Predictive Scenarios for Public Realms
Predictive Scenarios for Public Realms
DURATION
DURATION
CLIENT
Councils, Developers, Government, Campuses, Infrastructure, Airports
CLIENT
Councils, Developers, Government, Campuses, Infrastructure, Airports
Strategic Foresight
Strategic Foresight
Digital Twin
Digital Twin
Geospatial Intelligence
Geospatial Intelligence
Regulatory Insight
Regulatory Insight
PROJECT OVERVIEW
PROJECT OVERVIEW
Meta Moto delivered a privacy-first analytics platform using internationally recognised public-life metrics—such as people moving or staying, posture, group size and activities. Through multi-modal data sources (sensors, cameras, mobile), AI-informed modelling and dashboard visualisation, clients gain real‑time insight into how public spaces function. This supports data-driven briefs, crowd flow planning, activation strategy and investment cases, underpinned by transparent governance, open standards and ethical data handling.
Meta Moto delivered a privacy-first analytics platform using internationally recognised public-life metrics—such as people moving or staying, posture, group size and activities. Through multi-modal data sources (sensors, cameras, mobile), AI-informed modelling and dashboard visualisation, clients gain real‑time insight into how public spaces function. This supports data-driven briefs, crowd flow planning, activation strategy and investment cases, underpinned by transparent governance, open standards and ethical data handling.
The Challenge
The Challenge
Many local councils and precinct managers lack consistent, actionable insight into public space usage—relying on fragmented manual counts, surveys or anecdote. Without standard metrics and real‑time data, it’s difficult to plan functional amenities, design safe events or gauge precinct vitality. With growing expectations for open data, ethical governance and cross-sector interoperability, authorities need a trusted analytics framework to guide future‑ready planning across urban environments.
Many local councils and precinct managers lack consistent, actionable insight into public space usage—relying on fragmented manual counts, surveys or anecdote. Without standard metrics and real‑time data, it’s difficult to plan functional amenities, design safe events or gauge precinct vitality. With growing expectations for open data, ethical governance and cross-sector interoperability, authorities need a trusted analytics framework to guide future‑ready planning across urban environments.
WHAT WE DID
WHAT WE DID
Meta Moto devised independent, evidence‑based planning frameworks to guide councils and precinct owners—including during transformative Brisbane projects such as Cross River Rail, Queen’s Wharf, Howard Smith Wharves and Brisbane Metro. These frameworks establish automated, privacy-first analytics workflows capturing visitor dynamics like dwell‑time, movement flows, activity patterns, group size and demographic profiles. Through secure, federated dashboards and benchmarked insights, we inform urban design briefs and capital strategies. Our advisory input shaped spatial design interventions—shade structures, seating zones, wayfinding layouts—enhancing comfort and usability around new station precincts, plazas and activation zones. Predictive scenario modelling enables dynamic precinct management and strong business cases for integrated infrastructure and public realm investment.
Meta Moto devised independent, evidence‑based planning frameworks to guide councils and precinct owners—including during transformative Brisbane projects such as Cross River Rail, Queen’s Wharf, Howard Smith Wharves and Brisbane Metro. These frameworks establish automated, privacy-first analytics workflows capturing visitor dynamics like dwell‑time, movement flows, activity patterns, group size and demographic profiles. Through secure, federated dashboards and benchmarked insights, we inform urban design briefs and capital strategies. Our advisory input shaped spatial design interventions—shade structures, seating zones, wayfinding layouts—enhancing comfort and usability around new station precincts, plazas and activation zones. Predictive scenario modelling enables dynamic precinct management and strong business cases for integrated infrastructure and public realm investment.
