Skip to main content

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

Popular posts from this blog

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

RADIOMETRIC CORRECTION

  Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

Radar Sensors in Remote Sensing

Radar sensors are active remote sensing instruments that use microwave radiation to detect and measure Earth's surface features. They transmit their own energy (radio waves) toward the Earth and record the backscattered signal that returns to the sensor. Since they do not depend on sunlight, radar systems can collect data: day or night through clouds, fog, smoke, and rain in all weather conditions This makes radar extremely useful for Earth observation. 1. Active Sensor A radar sensor produces and transmits its own microwaves. This is different from optical and thermal sensors, which depend on sunlight or emitted heat. 2. Microwave Region Radar operates in the microwave region of the electromagnetic spectrum , typically from 1 mm to 1 m wavelength. Common radar frequency bands: P-band (70 cm) L-band (23 cm) S-band (9 cm) C-band (5.6 cm) X-band (3 cm) Each band penetrates and interacts with surfaces differently: Lo...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...