Pui Kwan Cheung, Kerry A. Nice, Stephen J. Livesley, Impacts of irrigation scheduling on urban green space cooling, Landscape and Urban Planning, 2024. Journal article | PDF |
The increasing heat stress in cities due to climate change and urbanisation can prevent people from using urban green spaces. Irrigating vegetation is a promising strategy to cool urban green spaces in summer. Irrigation scheduling, such as daytime vs night-time irrigation and the frequency of irrigation in a day, can influence the cooling benefit of irrigation. This study aimed to investigate whether irrigation scheduling can be optimised to increase the cooling benefit and determine how the cooling benefit changes with weather conditions. A field experiment with twelve identical turfgrass plots (three replicates × four irrigation treatments) was set up to measure the afternoon cooling benefits of irrigation. The four treatments included: no irrigation, single night-time irrigation (4 mm d–1), single daytime irrigation (4 mm d–1) and multiple daytime irrigation (total = 4 mm d–1). The cooling benefit was defined as the air temperature difference measured at 1.1 m above the turfgrass between the irrigated and unirrigated treatments (air temperature sensor accuracy ± 0.2 °C). The afternoon (12:00–15:59) mean cooling benefit of multiple daytime irrigation (–0.9 °C) which was significantly stronger than that of single night-time irrigation (–0.6 °C) and single daytime irrigation (–0.5 °C). Regardless of irrigation scheduling, the afternoon mean cooling benefits of irrigation were greater for days when background air temperature, vapour pressure deficit and incoming shortwave radiation were greater. The findings suggested that irrigation scheduling can be optimised to increase the cooling benefit of urban green space irrigation without increasing overall water use.
Pui Kwan Cheung, Naika Meili, Kerry A. Nice, Stephen J. Livesley, Identifying the mechanisms by which irrigation can cool urban green spaces in summer, Urban Climate, 2024. Journal article | Dataset | PDF |
High temperatures in summer can prevent people from using urban green spaces. Irrigating urban green spaces is a promising strategy to reduce temperatures. In this study, we aimed to a) identify the proportional contribution of different irrigation cooling mechanisms and b) quantify the impacts of different irrigation amounts (from 2 to 30 mm d−1) on the cooling effect of irrigating turfgrass in Melbourne, Australia. We first used a field experiment in Melbourne to provide empirical data to calibrate and verify the performance of an urban ecohydrological model, UT&C. Then, we used UT&C to predict the impacts of irrigating turfgrass on evapotranspiration, the energy balance and microclimate. UT&C predicted that irrigating turfgrass 4 mm d−1 would in- crease the evaporation from grass canopy and soil surface by 0.2 and 0.6 mm d−1, respectively, whereas it would reduce transpiration by 0.6 mm d−1 due to intercepted water covering part of the grass canopy following the irrigation. UT&C predicted that daytime (10:00–16:59) mean air temperature reductions would increase from 0.2 to 0.4◦C when the irrigation amount increased from 2 to 4 mm d−1. However, increasing the irrigation amount beyond 4 mm d−1 would not increase the cooling benefits.
Branislava Godic, Selin Akaraci, Rajith Vidanaarachchi, Kerry Nice, Sachith Seneviratne, Suzanne Mavoa, Ruth Hunter, Leandro Garcia, Mark Stevenson, Jasper Wijnands, Jason Thompson, A comparison of content from across contemporary Australian population health surveys, Australian and New Zealand Journal of Public Health (Accepted).
