Geovisualization

Geographic visualization (geovisualization) is the process of visually representing spatial data to facilitate understanding, analysis, and decision-making. It combines techniques from cartography, computer graphics, and geospatial analysis to explore both observational and simulated datasets.

Geospatial Data – Data that is associated with a specific location on Earth's surface. It can be in vector (points, lines, polygons) or raster (gridded) format.
Cartography – The art and science of map-making, which plays a crucial role in geovisualization.
Spatial Analysis – The process of examining the locations, attributes, and relationships of geographic features.
Scale and Resolution – The level of detail in a geospatial representation, affecting the accuracy and usability of the visualization.
Geospatial Information System (GIS) – A system designed to capture, store, analyze, and visualize geographic data.

Geovisualization leverages different mapping techniques to represent geographic patterns, trends, and relationships. It helps in:

Displaying spatial patterns (e.g., population distribution, climate change, or land use changes).
Analyzing observational and simulated datasets to derive meaningful insights (e.g., predicting traffic congestion or environmental changes).
Understanding Earth's surface and solid Earth processes such as plate tectonics, weather phenomena, and landform changes.

Geovisualization Techniques

Dot Density Map – Represents individual occurrences with dots, commonly used to show clustering of disease cases or crime incidents.
- Example: A COVID-19 dot density map showing infection hotspots in a city.
Heat Map – Uses color gradients to represent intensity or density of a phenomenon.
- Example: A weather heat map indicating temperature variations across a region.
Hexagonal Binning Map – Divides an area into hexagons, each colored based on data density.
- Example: A hexagonal binning map showing air pollution levels in an urban area.
Network Models – Represents connections between locations, used in transport, logistics, and urban planning.
- Example: A transportation network model visualizing traffic flow in a city.

Techniques

1D, 2D, and 3D Visualization
- 1D: Timeline graphs for temporal geospatial data.
- 2D: Flat maps with color-coded attributes.
- 3D: Terrain models, cityscape visualizations.
Icon-Based Visualization – Uses icons or symbols to represent different geographic elements.
- Example: Earthquake epicenters marked with different-sized circles indicating magnitude.
Geometrically Transformed Displays – Distorts the map to highlight certain features.
- Example: Cartograms, where country sizes are adjusted based on population.
Pixel-Oriented Displays – Uses pixel colors to encode data values, useful for high-resolution imagery.
- Example: Satellite images showing vegetation cover using NDVI.
Graph or Hierarchy-Based Visualization – Uses network graphs and tree structures to represent relationships.
- Example: A spatial hierarchy graph showing city, district, and neighborhood relationships.

Comments

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?" Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes . Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc. Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

Development and scope of Environmental Geography and Recent concepts in environmental Geography

Environmental Geography studies the relationship between humans and nature in a spatial (place-based) way. It combines Physical Geography (natural processes) and Human Geography (human activities). A. Early Stage 🔹 Environmental Determinism Concept: Nature controls human life. Meaning: Climate, landforms, and soil decide how people live. Example: People in deserts (like Sahara Desert) live differently from people in fertile river valleys. 🔹 Possibilism Concept: Humans can modify nature. Meaning: Environment gives options, but humans make choices. Example: In dry areas like Rajasthan, people use irrigation to grow crops. 👉 In this stage, geography was mostly descriptive (explaining what exists). B. Evolution Stage (Mid-20th Century) Environmental problems increased due to: Industrialization Urbanization Deforestation Pollution Geographers started studying: Environmental degradation Resource management Human impact on ecosystems The field became analytical and problem-solving...

Geographic phenomena fields objects boundaries.

In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields , objects , and boundaries . Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS). 1. Fields A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area. Characteristics : Continuity : Fields have no discrete boundaries; the data is continuous. Gradual Variability : The values of a field change gradually across space. Representation : Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell. Examples : Temperature Map : Shows temperature variation across a region. Rainfall Distribution : Displays rainfall levels over a large g...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination. Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

Vineesh V, Geography

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