Melbourne is Australia’s second largest city and is globally renowned for its liveability and commitment to great places and urban form. As the “worlds most locked down city” during the Covid-19 pandemic, the inner city has seen dramatic changes in how people engage with and activate the city, a changing world of work and building usage, shifts in urban living and an evolution in the role of parks and public spaces.

The Melbourne Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.

Core Melbourne Data Model includes:

Data

• Core Capital city model includes data for – 2019, 2020, 2021, 2022.
• Add on 2023/2024 monthly refresh to all model layers at any time.

Geography

• City of Melbourne LGA / 37,558.3km2
• Includes 14 suburbs: Carlton, Docklands, East Melbourne, Fishermans Bend, Flemington Racecourse, Kensington, Melbourne, North Melbourne, Parkville, Port Melbourne, South Yarra, Southbank, West Melbourne

Layers

• Activity – H3 L11 – 2,150M2 per cell. Filter by hour, day, week, year, percentile, suburb, land use type 
• Dwell – H3 L11 – 2,150M2 per cell. Filter by dwell time bin, percentile, suburb, land use type
• 1500+ Meshblocks – activity enriched with ABS land use classification. Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Primary street address)
• 7,400+ Buildings – Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Building Name and/or Primary street address)

Log-in

• Price includes 1 user log-in for 1 year of access
• Add on additional log-ins at any time

Add on any anytime:

Data

• Add on 2023/2024 monthly refresh to all model layers at any time.
• Add on offline data files for custom analytics (.geojson and .csv)

Layers

• Audience Origins – Reveal catchment of place. Includes % visitor vs % local over time. National audience catchment, filter by distance, weekday/weekend, month, year. 
• Cross visit and relationship model – Building to building cross visit model reveals the hierarchy of connections and directional flows between places. 
• Mobility Model – 1,900+ road/rail segments – Filter by category (Road/Rail), Type (13 types), Name (Street name)

Log-in

• Add on additional log-ins at any time

Capital City Data Models – Exclusions and Notes

Data models do not include the following:

• Active military bases, jails and detention centres have been removed from the data models
• Single use residential dwellings have been aggregated into statistical blocks or have been removed

Notes on Land Use Classification

• Land use classification has been undertaken using ABS mesh block categories
• Building and H3 land use classification references the ABS mesh block the building is located in.

Notes on Filtering the Data Models

• Filter the models by time (hour, day, week, year), geography (suburb, mesh block, building), land use type (ABS) to extract insights about patterns of activity.

Sydney is Australia’s most populated city with a unique urban form that is shaped by a dramatic natural geography, diversified economy, multi-modal transport network, major green spaces and significant development zones. Impacts of the Covid-19 pandemic lockdowns and border closures and evolving relationships with work and urban living have seen dramatic changes in activity patterns across the inner city in recent years.

The Sydney Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.

Core Sydney Data Model includes:

Data

• Core Capital city model includes data for – 2019, 2020, 2021, 2022.
• Add on 2023/2024 monthly refresh to all model layers at any time.

Geography

• City of Sydney LGA /26,850.3km2
• Includes 33 Suburbs: Alexandria, Annandale, Barangaroo, Beaconsfield, Camperdown, Centennial Park, Chippendale, Darlinghurst, Darlington, Dawes Point, Elizabeth Bay, Erskineville, Eveleigh, Forrest Lodge, Glebe, Haymarket, Millers Point, Moore Park, Newtown, Paddington, Potts Point, Pyrmont, Redfern, Rosebery, Rushcutters Bay, St Peters, Surry Hills, Sydney, The Rocks, Ultimo, Waterloo, Woolloomooloo, Zetland

Layers

• Activity – H3 L11 – 2,150M2 per cell. Filter by hour, day, week, year, percentile, suburb, land use type 
• Dwell – H3 L11 – 2,150M2 per cell. Filter by dwell time bin, percentile, suburb, land use type
• 2500+ Meshblocks – filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Primary street address)
• 10,270+ Buildings – filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Building Name and/or Primary street address)

Log-in

• Price includes 1 user log-in for 1 year of access
• Add on additional log-ins at any time

Add on at any time:

Data

• Add on 2023/2024 monthly refresh to all model layers at any time.
• Add on offline data files for custom analytics (.geojson and .csv)

Layers

• Audience Origins – Reveal catchment of place. Includes % visitor vs % local over time. National audience catchment, filter by distance, weekday/weekend, month, year. 
• Cross visit and relationship model – Building to building cross visit model reveals the hierarchy of connections and directional flows between places. 
• Mobility Model – 2400+ road/rail segments – Filter by category (Road/Rail), Type (13 types), Name (Street name)

Log-in

• Add on additional log-ins at any time

Capital City Data Models – Exclusions and Notes

Data models do not include the following:

• Active military bases, jails and detention centres have been removed from the data models
• Single use residential dwellings have been aggregated into statistical blocks or have been removed

Notes on Land Use Classification

• Land use classification has been undertaken using ABS mesh block categories
• Building and H3 land use classification references the ABS mesh block the building is located in.

