# 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. Common options in GeoPard are AUTO classification, Natural Breaks, Equal Interval, Equal Count (Area), and Spatially Localized classification. Each method fits a different use case, as described below: ## AUTO classification Auto classification selects an appropriate classification approach **based on the data distribution and zones areas**. It helps you get to a usable Zones Map faster, with less trial-and-error when comparing classification methods manually. This option is useful when you want a strong starting point and need to save time during map creation. You can still review the result and adjust other zone settings before saving.

## **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.

## **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.

## **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](https://geopard.tech/blog/432ca9jhnt-zones-quality/).

## 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.