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    [{"authors":null,"categories":null,"content":"With a background in geospatial and computer science, Dr Alan Both’s research focuses on developing spatial indicators for quantifying the health and liveability of the urban environment as well as adding a spatial context to agent-based modelling.\nThrough projects including JIBE, THAT-Melbourne, and the Australian Urban Observatory, Dr Both has developed automated processes for deriving a variety of spatial indicators covering the health impacts of increased physical activity through active transport, access to and visibility of public greenspace, walkability, and access to amenities.\nDr Both is currently developing algorithms to generate transport networks, synthetic population and travel demand models, along with other spatial indicators for use in evaluating the health impacts of transport interventions.\n","date":1751328000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1751328000,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"","publishdate":"2025-03-01T00:00:00Z","relpermalink":"","section":"authors","summary":"With a background in geospatial and computer science, Dr Alan Both’s research focuses on developing spatial indicators for quantifying the health and liveability of the urban environment as well as adding a spatial context to agent-based modelling.\n","tags":null,"title":"Alan Both","type":"authors"},{"authors":["Corin Staves","Irena Itova","Belen Zapata-Diomedi","Audrey de Nazelle","Jenna Panter","Lucy Gunn","Alan Both","Yuchen Li","Ismail Saadi","James Woodcock","SM Labib"],"categories":null,"content":"","date":1751328000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1751328000,"objectID":"87d8a8619b73126ac04849a68bcb4f92","permalink":"https://alanboth.github.io/publication/2025-jibe-network/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/2025-jibe-network/","section":"publication","summary":"Accessibility models explore how land use and transport systems interact to facilitate access to activities and daily needs. Existing applications generally model accessibility based on distance or travel time. For pedestrians and cyclists, the street-level environment (e.g., green visibility, streetside amenities, dedicated infrastructure) significantly influences people’s willingness and ability to travel. Incorporating these features into accessibility models can help them to be more representative of active travellers’ experienced environment. \n\nThis study presents a methodology for incorporating the street-level environment into active mode accessibility. First, micro-scale built environment data from multiple sources are harmonised into a high-resolution digital representation of the land use and transport system. Second, a compute-optimised framework is developed for modelling accessibility at the micro-scale (i.e., each dwelling separately) incorporating the street-level environment. The methods build upon the open geodatabase OpenStreetMap and open-source MATSim project, facilitating expandability and transferability to other contexts. We apply this methodology to develop policyrelevant accessibility indicators for Greater Manchester. \n\nIn the results, we observe that the street-level environment can cause accessibility indicators to vary at the micro-scale, especially in less connected neighbourhoods where the choice of routes is limited. We also observed that for cyclists, the accessibility advantage over walking reduces substantially when traffic stress is considered. Our findings support further adoption of micro-scale built environment data and high-resolution analysis methods for active travel accessibility modelling in research and practice.","tags":null,"title":"Modelling active travel accessibility at the micro-scale using multi-source built environment data","type":"publication"},{"authors":["Afshin Jafari","Steve Pemberton","Dhirendra Singh","Tayebeh Saghapour","Alan Both","Lucy Gunn","Billie Giles-Corti"],"categories":null,"content":"","date":1743638400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1743638400,"objectID":"f87e9f33f6ff7593970dca4473ba6a9f","permalink":"https://alanboth.github.io/publication/2025-cycling-corridors/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/2025-cycling-corridors/","section":"publication","summary":"In car-dominated cities like Melbourne, Australia, limited data on cyclists’ travel patterns and socio-demographic differences complicate understanding of the effectiveness of infrastructure investment interventions aimed at promoting cycling. Recent advancements in city-scale transport modelling enable virtual testing of such interventions. However, the application of agent- and activity-based models for large-scale cycling simulations has been constrained by data and complexity. \n\nIn this study, we developed a city-scale agent-based simulation model for Greater Melbourne to evaluate changes in travel mode share from cycling infrastructure modifications. We clustered bicycle riders into five demographic groups: Maverick Males, Motivated Adults, Conscientious Commuters, Young Sprinters, and Relaxed Cruisers, estimating mode choice parameters for each group. Using aggregated smartphone application data, we developed a cycling trip routing methodology to incorporate road infrastructure impacts. \n\nResults indicated that travel time significantly influences mode choice across all clusters. Cycling infrastructure was crucial for four clusters, and travel cost influenced four clusters. The calibrated model assessed the potential impact of fully implementing Greater Melbourne’s strategic cycling corridors, a network of key cycling routes. Simulations suggested an initial 30% increase in cycling use, raising the mode share to approximately 2.6%, indicating a modest overall impact. Further analysis showed that even with full implementation, on average about half of the lengths of the routed bikeable trips would still occur on roads without any cycling infrastructure. This underscores the need to improve infrastructure on both major corridors and minor roads, and to complement these improvements with behavioural interventions.","tags":null,"title":"“Understanding the Impact of City-Wide Cycling Corridors on Cycling Mode Share among Different Demographic Clusters in Greater Melbourne, Australia","type":"publication"},{"authors":["Afshin Jafari","Dhirendra Singh","Lucy Gunn","Alan Both","Billie Giles-Corti"],"categories":null,"content":"","date":1740787200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1740787200,"objectID":"8cf55a0479f6604a2394145ea1a5830f","permalink":"https://alanboth.github.io/publication/2025-cycling-infrastructure/","publishdate":"2025-03-01T00:00:00Z","relpermalink":"/publication/2025-cycling-infrastructure/","section":"publication","summary":"Cycling for transport is a sustainable alternative to using motorised vehicles for daily trips and is a key form of micromobility. Travel time is a critical factor influencing cycling route choice behaviour and uptake. Thus, it is important to understand the factors affecting cycling travel time and speed and their impact on cycling behaviour. In this study, an agent-based transport simulation model with heterogeneous cycling speeds was developed and used for Melbourne to study the impact of a hypothetical traffic signal optimisation intervention along six key cycling corridors. \n\nLinear regression and random forest models were used to identify factors affecting cycling speed, which informed the parameters of the agent-based model. Simulation outputs showed, on average, an increase of 4.1 % in the number of cyclists on the corridors, as existing cyclists chose to use these corridors, and an average reduction in cyclists’ moving travel time of 6.2 % for those using the intervention corridors (excluding time spent waiting at traffic signals). The findings provide insights into the effects of road attributes on cycling speed and behaviour, as well as the effectiveness of interventions aimed at reducing cycling delays. These insights are valuable for developing solutions to optimise urban infrastructure for micromobility, enhancing the efficiency and appeal of cycling as a viable transport option.","tags":null,"title":"Modelling the impact of road infrastructure on cycling moving speed","type":"publication"},{"authors":["Faith Macale"],"categories":null,"content":"","date":1732320000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1732320000,"objectID":"ca959755756246d34c86e8f812cc583a","permalink":"https://alanboth.github.io/news/20241123-starweekly/","publishdate":"2024-11-23T00:00:00Z","relpermalink":"/news/20241123-starweekly/","section":"news","summary":"*Star Weekly* \n\nNew data from the National Growth Areas Alliance (NGAA) shows that one-in-five Australians face inadequate access to doctors, schools, sports …","tags":null,"title":"NGAA proposes new funding model for Growth Areas","type":"news"},{"authors":["Cait Kelly","Josh Nicholas"],"categories":null,"content":"","date":1732147200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1732147200,"objectID":"a4b49afeeddb0dddc48b68de9a786c2c","permalink":"https://alanboth.github.io/news/20241121-theguardian/","publishdate":"2024-11-21T00:00:00Z","relpermalink":"/news/20241121-theguardian/","section":"news","summary":"*The Guardian* \n\nToo many new developments lack green spaces and have fast food outlets built before essential infrastructure, peak body warns","tags":null,"title":"Australians in outer suburbs have far less access to schools, healthcare and public transport, report finds","type":"news"},{"authors":["Nicola Smith"],"categories":null,"content":"","date":1732147200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1732147200,"objectID":"d1d67e46948a4ceb37d1b88f0da4a4bd","permalink":"https://alanboth.github.io/news/20241121-thenightly/","publishdate":"2024-11-21T00:00:00Z","relpermalink":"/news/20241121-thenightly/","section":"news","summary":"*The Nightly* \n\nOne in five Australians living in outer-metropolitan suburbs suffers inadequate access to schools, healthcare and other amenities and basic infrastructure, according to a new report.","tags":null,"title":"Suburbs set to be left behind as population booms","type":"news"},{"authors":["Matt Dennien"],"categories":null,"content":"","date":1732147200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1732147200,"objectID":"6d281946964bbed87204e5608eefafe3","permalink":"https://alanboth.github.io/news/20241121-brisbanetimes/","publishdate":"2024-11-21T00:00:00Z","relpermalink":"/news/20241121-brisbanetimes/","section":"news","summary":"*Brisbane Times* \n\nA report on liveability across five Australian capital cities has highlighted gaps between established and growing areas. In Brisbane, one issue stands out.","tags":null,"title":"The outer Brisbane growth areas being left behind on public transport","type":"news"},{"authors":["Melanie Davern","Ori Gudes","Alan