Notes on Filtering the Data Models

• Filter the models by time (hour, day, week, year), geography (suburb, mesh block, building), land use type (ABS) to extract insights about patterns of activity.

Brisbane is the capital city of Queensland and the third most populous city in Australia. Like many Australian cities in recent years, Brisbane has experienced significant changes due to the lasting effects of the Covid-19 pandemic and border closures, major infrastructure investments, population growth and the responsibility of hosting the 2032 Summer Olympics and Paralympics Games.

The Brisbane CBD Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies to support urban research and discovery.

Perth – Australia’s most western capital city – has been undergoing significant growth and change in recent years. Major urban investments, the impacts of the Covid-19 pandemic border closures, changing relationship with work and urban living, population growth and major new developments have seen significant changes in activity patterns across the inner city in recent years.

The Perth Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.

Core Perth Data Model includes:

Data

• Core Capital city model includes data for – 2019, 2020, 2021, 2022.
• Add on 2023/2024 monthly refresh to all model layers at any time.

Geography

• City of Perth LGA / 13,931.5km2
• Includes 7 suburbs: Crawley, East Perth, Kings Park, Nedlands, Northbridge, Perth, West Perth

Layers

• Activity – H3 L11 – 2,150M2 per cell. Filter by hour, day, week, year, percentile, suburb, land use type 
• Dwell – H3 L11 – 2,150M2 per cell. Filter by dwell time bin, percentile, suburb, land use type
• 490+ Meshblocks – enriched with ABS land use classification. Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Primary street address)
• 2,600+ Buildings – Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Building Name and/or Primary street address)

Log-in

• Price includes 1 user log-in for 1 year of access
• Add on additional log-ins at any time

Add on any anytime:

Data

• Add on 2023/2024 monthly refresh to all model layers at any time.
• Add on offline data files for custom analytics (.geojson and .csv)

Layers

• Audience Origins – Reveal catchment of place. Includes % visitor vs % local over time. National audience catchment, filter by distance, weekday/weekend, month, year. 
• Cross visit and relationship model – Building to building cross visit model reveals the hierarchy of connections and directional flows between places. 
• Mobility Model – 530+ road/rail segments – Filter by category (Road/Rail), Type (13 types), Name (Street name)

Log-in

• Add on additional log-ins at any time

Capital City Data Models – Exclusions and Notes

Data models do not include the following:

• Active military bases, jails and detention centres have been removed from the data models
• Single use residential dwellings have been aggregated into statistical blocks or have been removed

Notes on Land Use Classification

• Land use classification has been undertaken using ABS mesh block categories
• Building and H3 land use classification references the ABS mesh block the building is located in.

Notes on Filtering the Data Models

• Filter the models by time (hour, day, week, year), geography (suburb, mesh block, building), land use type (ABS) to extract insights about patterns of activity.

Adelaide has a unique urban form defined by significant green space and urban parks bounding the urban core. Impacts of the Covid-19 pandemic border closures and a changing relationship with work and urban living have seen dramatic changes in activity patterns across the inner city in recent years. Housing accessibility, strategic planning initiatives and infrastructure investment continue to influence the shape and function of the city.

The Adelaide Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.

Core Adelaide Data Model includes:

Data

• Core Capital city model includes data for – 2019, 2020, 2021, 2022.
• Add on 2023/2024 monthly refresh to all model layers at any time.

Geography

• City of Adelaide LGA / 15.77km2
• Includes 2 suburbs: Adelaide, North Adelaide

Layers

• Activity – H3 L11 – 2,150M2 per cell. Filter by hour, day, week, year, percentile, suburb, land use type 
• Dwell – H3 L11 – 2,150M2 per cell. Filter by dwell time bin, percentile, suburb, land use type
• 650+ Meshblocks – enriched with ABS land use classification. Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Primary street address)
• 3,870+ Buildings – Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport), Name (Building Name and/or Primary street address)

Log-in

• Price includes 1 user log-in for 1 year of access
• Add on additional log-ins at any time

Add on any anytime:

Data

• Add on 2023/2024 monthly refresh to all model layers at any time.
• Add on offline data files for custom analytics (.geojson and .csv)

