🌃 FAST MAP VIEWER 🌃
Redefining Relocation: Bridging the Gap Between Raw Geospatial Data and Quality of Life.
Finding a place to live is often a chaotic process dictated by biased real estate listings and fragmented map data. Loo-k is a comprehensive neighbourhood analysis platform designed to rewrite this narrative for residents, students, and families.
By aggregating complex geospatial data, we ensure every location is evaluated with absolute fairness and transparency. More importantly, we translate raw transit schedules, zoning data, and routing algorithms into an intuitive visual bridge—connecting prospective residents with their ideal neighbourhoods in the Tri-Cities.
- Analyze: Empowering home-buyers and renters to take control of their search with data-driven insights.
- Optimize: Hacking through the noise of traditional real estate platforms by focusing on what truly matters: commute efficiency and livability.
- Personalize: Deleting the one-size-fits-all approach by offering highly customizable criteria for every unique household.
- Unbiased Geospatial Evaluation: Comprehensive and fair data coverage across the entire Waterloo Region (Waterloo, Kitchener, Cambridge), ensuring no neighbourhood is overlooked.
- Advanced Commute & Route Analysis: A dynamic routing engine that breaks free from traditional university-centric models. Users can target local downtowns, corporate hubs, or custom destinations across multiple transportation modes (drive, transit, bike).
- Personalized Preference Tracking: Seamlessly integrated user profiles that record, manage, and recall specific lifestyle preferences, making the platform adaptable for single professionals, students, and large families alike.
- Immersive Data Visualization: A high-performance interactive map dashboard built to render complex localized data without sacrificing aesthetic appeal or user experience.
A collaborative engineering group working on geospatial data processing and interactive web applications.
| Member | Focus & Expertise | Connectivity |
|---|---|---|
 Aaron Shangguan |
Front End Architect Developed the main web portal and map interface using HTML, CSS, and JavaScript. Implemented the layout of the frontend site and integrated the geospatial visualization components used to display neighbourhood data. |
LinkedIn · GitHub |
 Tony |
Full Stack Engineer Implemented the authentication system using Auth0 and built the user information panel for collecting user preferences. Developed the interface for submitting user criteria and connected the input system to the analysis pipeline. Integrated external services including Google Maps for location-based routing and destination analysis. |
LinkedIn · GitHub |
 Ethan |
Back End Engineer Implemented the Python-based backend logic responsible for processing geographic and user input data. Developed the data analysis workflow used to evaluate neighbourhoods based on commute and livability metrics. Integrated data storage and retrieval using blackboard.io and handled the data processing pipeline used by the platform. |
LinkedIn · GitHub |
 Catherine |
Data & Presentation Collected and organized datasets used by the platform and contributed to preparing the project presentation and documentation. |
LinkedIn · GitHub |
- Frontend Architecture: HTML5 / CSS3 / JavaScript
- Mapping Engine: Leaflet.js
- Backend & Data Processing: Python
- Identity & Access Management: Auth0
- User Preference Database: blackboard.io
- Deployment & Hosting: GitHub Pages
- Commute & Transportation Calculations: Google Places API & Google Maps