Both"],"categories":null,"content":"","date":1728864000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1728864000,"objectID":"d38c8289f77996ad09f59a99e3778d0c","permalink":"https://alanboth.github.io/grants/2024-scorecards/","publishdate":"2024-10-14T00:00:00Z","relpermalink":"/grants/2024-scorecards/","section":"grants","summary":"\n**Project dates:** October 2024 -- June 2025 \n\n\nGrowth Areas Liveability Scorecards were developed in partnership between the [National Growth Areas Alliance](https://ngaa.org.au/) and the Australian Urban Observatory. The main aim was to investigate and communicate the liveability of Growth Areas in comparison to other areas of capital cities. Growth Area Liveability Scorecards are available for [Adelaide](https://auo.org.au/wp-content/uploads/2024/11/Adelaide_Growth_Areas_City_Liveability_Scorecard.pdf), [Brisbane](https://auo.org.au/wp-content/uploads/2024/11/Brisbane_Growth_Areas_City_Liveability_Scorecard.pdf), [Melbourne](https://auo.org.au/wp-content/uploads/2024/11/Melbourne_Growth_Areas_City_Liveability_Scorecard.pdf), [Sydney](https://auo.org.au/wp-content/uploads/2024/11/Sydney_Growth_Areas_City_Liveability_Scorecard.pdf) and [Perth](https://auo.org.au/wp-content/uploads/2024/11/Perth_Growth_Areas_City_Liveability_Scorecard.pdf) and include comparison of liveability indicators between Growth Areas, non-growth areas and the overall city averages. All results are based on previously released Australian Urban Observatory 2021 city-level liveability [scorecards](https://auo.org.au/measure/scorecards/), while Growth Areas Liveability Scorecards can be downloaded from the map below. \n\nGrowth Areas are defined as the fastest growing Local Government Areas in the outer metropolitan regions of capital cities and spill over peri-urban regions on the fringe of these cities. The areas are characterised by rapid annual population growth, with a high proportion of young families in new residential urban development. These [Growth Areas of interest](https://ngaa.org.au/about) have experienced this type of development in either the past 20 years, or are expected to see rapid growth in the next 20 years. \n\nCity Scorecards provide observation to support understanding and further action. The reports highlight liveability inequity across Australian cities that includes areas with high liveability and areas where liveability could be enhanced through targeted liveability policy and planning action. Additional information on the liveability indicators included in the Growth Area Liveability Scorecards is available [here](https://auo.org.au/measure/) and detailed suburb and neighbourhood liveability results can be visually explored via the [Australian Urban Observatory map](https://new.map.auo.org.au/#register).","tags":null,"title":"Growth Area Scorecards by the Australian Urban Observatory","type":"grants"},{"authors":["Sarah Keszler"],"categories":null,"content":"","date":1726876800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1726876800,"objectID":"3b9e91601beede92a28226cf5c3db6ce","permalink":"https://alanboth.github.io/news/20240921-7news/","publishdate":"2024-09-21T00:00:00Z","relpermalink":"/news/20240921-7news/","section":"news","summary":"*7 News* \n\nNo Australian cities made the list of the top 15-minute cities, which included over 10,000 locations around the world.","tags":null,"title":"Australia flops in global study of ‘15-minute cities’","type":"news"},{"authors":["Ahmed Yussuf"],"categories":null,"content":"","date":1726531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1726531200,"objectID":"23f3fb7c43b16ba1e3e7fe465f644845","permalink":"https://alanboth.github.io/news/20240917-abcnews/","publishdate":"2024-09-17T00:00:00Z","relpermalink":"/news/20240917-abcnews/","section":"news","summary":"*ABC News* \n\nFew cities in the world could be classified as a 15-minute city, according to new international research.","tags":null,"title":"Europe doing better at creating 15-minute cities than Australia, research finds","type":"news"},{"authors":["Ahmed Yussuf"],"categories":null,"content":"","date":1720310400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1720310400,"objectID":"d0dd9d234da1f1270e86f98005a44391","permalink":"https://alanboth.github.io/news/20240707-abcnews/","publishdate":"2024-07-07T00:00:00Z","relpermalink":"/news/20240707-abcnews/","section":"news","summary":"*ABC News* \n\nThe time it takes Australian workers to get to work is increasing. What do experts think will help improve them?","tags":null,"title":"Most Australians take more than 30 minutes to get to work. How does that compare to other countries?","type":"news"},{"authors":["Afshin Jafari","Dhirendra Singh","Alan Both","Mahsa Abdollahyar","Lucy Gunn","Steve Pemberton","Billie Giles-Corti"],"categories":null,"content":"","date":1719532800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1719532800,"objectID":"191c7004ba27b1bb78211e339b0dadd0","permalink":"https://alanboth.github.io/publication/2024-atom2/","publishdate":"2024-06-28T00:00:00Z","relpermalink":"/publication/2024-atom2/","section":"publication","summary":"Activity- and agent-based models for simulating transport systems have attracted significant attention in recent years. However, building these types of models at a city-wide level and including motorized (i.e. cars and public transport) and non-motorized (i.e. walk and bicycle) modes of transport is a complicated and involved task. This paper presents an open workflow for creating large-scale multi-modal agent-based transport simulation models. The workflow brings together a number of external tools, for example, an activity-based demand generation tool and a road network generation tool, and a set of tools developed for the agent-based model parameter estimation, calibration, and simulation post-processing. \n\nWe used this workflow to create an activity- and agent-based model for Melbourne and compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behavior of travelers. The calibrated model could be used to test road network change interventions. In addition, a similar workflow can be applied for building simulation models for other cities or could be expanded to include more complicated travel behaviors.","tags":null,"title":"Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne","type":"publication"},{"authors":["Annika Burgess"],"categories":null,"content":"","date":1719100800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1719100800,"objectID":"e1149e6f7d35c550ddbd5a6149407608","permalink":"https://alanboth.github.io/news/20240623-abcnews/","publishdate":"2024-06-23T00:00:00Z","relpermalink":"/news/20240623-abcnews/","section":"news","summary":"*ABC News* \n\nGentrification continues to expand further away from the centre of major cities. There are several indicators that can give you a hint your suburb is heading for a transformation.","tags":null,"title":"There goes the neighbourhood. How to tell if your area is next in the gentrification firing line","type":"news"},{"authors":["Melanie Davern","Afshin Jafari","Alan Both","Jago Dodson","Lucy Gunn","Qian (Chayn) Sun"],"categories":null,"content":"","date":1717372800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1717372800,"objectID":"7a3a88dede86d08bd222adfbe7338663","permalink":"https://alanboth.github.io/publication/2024-cycling-article/","publishdate":"2024-06-03T00:00:00Z","relpermalink":"/publication/2024-cycling-article/","section":"publication","summary":"We want healthy, liveable cities and to cut emissions to net zero. Getting more people to use bicycles instead of cars will go a long way towards achieving these goals.","tags":null,"title":"Why do so few people cycle for transport in Australia? 6 ideas on how to reap all the benefits of bikes","type":"publication"},{"authors":["Cameron Atfield"],"categories":null,"content":"","date":1708473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1708473600,"objectID":"a6a58ed20533807f972027958b101550","permalink":"https://alanboth.github.io/news/20240221-smh/","publishdate":"2024-02-21T00:00:00Z","relpermalink":"/news/20240221-smh/","section":"news","summary":"*The Sydney Morning Herald* \n\nFor most, the quality – or otherwise – of their life can come down to one word – access. So how does Brisbane fare?","tags":null,"title":"If you build it, will they come? Calls for a rethink on social infrastructure planning","type":"news"},{"authors":["Cameron Atfield"],"categories":null,"content":"","date":1708387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1708387200,"objectID":"873fff58540c126aeed8dbf25f04c18d","permalink":"https://alanboth.github.io/news/20240220-smh/","publishdate":"2024-02-20T00:00:00Z","relpermalink":"/news/20240220-smh/","section":"news","summary":"*The Sydney Morning Herald* \n\nResearch suggests inner Brisbane is great for pedestrians, but advocates say it doesn’t tell the whole story.","tags":null,"title":"Striding or stumbling? Walkability in Brisbane a tale of two cities","type":"news"},{"authors":["Melanie Davern","Qian (Chayn) Sun","Afshin Jafari","Alan Both","Lucy Gunn","Jago Dodson"],"categories":null,"content":"","date":1704153600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1704153600,"objectID":"4bc016448c30dad0e61fc2f969afdc11","permalink":"https://alanboth.github.io/grants/2024-ianpotter/","publishdate":"2024-01-02T00:00:00Z","relpermalink":"/grants/2024-ianpotter/","section":"grants","summary":"**Budget:** $600,000 \n\n**Project dates:** January 2024 -- December 2029 \n\nThis grant supports a project by RMIT's Centre for Urban Research that will link multi-disciplinary research evidence on health and place to transform the development of urban planning that influences chronic ill health. The research team will evaluate existing infrastructure, cycling behaviour and area-based health outcomes to support healthy city design using the Australian Urban Observatory's digital liveability platform. \n\nPlanners and governments are struggling to support physically active communities, address the health impacts of climate change, support zero emissions and translate research evidence into healthy city planning. Cycling is an affordable, pollution-free, physically active transport mode that can prevent chronic ill health with the potential to replace short to medium-distance car trips that support local living. \n\nCurrent levels of cycling are impeded by a lack of safe cycling infrastructure, built and natural environment factors, traffic speeds and volumes and socio-cultural factors. More knowledge is needed on the relationship between cycling behaviour and infrastructure that incentivises cycling for transport. Cycling is an undervalued transport mode critical for net-zero policy ambition with public health benefits. \n\nWith this funding, the research team aim to address evidence and policy gaps by developing easy-to-use tools and research evidence that connects the presence of cycling infrastructure to cycling behaviour and health outcomes. \n\nThis project is a significant