Work

The City of Lake Forest needed residents and visitors to understand Central Business District parking at a glance — including the question everyone asks, “how far is this lot from where I'm going?” — and off-the-shelf ArcGIS Experience Builder couldn't deliver that experience.

I designed, built, and deployed a custom React + ArcGIS application: every CBD lot mapped with color-coded restrictions and detailed profiles, on-demand walk-time routing from any user-set origin, and a mobile-first interface — all driven by a single JSON config that makes it portable to other communities.

MGP's first custom-coded GIS web application — delivering functionality off-the-shelf tools couldn't, and now a reusable template already carried forward to a second municipality (La Grange).

Over a century of handwritten water service records were trapped on deteriorating paper cards — unsearchable and disconnected from modern systems — while field crews kept capturing new records in inconsistent formats.

One initiative, two directions. Backward: a local vision-AI model (Qwen2.5-VL) reads the handwritten cards and extracts structured, searchable data at scale. Forward: a structured Survey123 + Field Maps intake form so crews capture clean, GIS-ready records from here on.

A century of infrastructure knowledge made searchable and integrated into GIS — roughly 40+ hours of manual entry completed in about 4 hours of review — plus a standardized capture process that keeps the archive clean going forward.

Preemption device status, location, and component health were scattered across sources, making it hard to know at a glance which intersections were healthy and which needed attention.

Built a real-time ArcGIS dashboard consolidating device status, location, and component health into a single operational view for emergency-response coordination.

Single-pane situational awareness that improves response coordination — without adding headcount.

Manual feature extraction from spatial imagery is slow, error-prone, and doesn't scale with growing municipal data demands.

Built an AI-powered tool that uses computer-vision models to detect and extract geographic features from imagery — turning pixels into GIS-ready features instead of hand-digitized ones.

Automated feature extraction from spatial imagery, dramatically reducing manual digitizing while improving consistency and data fidelity.

Thousands of municipal address records needed verification before large-scale system migrations, with no efficient way to catch invalid or partially correct entries.

Built an internal validation tool that checks records against the official USPS database, with an interactive dashboard, map visualization, and pause/resume for multi-day jobs.

Supports large-scale municipal migrations (BS&A to BS&A Cloud), catching address errors before records reach production and sparing staff hours of downstream cleanup.

Public crime data is often locked in formats no resident can use — and publishing it openly raises real privacy concerns about pinpointing incidents to a single household.

Built a Python pipeline that ingests incident data, anonymizes each record to the 100-block level to protect privacy, and auto-publishes a clean public dashboard to ArcGIS Online — replacing a manual update process with a repeatable, scheduled one.

What took 60–90 minutes of manual work per update now runs in about 5 — giving residents a transparent, privacy-respecting, always-current view of local incidents while reclaiming staff capacity.