Workflow Narrative

Network Readiness and Diagnostics

Problem It Solves

Is this network structurally and cost-wise ready for reliable routing and service optimization workflows?

Who It Is For

Routing analysts, municipal transportation planning teams, and utility network operations.

Primary User

Municipal/public works GIS teams, utilities, and logistics operations requiring reproducible network QA gates.

What It Does

Audits line-based transportation/utility networks for operational routing readiness.
Detects dead-end concentration and cost-consistency anomalies before routing runs.
Emits a machine-readable readiness score with pass/fail quality gate and diagnostics outputs.

How It Works

Loads a line network and validates geometry is line-based (`LineString`/`MultiLineString`).
Builds node degree counts from line endpoints to quantify dead-end prevalence.
Computes per-segment length costs and assesses variance using z-score outlier detection.
Computes weighted readiness score from connectivity and cost-consistency components.
Indicative formula: `overall = 0.6 * connectivity_score + 0.4 * cost_consistency_score`.

Why It Wins

Replaces manual topology spot-checks with a reproducible score + diagnostics package that is machine-checkable and reportable.

Typical Buying Trigger

Teams encounter unstable routing outcomes and need an auditable pre-routing network quality gate.

Typical Presets

default: balanced scoring for day-to-day network readiness checks.
pre-routing gate: run before route optimization or service-area generation.
data onboarding QA: run when ingesting new road/utility centerline deliveries.

Inputs

Parameter	Optional	Description
network	no	Input line network layer (street, transit, or utility network).
qa_report	no	Output CSV path for detailed QA findings and issue counts.
diagnostics_layer	no	Output GeoJSON/GeoPackage path containing diagnostic geometries.
readiness_score	no	Output JSON path with readiness score, component scores, and pass/fail gate.

Outputs

Parameter	Type	Description
qa_report	CSV	Tabular QA findings including severity, check type, count, and descriptive diagnostics.
diagnostics_layer	GeoJSON/GeoPackage	Spatial diagnostics layer for issue visualization and spatial triage.
readiness_score	JSON	Machine-readable readiness contract with overall score, component scores, penalties, and pass/fail gate.
html_report	HTML	Human-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.network_readiness_and_diagnostics_intelligence(
    network="data/street_network.shp",
    qa_report="output/network_readiness_qa.csv",
    diagnostics_layer="output/network_readiness_diagnostics.geojson",
    readiness_score="output/network_readiness_score.json",
)

print(result)

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Project Links

WbW Homepage User Manual Learn More