step towards providing evidence for urban planners, councils, and governments to adopt a public health perspective in city development.","tags":null,"title":"Linking health, place and urban planning through the Australian Urban Observatory","type":"grants"},{"authors":["Afshin Jafari","Tayebeh Saghapour","Chris De Gruyter","Belén Zapata-Diomedi","Alan Both"],"categories":null,"content":"","date":1704067200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1704067200,"objectID":"70ce570ebe5ead64857c418b87c54990","permalink":"https://alanboth.github.io/grants/2024-imove/","publishdate":"2024-01-01T00:00:00Z","relpermalink":"/grants/2024-imove/","section":"grants","summary":"**Budget:** $450,000 \n\n**Project dates:** January 2024 -- April 2026 \n\n\nThe Victorian State Government adopted a formal commitment of a 25% of walking or bike riding trips target by 2030. Fast-tracking the development of the bike riding network is a significant State priority over the next seven years. Turbo-charging bicycle infrastructure investments in regional areas is of high strategic importance. However, with the immense geographic scale of the state, it can be difficult to ascertain what investments should be prioritised. \n\nThere is a unique research opportunity to develop a framework for modelling benefits of bike/bicycle infrastructure investments and design a project prioritisation tool to generate evidence and compare benefits. \n\nThere is also a critical lack of bicycle infrastructure planning focus on regional spatial equity in Australia. \n\nTo achieve this, the Department of Transport and Planning (DTP) and the City of Greater Bendigo are partnering with RMIT University to develop the Victoria Bicycle Simulation and Prioritisation Modelling tool with a case study in Bendigo, that combines spatial, infrastructure and safety data integration, demand forecasting and econometric scenario projections.","tags":null,"title":"Framework for Assessing Benefits for Active Transport Investments in Regional Areas","type":"grants"},{"authors":["Department of Health and Aged Care"],"categories":null,"content":"","date":1701820800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1701820800,"objectID":"5e110cc8506e038a030c719a8674fb7f","permalink":"https://alanboth.github.io/news/20231206-health/","publishdate":"2023-12-06T00:00:00Z","relpermalink":"/news/20231206-health/","section":"news","summary":"The National Health and Climate Strategy sets out a whole-of-government plan to address the health and wellbeing impacts of climate change and address the contribution of the health system to climate change.","tags":null,"title":"National Health and Climate Strategy","type":"news"},{"authors":["Belen Zapata-Diomedi","Alan Both","Ali Abbas","James Woodcock","Annette Kroen","Melanie Davern","Lucy Gunn"],"categories":null,"content":"","date":1688169600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688169600,"objectID":"1d4b7899485f3890ffd268e1945075f1","permalink":"https://alanboth.github.io/publication/2023-shifting-car-travel/","publishdate":"2023-07-01T00:00:00Z","relpermalink":"/publication/2023-shifting-car-travel/","section":"publication","summary":"*Introduction* \n\nBeing physically active has multiple health benefits and contributes to the reduction of co-morbidities and mortality from chronic diseases. Active transport (walking and cycling) contributes to population health by enabling physical activity. We developed a simulation model to measure health impacts of transport scenarios for Melbourne, Australia. Our aim was to demonstrate active transport health impacts and support the materialization of policies for healthy cities and people. The model measures health impacts of increased physical activity from replacing short car trips for any purpose or for commuting under 5 km by walking and cycling. \n\n*Methods* \n\nWe developed a micro-simulation model of physical activity and disease risk in combination with the well-established proportional multi-state life table model. We quantified life course health including health adjusted-life years, life years, new cases of diseases prevented, and deaths prevented for 14 chronic diseases associated with physical inactivity for the adult population of people from Melbourne, Australia in 2017. \n\n*Results* \n\nOver the life course of the Melbourne adult population of 3.6 million people in 2017, gains in health-adjusted life years ranged from 5,100 (95% Uncertainty Interval (UI) 3,700 to 6,500) for the scenario replacing commute trips by car under 1 km with walking up to 738,800 (95% UI 546,000 to 935,000) when replacing car trips under 2 km with walking and between 2 km and 5 km with cycling. We also estimated benefits in terms of reductions of new cases of diseases and deaths prevented, with the greatest gains for ischemic heart disease, stroke, Alzheimer's and other dementias and type 2 diabetes. \n\n*Conclusions* \n\nWe found that shifting car travel to active modes would accrue important health benefits for the 2017 Melbourne population. Our results support policies and strategies for sustainable transport planning to contribute to reduce the burden from chronic diseases and environmental impact of car-oriented cities.","tags":null,"title":"Shifting car travel to active modes to improve population health and achieve transport goals: A simulation study","type":"publication"},{"authors":["Mostafa Rachwani"],"categories":null,"content":"","date":1686182400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1686182400,"objectID":"d55c676a7a8fbd961c57e2c03937bbd6","permalink":"https://alanboth.github.io/news/20230608-theguardian2/","publishdate":"2023-06-08T00:00:00Z","relpermalink":"/news/20230608-theguardian2/","section":"news","summary":"*The Guardian* \n\nTeachers, nurses, social workers and emergency responders are already clustered in areas where housing is cheaper, data shows","tags":null,"title":"Essential workers priced out of housing near Sydney workplaces face even longer commutes","type":"news"},{"authors":["Josh Nicholas"],"categories":null,"content":"","date":1686182400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1686182400,"objectID":"79534dbf8d2d23bd1f4953001830f8a9","permalink":"https://alanboth.github.io/news/20230608-theguardian/","publishdate":"2023-06-08T00:00:00Z","relpermalink":"/news/20230608-theguardian/","section":"news","summary":"*The Guardian* \n\nExclusive: A third of suburbs in Sydney and Adelaide are already highly gentrified – and other capitals are on a similar trajectory","tags":null,"title":"How gentrified is your postcode? Search our map of Australia’s capital cities","type":"news"},{"authors":["Melanie Davern","Alan Both","Katherine Murray","Rebecca Roberts","Fadhillah Norzahari"],"categories":null,"content":"","date":1677456000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1677456000,"objectID":"7c5ca0c2ddf091b995879f1f87edfd27","permalink":"https://alanboth.github.io/publication/2023-liveability-research/","publishdate":"2023-02-27T00:00:00Z","relpermalink":"/publication/2023-liveability-research/","section":"publication","summary":"Urbanisation is occurring globally and rapidly with potential to compromise the development of sustainable, liveable and healthy cities. Urban observatories have also existed for many years addressing a range of relevant urban issues. These observatories provide a unique method to translate research into practice, support evidence-informed policy and planning, target actions of the sustainable development goals, address spatially based health inequities and improve the liveability of cities. This paper provides an analysis of the Australian Urban Observatory, a digital liveability planning platform using urban analytics to observe and enhance understanding of liveability inequities in Australian cities that is linked to policy and planning. The analysis aims to share learnings about development of the Australian Urban Observatory, including the conceptual framework of liveability, planning tools, and the resulting impact in policy and planning applications. This is the first urban observatory in Australia that will continue to expand and develop over time, supporting urban governance, democratic process and creating real world policy impact through partnership between academia, government, industry and the community.","tags":null,"title":"Liveability research creating real world impact: connecting urban planning and public health through the Australian Urban Observatory","type":"publication"},{"authors":["Abdur Forkan","Alan Both","Chris Bellman","Matt Duckham","Hamish Anderson","Nenad Radosevic"],"categories":null,"content":"","date":1677196800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1677196800,"objectID":"b66ce140925c359e8a33c4c57e811aec","permalink":"https://alanboth.github.io/publication/2023-kspan/","publishdate":"2023-02-24T00:00:00Z","relpermalink":"/publication/2023-kspan/","section":"publication","summary":"This paper describes the design, development, and testing of a general-purpose scientific-workflows tool for spatial analytics. Spatial analytics processes are frequently complex, both conceptually and computationally. Adaptation, documention, and reproduction of bespoke spatial analytics procedures represents a growing challenge today, particularly in this era of big spatial data. Scientific workflow systems hold the promise of increased openness and transparency with improved automation of spatial analytics processes. In this work, we built and implemented a KNIME spatial analytics (“K-span”) software tool, an extension to the general-purpose open-source KNIME scientific workflow platform. The tool augments KNIME with new spatial analytics nodes by linking to and integrating a range of existing open-source spatial software and libraries. The implementation of the K-span system is demonstrated and evaluated with a case study associated with the original process of construction of the Australian national DEM (Digital Elevation Model) in the Greater Brisbane area of Queensland, Australia by Geoscience Australia (GA). The outcomes of translating example spatial analytics process into a an open, transparent, documented, automated, and reproducible scientific workflow highlights the benefits of using our system and our general approach. These benefits may help in increasing users’ assurance and confidence in spatial data products and in understanding of the provenance of foundational spatial data sets across diverse uses and user groups.","tags":null,"title":"K-span: Open and reproducible spatial analytics using scientific workflows","type":"publication"},{"authors":["Belén Zapata-Diomedi","Afshin Jafari","James Woodcock","Rolf Moeckel","Alan Both"],"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"fd8bd84112774ec21bcfbda7796ce42d","permalink":"https://alanboth.github.io/grants/2023-vichealth/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/grants/2023-vichealth/","section":"grants","summary":"**Budget:** $230,000 \n\n**Project