Skip to main content

Graduated Symbol with Quantile Classification

Graduated Symbol with Quantile Classification

Geographical data visualization plays a crucial role in GIS-based research, helping to reveal spatial patterns and distributions. One such method is the Graduated Symbol Map with Quantile Classification, which combines statistical categorization with symbolic representation for effective data interpretation.

1. The Concept of Graduated Symbols

Graduated symbols in GIS are proportional representations of numerical data assigned to geographical features. The size of each symbol changes according to the magnitude of the associated data attribute. This technique is commonly used for:

  • Visualizing variation in spatial datasets (e.g., crime rates, GDP, population density).
  • Highlighting relative differences rather than absolute values.
  • Avoiding misinterpretation often caused by color-based representations in choropleth maps.

For instance, in a crime rate map, cities with higher crime rates would be represented with larger circles, while those with lower crime rates would have smaller circles.

2. Quantile Data Classification: Statistical Basis

Quantile classification is a statistical approach that divides data into equal-sized groups. If the data is divided into four groups (quartiles), each class contains 25% of the total observations.

Mathematical Explanation

Given a dataset D with n observations, a quantile classification finds the k-th percentile (Qk) by:

Qk=X(k×n)Q_k = X_{(k \times n)}

where:

  • kk is the quantile (e.g., 0.25 for the first quartile, 0.50 for the median, etc.).
  • X(k×n)X_{(k \times n)} is the value at the respective position when data is sorted.

Example Dataset

CityCrime Rate (per 100,000 people)
A125
B200
C350
D450
E500
F750
G800
H950

Sorting the data:

125,200,350,450,500,750,800,950125, 200, 350, 450, 500, 750, 800, 950

For quartile-based classification (4 groups):

  • Q1 (25%) → 287.5 (between 200 and 350)
  • Q2 (50%) → 475 (between 450 and 500)
  • Q3 (75%) → 775 (between 750 and 800)

Thus, the class intervals would be:

  1. 125 - 287.5 (Smallest symbols)
  2. 287.6 - 475
  3. 476 - 775
  4. 776 - 950 (Largest symbols)

3. Analytical Benefits and Drawbacks

Benefits

  1. Uniform Distribution of Data in Classes

    • Ensures each class contains an equal number of data points.
    • Helps in avoiding class imbalance that can occur in natural breaks or standard deviation-based classification.

  2. Better Visualization for Skewed Data

    • If the data distribution is highly skewed (i.e., clustered towards one end), quantile classification ensures all data ranges are equally represented.
    • Helps in highlighting contrasts even in small differences.

  3. Easier Interpretation

    • Since each class contains an equal number of data points, comparison across different regions is straightforward.

Drawbacks

  1. Artificial Grouping of Data

    • In cases where the data is not evenly distributed, boundaries might not represent real-world differences.
    • For example, two cities with crime rates of 799 and 801 might be placed in separate categories, creating an artificial break.

  2. Size Misrepresentation in Graduated Symbols

    • If values in a category vary significantly, symbol sizes might exaggerate or understate real differences.
    • For instance, a city with a crime rate of 500 would receive the same symbol size as another with 750, despite a notable difference.

4. Applied Example in GIS

If applying this technique in ArcGIS, QGIS, or Google Earth Engine, the workflow would be:

  1. Data Collection: Import the geospatial dataset (e.g., crime rates, population density).
  2. Sorting and Classification: Use quantile classification to divide the dataset into equal-size groups.
  3. Symbol Scaling: Assign graduated symbols (e.g., circle size increases with crime rate).
  4. Map Interpretation: Analyze spatial distribution and identify hotspots or patterns.


Implementing Graduated Symbols with Quantile Classification in ArcGIS

ArcGIS allows you to apply graduated symbols and classify data using quantiles for effective spatial analysis. Below is a step-by-step guide to implementing this technique.

Step 1: Load the Data

  1. Open ArcGIS Pro or ArcMap.
  2. Click Add Data → Select the shapefile or geodatabase feature class that contains your spatial data (e.g., crime rates, population).
  3. Ensure your dataset includes a numerical field for classification (e.g., "Crime Rate per 100,000 people").

Step 2: Open the Symbology Panel

  1. Right-click on the layer in the Table of Contents.
  2. Select Symbology.
  3. Choose Graduated Symbols.

Step 3: Configure the Classification

  1. In the Symbology tab:
    • Choose the Value Field (e.g., "Crime Rate").
    • Set Normalization (optional, e.g., dividing crime counts by population size).
  2. Under Classification, select Quantile (Equal Count).
  3. Set the number of classes (e.g., 4 for quartiles, 5 for quintiles).
  4. Click Classify to generate class breaks.

Step 4: Customize Symbol Sizes

  1. Adjust the minimum and maximum symbol sizes for clear differentiation.
  2. Use proportional scaling to ensure readability.
  3. Optionally, choose circle, square, or other symbols to best represent the data.

Step 5: Finalize and Export

  1. Click Apply to preview the changes.
  2. Click OK to finalize the symbology.
  3. To export the map:
    • Go to Layout View.
    • Add a Legend, Title, Scale Bar, and North Arrow.
    • Export as PDF, PNG, or GeoTIFF.

Example Use Case: Crime Rate Mapping

  • Dataset: Crime rates in different districts.
  • Classification: Quantile (4 classes)
    • 0–250 crimes: Smallest symbol
    • 251–500 crimes: Medium symbol
    • 501–750 crimes: Large symbol
    • 751+ crimes: Largest symbol
  • Output: A clear spatial pattern showing high-crime areas.






Quantile Classification






The Graduated Symbol with Quantile Classification is a powerful GIS visualization tool that balances spatial representation with statistical fairness. It ensures that all areas receive equal emphasis, which is useful in urban planning, socio-economic studies, and environmental monitoring. However, careful interpretation is required to avoid artificial class separations and misrepresentation due to symbol scaling.

Comments

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...
Post a Comment
Read more

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...
Post a Comment
Read more