Random Choropleth Bin Boundary Dataset Generator

Description

Assign synthetic near-break values to supplied region IDs with explicit lower-closed class labels and outside controls; no map geometry is supplied.

A chart dataset contains values plus the labels, grouping, totals, or missing-value conventions needed to interpret them. Separating the numeric construction from plotting makes conservation rules and deliberate edge cases visible before a chart is rendered.

Assign synthetic near-break values to supplied region IDs with explicit lower-closed class labels and outside controls; no map geometry is supplied.

When to use this tool

Use this generator to create reproducible choropleth bin boundary dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply regions, edges, epsilon in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read rows, classCounts. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Draw each region value at or near supplied class edges and assign a left-closed class, including both outermost edges. Return region/value/class records and counts per class.^[1]

Input field	Meaning and units	Accepted input
regions	Regions	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 32.
edges	Edges	Required. Array of Safe integer.; minimum items 2; maximum items 17.
epsilon	Epsilon	Required. Safe integer; minimum 1.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
rows	Rows	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 32.
rows[].region	Region	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
rows[].value	Value	Required. Safe integer.
rows[].classIndex	Class Index	Required. Safe integer.
classCounts	Class Counts	Required. Array of Safe integer.; minimum items 1; maximum items 16.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "regions": [
      "a",
      "b"
    ],
    "edges": [
      0,
      10,
      20
    ],
    "epsilon": 1,
    "seed": 42
  },
  "output": {
    "rows": [
      {
        "region": "a",
        "value": 10,
        "classIndex": 1
      },
      {
        "region": "b",
        "value": 21,
        "classIndex": -1
      }
    ],
    "classCounts": [
      0,
      1
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

A value outside the edge span has classIndex minus one and is not included in classCounts. Regions are labels rather than map geometry; no choropleth is rendered or real regional measurements inferred.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Choropleth Bin Boundary Dataset Generator — tool.io

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