Kerry A. Nice, Matthias Demuzere, Andrew Coutts, Nigel Tapper, Present day and future urban cooling enabled by integrated water management, Frontiers in Sustainable Cities, 2024. Journal article | Dataset | PDF |
The process of urbanisation has increased public health risks due to urban heat, risks that will be further exacerbated in future decades by climate change. However, the growing adoption of integrated water management (IWM) practices (coordinated stormwater management of water, land, and resources) provides an opportunity to support urban heat amelioration through water supply provision and irrigated and vegetated infrastructure that can provide cooling benefits. This study examines the thermal impacts of future implementations of IWM for nine Australian cities based on a review of Government policy documents in the present and over two future time frames (2030 and 2050) under different greenhouse gas emissions scenarios (SSPs 1.2-6, 3.7-0 and 5.8-5). Statistical analysis of the future climate data using historical data shows that future warming is nuanced, with changes variable in both time and place, and with extremes becoming more pronounced in future. We have developed a unique approach to morph the future climate projections onto historical data (derived from the ERA5 Reanalysis product) for the 2010-2020 period. Additionally, we use locally appropriate Local Climate Zones (LCZs) for Australian cities, resulting from a holistic and global approach that is widely adopted by the urban climate modelling community. We developed scenarios for business-as-usual as well as implementation of moderate and high levels of IWM across each of the Australian LCZs and modelled them using TARGET (The Air temperature Response to Green infrastructure Evaluation Tool). Results generated at the LCZ level are aggregated to Australian statistical areas (SA4, the largest sub-city area) and city-wide levels. The thermal impacts associated with the various degrees of IWM were marked and geographically differentiated, depending on the climatic characteristics of the various cities. For the current climate, high IWM intervention provided reductions in annual mean daily maximum temperature ranging from -0.77C in Darwin, up to -1.86C in Perth. Generally, the drier southern cities of Sydney, Canberra, Albury, Melbourne, Adelaide, and Perth produced the greatest thermal response to implementation of IWM and the more tropical cities with higher rainfalls the least response. For some southern cities cooling was > -3.0 C at the time of maximum summer temperatures. Interestingly high levels of IWM in winter produced modest warming of minimum overnight temperatures, especially for the cooler southern cities. The cooling benefits of IWM were seen across all future climate scenarios and are a real opportunity to offset-projected temperature increases resulting from climate change.
Ben Beck, Chris Pettit, Meghan Winters, Trisalyn Nelson, Hai L. Vu, Kerry Nice, Sachith Seneviratne, Meead Saberi, Association between network characteristics and bicycle ridership across a large metropolitan region, International Journal of Sustainable Transportation, 2024. Pre-print | Journal article | PDF |
Numerous studies have explored associations between bicycle network characteristics and bicycle rider ship. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership. We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated (length of the bicycle network, betweenness centrality, degree centrality, network density, network coverage, intersection density and average weighted slope) and Bayesian spatial models were used to explore associations between these network characteristics and bicycle ridership. We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region. These findings challenge the utility of approaches based on spatially modeling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. Further efforts are required to be able to quantify network characteristics that reflect the myriad of factors that influence comfort and safety for people of all ages and abilities.
2023
Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, Seung-Bu Park, Michael Roth, Robert Schoetter, Andres Simon, Gert-Jan Steeneveld, Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, Chenghao Wang, Zhi-Hua Wang, Andrew Pitman, Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results, Quarterly Journal of the Royal Meteorological Society, 2023. Journal article | PDF |
Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban
resilience. Multi-model evaluations can lead to model improvements, however there have been no major
intercomparisons of urban-focused land surface models in over a decade. Here, in Phase 1 of the Urban-PLUMBER
project, we evaluate 30 land surface models' ability to simulate surface energy fluxes critical to atmospheric
meteorological and air quality simulations. We establish minimum and upper performance expectations for participating
models using simple information-limited models as benchmarks. Compared with the last major model intercomparison at
the same site, we find broad improvement in the current cohort's predictions of shortwave radiation, sensible and latent
heat fluxes, but little or no improvement in longwave radiation and momentum fluxes. Models with a simple urban
representation (e.g. “slab” schemes) generally perform well, particularly when combined with sophisticated
hydrological/vegetation models. Some mid-complexity models (e.g. “canyon” schemes) also perform well, indicating
efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve
three-dimensional interactions between buildings in general did not perform as well as other categories. However, these
models also tended to have the simplest representations of hydrology and vegetation. Models without any urban
representation (i.e. vegetation-only land surface models) performed poorly for latent heat fluxes, and reasonably for
other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that
substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude
that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating
scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of
Urban-PLUMBER will evaluate models across twenty sites in different urban and regional climate zones.
