The Method
of StopICE
The Stop ICE Raids Alert Network uses a multi-step workflow that integrates community reporting, data processing, and public-facing visualization. These methods transform individual alerts into a collective digital resource that supports transparency, safety, and public education. Each step reflects Digital Humanities principles of openness, participatory design, and ethical information management.
Overview of Workflow
The project’s methods follow a clear, repeatable process:
- Data Collection: Community members submit alerts via SMS or the website.
- Data Cleaning & Verification: Submissions are processed using Python tools to remove duplicates, categorize alert status, and generalize location data for privacy.
- Data Visualization & Output: Cleaned alerts are sent to the live map using Leaflet.js and distributed to subscribers through SMS.
This pipeline ensures that Stop ICE remains both community-driven and technically reliable, combining grassroots action with structured digital practices.
Workflow Steps Breakdown
Step 1: Participatory Data Submission
The most important foundation of the project is its participatory reporting system, which allows users to submit sightings, descriptions, or concerns about ICE activity. Reports arrive through SMS or a web form and may include text notes, approximate location, and optional media. Because the data comes directly from community members, the system elevates marginalized voices and aligns with Stop ICE’s goals of empowerment and visibility. This step embodies DH values by creating knowledge collaboratively rather than relying on institutional sources alone.
Step 2: Data Cleaning, Categorization, and Ethical Processing
Once submissions are received, Stop ICE uses Python and Pandas to clean and categorize each alert. Duplicate reports are merged, vague or conflicting entries are filtered, and locations are generalized to ZIP-area coordinates rather than precise GPS points to protect user safety. Alerts are then assigned a verification category such as UNCONFIRMED, SIGHTING, or CONFIRMED.
This step is essential for maintaining accuracy, transparency, and ethical data stewardship—ensuring that crowd-sourced information is reliable without exposing individuals to risk.
Step 3: Mapping and Public Distribution
After processing, alerts are pushed to the public-facing Leaflet.js interactive map, where they appear as visual markers with color-coded verification labels. Simultaneously, the system sends out SMS alerts to subscribers located within the affected region. This dual-output approach transforms processed data into actionable information—allowing users to visually analyze enforcement patterns while receiving time-sensitive updates that can support safety and rapid response.
This step directly supports the project’s goals of transparency (public mapping) and community safety (SMS distribution), while also demonstrating DH priorities such as open visualization, accessible communication, and ethically designed public data systems.