In this article, we will discuss how data is represented using vector data model and what are different types of vector data models and vector data structures.

Data model is a conceptual model which is used for representing real world data. Data model helps in visualizing and understanding about an object through different types of analysis. Data in GIS is represented mainly using 2 different types of data models. 1. Vector Data Model and 2. Raster Data Model

Each model has its unique way in representing the data. Each data model in GIS also comes with its own advantages and disadvantages while representing the data.

In this current article, we will discuss in detail about what is vector data model, how data is represented using vector data model and different types of vector data structures.

Related articles: **Components of GIS**

**6 Important Characteristics of Data**

## What is Vector Data Model?

It is a data model which is used in Geographical Information System (GIS) for representing real world data. A vector data model makes use of points, lines and polygon features for storing and representing geographical data in a GIS system.

## Data Representation in Vector Data Model:

As already stated, vector data model consists of 3 different types of features i.e., points, lines and polygons to represent data.

### Point Features:

Point features are normally used for representing single and discrete objects in a map. Points are depicted using a pair of coordinates (X, Y) in map. They convey the location detail of a given feature.

Objects such as wells, poles, buildings, villages, towns etc., may be represented using point features.

How ever, one should understand that same object or feature such as a village in the map may be shown as a point or polygon depending on the scale used for creating the map.

### Line Features:

Linear features such as roads, streams, fault lines etc., are represented using lines. Lines are formed by connected set of points (2 or more). Lines are one dimensional features and have the property of length.

### Polygon Features:

Enclosed phenomena or objects with boundary such as villages, mandals, districts etc., are represented using polygon features in GIS. Polygons are created by joining different line segments in such a way the connected segments form a closed figure.

In a polygon feature, coordinates of the first point of starting line, and last coordinates of the closing line are same.

Polygons have the property of location, perimeter and area.

## What type of Data Can be Represented Using Vector Data Model?

Vector data model is best suitable for representing discrete objects and phenomena such as bore wells, district boundaries, roads etc. These features such as bore wells, roads etc., are present over a finite space in reality. Using points, lines and polygons all such features can be represented very precisely and accurately.

However, continuous nature features such as temperature, rainfall, humidity etc., are not best described in a vector data model.

## Different types of Vector Data Structure:

### Spaghetti Data Model:

Spaghetti data model is the simplest vector data structure.

In spaghetti data structure, all vector features such as points, lines and polygons are represented using of X, Y coordinates (single or many depending on feature). All features are called as spaghettis, and complex spaghettis are formed by integration of simple spaghetti such as point spaghetti features.

In this data structure model, even if some features (entities) are sharing common geometry, they have to formed by their own spaghettis. For example, if A and B are 2 adjacent polygons in a map, if spaghetti model is used for constructing the map, both A and B are drawn using their own spaghettis.

### Disadvantages of Spaghetti Data Model:

Above structure creates data redundancy as common features are stored multiple times along the shared geometry. Further, this type of structure doesn’t allow implicit relationships among the features. Hence, topology is not present or weakly present in the spaghetti data structure.

### Advantages of Spaghetti Data Model:

Though few disadvantages are present, it is most simple model to construct and understand. If user is interested in creating only visual maps and is not worried about the spatial relationship and analysis, spaghetti model is most easy to construct the map.

### Topological Data Model:

Topological model ensures topology is maintained among the features in vector data sets. Topology in a given data set is explained using following 3 types of topological relationships or concepts.

- Connectivity
- Contiguity
- Containment

### Connectivity:

Connectivity between features in a vector data model indicates presence of arc-node topology.

As per arc-node topology, arcs/lines in a network are said to be connected provided if they share a common node. Each arc consists of a from node and to node, this also indicates direction of arc. Nodes indicate starting and ending points of arcs and also intersection points of arcs or line segments.

If 2 arcs or line segments are sharing a node, they are said to be connected.

For example, In case of road network in a GIS, moving from one road to another is possible only at the nodes. If common node is not available even if 2 roads intersect, movement is not possible from one road to another.

### Contiguity:

Adjacency related relationships in a vector data set are defined using contiguity.

It is also called as polygon topology. 2 polygons are said to adjacent or contiguous only if they share a common arc. As mentioned in the arc-node topology, a node contains information related to direction (obtained using from and to nodes), it helps in identifying left and right side features in a data set.

### Containment:

All features in the real world are not that simple to analyze. Some features are difficult to represent graphically, and some may also pose difficulties while processing.

For example, representing a group of islands such as Nicobar or Lake with an island at the center etc.

Lake with an island at center is a classic example for understanding containment.

Containment explains relationship of containing i.e., one feature is present inside another feature. Such relationship is explained using polygon-arc topology.

Here outer polygon is represented using a string of arcs, while contained polygon is represented using a single arc.

The arcs are represented using standard procedures specified as per topological model in GIS.

**Related Video:**

**For more understanding and reading following articles are suggested:**

1. NPTEL — Link

2. Saylor Blog – Link

**Related Presentation:**

The shared presentations may not contain complete information of vector data structures and readers are advised to proceed with their own discretion.

[slideshare id=76693699&doc=datamodelsingisfinal-170606134708]