Marzie Naserikia, Melissa A. Hart, Negin Nazarian, Benjamin Bechtel, Mathew Lipson, Kerry A. Nice, Land surface and air temperature dynamics: The role of urban form and seasonality, Science of the Total Environment, 2023. Journal article | PDF |
Due to the scarcity of air temperature (Ta) observations, urban heat studies often rely on satellite-
derived Land Surface Temperature (LST) to characterise the near-surface thermal environment.
However, there remains a lack of a quantitative understanding - on how LST differs from Ta within
urban areas and what are the controlling factors of their interaction. We use crowdsourced air
temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate
Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the interplay of urban
form and fabric on the interaction between Ta and LST. Results show that LST and Ta have distinct
lspatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and
building morphology. We found greater seasonal variability in LST compared to Ta, along with more
pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a
greater temperature difference between LST and Ta in the built environment compared to the natural
LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees)
showed stronger LST-Ta relationships compared to built areas. In particular, we observe that built
areas with higher building density (where the heat vulnerability is likely more pronounced) show
insignificant or negative relationships between LST- Ta in summer. Our results also indicate that
surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot
and cold spots for LST and Ta. The spatial distribution for Ta hot spots does not always overlap with
LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is
inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide
new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.
Xinye Wanyan, Sachith Seneviratne, Kerry Nice, Jason Thompson, Marcus White, Nano Langenheim, Mark Stevenson, Scalable Label-efficient Footpath Network Generation using Remote Sensing Data and Self-supervised Learning, DICTA 2023, 2023 International Conference on Digital Image Computing: Techniques and Applications, Port Macquarie, NSW, Australia on 29 November-1 December 2023. Conference paper | Preprint | PDF |
Footpath mapping, modeling, and analysis can provide important geospatial insights to many fields of study,
including transport, health, environment and urban planning.
The availability of robust Geographic Information System (GIS) layers can benefit the management of infrastructure inventories,
especially at local government level with urban planners responsible for the deployment and maintenance of such infrastructure.
However, many cities still lack real-time information on the location, connectivity, and width of footpaths, and/or employ
costly and manual survey means to gather this information. This work designs and implements an automatic pipeline for
generating footpath networks based on remote sensing images using machine learning models. The annotation of segmentation
tasks, especially labeling remote sensing images with specialized requirements, is very expensive, so we aim to introduce a pipeline
requiring less labeled data. Considering supervised methods require large amounts of training data, we use a self-supervised
method for feature representation learning to reduce annotation requirements. Then the pre-trained model is used as the encoder
of the U-Net for footpath segmentation. Based on the generated masks, the footpath polygons are extracted and converted to
footpath networks which can be loaded and visualized by geographic information systems conveniently. Validation results
indicate considerable consistency when compared to manually collected GIS layers. The footpath network generation pipeline
proposed in this work is low-cost and extensible, and it can be applied where remote sensing images are available. Github:
https://github.com/WennyXY/FootpathSeg.