dates:** August 2023 -- January 2025 \n\nIncreasing active transport (walking and cycling) and decreasing private car use have significant health and environmental benefits and is a recognised priority across multiple sectors. The urgency to build neighbourhoods that encourage active transport has been accelerated locally and globally through the COVID-19 pandemic. Yet achieving equitable access to walkable and cyclable neighbourhoods is challenging in low-density sprawling cities like Melbourne. Policymakers and health advocates are keen to understand how to change policies to deliver local living and a cycling-friendly Melbourne and the health and equity impacts of these policies. This is particularly relevant for those living in the most car-dependant areas and experiencing greater systemic spatial and health inequities. At RMIT’s Healthy Liveable Cities Lab (HLCL) we are building a city-wide computer transport and health simulation model for Greater Melbourne capable of assessing the impact of built environment change scenarios on individuals’ transport behaviour choices, their level of physical activity, exposure to air and noise pollution and greenspace, injury risk and health. However, the complexity of the model is a major obstacle in effective knowledge translation and therefore impact. \n\nThe VicHealth Impact Grant will catalyse the potential impact of our work by creating a tool to enable knowledge translation with transport, planning and health policymakers, technical practitioners, and advocates. We will co-create a visualisation tool for our transport and health model results with stakeholders to support creating and implementing healthy built environment policies. The tool will provide policy-relevant evidence on built environment change scenarios that maximise active transport uptake, redress spatial and health inequities and inform investments in high impact-built environment interventions. This project will benefit from our well-established collaborations with government and non-government organisations facilitated through HLCL’s Policy Advisory Group (PAG). The PAG is chaired by the CEO of Infrastructure Victoria and includes all responsible sectors for delivering a healthy built environment for all Melburnians, including Department of Transport and Planning.","tags":null,"title":"Developing tools for knowledge translation in transport and health modelling","type":"grants"},{"authors":["Department of Infrastructure, Transport, Regional Development, Communications and the Arts"],"categories":null,"content":"","date":1670371200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1670371200,"objectID":"6bc26d32311a92368b007cd35f0259b8","permalink":"https://alanboth.github.io/news/20221207-infrastructure/","publishdate":"2022-12-07T00:00:00Z","relpermalink":"/news/20221207-infrastructure/","section":"news","summary":"The Bureau of Communications, Arts and Regional Research (BCARR) has undertaken a South East Queensland (SEQ) research project to provide an evidence base on the spatial distribution of population growth, jobs, connectivity and liveability of SEQ. This evidence base can be used to monitor how population, jobs, connectivity and liveability change over time and respond to investment. The report aims to support the Department's policy and project delivery.","tags":null,"title":"South East Queensland population, housing, jobs, connectivity and liveability","type":"news"},{"authors":["Billie Giles-Corti","Tayebeh Saghapour","Gavin Turrell","Lucy Gunn","Alan Both","Melanie Lowe","Julianna Rozek","Rebecca Roberts","Paula Hooper","Andrew Butt","Carl Higgs"],"categories":null,"content":"","date":1665532800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1665532800,"objectID":"09ac4b1b3c234da7f3439e696377de3c","permalink":"https://alanboth.github.io/publication/2022-spatial-inequities/","publishdate":"2022-10-12T00:00:00Z","relpermalink":"/publication/2022-spatial-inequities/","section":"publication","summary":"Spatial and area-level socioeconomic variation in urban liveability (access to social infrastructure, public transport, open space, healthy food choices, local employment, street connectivity, dwelling density, and housing affordability) was examined and mapped across 39,967 residential statistical areas in Australia's metropolitan (n = 7) and largest regional cities (n = 14). Urban liveability varied spatially, with inner-city areas more liveable than outer suburbs. Disadvantaged areas in larger metropolitan cities were less liveable than advantaged areas, but this pattern was reversed in smaller cities. Local data could inform policies to redress inequities, including those designed to avoid disadvantage being suburbanised as cities grow and gentrify.","tags":null,"title":"Spatial and socioeconomic inequities in liveability in Australia’s 21 largest cities: Does city size matter?","type":"publication"},{"authors":["Melanie Davern","Alan Both","Jago Dodson","Tiebei (Terry) Li"],"categories":null,"content":"","date":1665360000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1665360000,"objectID":"91e9c91daf6cda971ef6f49d4e9e6d89","permalink":"https://alanboth.github.io/publication/2022-jtw-article/","publishdate":"2022-10-10T00:00:00Z","relpermalink":"/publication/2022-jtw-article/","section":"publication","summary":"City planning needs up-to-date data on where people work, how they get to work and how far they travel. Normally the census provides that, but this time round our biggest cities were in lockdown.","tags":null,"title":"COVID skewed journey-to-work census data. Here’s how city planners can make the best of it","type":"publication"},{"authors":["Carl Higgs","Amanda Alderton","Julianna Rozek","Deepti Adlakha","Hannah Badland","Geoff Boeing","Alan Both","Ester Cerin","Manoj Chandrabose","Chris De Gruyter","Alysha De Livera","Lucy Gunn","Erica Hinckson","Shiqin Liu","Suzanne Mavoa","James F. Sallis","Koen Simons","Billie Giles-Corti"],"categories":null,"content":"","date":1662336000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1662336000,"objectID":"6c618ba84ff5fa6b61ee501bf936ccea","permalink":"https://alanboth.github.io/publication/2022-policy-relevant-spatial-indicators/","publishdate":"2022-09-05T00:00:00Z","relpermalink":"/publication/2022-policy-relevant-spatial-indicators/","section":"publication","summary":"Urban liveability is a global priority for creating healthy, sustainable cities. Measurement of policy-relevant spatial indicators of the built and natural environment supports city planning at all levels of government. Analysis of their spatial distribution within cities, and impacts on individuals and communities, is crucial to ensure planning decisions are effective and equitable. This paper outlines challenges and lessons from a 5-year collaborative research program, scaling up a software workflow for calculating a composite indicator of urban liveability for residential address points across Melbourne, to Australia’s 21 largest cities, and further extension to 25 global cities in diverse contexts.","tags":null,"title":"Policy-Relevant Spatial Indicators of Urban Liveability And Sustainability: Scaling From Local to Global","type":"publication"},{"authors":["Department of Infrastructure, Transport, Regional Development, Communications and the Arts"],"categories":null,"content":"","date":1658361600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1658361600,"objectID":"5f1e27908c5b5cca2489232536af7b3e","permalink":"https://alanboth.github.io/news/20220721-infrastructure/","publishdate":"2022-07-21T00:00:00Z","relpermalink":"/news/20220721-infrastructure/","section":"news","summary":"This chart pack provides a summary of population, employment, housing and liveability trends for our cities and regions. Key findings include:\n * The population in capital cities is expected to grow more rapidly than regional Australia over the next 12 years\n * Service industries will drive employment growth both in capital cities and regional Australia over the five years to November 2025.\n * Inner regional, Outer regional and Remote areas experienced strong growth in their dwelling approvals from 2016 to 2021.\n * Capital cities have better access to health and education services and greater walkability.","tags":null,"title":"Snapshot of Cities and Regions—chart pack","type":"news"},{"authors":["Alan Both","Lucy Gunn","Carl Higgs","Melanie Davern","Afshin Jafari","Claire Boulange","Billie Giles-Corti"],"categories":null,"content":"","date":1642032000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1642032000,"objectID":"fc67b1bbd308dbb72e83daf2e3dd1237","permalink":"https://alanboth.github.io/publication/2022-30-minute-cities/","publishdate":"2022-01-13T00:00:00Z","relpermalink":"/publication/2022-30-minute-cities/","section":"publication","summary":"Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, is being pursued in some cities to reduce congestion and foster local living. \n\nThis paper examines the spatial relationship between employment, the skills of residents, and transport opportunities, to answer three questions about Australia’s 21 largest cities: (1) What percentage of workers currently commute to their workplace within 30 min? (2) If workers were to shift to an active transport mode, what percent could reach their current workplace within 30 min? and (3) If it were possible to relocate workers closer to their employment or relocate employment closer to their home, what percentage could reach work within 30 min by each mode? \n\nActive transport usage in Australia is low, with public transport, walking, and cycling making up 16.8%, 2.8%, and 1.1% respectively of workers’ commutes. Cycling was found to have the most potential for achieving the 30 min city, with an estimated 29.5% of workers able to reach their current workplace were they to shift to cycling. This increased to 69.1% if workers were also willing and able to find a similar job closer to home, potentially reducing commuting by private motor vehicle from 79.3% to 30.9%.","tags":null,"title":"Achieving ‘Active’ 30 Minute Cities: How Feasible Is It to Reach Work within 30 Minutes Using Active Transport Modes?","type":"publication"},{"authors":["Afshin Jafari","Alan Both","Dhirendra Singh","Lucy Gunn","Billie Giles-Corti"],"categories":null,"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"0ec9c58ef753b593fb72daf92f451358","permalink":"https://alanboth.github.io/publication/2022-network/","publishdate":"2022-01-01T00:00:00Z","relpermalink":"/publication/2022-network/","section":"publication","summary":"City-scale simulation modelling of active modes of transportation (i.e., walking and cycling) is becoming increasingly popular in recent years. The heterogeneous and complex behaviour of these transportation modes, however, indicates the need for a shift from the traditional car and public transport centred modelling approaches towards incorporating the requirements for walking and cycling behaviour, while maintaining the run-time