Understanding Fragmentation Pattern Analysis
Fragmentation pattern analysis studies how observations split across classes, bins, regions, or size groups. It is useful when a total breaks into many measurable parts. Examples include particle sizes, document sections, market segments, habitat patches, and event types. The calculator treats each bin count as part of one distribution. It then converts counts into proportions. Those proportions show how strongly the total is spread or concentrated.
Distribution Shape
A balanced pattern has similar proportions in most bins. A concentrated pattern has one or two dominant bins. Entropy, Simpson diversity, dominance, and evenness describe these differences. They help summarize many counts with a few clear indicators. The coefficient of variation also shows whether bin counts are stable or uneven. A high value means the bins vary widely.
Expected Pattern Testing
The expected pattern test adds another layer. You can enter reference counts from a model, benchmark, or older sample. The tool scales expected values when needed. It then uses a chi-square statistic. This compares observed and expected frequencies. A small p-value suggests the current pattern differs from the reference. That result is useful for quality control, sampling studies, and monitoring work.
Material Based Review
The optional material fields help connect count data with physical size. Total material can be mass, length, area, value, or another unit. Largest fragment size estimates concentration in the biggest piece. The largest share and pulverization index then describe how much material remains outside the largest fragment. These measures support practical review when counts alone are not enough.
Data Quality
Good results need sensible bins. Use mutually exclusive groups. Keep labels clear. Avoid expected values of zero. Combine sparse bins when counts are too small. Always check whether the categories match the question being studied. A strong statistic cannot fix poor grouping.
Decision Use
Use the report as a decision aid, not as automatic proof. Sampling method, measurement error, and domain context still matter. Compare several samples when possible. Save the CSV for spreadsheets. Save the report when a compact record is needed. With clear inputs, the calculator gives a complete view of spread, dominance, fit, and practical fragmentation.
Results should be reviewed over time. Repeated patterns can reveal drift, clustering, or recovery. Sudden changes may suggest process shifts, new causes, or sampling problems needing attention from senior analysts.