URBANLY
ACCELERATE GROUNDED, BETTER-INFORMED DECISIONS IN URBAN CONTEXTS.
EMPOWERING DECISION-MAKERS BY COMBINING EXPERT DOMAIN KNOWLEDGE WITH MODEL-BASED ALGORITHMS IN ORDER TO GENERATE A DEPENDABLE AND EVOLVING VISION THAT HELPS TO MAKE INFORMED DECISIONS.
Data Collection
Data Management & Analysis
Knowledge
Best Decisions
With CitiyCompass, it is possible to analyze both present and future accessibility in real-time. Visualize the current transit lines for the different modes, add urban equipment in future years and see how accessibility indicators change, also affecting incentives for demand.
In CityCompass is possible to configure different sets of Real Estate Agents, targeting specific parcel sizes, building types and uses. The tool computes proformas for each agent and parcel of the study area in a few seconds. This information is then used for assessing the decisions of agents competing for the available land.
CityCompass allows the design of scenarios around a set of planned projects, that generate urban effects in their surroundings. The impact of each project could be precisely tuned by the user, and during simulation time will affect prices and incentives for offer and demand.
Underlying the CityCompass interface resides a powerful simulation engine that can discover interactions between agents that are very difficult to anticipate with just planning analysis. Each demand agent represent a particular household type, that will bid for available properties affecting how prices evolve over time.
URBANLY WAS CREATED MOTIVATED BY THE DESIRE TO LIVE IN COMMUNITIES DESIGNED FOR THE FUTURE AND PREPARED FOR GROWTH.
Using Opensource and proprietary tools we facilitate the task of communities decisions makers by providing our experience in Data Management and analysis to convert them into specific indicators and, through algorithms based on models, generate future scenarios to make informed decisions.
Federico has been working in urban software technology for the last decade, including land use simulation, procedural geometry generators and algorithms for automatic design of architectural floorplans. He is passionate about solving real-world problems, connecting state of the art research to public sector needs.
Mariano has been working in software development and infrastructure for over 20 years. In the last 10 years he has participated in different projects related to community analysis including map data visualization tools, traffic and micro-mobility fleets data analysis and reports. He focuses on the automation of tasks motivated by the optimization of time and results.
Gregorio is finishing his CS. Master's thesis with a research on improvements in algorithms for modeling large-scale urban simulations. He has been working for the last 5 years on urban tooling and optimizations in geometric data management and visualization.