efficiency of the models. In this paper, we introduce and test our algorithm to create road network representations, designed and optimised to be used in city-scale active transportation modelling. The algorithm relies on open and universal data. In addition to the major roads and attributes typically used in transport modelling (e.g., speed limit, number of lanes, permitted travel modes), the algorithm also captures minor roads usually favoured by pedestrians and cyclists, along with road attributes such as bicycle-specific infrastructure, traffic signals, road gradient and road surface type. Furthermore, it simplifies the complex geometries of the network and merges parallel roads, if applicable, to make it suitable for large-scale simulations. \n\nTo examine the utility and performance of the algorithm, we used it to create a network representation for Greater Melbourne, Australia, and compared the output with a network created using an existing simulation toolkit along with another network from an existing city-scale transport model from the Victorian government. Through simulation experiments with these networks, we illustrated that for routed trips on our network for walking and cycling, it is of comparable accuracy to the common network conversion tools in terms of travel distance of the shortest paths while being more than two times faster when used for simulating different sample sizes. Therefore, our algorithm offers a flexible and adjustable solution for users to create road networks for city-scale active transport modelling while balancing between their desired simulation accuracy and run-time.","tags":null,"title":"Building the road network for city-scale active transport simulation models","type":"publication"},{"authors":["Afshin Jafari","Dhirendra Singh","Alan Both","Lucy Gunn","Steve Pemberton","Billie Giles-Corti"],"categories":null,"content":"","date":1639526400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1639526400,"objectID":"8478407eb83ea086f230923c518fe040","permalink":"https://alanboth.github.io/publication/2021-atom/","publishdate":"2021-12-15T00:00:00Z","relpermalink":"/publication/2021-atom/","section":"publication","summary":"Agent-based and activity-based models for simulating transportation systems have attracted significant attention in recent years. Few studies, however, include a detailed representation of active modes of transportation - such as walking and cycling - at a city-wide level, where dominating motorised modes are often of primary concern. This paper presents an open workflow for creating a multi-modal agent-based and activity-based transport simulation model, focusing on Greater Melbourne, and including the process of mode choice calibration for the four main travel modes of driving, public transport, cycling and walking. \n\nThe synthetic population generated and used as an input for the simulation model represented Melbourne's population based on Census 2016, with daily activities and trips based on the Victoria's 2016-18 travel survey data. The road network used in the simulation model includes all public roads accessible via the included travel modes. We compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behaviour of travellers.","tags":null,"title":"Activity-based and agent-based Transport model of Melbourne (AToM): an open multi-modal transport simulation model for Greater Melbourne","type":"publication"},{"authors":["Sarah Hill","Zena Cumpston","Gabriela Quintana Vigiola","Tanya Koeneman"],"categories":null,"content":"","date":1638316800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1638316800,"objectID":"158082c017454099821080ce5945ccf9","permalink":"https://alanboth.github.io/news/20211201-soe/","publishdate":"2021-12-01T00:00:00Z","relpermalink":"/news/20211201-soe/","section":"news","summary":"Australia is one of the most urbanised countries in the world – more than 96% of the Australian population (around 24.5 million) live in urban areas and 68% live within the greater metropolitan areas of Australia’s 8 capital cities.","tags":null,"title":"Livability | Australia state of the environment 2021","type":"news"},{"authors":["Alan Both","Dhirendra Singh","Afshin Jafari","Billie Giles-Corti","Lucy Gunn"],"categories":null,"content":"","date":1637280000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1637280000,"objectID":"652ad3f3c2971a0fc52b043bd3f3dbc5","permalink":"https://alanboth.github.io/publication/2021-virtual-population/","publishdate":"2021-11-19T00:00:00Z","relpermalink":"/publication/2021-virtual-population/","section":"publication","summary":"In this paper, we present an algorithm for creating a synthetic population for the Greater Melbourne area using a combination of machine learning, probabilistic, and gravity-based approaches. We combine these techniques in a hybrid model with three primary innovations: 1. when assigning activity patterns, we generate individual activity chains for every agent, tailored to their cohort; 2. when selecting destinations, we aim to strike a balance between the distance-decay of trip lengths and the activity-based attraction of destination locations; and 3. we take into account the number of trips remaining for an agent so as to ensure they do not select a destination that would be unreasonable to return home from. Our method is completely open and replicable, requiring only publicly available data to generate a synthetic population of agents compatible with commonly used agent-based modeling software such as MATSim. The synthetic population was found to be accurate in terms of distance distribution, mode choice, and destination choice for a variety of population sizes.","tags":null,"title":"An Activity-Based Model of Transport Demand for Greater Melbourne","type":"publication"},{"authors":["Valentina Cerutti","Chris Bellman","Alan Both","Matt Duckham","Bernhard Jenny","Rob Lemmens","Frank Ostermann"],"categories":null,"content":"","date":1630540800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1630540800,"objectID":"303b2422a6b53c510a59096593e5b6b2","permalink":"https://alanboth.github.io/publication/2021-scientific-workflows/","publishdate":"2021-09-02T00:00:00Z","relpermalink":"/publication/2021-scientific-workflows/","section":"publication","summary":"Reproducibility is widely regarded as crucial for scientific studies, yet there is still a lack of reproducibility in geospatial research. New sources of crowdsourced geoinformation provide new opportunities, but also complicate the reproducibility situation. Consequently, there is untapped potential in the domain of disaster response to reuse scientific methodology. Shared, executable scientific workflows can help in improving reproducibility. \n\nIn this paper, we created reproducible scientific workflows for disaster response from three published studies using geosocial media sources. They have been adapted to a scientific workflow management system to investigate and evaluate their suitability for the creation of geospatial footprints of wildfire events from Twitter data. We investigated how scientific workflows adapt to various analytical processes and compared their performance using MODIS active fires data as ground truth. A systematic qualitative and quantitative evaluation demonstrated that scientific workflows can help increase the reproducibility of geospatial analytics.","tags":null,"title":"Improving the reproducibility of geospatial scientific workflows: the use of geosocial media in facilitating disaster response","type":"publication"},{"authors":["Yaguang Tao","Alan Both","Rodrigo I. Silveira","Kevin Buchin","Stef Sijben","Ross Purves","Patrick Laube","Dongliang Peng","Kevin Toohey","Matt Duckham"],"categories":null,"content":"","date":1625356800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1625356800,"objectID":"76e64d063e57af45a25436b61034baba","permalink":"https://alanboth.github.io/publication/2021-trajectory/","publishdate":"2021-07-04T00:00:00Z","relpermalink":"/publication/2021-trajectory/","section":"publication","summary":"Computing trajectory similarity is a fundamental operation in movement analytics, required in search, clustering, and classification of trajectories, for example. Yet the range of different but interrelated trajectory similarity measures can be bewildering for researchers and practitioners alike. This paper describes a systematic comparison and methodical exploration of trajectory similarity measures. Specifically, this paper compares five of the most important and commonly used similarity measures: dynamic time warping (DTW), edit distance (EDR), longest common subsequence (LCSS), discrete Fréchet distance (DFD), and Fréchet distance (FD). \n\nThe paper begins with a thorough conceptual and theoretical comparison. This comparison highlights the similarities and differences between measures in connection with six different characteristics, including their handling of a relative versus absolute time and space, tolerance to outliers, and computational efficiency. The paper further reports on an empirical evaluation of similarity in trajectories with contrasting properties: data about constrained bus movements in a transportation network, and the unconstrained movements of wading birds in a coastal environment. \n\nA set of four experiments: a. creates a measurement baseline by comparing similarity measures to a single trajectory subjected to various transformations; b. explores the behavior of similarity measures on network-constrained bus trajectories, grouped based on spatial and on temporal similarity; c. assesses similarity with respect to known behavioral annotations (flight and foraging of oystercatchers); and d. compares bird and bus activity to examine whether they are distinguishable based solely on their movement patterns. \n\nThe results show that in all instances both the absolute value and the ordering of similarity may be sensitive to the choice of measure. In general, all measures were more able to distinguish spatial differences in trajectories than temporal differences. The paper concludes with a high-level summary of advice and recommendations for selecting and using trajectory similarity measures in practice, with conclusions spanning our three complementary perspectives: conceptual, theoretical, and empirical.","tags":null,"title":"A comparative analysis of trajectory similarity measures","type":"publication"},{"authors":["Luke Wallace","Qian (Chayn) Sun","Bryan Hally","Samuel Hillman","Alan Both","Joe Hurley","Daisy San Martin Saldias"],"categories":null,"content":"","date":1622505600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1622505600,"objectID":"827e0d08807f15cc8b580414678eca5e","permalink":"https://alanboth.github.io/publication/2021-urban-tree/","publishdate":"2021-06-01T00:00:00Z","relpermalink":"/publication/2021-urban-tree/","section":"publication","summary":"Urban trees provide a range of vital social and environmental services. Currently, inventories of individual urban trees are conducted in-situ by