Nan Xu, Kerry Nice, Sachith Seneviratne, Mark Stevenson, Leveraging Segment-Anything model for automated zero-shot road width extraction from aerial imagery, DICTA 2023, 2023 International Conference on Digital Image Computing: Techniques and Applications, Port Macquarie, NSW, Australia on 29 November-1 December 2023. Conference paper | PDF |
Segment-Anything model (SAM) is a foundation
segmentation model published in April 2023. Trained on an unprecedented 11 million annotated images, the model can generate
segmented masks bearing clear-cut contours by integrating user-provided prompts. It is zero-shot transferable, requiring no
task-specific training. Nevertheless, its applicability for geographic vision tasks has not been fully evaluated. There is no automated
prompt-feeding method incorporating with SAM that can work efficiently for purposeful batch processing as well. To fill these
gaps, we developed a process that can be executed automatically from visual-prompts extraction to road width measurement,
utilizing OpenStreetMap (OSM) and SAM. By examining the quality of segmentation in various image contexts, we evaluated
the capacity and limitations of SAM working on aerial imagery. Through comparing measured widths to VicRoads records, we
validated the specially designed width-measuring algorithm for high precision and accuracy. After this process, prompt-indicated
zero-shot approach in solving basic geographic vision tasks is to be shaped synchronously on both theory and application ends.
Pui Kwan Cheung, C.Y. Jim, Kerry A. Nice, Stephen J. Livesley, Measuring the instantaneous cooling effect of turf irrigation in Melbourne, Australia, in P. Rajagopalan, V. Soebarto and H. Akbari (Eds.), 6th International Conference on Countermeasures to Urban Heat Islands (IC2UHI), pp. 1–9. 2023, RMIT University, Melbourne, Australia. PDF |
Private backyards are an important space for social and physical activities. Summer
heat stress can reduce people’s willingness to use their backyards. Long-term turf irrigation
has been demonstrated to reduce daytime mean air temperature because it can maintain soil
moisture content at or near field capacity for a longer period, thereby increasing evaporation
from soil surface and turf transpiration. This cooling effect is likely to be stronger during and
immediately after irrigation because of direct evaporation as the sprayed water streams pass
through the air and from water intercepted by the turf grass canopy. This study aims to
measure the magnitude and duration of this instantaneous cooling effect from turf irrigation.
The field experiment consisted of two 6m × 6m plots (one irrigated and one unirrigated)
fenced to mimic a backyard environment. Daily irrigation of 2 mm was applied at 13:00 local
time. The experiment was conducted in summer 2021 in Melbourne, Australia and lasted for
six weeks. The instantaneous cooling effect from irrigation lasted for approximately two
hours (13:00–15:59, instantaneous cooling) before the air temperature and turf surface
temperature returned to the levels measured before irrigation (10:00–12:59, baseline cooling).
The instantaneous cooling effect for air temperature and turf surface temperature were –0.54°C and –1.01°C, respectively. The results suggest that the daytime cooling effect of irrigation may be strengthened by applying smaller amounts at multiple times throughout the
warmest part of the day, thereby increasing the direction evaporation of water during and after
irrigation.
2022
Debjit Bhowmick, Ben Beck, Meead Saberi, Mark Stevenson, Jason Thompson, Meghan Winters, Trisalyn Nelson, Simone Zarpelon Leao, Sachith Seneviratne, Christopher Pettit, Le Hai Vu, Kerry Nice, A systematic scoping review of methods for estimating link-level bicycle volumes, Transport Reviews, 2022. Journal article | PDF |
Estimation of bicycling volumes is essential for the strategic
implementation of infrastructure and related transport elements and policies. Link-level volume estimation models (models that
estimate volumes on individual street segments) allow for understanding variation in bicycling volumes across an entire
network at higher spatial resolution than area-level models. Such models assist transport planners to efficiently monitor network
usage, to identify opportunities to enhance safety and to evaluate the impact of policy and infrastructure interventions. However,
given the sparsity and scarcity of bicycling data as compared to its motorised counterparts, link-level bicycling volume estimation
literature is relatively limited. This paper conducts a scoping review of link-level bicycling volume estimation methods by implementing
systematic search strategies across relevant databases, thereby identifying appropriate studies for the review. The review resulted
in some interesting findings. Among all the methods implemented, direct demand modelling was the predominant one. Not a single
study implemented multiple modelling approaches in the same study area, thereby not allowing for comparison of these
approaches. Most studies were conducted in the United States. It was also observed that there exists a lot of heterogeneity in the
reporting of basic study characteristics and validation results, sometimes to the extent of not reporting these at all. The study
presents the different types of data used in modelling (count, travel survey, GPS data) along with an array of popular explanatory
variables that can inform future studies about data collection and variable selection for modelling. The study discusses the strengths
and limitations of different methods and finally presents recommendations for future research.
