TraverseOps

01 / Summary

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

Field-operations teams lose time when location, records, state, and team permissions are split across spreadsheets, map viewers, and ticket lists. TraverseOps shows how a MapLibre map, Supabase-shaped relational model, selected-asset context, inspections, work orders, imports, reports, and role-aware screens can fit together using public-safe sample records.

Why this matters

It connects field data to the work people actually need to do.

Production readiness

The data model is solid; the public version uses sample records.

02 / What I built

The product model, task structure, and public case-study framing.

• Modeled assets, inspections, work orders, imports, reports, and team roles around field-crew and supervisor tasks.
• Built a map-first asset path with status filtering, selected-asset panels, and direct inspection/work-order screens.
• Designed sample data relationships so the app can represent municipal-style operations without exposing real field records.
• Documented public boundaries and production needs, including authentication, role permissions, offline sync, audit logs, validation, and mobile field testing.
• Framed the case study so reviewers can evaluate product modeling, map UI decisions, and production gaps without opening the app.

External and target-architecture concepts include MapLibre and a Supabase-style relational model. The public app uses sample hydrant-style records rather than real municipal operating data or a production backend.

03 / Problem

Field crews need shared context at the asset, not after the fact.

Hydrant-style asset operations break down when crews use one tool for maps, another for asset records, another for inspection notes, and another for work orders. The result is duplicated typing, delayed updates, uncertain asset status, and weak handoff between the person in the field and the person assigning or reviewing work.

TraverseOps addresses that by treating the map as the entry point and the asset record as the hub. From one selected asset, the user should be able to see the location, understand condition/status, record an inspection, create a work order, and later report on the operational picture.

04 / Target users

Who the interface is designed for

05 / Field loop

The core loop starts on the map and ends in accountable work.

1. Find an asset Use the map, filters, or asset list to locate the hydrant-style record.
2. Review context Open the selected asset panel to see status, location, type, inspection history, and open work.
3. Record field activity Create an inspection with condition, checklist results, notes, and follow-up flags.
4. Turn issues into work Create or update a work order, assign responsibility, set priority, and keep the record tied back to the asset.
5. Report and improve Use reports to spot overdue inspections, unresolved work, import exceptions, and asset status by area.

06 / Tech stack

Stack chosen for a credible public sample app.

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

The important technical decision is the app shape: map interaction, panels, and relational records are treated as separate concerns. That lets the public version show how sample data could grow into authenticated field operations without changing the core user model.

07 / Map/interface architecture

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

In plain terms: public data stays inside a sample-data boundary. Users start from a field or admin role, import or review asset records, use the map to select an asset, then create inspections and work orders from that record. Reports and admin views summarize the same records without exposing real municipal data.

• The map layer answers where the asset is and which status or area needs attention.
• The inspection and work-order layers answer what happened in the field and what follow-up work exists.
• The reporting/admin layer answers what supervisors need to review across assets, imports, roles, and open work.

08 / Data model

The data model follows field operations.

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);
}

09 / Imports

Imports are treated as validation, not a blind upload.

A production field system often starts with legacy spreadsheets or GIS exports. TraverseOps frames imports as steps: choose a file, map incoming columns to known fields, validate coordinates and IDs, preview rejected rows, then commit validated records into the asset registry.

• Column mapping makes the import explainable instead of assuming every source file already matches the target schema.
• Validation catches missing coordinates, duplicate IDs, unknown asset types, and invalid status values before records reach the map.
• Import summaries create a review point for administrators and a future audit trail for production use.

10 / Inspection and work-order flow

Inspections create operational decisions.

The inspection flow is designed around field decision-making. A crew member selects an asset, records condition and checklist information, adds notes, and flags whether follow-up work is required. When follow-up is needed, that inspection can become a work order tied back to the same asset.

11 / Public limits

The public app is intentionally bounded.

• Uses sample data only; it is not connected to an active municipal dataset or active field crew.
• Role and permission concepts are represented for product context, but production authorization would need authenticated accounts and server-side policy enforcement.
• Import flows should be treated as UI evidence, not as a guarantee that arbitrary operational files are safe to upload.
• Offline sync, GPS permission handling, photo attachments, and conflict resolution would need deeper production engineering before field deployment.
• The public page does not currently expose a public TraverseOps source repository link.

12 / Production plan

What would need to change before production deployment

The public TraverseOps app shows the product shape and data model. A deployed field system would need identity, authorization, data quality, offline behavior, security, and recovery guarantees before crews or municipal records depended on it.

13 / Testing and QA notes

Manual QA covers field risk and device assumptions.

14 / Deployment and run notes

The public version runs in the browser with sample data.

15 / Results

What the project proves

TraverseOps gives hiring reviewers a concrete product surface to evaluate: a domain with real constraints, a map-first field path, multi-entity records, role-aware operations, and a path from sample app to production deployment. It shows the ability to reason about software beyond isolated UI components.

• Models a full operational loop from map lookup to inspection to work order to reporting.
• Uses realistic domain entities instead of a single-entity CRUD structure.
• Documents limitations honestly so the public app does not pretend to be production software.

16 / Screenshots and states

Screenshots

17 / Next improvements

What I would improve next

• Add a public technical appendix with schema examples, import validation examples, and state-machine diagrams for inspections and work orders.
• Add short GIFs showing import validation, inspection submission, assignment, and report updates end to end.
• Expose a safe source package or architecture notes if the TraverseOps app source cannot be public.
• Run a deeper accessibility pass on map controls, keyboard alternatives, focus order, and mobile field-entry forms.