01 / Summary

Records, maps, and follow-up work in one app.

TraverseOps connects map location, asset state, inspections, work orders, imports, reports, and roles with public-safe sample records.

Technical problem

Location, history, work state, imports, reports, and roles need one selected record.

Constraints

The public app uses sample data and omits auth, offline sync, audit trails, and production GIS.

Approach

I modeled a MapLibre workspace with relational sample data, filters, panels, and direct inspection/work-order paths.

Tradeoff

The public version favors easy review over permissions, audit logging, and offline conflict resolution.

Next improvement

Production would add RBAC, offline sync, import validation, audit logs, device testing, and reliable map sources.

What it demonstrates: Product modeling, map UI decisions, multi-entity data, field-device thinking, and production-risk awareness.

Why this matters

It connects field data to field work.

Operational problem

Crews and supervisors need maps, records, inspections, and work orders in one path.

Useful approach

Location is the entry point; the selected asset owns history, status, imports, reports, and follow-up work.

Employer signal

The project shows multi-entity data thinking, mobile UI judgment, and production planning.

Production readiness

Solid model; sample records.

Already solid

Assets, inspections, work orders, imports, reports, and roles tie to the selected map record.

Public limits

Sample data only; no accounts, permissions, audit trails, offline sync, or GIS feeds.

Production work

Production needs auth, roles, import validation, audit logs, offline conflicts, device tests, map monitoring, and clearer errors.

Security and ops

Operational data needs tenant isolation, least privilege, PII review, backups, logs, and alerts.

02 / What I built

Product model, task structure, and public framing.

Modeled assets, inspections, work orders, imports, reports, and roles around field tasks.
Built a map-first asset path with filters, selected panels, and inspection/work-order screens.
Used sample relationships to show municipal-style operations without real records.
Documented public limits and production needs: auth, roles, offline sync, audit logs, validation, and device testing.
Framed product modeling, map UI decisions, and production gaps before the app link.

Uses MapLibre and Supabase-style concepts with sample records, not real municipal data or a production backend.

03 / Problem

Crews need shared context at the asset.

Asset work breaks when maps, records, inspections, and work orders live apart. Crews get duplicate typing, delays, and weak handoff.

TraverseOps makes the map the entry point and the selected asset the hub for condition, inspections, work orders, and reports.

04 / Target users

Who the interface is designed for

Field technicians

Need fast lookup, mobile detail views, checklist entry, and obvious next actions.

Supervisors

Need condition trends, prioritized work orders, and clear assets needing attention.

Operations administrators

Need imports, reports, roles, and traceable changes.

05 / Field loop

Map lookup to accountable work.

Find an asset Use the map, filters, or asset list.
Review context Open status, location, type, history, and open work.
Record field activity Create an inspection with condition, checklist, notes, and follow-up.
Turn issues into work Create or update a work order tied to the asset.
Report and improve Spot overdue inspections, unresolved work, import exceptions, and area status.

06 / Tech stack

Stack for a public sample app.

MapLibre
JavaScript
Supabase-style model
Sample data
GitHub Pages
Mobile field UI

Map interaction, panels, and relational records stay separate so sample data can grow toward authenticated field operations.

07 / Map/interface architecture

Map, record, task, and reporting layers stay separate.

TraverseOps architecture Roles use a map workspace with sample assets, imports, inspections, work orders, and reports inside a public boundary.

Roles, records, map selection, field tasks, and reporting stay separate inside a public sample app.

Public data stays inside a sample boundary. Users select an asset, create inspections and work orders, and view reports from the same records.

The map layer answers where and what status needs attention.
Inspection and work-order layers capture field events and follow-up work.
Reporting/admin layers summarize assets, imports, roles, and open work.

08 / Data model

The data model follows field operations.

Core TraverseOps entities and why they exist.
Entity	Typical fields	Why it matters
Assets	ID, type, coordinates, area, status, condition, last inspected date.	Anchor for map display, history, inspection, work, and reporting.
Inspections	Asset ID, inspector, timestamp, checklist values, condition, notes, follow-up flag.	Captures field observations and history.
Work orders	Asset ID, issue summary, priority, status, assignee, due date, source inspection.	Turns findings into trackable work.
Imports	File name, column mapping, validation result, imported count, rejected rows.	Shows bulk data entry without silent corruption.
Reports	Date range, area, status, overdue count, completed work, exception groups.	Converts records into supervisor status and planning views.
Team roles	User, role, permission scope, assigned area, active status.	Models field entry, supervision, and admin control.

Status state stays explicit and filters the same records used by map, panels, reports, inspections, and work orders.

const traverseState = {
  assets: [...traverseAssets],
  inspections: [...baseInspections],
  workOrders: [...baseWorkOrders],
  selectedAssetId: "NH-014",
  filter: "all"
};

function visibleAssets() {
  if (traverseState.filter === "all") return traverseState.assets;
  return traverseState.assets.filter((asset) => asset.status === traverseState.filter);
}

Sample-data excerpt; no private municipal records.

09 / Imports

Imports are validation, not blind upload.

