Usage of Data Classification

Data classification is a crucial step in the analysis and visualization of geographic data. GeoPard offers several classification methods to help users effectively understand and interpret their data. Three commonly used data classification types in GeoPard are Natural Breaks, Equal Interval, and Equal Count (Area). Each classification method has its unique characteristics and application scenarios, as described below:

1. Natural Breaks Classification

The Natural Breaks classification identifies "natural" thresholds or breakpoints in the data distribution to create distinct groups. It maximizes differences between classes and minimizes differences within each class. Natural Breaks is useful for data with clear patterns or clusters, allowing effective exploration and analysis.

Natural Breaks Classification

2. Equal Interval Classification

The Equal Interval classification divides the data range into equal intervals or bins. It provides a balanced representation of data distribution, making it easy to interpret and compare values within each interval. Equal Interval is suitable for evenly distributed data without distinct patterns.

Equal Interval Classificaiton

3. Equal Count (Area) Classification

The Equal Count classification ensures an equal number of data values in each class. It maintains a balanced representation, especially for skewed or unevenly distributed data. Equal Count enables fair comparisons between areas or regions, providing consistent analysis and visualization.

The goal is to create zones with relatively similar area sizes, but rounding operations and zone quality enhancements may introduce slight variations. Therefore, using vegetation indexes with higher granularity, such as EVI2, MCARI1, or WDRVI, results in more precise outcomes. And the final geometries of the zones are fine-tuned to improve accuracy.

Equal Count (Area) Classification

4. Spatially Localized Classification

The Spatially Localized classification clusters data geospatially, creating localized zones. Its primary use case is planning Zones for Soil Sampling, enabling efficient segmentation of Fields into manageable areas.

To offer greater flexibility, the Spatial Localized classification includes three distinct options: towards Spatial, towards Values, and Balanced, allowing you to customize the clustering process based on specific needs.

4.1. Balanced Option of Spatially Localized

The Balanced option of Spatially Localized Classification provides a middle ground between the toward Spatial and towards Values options. It creates a ZonesMap with clusters that achieve a balance between geographic proximity and data value similarity. This approach is useful when both spatial compactness and data consistency are important, offering a well-rounded solution for most general use cases.

Spatially Localized Classification (Balanced Option)

4.2. Towards Values of Spatially Localized

Towards Values option of Spatially Localized Classfication, in contrast, produces zones that are clustered based on data values rather than geographic proximity. This option groups areas with similar data attributes, such as vegetation or soil quality, to create a ZonesMap where the primary focus is on data consistency within each zone. This is best suited for use cases where the uniformity of the data within zones is more critical than their spatial arrangement.

Spatially Localized Classification (towards Values Option)

4.3. Towards Spatial of Spatially Localized

Towards Spatial option of Spatially Localized Classification focuses on creating zones that are more geographically concentrated. This produces a ZonesMap with clusters that prioritize proximity, ensuring that each zone is spatially compact. It is ideal for applications where the physical location of the zones is the primary concern, such as logistics or spatial-based sampling.

Spatially Localized Classification (towards Spatial Option)