Skip to main content

Model GIS object attribute entity

These concepts explain different ways of organizing, storing, and representing geographic information in a Geographic Information System (GIS). They include database design models (ER model), data structure models (Object and Attribute models), and spatio-temporal representations that integrate location, entities, and time. Together, they help GIS manage both spatial data (where things are) and descriptive information (what they are and how they change over time).

1. Object-Based Model (Object-Oriented Data Model)

The Object-Based Model treats geographic features as independent objects that combine spatial geometry and descriptive attributes within a single structure.

Core Concept:
Each geographic feature (such as a building, road, or river) is represented as a self-contained object that stores both:

  • Geometry – location and shape (point, line, polygon)

  • Attributes – descriptive properties (name, type, length, capacity)

Unlike older georelational models, which stored spatial data and attributes separately, object-based models integrate both into one system.

Key Characteristics

  • Encapsulation: Geometry and attributes are stored together in one object.
    Example: A "Fire Hydrant" object contains its coordinates along with properties like water pressure and color.

  • Behavior (Methods): Objects can perform automatic operations.
    Example: A "Parcel" object can automatically calculate its area when its boundary changes.

  • Inheritance: Objects can be organized in hierarchical classes.
    Example:

    • Parent class: Road

    • Child classes: Highway, Street, Footpath
      These inherit common properties but may also have specialized attributes.

This model is widely used in modern GIS and spatial databases such as object-relational GIS systems.

2. Attribute Model

The Attribute Model explains how non-spatial descriptive information about geographic features is stored and managed.

In GIS, spatial data answers "Where is the feature?", while attribute data answers "What is the feature?".

Structure

Attributes are stored in tables consisting of rows and columns:

  • Rows (Records): Represent individual geographic features

  • Columns (Fields): Represent characteristics of those features

Example

Lake_IDNameDepth (m)Salinity
101Lake A25Low
102Lake B40Medium

Relational Database Approach

Most GIS systems use the Relational Database Model, where tables are connected using a unique identifier (Primary Key).

Example:

  • Spatial layer: Cities

  • Attribute table: Population statistics

  • Linked by City_ID

Data Types

Attributes are stored using appropriate data formats such as:

  • Integer: population, number of houses

  • Float: rainfall, temperature

  • Text/String: place names

  • Date: time of observation

Thus, the attribute model provides structured storage and efficient querying of descriptive geographic data.

3. Entity–Relationship (ER) Model

The Entity–Relationship (ER) Model is a conceptual database design tool used before building a GIS database. It helps represent real-world geographic systems in a structured form.

An ER model is usually shown as a diagram consisting of three components.

Entities
Entities represent real-world objects or phenomena.

Examples:
Forest, River, Village, Ranger Station

Attributes
Attributes describe the characteristics of entities.

Example:
Forest → tree species, area, density

Relationships
Relationships define how entities are connected.

Example:
A River flows into a Lake

Purpose of the ER Model

  • Reduces data redundancy

  • Improves database organization

  • Supports complex spatial queries

Example query:
"Which Ranger Station manages a particular Forest Area?"

Thus, the ER model acts as a blueprint for designing efficient GIS databases.

Spatio-Temporal Representations in GIS

(Location–Entity–Time Triad)

Traditional GIS mainly represents static spatial information (a snapshot of space). However, many geographic phenomena change over time.

To address this, Donna Peuquet proposed the Spatio-Temporal Triad, which organizes geographic data using three perspectives:

  • Location

  • Entity

  • Time

These perspectives help answer where, what, and when questions in GIS.

4. Location-Based Representation

The Location-Based Representation organizes information according to fixed geographic positions.

Key Question:
"What information exists at this location over time?"

Structure

  • Often implemented using raster (grid) data models

  • Each cell represents a fixed location

  • The attribute value of that cell changes through time

Example

Monitoring soil moisture at coordinate (X,Y):

TimeSoil Moisture
Day 130%
Day 235%
Day 340%

Applications

  • Climate monitoring

  • Pollution distribution

  • Land cover change detection

  • Temperature mapping

This model is ideal for studying continuous spatial phenomena.

5. Entity-Based Representation

The Entity-Based Representation organizes data according to specific geographic features or objects.

Key Question:
"Where was this object at different times?"

Structure

  • Usually uses vector data models

  • Each entity has a unique ID

  • The system records how its location, shape, or attributes change over time

Example

Tracking Wildlife Migration

TimeLocation
Day 1Point A
Day 2Point B
Day 3Point C

Applications

  • Vehicle or fleet tracking

  • Wildlife movement studies

  • Hurricane path tracking

  • Urban boundary expansion

This model is best for discrete objects that move or change shape over time.

6. Time-Based Representation

The Time-Based Representation organizes geographic information according to temporal events or time sequences.

Key Question:
"What happened at this particular time?"

Structure

  • Data is arranged along a timeline

  • Each time point stores all spatial changes occurring at that moment

Example timeline:

YearEvent
2000New road constructed
2005Urban expansion
2010Flood event

Applications

  • Disaster analysis

  • Urban development history

  • Environmental change studies

  • Infrastructure development tracking

This representation is useful for event reconstruction and historical analysis.

In summary, these GIS concepts represent different ways of managing geographic data:

  • Object Model – integrates geometry, attributes, and behavior in spatial objects

  • Attribute Model – organizes descriptive data in relational tables

  • ER Model – designs database structure conceptually

  • Location Representation – studies changes at a fixed place

  • Entity Representation – tracks changes of a specific object

  • Time Representation – records events in chronological order

Together, they provide a comprehensive framework for representing spatial and temporal dynamics in modern GIS systems.


Comments

Post a Comment

Popular posts from this blog

Remote Sensing Technology

Remote sensing is a rapidly evolving geospatial technology used to collect information about the Earth's surface and atmosphere without direct physical contact . It involves detecting and measuring electromagnetic radiation (EMR) reflected or emitted from objects using sensors mounted on satellites, aircraft, or drones. Remote sensing systems are fundamentally classified based on (1) the energy source used for illumination and (2) the region of the electromagnetic spectrum utilized for sensing . 1. Types of Remote Sensing Based on Energy Source Remote sensing systems are commonly categorized according to whether the sensor generates its own energy or relies on naturally available radiation . Passive Remote Sensing Principle: Passive remote sensing relies on natural sources of electromagnetic energy , primarily solar radiation reflected from the Earth's surface or thermal radiation emitted by objects. Operation: Most passive sensors operate during daylight when sunlight is av...
Post a Comment
Read more

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...
Post a Comment
Read more

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...
Post a Comment
Read more

Spatial Entity and Spatial Object

Concepts Spatial Entity : Refers to any real-world feature or phenomenon that exists in a specific location and can be identified in space. This emphasizes the actual physical or conceptual presence of the feature. Spatial Object : Represents the digital or computational representation of a spatial entity within a Geographic Information System (GIS). This includes its geometry (e.g., points, lines, polygons) and associated attributes. Key Distinction : While the terms are often interchangeable, spatial entity tends to focus on the real-world phenomenon, whereas spatial object highlights its representation in GIS. Key Terminologies Geographic Coordinates : Define the location of spatial entities using a coordinate system (e.g., latitude and longitude). Example: A building at 40.748817° N, 73.985428° W . Geometry Types : Point : Represents a single location (e.g., a well or a bus stop). Line : Represents linear features (e.g., roads, rivers). Polyg...
Post a Comment
Read more

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl...
Post a Comment
Read more