TraverseOps frames imports as steps: choose a file, map columns, validate coordinates and IDs, preview rejected rows, then commit valid records.

Column mapping makes imports explainable.
Validation catches missing coordinates, duplicate IDs, unknown types, and invalid statuses.
Import summaries create an admin review point.

10 / Inspection and work-order flow

Inspections create decisions.

A crew selects an asset, records condition and checklist notes, and flags follow-up. Follow-up becomes a work order tied to the same asset.

Inspection entry

Captures condition, checks, notes, and field context.

Work-order creation

Converts findings into priority, status, assignment, due date, and source record.

Status visibility

Keeps open work and inspection freshness visible by asset, area, or urgency.

11 / Public limits

The public app is intentionally bounded.

Sample data only; no active municipal dataset or field crew.
Roles are represented, but production needs authenticated accounts and server policy.
Import flows are UI evidence, not arbitrary-file safety guarantees.
Offline sync, GPS, photos, and conflict resolution need production engineering.
No public TraverseOps source repository is linked.

12 / Production plan

Before production deployment

The public app shows product shape and data model. Production needs identity, authorization, data quality, offline behavior, security, and recovery guarantees.

Authentication

Accounts and permissions

Public: crew, supervisor, and admin-style flows.

Production: accounts, server roles, row permissions, least privilege, session expiry, and action boundaries.

Offline sync

Reliability in poor connectivity

Public: mobile-friendly field path.

Production: drafts, retry queues, conflicts, sync status, recovery, and unsynced warnings.

Validation

Strict data and state rules

Public: curated sample lifecycle.

Production: validate fields, coordinates, status transitions, priorities, due dates, duplicates, and state changes.

Audit logs

Operational traceability

Public: records that could be auditable.

Production: record who changed assets, inspections, imports, assignments, statuses, and reports.

Error handling

Recovery instead of silence

Public: intended path and product structure.

Production: handle failed saves, map failures, denied permissions, stale records, conflicts, rejected imports, and retries.

Imports

Defensive import validation

Public: imports as a review concept.

Production: file limits, mapping, previews, duplicate checks, coordinate checks, rollback, rejected-row exports, logs, and bulk permissions.

Mobile QA

Field-device testing

Public: desktop/mobile layouts.

Production: phones/tablets, GPS, battery, photos, sunlight readability, and intermittent networks.

Security

Protect records

Public: sample data.

Production: TLS, secrets, rate limits, upload controls, patching, backups, and privacy-aware logs.

Map data

Reliable basemaps and sources

Public: location and status drive the UI.

Production: tile SLAs, cache strategy, fallbacks, geocoding limits, coordinate systems, ownership, and stale-data alerts.

13 / Testing and QA notes

Manual QA covers field assumptions.

Automated tests

No public automated suite yet. Current evidence is manual browser testing, responsive checks, and documented gaps.

Manual checklist

Verify reset, roles, tabs, map selection, filters, forms, imports, and reports.

Browser and device checks

Check maps, forms, tabs, and buttons at phone, tablet, and desktop widths.

Edge cases

Exercise empty data, resets, invalid imports, missing assets, sparse filters, role changes, and long labels.

Known limitations

No automated E2E, GIS validation, offline-conflict simulation, permission tests, or field-device lab testing yet.

14 / Deployment and run notes

Runs in the browser with sample data.

Hosted/run location

Hosted on GitHub Pages. traverseops-demo.html and traverseops-demo.js run client-side with sample data.

Environment/config

No env vars. Roles, filters, imports, reports, and forms use in-browser sample state.

External APIs/services

Explains MapLibre and Supabase-style concepts, but connects to no municipal records, private GIS, or Supabase.

Local development

Run python -m http.server 8000, then open /traverseops-demo.html or /traverseops-case-study.html. No build step.

Secrets and data safety

No secrets belong in the public page. Production would keep keys, GIS credentials, accounts, and records out of client code.

15 / Results

What the project proves

TraverseOps gives visitors a concrete surface to evaluate: map-first field flow, multi-entity records, roles, and production gaps.

Models map lookup, inspection, work order, and reporting.
Uses realistic entities instead of single-entity CRUD.
States public limits clearly.

16 / Screenshots and states

Screenshots

TraverseOps map workspace showing asset markers and a selected asset detail panel — Map workspace with selected-asset context.

TraverseOps import tab showing sample import validation, accepted rows, rejected rows, and staging controls — Import screen with accepted/rejected sample rows.

TraverseOps inspection screen showing asset selection, condition notes, checklist items, and recent inspection records — Inspection form and recent records tied to sample assets.

TraverseOps reports tab showing sample operational summary cards for assets, inspections, and work orders — Reports for sample assets, inspection coverage, and work-order state.

17 / Next improvements

Next improvements

Add schema, import validation, and state-machine examples.
Expose safe source or architecture notes.
Run deeper accessibility checks on map controls, focus order, and mobile forms.

TraverseOps

TraverseOps in 30 seconds.

What this proves

Core technical work

Best path

Known limits

Try this in 2 minutes.

Map context drives the workflow.

What to notice

Open the app and evidence.

TraverseOps in 15 seconds.