DATABASE* STRUCTURE

Record D1 (Name) D2 (pH) D3 (SAR) D4(DIAMETER)

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Record 2

Sand Clay

4 7

0.87 0.41

4mm 0.1mm

Retrieval of data is based on a query. Hence called a dbase. This is a simple method of query. But relational data is complex phenomenon. For the same record simple fields such as pH determination is easy but the additional data such as bearing capacity of each of the soil in a

different table is not possible, which then may have to be linked through a common field to the first

table (pointer), to retrieve the data.

"Relational data is linking though a common field for retrieval". The common field is also called as "pointer".

Ex. Traffic details

Node Table Attributes

Line Node

No.of lanes

From To

I II III

4 1

1 2

11 2

Line Length Type No.of lanes

Name Easting Northing

1 - - 2 - - 3 - - 4 - -

4

Name Signals Lamp post

1 2 3 4

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II

380 180

Concrete Tar

11 2

Polygon Table Attribute Table

(vii) Digital line Graph (DLG)

Four layers 1. Boundary - Town, Village etc.

2. Hydrographic details - stream, drainage etc.

3. Road network - road, rail etc.

4. Land Survey details - Parcel no. (Survey no. ownership) Organisation is different - it is used in SOI. Give a code for a major class called major code.

Major code Category

20 Hydrography (Contour details)

50 Hydrography (Stream, well etc)

70 Land surface

followed by a minor code

0-99 nodes - points

100 -199 Area - lines 200 - 299 Lines - Areas Ex. 100 - Road

101 – Settlement 102 - Stream 1 103 - Stream 2

Area No. of roads Remarks

A 25 4 lane traffic -

none

Area Area Survey No.

A Mylapore 143 to 165

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To code - the stream - 50102 is followed by locations (x,y).

Hence each point is coded by a number.

(Viii) TIGER (Topological Integrated Geographic Encoded Reference)

Both DLG & DIME are included in this method of structuring. Data can be obtained in 1:25,000 scale from SOI on floppies, cartridges etc.

Topology - study of science of shape, which gives you the relation between vertices edges, faces of an object. Main aim in vector data structure is building Topology, a simple polygon has no topology. Arc node topology exists, even in DIME and DLG topology exists, i.e. when you establish a link between point line and an area topology exists. Only for vector data topology is there not for raster data.