SnapScope: A Platform for City-Scale Collection and Exploration of Public Snap Map Data

2026-08-06

AI Summary

This study addresses the lack of a public API for Snap Map, which hinders reproducible collection and analysis of its publicly geotagged content at urban scales. To overcome this limitation, the authors propose a city-agnostic integrated platform that combines a grid-based automated data collection pipeline, deduplication mechanisms, and an interactive web frontend to support task management, data exploration, neighborhood comparisons, and data export. This system enables, for the first time, reproducible city-scale harvesting and analysis of Snap Map data. Deployed in Riyadh over 23 days, it collected 515,364 unique public Snaps—despite a high duplication rate of 94.8%—and released the aggregated dataset under a CC BY 4.0 license.

Abstract

Snapchat's Snap Map is an ephemeral stream of geotagged public video and image stories, but the platform provides no documented API, no prior work describes a reproducible system for collecting this data at city scale, and no tool exists for managing and exploring the collected data interactively. We present SnapScope, an integrated platform that pairs a back-end collection pipeline with a web-based front end for scraper management, interactive data exploration, side-by-side neighborhood comparison, and data export. We deploy the platform over Riyadh, Saudi Arabia, collecting 515,364 unique public snaps across 23 days on a 1 km grid of 2,740 query points. A saturation probe over 21 consecutive runs shows that 94.8% of returned observations are duplicates of already-stored records. We provide a privacy-safe aggregate dataset under CC BY 4.0. The platform is city-agnostic and redeployable by substituting grid coordinates and boundary polygons.

Problem

• Snap Map
• ephemeral geotagged data
• city-scale data collection
• public social media data
• data exploration

Innovation

• Snap Map
• city-scale data collection
• ephemeral geosocial data
• interactive data exploration
• reproducible scraping pipeline