Pui Kwan Cheung, C.Y. Jim, Nigel Tapper, Kerry A. Nice, Stephen J. Livesley, Daytime irrigation leads to significantly cooler private backyards in summer, Urban Climate, 2022. Journal article | Dataset | PDF |
Backyards play important roles for individual households because they provide a private and safe
green space for social and environmental interactions, relaxation, gardening and children’s activities. The use of backyards is highly dependent on their thermal conditions. Turf is a common surface type in backyards but unirrigated turf can be as warm as pavement, bringing thermal
discomfort and discouraging people from using them. Under certain conditions, turf irrigation
provides an opportunity to reduce thermal stress by increasing evapotranspiration. This study
aims to measure the impacts of turf irrigation on microclimate in a backyard environment in the
warm season in Melbourne, Australia. The experiment consisted of four 6m × 6m turf-covered
plots. Daily irrigation was applied at four amounts: 0, 2, 4 and 7mm for six weeks. In Week 6, the
4-mm irrigation reduced daytime soil temperature, turf surface temperature, air temperature and
universal thermal climate index by 1.7, 2.3, 0.6 and 0.4◦C, respectively. All daytime impacts
were significant (p<0.05, t-test). Irrigation has the potential to significantly improve the thermal
conditions of backyards in combination with the use of tree shade.
Kerry A. Nice, Negin Nazarian, Mathew J. Lipson, Melissa A. Hart, Sachith Seneviratne, Jason Thompson, Marzie Naserikia, Branislava Godic, and Mark Stevenson, Isolating the impacts of urban form and fabric from geography on urban heat and human thermal comfort, Building and Environment, 2022. Preprint | Journal article | PDF |
Public health risks resulting from urban heat in cities are increasing due to rapid urbanisation and climate
change, motivating closer attention to urban heat mitigation and adaptation strategies that enable climate-
sensitive urban design and development. These strategies incorporate four key factors influencing heat stress
in cities: the urban form (morphology of vegetated and built surfaces), urban fabric, urban function (including
human activities), and background climate and regional geographic settings (e.g. topography and distance
to water bodies). The first two factors can be modified and redesigned as urban heat mitigation strategies
(e.g. changing the albedo of surfaces, replacing hard surfaces with pervious vegetated surfaces, or increasing
canopy cover). Regional geographical settings of cities, on the other hand, cannot be modified and while human
activities can be modified, it often requires holistic behavioural and policy modifications and the impacts of
these can be difficult to quantify. When evaluating the effectiveness of urban heat mitigation strategies in
observational or traditional modelling studies, it can be difficult to separate the impacts of modifications to
the built and natural forms from the interactions of the geographic influences, limiting the universality of
results. To address this, we introduce a new methodology to determine the influence of urban form and fabric
on thermal comfort, by utilising a comprehensive combination of possible urban forms, an urban morphology
data source, and micro-climate modelling. We perform 9814 simulations covering a wide range of realistic built
and natural forms (building, roads, grass, and tree densities as well as building and tree heights) to determine
their importance and influence on thermal environments in urban canyons without geographical influences. We
show that higher daytime air temperatures and thermal comfort indices are strongly driven by increased street
fractions, with maximum air temperatures increases of up to 10 and 15◦C as street fractions increase from 10%
(very narrow street canyons and/or extensive vegetation cover) to 80 and 90% (wide street canyons). Up to
5◦C reductions in daytime air temperatures are seen with increasing grass and tree fractions from zero (fully
urban) to complete (fully natural) coverage. Similar patterns are seen with the Universal Thermal Climate
Index (UTCI), with increasing street fractions of 80% and 90% driving increases of 6 and 12◦C, respectively.