Layers

• Audience Origins – Reveal catchment of place. Includes % visitor vs % local over time. National audience catchment, filter by distance, weekday/weekend, month, year. 
• Cross visit and relationship model – Building to building cross visit model reveals the hierarchy of connections and directional flows between places. 
• Mobility Model – 1,100+ road/rail segments – Filter by category (Road/Rail), Type (13 types), Name (Street name)

Log-in

• Add on additional log-ins at any time

Capital City Data Models – Exclusions and Notes

Data models do not include the following:

• Active military bases, jails and detention centres have been removed from the data models
• Single use residential dwellings have been aggregated into statistical blocks or have been removed

Notes on Land Use Classification

• Land use classification has been undertaken using ABS mesh block categories
• Building and H3 land use classification references the ABS mesh block the building is located in.

Notes on Filtering the Data Models

• Filter the models by time (hour, day, week, year), geography (suburb, mesh block, building), land use type (ABS) to extract insights about patterns of activity.

Darwin is Australia’s most northern capital city with a tropical climate and vast geography. Impacts of the Covid-19 pandemic border closures, changing relationships with work and urban living, and changes in travel and tourism have seen dramatic shifts in activity patterns across the city in recent years.

The Darwin Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.

Core Adelaide Data Model includes:

Data

• Core Capital city model includes data for – 2019, 2020, 2021, 2022.
• Add on 2023/2024 monthly refresh to all model layers at any time.

Geography

• City of Darwin LGA / 112,742.2km2
• Includes 40 suburbs: Alawa, Anula, Bayview, Berrimah, Brinkin, Buffalo Creek, Casuarina, Coconut Grove, Coonawarra, Darwin City, East Point, Eaton, Fannie Bay, Hidden Valley, Holmes, Jingili, Karama, Larrakeyah, Leanyer, Lee Point, Ludmilla, Lyons, Malak, Marrara, Millner, Moil, Muirhead, Nakara, Nightcliff, Parap, Rapid Creek, Stuart Park, The Gardens, The Narrows, Tiwi, Wagaman, Wanguri, Winnellie, Woolner, Wulagi

Layers

• Activity – H3 L11 – 2,150M2 per cell. Filter by hour, day, week, year, percentile, suburb, land use type 
• Dwell – H3 L11 – 2,150M2 per cell. Filter by dwell time bin, percentile, suburb, land use type
• 1680+ Meshblocks – enriched with ABS land use classification. Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport, Other), Name (Primary street address)
• 5,500+ Buildings – Filter by Category (Suburb), Type (Commercial, Education, Hospital/Medical, Industrial, Parkland, Residential, Transport, Other), Name (Building Name and/or Primary street address)

Log-in

• Price includes 1 user log-in for 1 year of access
• Add on additional log-ins at any time

Add on any anytime:

Data

• Add on 2023/2024 monthly refresh to all model layers at any time.
• Add on offline data files for custom analytics (.geojson and .csv)

Layers

• Audience Origins – Reveal catchment of place. Includes % visitor vs % local over time. National audience catchment, filter by distance, weekday/weekend, month, year. 
• Cross visit and relationship model – Building to building cross visit model reveals the hierarchy of connections and directional flows between places. 
• Mobility Model – 2,300+ road/rail segments – Filter by category (Road/Rail), Type (13 types), Name (Street name)

Log-in

• Add on additional log-ins at any time

Capital City Data Models – Exclusions and Notes

Data models do not include the following:

• Active military bases, jails and detention centres have been removed from the data models
• Single use residential dwellings have been aggregated into statistical blocks or have been removed

Notes on Land Use Classification

• Land use classification has been undertaken using ABS mesh block categories
• Building and H3 land use classification references the ABS mesh block the building is located in.

Notes on Filtering the Data Models

• Filter the models by time (hour, day, week, year), geography (suburb, mesh block, building), land use type (ABS) to extract insights about patterns of activity.

Australia’s national capital city displays a distinctive planned urban form that continues to evolve. Like many Australian cities in recent years, Canberra has experienced significant change due to the effects of the Covid-19 pandemic, black summer fires, population growth and major new infrastructure interventions.

The Canberra Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.

Australia’s most southern capital city has seen significant change in recent years as it adapts to rapid growth predictions with interventions in housing, transport and education. Impacts of the Covid-19 pandemic border closures, housing accessibility, changing relationships with work and urban living and an evolving visitor economy have seen significant changes in activity patterns across the city in recent years.

The Hobart Capital City Data Model reveals unprecedented insights on 4+ years of changes in activity, dwell time, audience mix and building use over time in the commercial and retail cores, major universities, mobility networks, health and cultural precincts, major parks, and a variety of land use and place typologies.