GIS Topology Errors

GIS topology defines spatial relationships between geometric elements such as points, lines, and polygons. Ensuring correct topology is essential for accurate spatial analysis, as topology errors can lead to incorrect data interpretation and analysis results. Below are common topology errors with explanations and examples:

1. Loopbacks – Self-Intersections Anomaly

Concept:

Occurs when a single line or polygon boundary intersects itself, creating an invalid topology.
Often results from digitization errors or incorrect snapping settings.

Example:

A road network where a single road segment loops back on itself.
A river polyline that intersects itself, creating an incorrect junction.

2. Unclosed Polygons/Rings Anomaly

Concept:

Happens when a polygon's boundary is not fully closed, leaving a gap or break in the shape.
Common in digitization when the start and end points of a polygon do not connect.

Example:

A land parcel that is missing a boundary segment, causing errors in area calculations.

3. Internal Polygons with Incorrect Rotation Anomaly

Concept:

Some GIS systems use specific vertex orientations (clockwise or counterclockwise) to define polygon interiors.
If the rotation is incorrect, internal polygons may not be recognized properly.

Example:

An island inside a lake polygon that is misinterpreted due to incorrect rotation.

4. Duplicated Points Anomaly

Concept:

Occurs when multiple identical coordinate points exist at the same location unnecessarily.
May result from improper data import or redundant digitization.

Example:

A survey dataset with multiple identical GPS points for the same location.

5. Kickbacks Anomaly

Concept:

A line that suddenly changes direction and returns to nearly the same point, creating unnecessary bends or distortions.
Often results from digitization errors or poorly simplified data.

Example:

A road network with an unnatural sharp turn and return movement within a small distance.

6. Spikes Anomaly

Concept:

Spikes are unwanted protrusions on a polygon boundary or line due to inaccurate vertex placement.
Caused by errors in digitization or data generalization.

Example:

A building footprint polygon with a sharp, unintended triangular protrusion.

7. Small Areas (Polygon Smaller than a Specified Size) Anomaly

Concept:

Very small polygons that are below a defined threshold may indicate unnecessary features or data errors.
Often caused by incorrect digitization or unnecessary subdivision of polygons.

Example:

A land parcel dataset where tiny, unintended polygons appear due to errors in boundary delineation.

8. Slivers or Gaps Anomaly

Concept:

Narrow, unintended gaps between adjacent polygons caused by misalignment.
Typically occurs when datasets from different sources or scales are combined.

Example:

Land-use polygons that should be adjacent but have thin gaps due to coordinate misalignment.

9. Overlapping Polygons Anomaly

Concept:

Occurs when two or more polygons overlap in an area where only one should exist.
Can result from duplicate data entry or improper polygon snapping.

Example:

Two administrative boundaries overlapping when they should be adjacent.

10. Duplicate Polygons (Polygons with Identical Attributes) Anomaly

Concept:

When two or more polygons exist in the same location with the same attribute values.
Often results from redundant data import or dataset merging issues.

Example:

Two identical parcels of land recorded twice in a land registry database.

11. Short Segments Anomaly

Concept:

Line segments that are unnecessarily small and do not contribute to spatial accuracy.
Often caused by poor vectorization or excessive vertex density.

Example:

A road network with numerous tiny line segments instead of smooth curves.

12. Null Geometry - Table Records with Null Shape Anomaly

Concept:

When an attribute table contains records that lack corresponding geometric shapes.
Usually occurs due to incorrect data imports or missing spatial information.

Example:

A city boundary dataset with a record for a new district but no corresponding polygon.

13. Empty Parts (Geometry Has Multiple Parts and One is Empty) Anomaly

Concept:

A multi-part geometry that includes one or more empty components.
Typically results from incorrect spatial operations.

Example:

A river system represented as a multi-part line feature where one part contains no coordinates.

14. Inconsistent Polygon Boundary Node Anomaly

Concept:

Happens when polygons that should share boundaries do not properly align at their nodes.
Can cause visual gaps or errors in spatial analysis.

Example:

Two adjacent districts in a political boundary dataset that do not match perfectly at their borders

Comments

Linear Arrays Along-Track Scanners or Pushbroom Scanners

Multispectral Imaging Using Linear Arrays (Along-Track Scanners or Pushbroom Scanners) Multispectral Imaging: As previously defined, this involves capturing images using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. Linear Array of Detectors (A): This refers to a row of discrete detectors arranged in a straight line. Each detector is responsible for measuring the radiation within a specific wavelength band. Focal Plane (B): This is the plane where the image is formed by the lens system. It is the location where the detectors are placed to capture the focused image. Formed by Lens Systems (C): The lens system is responsible for collecting and focusing the incoming radiation onto the focal plane. It acts like a camera lens, creating a sharp image of the scene. Ground Resolution Cell (D): As previously defined, this is the smallest area on the ground that can be resolved by a remote sensing sensor. In the case of linear array scanne...

Discrete Detectors and Scanning mirrors Across the track scanner Whisk broom scanner.

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Across-Track Scanner or Whisk Broom Scanner) Multispectral Imaging: This technique involves capturing images of the Earth's surface using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. This allows for the identification of various features and materials based on their spectral signatures. Discrete Detectors: These are individual sensors that are arranged in a linear or array configuration. Each detector is responsible for measuring the radiation within a specific wavelength band. Scanning Mirrors: These are optical components that are used to deflect the incoming radiation onto the discrete detectors. By moving the mirrors, the sensor can scan across the scene, capturing data from different points. Across-Track Scanner or Whisk Broom Scanner: This refers to the scanning mechanism where the mirror moves perpendicular to the direction of flight. This allows for t...

Natural Disasters

A natural disaster is a catastrophic event caused by natural processes of the Earth that results in significant loss of life, property, and environmental resources. It occurs when a hazard (potentially damaging physical event) interacts with a vulnerable population and leads to disruption of normal life . Key terms: Hazard → A potential natural event (e.g., cyclone, earthquake). Disaster → When the hazard causes widespread damage due to vulnerability. Risk → Probability of harmful consequences from interaction of hazard and vulnerability. Vulnerability → Degree to which a community or system is exposed and unable to cope with the hazard. Resilience → Ability of a system or society to recover from the disaster impact. 👉 Example: An earthquake in an uninhabited desert is a hazard , but not a disaster unless people or infrastructure are affected. Types Natural disasters can be classified into geophysical, hydrological, meteorological, clim...

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

Trans-Himalayas

1. Location and Extent The Trans-Himalayas , also known as the Tibetan Himalayas , form the northernmost mountain system of India . Stretching in an east–west alignment , they run parallel to the Greater Himalayas , covering: Ladakh (Jammu & Kashmir, UT) Himachal Pradesh (north parts) Tibet (China) They mark the southern boundary of the Tibetan Plateau and act as a transition zone between the Indian Subcontinent and Central Asia . 2. Major Ranges within the Trans-Himalayas Karakoram Range World's second highest peak: K2 (8,611 m) . Contains Siachen Glacier and Baltoro Glacier . Geopolitical importance: forms part of India–Pakistan–China border. Ladakh Range Separates the Indus Valley from the Tibetan Plateau . Known for rugged barren mountains and cold desert conditions. Zanskar Range Lies south of the Ladakh Range, cut deeply by the Zanskar River . Famous for trekking and frozen river expeditions...

Vineesh V, Geography