We then apply the results at a city-wide scale, generating heat maps of several Australian cities showing the
impacts of present day urban form and fabric. The resulting method allows mitigation strategies to be tested
on modifiable urban form factors isolated from geography, topography, and local weather conditions, factors
that cannot easily be modified.
Mathew J. Lipson, Negin Nazarian, Melissa A. Hart, Kerry A. Nice and Brooke Conroy, A transformation in city-descriptive input data for urban climate models, Frontiers in Environmental Science: Environmental Informatics and Remote Sensing, 2022. Journal article (open access) | PDF |
In urban climate studies, datasets used to describe urban characteristics have traditionally
taken a class-based approach, whereby urban areas are classified into a limited number of
typologies with a resulting loss of fidelity. New datasets are becoming increasingly available
that describe the three-dimensional structure of cities at sub-metre micro-scale
resolutions, resolving individual buildings and trees across entire continents. These
datasets can be used to accurately determine local characteristics without relying on
classes, but their direct use in numerical weather and climate modelling has been limited by
their availability, and because they require processing to conform to the required inputs of
climate models. Here, we process building-resolving datasets across large geographical
extents to derive city-descriptive parameters suitable as common model inputs at
resolutions more appropriate for local or meso-scale modelling. These parameter
values are then compared with the ranges obtained through the class-based Local
Climate Zone framework. Results are presented for two case studies, Sydney and
Melbourne, Australia, as open access data tables for integration into urban climate
models, as well as codes for processing high-resolution and three-dimensional urban
datasets. We also provide an open access 300 m resolution building morphology and
surface cover dataset for the Sydney metropolitan region (approximately 5,000 square
kilometres). The use of building resolving data to derive model inputs at the grid scale better
captures the distinct heterogenetic characteristics of urban form and fabric compared with
class-based approaches, leading to a more accurate representation of cities in climate
models. As consistent building-resolving datasets become available over larger
geographical extents, we expect bottom-up approaches to replace top-down class-based frameworks.
Pui Kwan Cheung, Kerry Nice, Stephen Livesley, Irrigating urban greenspace for cooling benefits: the mechanisms and management considerations, Environmental Research: Climate, 2022. Journal article (open access) | PDF |
Evapotranspiration is an important cooling mechanism in urban green space (UGS). Irrigating
vegetated surfaces with potable water, collected stormwater or recycled sewage water has the
potential to increase the cooling effect of UGS by increasing evapotranspiration. Such cooling
effect may not always be strong because evapotranspiration is dependent on local and regional
factors such as background climate, seasonality and vegetation type. When using irrigation for
cooling, city managers also need to consider management issues such as irrigation water supply
and amenity use of the UGS. This study aims to develop a theoretical framework that explains the
physical and energetic mechanisms of irrigation cooling effect and a framework to assist city
managers to make decision about the use of irrigation for urban cooling. This is achieved by
reviewing the impacts of irrigation on local climate reported in the literature and identifying the
regional and local factors that influence irrigation cooling effect in warm seasons. The literature
suggests that irrigation can potentially reduce daily maximum air temperature and ground surface
temperature by approximately 2.5◦C and 4.9◦C, respectively, depending on weather conditions
and irrigation amount. Background climate is an important factor that influences the cooling
potentials of irrigation. Cities with dry and warm climates have the highest cooling potentials from
irrigation. The cooling potentials are also influenced by seasonality and weather, vegetation type,
irrigation time of day and irrigation amount. Cities with a dry and warm season can consider using
irrigation to mitigate urban heat within UGS because such climatic conditions can increase cooling
potentials. To maximise irrigation cooling effect, cities with abundant irrigation water supply can
use a soil moisture-controlled irrigation regime while those with limited supply can use a
temperature-controlled regime. More studies are required to understand the cooling potentials of
irrigating small, individual UGS.