professional arborists. Such an approach to urban tree inventories means they are challenging to maintain and only capture information describing trees on accessible land. Whilst remote sensing approaches have shown the potential to derive individual tree attributes, these studies rarely make use of existing inventory information. \n\nIn this study, we present a method to parameterise an algorithm for individual tree detection and delineation from airborne remote sensing data. The approach uses existing inventory data as training information firstly for the detection of the canopy area and secondly to parameterise a marker-based watershed segmentation algorithm. In this parameterisation, crown segmentation shape, as well as features derived from the remote sensing data, are used to determine if a segment contains one or more trees. If a segment contains more than one tree, it is split with the number of markers increased until each segment includes only one tree. \n\nThe approach was evaluated within three distinct urban areas: the central business district, an urban park and a residential area, to be determined. Commission and omission errors ranged between 11% and 27% across the three regions, with commission typically caused by land covers on private land that were unaccounted for in the training processes. In all areas the overall tree count was within two per cent of that defined by reference information. The accurate tree count produced by this approach suggests it has the potential to be adopted by government agencies for routine tree inventory maintenance.","tags":null,"title":"Linking urban tree inventories to remote sensing data for individual tree mapping","type":"publication"},{"authors":["Qian (Chayn) Sun","Tania Macleod","Alan Both","Joe Hurley","Andrew Butt","Marco Amati"],"categories":null,"content":"","date":1617235200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1617235200,"objectID":"855d03f5773ecb8e042fc35e91f76a0a","permalink":"https://alanboth.github.io/publication/2021-heat-mitigation/","publishdate":"2021-04-01T00:00:00Z","relpermalink":"/publication/2021-heat-mitigation/","section":"publication","summary":"Hot weather not only impacts upon human physical comfort and health, but also impacts the way that people access and experience active travel options such as walking and cycling. By evaluating the street thermal environment of a city alongside an assessment of those communities that are the most vulnerable to the effects of heat, we can prioritise areas in which heat mitigation interventions are most needed. \n\nIn this paper, we propose a new approach for policy makers to determine where to delegate limited resources for heat mitigation with most effective outcomes for the communities. We use eye-level street panorama images and community profiles to provide a bottom-up, human-centred perspective of the city scale assessment, highlighting the situation of urban tree shade provision throughout the streets in comparison with environmental and social-economic status. The approach leverages multiple sources of spatial data including satellite thermal images, Google street view (GSV) images, land use and demographic census data. \n\nA deep learning model was developed to automate the classification of streetscape types and percentages at the street- and eye-view level. The methodology is metrics based and scalable which provides a data driven assessment of heat-related vulnerability. The findings of this study first contribute to sustainable development by developing a method to identify geographical areas or neighbourhoods that require heat mitigation; and enforce policies improving tree shade on routes, as a heat adaptation strategy, which will lead to increasing active travel and produce significant health benefits for residents. The approach can be also used to guide post COVID-19 city planning and design.","tags":null,"title":"A human-centred assessment framework to prioritise heat mitigation efforts for active travel at city scale","type":"publication"},{"authors":["Billie Giles-Corti","Belen Zapata-Diomedi","Afshin Jafari","Alan Both","Lucy Gunn"],"categories":null,"content":"","date":1606780800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606780800,"objectID":"7e5eddc9c72f375006c7a2c6f06fb3e3","permalink":"https://alanboth.github.io/publication/2020-distrupted-cities/","publishdate":"2020-12-01T00:00:00Z","relpermalink":"/publication/2020-distrupted-cities/","section":"publication","summary":"*Background:* Since the late 19th century, city planners have struggled to cope with new types of urban transport and mobility that threatened the existing system, or even rendered it obsolete. \n\n*Purpose:* As city planners confront the range of disruptive urban mobilities currently on the horizon, this paper explores how we can draw on a vast body of evidence to anticipate and avoid unintended consequences to people's health and wellbeing. \n\n*Methods:* This commentary involved a rapid review of the literature on transport disruption. \n\n*Results:* We found that to avoid the unintended consequences of disruption, research, policy and practice must think beyond single issues (such as the risk of chronic disease, injury, or traffic management) and consider the broader consequences of interventions. For example, although autonomous vehicles will probably reduce road trauma, what will be the negative consequences for physical inactivity, sedentary behavior, chronic disease, land use, traffic congestion and commuting patterns? Research is needed that considers and informs how to mitigate the range of potential harms caused by disruptive mobilities. \n\n*Conclusion:* In the face of new disruptive mobilities, we must: (a) draw on existing evidence to shape new regulations that address the ‘who, when and where’ rules of introducing new mobilities (such as electric assisted bicycles (e-bikes) and scooters (e-scooters)) of which the health repercussions can be easily anticipated; (b) monitor and evaluate the implementation of any interventions through natural experiment studies; and (c) use innovative research methods (such as agent-based simulation and health-impact-assessment modelling) to assess the likely effects of emerging disruptive mobilities (e.g., autonomous vehicles) on health and wellbeing and on the environment.","tags":null,"title":"Could smart research ensure healthy people in disrupted cities?","type":"publication"},{"authors":null,"categories":null,"content":"","date":1606435200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606435200,"objectID":"f74ea358881e2e1ce974f214029dce76","permalink":"https://alanboth.github.io/project/atom/","publishdate":"2020-11-27T00:00:00Z","relpermalink":"/project/atom/","section":"project","summary":"Activity-based and agent-based Transport model of Melbourne (AToM) is a city-scale multi-modal transport simulation, modelling a full day in the transportation system of Melbourne for understanding intended and unintended consequences of a change in the environment on individuals’ travel behaviour. AToM is an open-source simulation model that to represent mobility on a typical mid-weekday in Melbourne. \n\nAToM includes transportation by car, public transport, cycling, and walking and models travel behaviours at an individual traveller and road segment level. Using the baseline mobility of Melbourne captured in AToM, it is possible to examine impacts of “what-if” built environment interventions on travel behaviour.","tags":null,"title":"AToM","type":"project"},{"authors":null,"categories":null,"content":"","date":1606435200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606435200,"objectID":"fbc963239397604ffcab77dad0580441","permalink":"https://alanboth.github.io/project/auo/","publishdate":"2020-11-27T00:00:00Z","relpermalink":"/project/auo/","section":"project","summary":" The Australian Urban Observatory is a digital liveability planning platform that transforms complex urban data into easily understood liveability maps across Australia’s 21 largest cities. The Observatory draws on over 10 years of policy-relevant research and is located within the Centre for Urban Research at RMIT University. \n\nThe Observatory maps key liveability indicators found to be associated with health and wellbeing, and provides a clear understanding of the liveability of cities. The Observatory provides information and understanding to support resource allocation, future policy action and support to create equitable, healthy and liveable places. ","tags":null,"title":"Australian Urban Observatory","type":"project"},{"authors":null,"categories":null,"content":"","date":1606435200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606435200,"objectID":"06fd7a4056116b9fcd9aaf30b3b538c4","permalink":"https://alanboth.github.io/project/that/","publishdate":"2020-11-27T00:00:00Z","relpermalink":"/project/that/","section":"project","summary":" *The Transport Health Assessment Tool for Brisbane (THAT-Brisbane)* was developed as a quantitative Health Impact Assessment model to support evidence-informed planning for healthier cities. *THAT-Brisbane* extends on the existing and award winning *THAT-Melbourne* tool and demonstrates health benefits and health care cost saving associated with changing short care trips, to walking and cycling. \n\nBoth the Melbourne and Brisbane tools quantify the impact of transport choices on health. However, *THAT-Brisbane* extends on previous modelling by assessing the health impacts on additional chronic diseases and health care cost savings from changing short car trips to active transport modes. ","tags":null,"title":"Transport Health Assessment Tools (THAT)","type":"project"},{"authors":["Melanie Davern","Alan Both","Carl Higgs","Lucy Gunn"],"categories":null,"content":"","date":1584316800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1584316800,"objectID":"cfdb531968a77095bad76a7dfc1d263d","permalink":"https://alanboth.github.io/publication/2020-regional-liveability-article/","publishdate":"2020-03-16T00:00:00Z","relpermalink":"/publication/2020-regional-liveability-article/","section":"publication","summary":"Geelong, Albury-Wodonga and Wollongong do as well on access to services as many capitals and even outperform a few, but poor planning is affecting liveability for too many regional city residents.","tags":null,"title":"The average regional city resident lacks good access to two-thirds of community services, and liveability suffers","type":"publication"},{"authors":["Billie Giles-Corti","James Woodcock","Belén Zapata-Diomedi","Lucy Gunn","Liton Kamruzzaman","Alan Both","Dhirendra Singh","Luke Knibbs","Gavin Turrell"],"categories":null,"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"be9654c9b2bc35b54ea63939d479fc2d","permalink":"https://alanboth.github.io/grants/2020-jibe/","publishdate":"2020-01-01T00:00:00Z","relpermalink":"/grants/2020-jibe/","section":"grants","summary":"**Budget:** $814,558 \n\n**Project dates:** 2020 -- 2024 \n\nCreating healthy, sustainable, ‘liveable’ cities is a priority in both Australia and the UK. Growing evidence suggests how we plan our cities can affect preventable health risks such as physical inactivity, obesity, noise and air pollution, and road trauma. \n\nBy testing and estimating the health impacts of scenarios in urban and transport planning interventions in different contexts, we can inform city planners and public health practitioners about what scenarios have the greatest chance of promoting good health for future planning. \n\nFunded by the UK Medical Research (UKRI) and the Australian National Health and Medical Research Council (NHMRC), this project brings together research linking the built environment, transport and other health behaviours to develop computer models that can better inform urban and transport planning policy and practice in Australia and the UK. \n\nThe project is led by Dr Belen Zapata-Diomedi at RMIT University and Professor James Woodcock at the University of Cambridge, and involves a multi-disciplinary team of leading researchers with complementary expertise across Australia (Monash University, University of Melbourne, University of Queensland) and England (Imperial College London, London School of Hygiene and Tropical Medicine, University of Leicester). Emeritus Professor Billie Giles-Corti was a member of the leadership team from 2020 until her retirement in December 2022.","tags":null,"title":"Joining Impact models of transport with spatial measures of the Built Environment (JIBE)","type":"grants"},{"authors":["Andrew Campbell","Alan Both","Qian (Chayn) Sun"],"categories":null,"content":"","date":1567296000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567296000,"objectID":"71c82cb3f50661a4860f5b269d4e158c","permalink":"https://alanboth.github.io/publication/2019-stop-signs/","publishdate":"2019-09-01T00:00:00Z","relpermalink":"/publication/2019-stop-signs/","section":"publication","summary":"Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. \n\nThis paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. \n\nThe experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset.","tags":null,"title":"Detecting and mapping traffic signs from Google Street View images using deep learning and GIS","type":"publication"},{"authors":["Joe Hurley","Alex Saunders","Alan Both","Qian (Chayn) Sun","Bryan Boruff","John Duncan","Marco Amati","Peter Caccetta","Joanne Chia"],"categories":null,"content":"","date":1564099200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564099200,"objectID":"5fe51bcaabcafdfd98943da2917e71d3","permalink":"https://alanboth.github.io/publication/2019-urban-vegetation/","publishdate":"2019-07-26T00:00:00Z","relpermalink":"/publication/2019-urban-vegetation/","section":"publication","summary":"This report presents a descriptive analysis of vegetation cover change between 2014 and 2018 in Melbourne, Australia. The project sits within a boarder research context, under the “Making greening happen in consolidating cities” project of the Clean Air and Urban Landscapes (CAUL) research hub of the Australian Government’s National Environmental Science Program. The project is a collaboration between RMIT University, The University of Western Australia, CSIRO and the Victorian Government Department of Environment, Land, Water and Planning (DELWP) as part of the “Cooling and Greening Melbourne” work for Plan Melbourne 2017-2050. \n\nThe goal of this research project is to understand the spatial distribution of urban vegetation and the relationship to landuse. The data assembled can support further investigation of the impacts of urban development on vegetation cover and the potential mediating role of land-use planning interventions in this. This report focuses on the extent and change in the spatial distribution of vegetation across Melbourne between 2014 and 2018, reported against major land-use classes and against metropolitan sub-regions. To do this the research integrates high resolution urban vegetation coverage (including canopy cover and total vegetation) at a modified Mesh Block level for two years – 2014 and 2018; with landuse information derived from ABS Mesh Block attributes.","tags":null,"title":"Urban vegetation cover change in Melbourne: 2014-2018","type":"publication"},{"authors":["Alan Both","Matt Duckham","Michael Worboys"],"categories":null,"content":"","date":1533859200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1533859200,"objectID":"62fd94a80a5d737da8108bc10ca3b489","permalink":"https://alanboth.github.io/publication/2018-surrounds/","publishdate":"2018-08-10T00:00:00Z","relpermalink":"/publication/2018-surrounds/","section":"publication","summary":"This article concerns the definition and identification of qualitative spatial relationships for the full and partial enclosure of spatial regions. The article precisely defines three relationships between regions—“surrounds,” “engulfs,” and “envelops”—highlighting the correspondence to similar definitions in the literature. \n\nAn efficient algorithm capable of identifying these qualitative spatial relations in a network of dynamic (mobile) geosensor nodes is developed and tested. The algorithms are wholly decentralized, and operate in-network with no centralized control. The algorithms are also “coordinate-free,” able to operate in distributed spatial computing environments where coordinate locations are expensive to capture or otherwise unavailable. \n\nExperimental evaluation of the algorithms designed demonstrates the efficiency of the approach. Although the algorithm communication complexity is dominated by an overall worst-case O(n2) leader election algorithm, the experiments show in practice an average-case complexity approaching linear, O(n1.1).","tags":null,"title":"Identifying Surrounds and Engulfs Relations in Mobile and Coordinate-Free Geosensor Networks","type":"publication"},{"authors":["Yaguang Tao","Alan Both","Matt Duckham"],"categories":null,"content":"","date":1530576000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1530576000,"objectID":"398ee04166be799d2e0ba788da87f8f7","permalink":"https://alanboth.github.io/publication/2018-checkpoints/","publishdate":"2018-07-03T00:00:00Z","relpermalink":"/publication/2018-checkpoints/","section":"publication","summary":"This article concerns the opportunities for analysis of data about movement past spatial checkpoints. Checkpoint data are generated by an object’s movement past fixed sensors (‘checkpoints’) distributed throughout geographic space. Example sources of checkpoint data include road-toll gantries, social media check-ins, WiFi hotspots, access swipe cards, and public transport smart cards. \n\nMany existing movement analytics techniques, which frequently rely on precise coordinate locations, are ill-suited to the inherent and variable spatial granularity of checkpoint data. However, this spatial granularity also brings advantages in linking movement more closely to its environmental context, in particular the characteristics of the regions through which an object is moving. In this article, we propose a general model for representing checkpoint data of moving objects, based on two fundamentally different types of sensors: transaction and presence. \n\nExperiments using real movement data illustrate the diversity of queries that can be efficiently supported by our model. An example movement classification task, identifying vehicle type from checkpoint data recording vehicle movement, provides an illustration of the opportunities for more closely linking movement with its environmental context through the use of checkpoint data analysis.","tags":null,"title":"Analytics of movement through checkpoints","type":"publication"},{"authors":["Chris Bellman","Matt Duckham","Alan Both","Hamish Anderson"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"5d1c6c7f4275562d4e72e6c75c2ec19f","permalink":"https://alanboth.github.io/grants/2018-crcsi/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/grants/2018-crcsi/","section":"grants","summary":"**Budget:** $100,000 \n\n**Project dates:** While funding was approved by the CRCSI, formal approval was not received from the partner organization prior to the commencement date, causing the project to fall through. This project would have run February 2018 -- January 2019. \n\nThe Open Spatial Analytics (OSA) project (Stages 1 and 2) has clearly demonstrated the potential of the “open” paradigm for making spatial analytics reliable, efficient, and shareable. In particular, Stage 2 of the project delivered a robust, scalable, and general-purpose version 1.0 of the OSA software. The project demonstrated the integration of spatial and non-spatial tools in an open, transparent, and scalable platform, enabling the case study of cloud-based construction of the National DEM. \n\nBuilding on this established national success, Stage 3 will make the transition to providing essential analytics infrastructure for the CRCSI and the spatial community, as the foundations of a national spatial analytics capability (NSAC). Achieving this goal requires further enhancements to OSA. These capabilities include enhanced 3D capabilities, including point cloud processing; enhanced integration with non-spatial domains, including artificial intelligence and machine learning; connections not only to data workflows but to business workflows, including data curation and quality control; and repositories and archiving, including searching and version control of workflows. \n\nNo currently available workflow software is able to meet these needs. With limited funding, the CRCSI’s OSA project has positioned itself today as arguably the world's leading research capabilities in the area of spatial scientific workflows.","tags":null,"title":"Project 2.23 Open Spatial Analytics, Stage 3","type":"grants"},{"authors":["Chris Bellman","Matt Duckham","Alan Both","Hamish Anderson"],"categories":null,"content":"","date":1483228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483228800,"objectID":"035e3c84319ec6365980c2da9425a8a5","permalink":"https://alanboth.github.io/grants/2017-crcsi/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/grants/2017-crcsi/","section":"grants","summary":"**Budget:** $62,000 \n\n**Project dates:** February 2017 -- June 2017 \n\nThe Open Spatial Analytics (OSA) concept seeks to unlock the final component of the ‘open’ paradigm and create an open National Spatial Analytics Capability that becomes essential infrastructure for anyone who undertakes spatial data processing and analysis. We have had open products for many years and more recently, open data has swept the world with innovative, democratised ways to use spatial data. However, the science, processes, techniques and decisions used in working with spatial data remain hidden and obscure, often residing in the head of the spatial analyst or in poorly documented instructions. The OSA concept within the CRCSI aims to change all this by drawing heavily on scientific workflow models to create an open, shareable