Jasper S. Wijnands, Kerry A. Nice, Sachith Seneviratne, Jason Thompson, and Mark Stevenson, The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques, Atmospheric Pollution Research, 2022. Journal article | Pre-print | PDF |
In response to the COVID-19 pandemic, most countries implemented public health ordinances that resulted
in restricted mobility and a resultant change in air quality. This has provided an opportunity to quantify the
extent to which carbon-based transport and industrial activity affect air quality. However, quantification of
these complex effects has proven to be difficult, depending on the stringency of restrictions, country-specific
emission source profiles, long-term trends and meteorological effects on atmospheric chemistry, emission
levels and in-flow from nearby countries. In this study, confounding factors were disentangled for a direct
comparison of pandemic-related reductions in absolute pollutions levels, globally. The non-linear relationships
between atmospheric processes and daily ground-level NO2 , PM10 , PM2.5 and O3 measurements were captured
in city- and pollutant-specific XGBoost models for over 700 cities, adjusting for weather, seasonality and
trends. City-level modelling allowed adaptation to the distinct topography, urban morphology, climate and
atmospheric conditions for each city, individually, as the weather variables that were most predictive varied
across cities. Pollution forecasts for 2020 in absence of a pandemic were generated based on weather and
formed an ensemble for country-level pollution reductions. Findings were robust to modelling assumptions
and consistent with various published case studies. NO2 reduced most in China, Europe and India, following
severe government restrictions as part of the initial lockdowns. Reductions were highly correlated with changes
in mobility levels, especially trips to transit stations, workplaces, retail and recreation venues. Further, NO2
did not fully revert to pre-pandemic levels in 2020. Ambient PM2.5 pollution, which has severe adverse health
consequences, reduced most in China and India. Since positive health effects could be offset to some extent
by prolonged exposure to indoor pollution, alternative transport initiatives could prove to be an important
pathway towards better health outcomes in these countries. Increased O3 levels during initial lockdowns have
been documented widely. However, our analyses also found a subsequent reduction in O3 for many countries
below what was expected based on meteorological conditions during summer months (e.g., China, United
Kingdom, France, Germany, Poland, Turkey). The effects in periods with high O3 levels are especially important
for the development of effective mitigation strategies to improve health outcomes.
Ben Beck, Meghan Winters, Trisalyn Nelson, Chris Pettit, Meead Saberi, Jason Thompson, Sachith
Seneviratne, Kerry Nice, Simone Zarpelon Leao, Mark Stevenson, Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics, Environment and Planning B: Urban Analytics and City Science, 2022. Pre-print | Journal article | PDF |
Extensive research has been conducted exploring associations between built environment
characteristics and biking. However, these approaches have often lacked the ability to understand
the interactions of the built environment, population and bicycle ridership. To overcome these
limitations, this study aimed to develop novel urban biking typologies using unsupervised machine
learning methods. We conducted a retrospective analysis of travel surveys, bicycle infrastructure
and population and land use characteristics in the Greater Melbourne region, Australia. To develop
the urban biking typology, we used a k-medoids clustering method. Analyses revealed 5 clusters.
We highlight areas with high bicycle network density and a high proportion of trips made by bike
(Cluster 1; reflecting 12% of the population of Greater Melbourne, but 57% of all bike trips) and
areas with high off-road and on-road bicycle network length, but a low proportion of trips made by
bike (Cluster 4, reflecting 23% of the population of Greater Melbourne and 13% of all bike trips).
Our novel approach to developing an urban biking typology enabled the exploration of the interaction of bicycle ridership, the bicycle network, population and land use characteristics. Such approaches are important in advancing our understanding of bicycling behaviour, but further
research is required to understand the generalisability of these findings to other settings.