workflow platform that can be used to capture, document and easily recreate the spatial analytics used in the generation of any spatial data product. \n\nSharing spatial analytics will reduce duplication, avoiding multiple bespoke solutions to similar problems; increase transparency, generating “executable documentation” and auditable processes, increasing confidence in data products and decisions; and increase scalability, seamlessly exploiting efficient cloud-based, multi-platform and parallelised computing environments. \n\nThe Stage 1 demonstrator project successfully delivered a proof-of-concept tool -  an OSA platform - that combines open spatial tools (including PostGIS, GeoTools, and GDAL) with an open (non-spatial) scientific workflow system (Knime). Together with Geoscience Australia, the demonstrator project has shown how this OSA platform made the construction of the National DEM automated, transparent, and easily repeatable ( https://www.youtube.com/watch?v=42Npvvc1Nls ).","tags":null,"title":"Project 2.23 Open Spatial Analytics, Stage 2","type":"grants"},{"authors":["Antony Galton","Matt Duckham","Alan Both"],"categories":null,"content":"","date":1450137600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1450137600,"objectID":"6abbb05dcca62acad25a5a731401388b","permalink":"https://alanboth.github.io/publication/2015-causality/","publishdate":"2015-12-15T00:00:00Z","relpermalink":"/publication/2015-causality/","section":"publication","summary":"This paper is concerned with the problem of detecting causality in spatiotemporal data. In contrast to most previous work on causality, we adopt a logical rather than a probabilistic approach. By defining the logical form of the desired causal rules, the algorithm developed in this paper searches for instances of rules of that form that explain as fully as possible the observations found in a data set. \n\nExperiments with synthetic data, where the underlying causal rules are known, show that in many cases the algorithm is able to retrieve close approximations to the rules that generated the data. However, experiments with real data concerning the movement of fish in a large Australian river system reveal significant practical limitations, primarily as a consequence of the coarse granularity of such movement data. In response, instead of focusing on strict causation (where an environmental event initiates a movement event), further experiments focused on perpetuation (where environmental conditions are the drivers of ongoing processes of movement). After retasking to search for a different logical form of rules compatible with perpetuation, our algorithm was able to identify perpetuation rules that explain a significant proportion of the fish movements. For example, approximately one fifth of the detected long-range movements of fish over a period of six years were accounted for by 26 rules taking account of variations in water-level alone.","tags":null,"title":"Extracting Causal Rules from Spatio-Temporal Data","type":"publication"},{"authors":["Alan Both"],"categories":null,"content":"","date":1441065600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1441065600,"objectID":"3d7a3d68a58db8968bef0d1437c82e82","permalink":"https://alanboth.github.io/publication/2015-thesis/","publishdate":"2015-09-01T00:00:00Z","relpermalink":"/publication/2015-thesis/","section":"publication","summary":"Consider the task of monitoring changes in the structure of dynamic spatial phenomena such as algal blooms or oil spills. Such phenomena consist of multiple disconnected region components, which can reconfigure over time. This work uses qualitative spatial reasoning to record only salient changes to the internal structure of these regions. \n\nTo detect and store such qualitative spatial information, this research proposes a collection of five in-network, decentralized algorithms. Unlike previous work, these algorithms are able to operate in networks of mobile geosensor nodes with no access to coordinate information. A decentralized approach to algorithm design allows for information processing to take place within the network, with the defining feature being that no single node has access to the entirety of data in the network. \n\nA fundamental aspect of decentralized algorithms is wireless communication between nodes. As such communication has high power requirements, the algorithms presented in this work are designed to minimize the amount of communication taking place, while still maintaining accuracy. Experimental evaluation of these algorithms found that meeting the criteria of sufficient node density, broadcast interval, and communication distance produced accurate results. These three factors were found to be dependent upon the characteristics of the phenomena being monitored. \n\nIt was also found that the modules comprising each of the decentralized algorithms exhibited either sub-polynomial, linear, or weakly polynomial scalability (with the worst case being O(n1.1)). The order of scalability produced by a module was found to be due to the type of decentralized algorithm that module was based on, with leader election based algorithms producing weakly polynomial scalability, and surprise flooding based algorithms producing linear or sub-polynomial scalability. \n\nThis work aims to provide long-term environmental monitoring to areas that have previously been unable to be monitored due to their location, the cost of deploying a suitable geosensor network, or the time-span required.","tags":null,"title":"Decentralized Computation of Qualitative Spatial Relationships in Mobile Geosensor Networks","type":"publication"},{"authors":["Sanjiang Li","Zhiguo Long","Weiming Liu","Matt Duckham","Alan Both"],"categories":null,"content":"","date":1438387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1438387200,"objectID":"6e6c7c43f0bc64ffaeee2852805ff1dc","permalink":"https://alanboth.github.io/publication/2015-topological-constraints/","publishdate":"2015-08-01T00:00:00Z","relpermalink":"/publication/2015-topological-constraints/","section":"publication","summary":"Redundancy checking is an important task in the research of knowledge representation and reasoning. In this paper, we consider redundant qualitative constraints. For a set Γ of qualitative constraints, we say a constraint (xRy) in Γ is redundant if it is entailed by the rest of Γ. A prime subnetwork of Γ is a subset of Γ which contains no redundant constraints and has the same solution set as Γ. It is natural to ask how to compute such a prime subnetwork, and when it is unique. We show that this problem is in general intractable, but becomes tractable if Γ is over a tractable subalgebra S of a qualitative calculus. Furthermore, if S is a subalgebra of the Region Connection Calculus RCC8 in which weak composition distributes over nonempty intersections, then Γ has a unique prime subnetwork, which can be obtained in cubic time by removing all redundant constraints simultaneously from Γ. \n\nAs a by-product, we show that any path-consistent network over such a distributive subalgebra is minimal and globally consistent in a qualitative sense. A thorough empirical analysis of the prime subnetwork upon real geographical data sets demonstrates the approach is able to identify significantly more redundant constraints than previously proposed algorithms, especially in constraint networks with larger proportions of partial overlap relations.","tags":null,"title":"On redundant topological constraints","type":"publication"},{"authors":["Alan Both","Matt Duckham"],"categories":null,"content":"","date":1386460800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1386460800,"objectID":"d571d634a17b9c26120476d29b399a31","permalink":"https://alanboth.github.io/publication/2013-qsr/","publishdate":"2013-12-08T00:00:00Z","relpermalink":"/publication/2013-qsr/","section":"publication","summary":"This paper proposes an in-network, decentralized algorithm for determining the qualitative spatial structure of complex a real objects, which may have multiple disconnected components. The algorithm determines both the containment relations between region components and the adjacency relations between Voronoi regions of those components. Combining this information yields finer-grained qualitative structure of the region, specifically where a component is surrounded (rather than strictly contained) by other region components. Unlike previous work, the algorithm is able to operate in networks of mobile geosensor nodes with no access to coordinate information. The algorithm is evaluated in terms of scalability (communication complexity) and veracity, with the finding that it performs efficiently and correctly.","tags":null,"title":"Qualitative Spatial Structure in Complex Areal Objects Using Location-Free, Mobile Geosensor Networks","type":"publication"},{"authors":["Alan Both","Matt Duckham","Patrick Laube","Tim Wark","Jeremy Yeoman"],"categories":null,"content":"","date":1385856000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1385856000,"objectID":"dd89bc22ac69eac04e731c2f46651f10","permalink":"https://alanboth.github.io/publication/2013-fish/","publishdate":"2013-12-01T00:00:00Z","relpermalink":"/publication/2013-fish/","section":"publication","summary":"This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. \n\nIn this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement information.","tags":null,"title":"Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints","type":"publication"},{"authors":["Alan Both","Werner Kuhn","Matt Duckham"],"categories":null,"content":"","date":1383609600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1383609600,"objectID":"e29bd581f6bcfbaa283c7ef8610cce34","permalink":"https://alanboth.github.io/publication/2013-sbv/","publishdate":"2013-11-05T00:00:00Z","relpermalink":"/publication/2013-sbv/","section":"publication","summary":"How does complex spatiotemporal behavior arise from, and from which, spatiotemporal knowledge? In an attempt to answer this question, we extend Valentino Braitenberg's thought experiment [3] by describing and implementing vehicles with explicit, and increasingly sophisticated, spatiotemporal knowledge. We then observe the corresponding spatiotemporal behavior that can result. These spatiotemporal vehicles are able to move about their environment. The paper shows how vehicles can be incrementally equipped with three fundamental spatial constructs: knowledge of places, of neighborhoods, and the ability to communicate with other nearby vehicles. In turn we demonstrate, using agent-based simulations, how the fundamental spatial concepts of fields, networks, objects, and reference frames can emerge from these basic constructs. Our approach contributes to ongoing efforts of identifying the core concepts of spatial information [11] and of understanding the relationships between interaction with space and spatial computation [6].","tags":null,"title":"Spatiotemporal Braitenberg vehicles","type":"publication"}]