Jason Thompson, Rod McClure, Tony Blakely, Nick Wilson, Michael G. Baker, Jasper S. Wijnands, Thiago Herick De Sa, Kerry Nice, Camilo Cruz, Mark Stevenson, Modelling SARS-CoV-2 disease progression in Australia and New Zealand: an account of an agent-based approach to support public health decision-making, Australian and New Zealand Journal of Public Health, 2022. https://doi.org/10.1111/1753-6405.13221 Journal article (open access) | PDF |
Objective: In 2020, we developed a public health decision-support model for mitigating
the spread of SARS-CoV-2 infections in Australia and New Zealand. Having demonstrated
its capacity to describe disease progression patterns during both countries’ first waves of
infections, we describe its utilisation in Victoria in underpinning the State Government’s then
‘RoadMap to Reopening’.
Methods: Key aspects of population demographics, disease, spatial and behavioural dynamics,
as well as the mechanism, timing, and effect of non-pharmaceutical public health policies
responses on the transmission of SARS-CoV-2 in both countries were represented in an
agent-based model. We considered scenarios related to the imposition and removal of non-
pharmaceutical interventions on the estimated progression of SARS-CoV-2 infections.
Results: Wave 1 results suggested elimination of community transmission of SARS-CoV-2 was
possible in both countries given sustained public adherence to social restrictions beyond 60
days’ duration. However, under scenarios of decaying adherence to restrictions, a second wave
of infections (Wave 2) was predicted in Australia. In Victoria’s second wave, we estimated in
early September 2020 that a rolling 14-day average of <5 new cases per day was achievable
on or around 26 October. Victoria recorded a 14-day rolling average of 4.6 cases per day on 25
October.
Conclusions: Elimination of SARS-CoV-2 transmission represented in faithfully constructed
agent-based models can be replicated in the real world.
Implications for public health: Agent-based public health policy models can be helpful to
support decision-making in novel and complex unfolding public health crises.
↑ (Published as above) Jason Thompson, Rod McClure, Tony Blakely, Nick Wilson, Michael G. Baker, Thiago Herick De Sa, Kerry Nice, Jasper Wijnands, Gideon Aschwanden, Camilo Cruz, Mark Stevenson, Modelling the estimated likelihood of eliminating the SARS-CoV-2 pandemic in Australia and New Zealand under public health policy settings: an agent-based-SEIR approach, Pre-print
2021
Sachith Seneviratne, Kerry A. Nice, Jasper Wijnands, Jason Thompson, Mark Stevenson, Self-supervision, Remote Sensing and Abstraction: Representation Learning across 3 million locations, Digital Image Computing: Techniques and Applications 2021, Gold Coast, 29 Nov-1 Dec 2021, 2021. Conference paper and Conference presentation | PDF |
Self-supervision based deep learning classification approaches have received considerable attention in academic
literature. However, the performance of such methods on remote sensing imagery domains remains under-explored. In this work,
we explore contrastive representation learning methods on the task of imagery-based city classification, an important problem
in urban computing. We use satellite and map imagery across 2 domains, 3 million locations and more than 1500 cities. We show
that self-supervised methods can build a generalizable representation from as few as 200 cities, with representations achieving over
95% accuracy in unseen cities with minimal additional training.
We also find that the performance discrepancy of such methods, when compared to supervised methods, induced by the domain
discrepancy between natural imagery and abstract imagery is significant for remote sensing imagery. We compare all analysis
against existing supervised models from academic literature and open-source our models for broader usage and further criticism.
Pui Kwan Cheung, Stephen J. Livesley, Kerry A. Nice, Estimating the cooling potential of irrigating green spaces in 100 global cities with arid, temperate or continental climates, Sustainable Cities and Society, 2021. DOI:10.1016/j.scs.2021.102974, Journal article | PDF |
Modern agricultural irrigation can produce extensive cooling that is strong enough to mask the current effect of
global climate change. Irrigating urban green spaces therefore has the potential to mitigate heat stress in cities.
However, the cooling potentials of irrigating urban green space in different climate regions of the world have
never been estimated. Here we conducted a systematic literature review to determine air temperature reductions
in past experimental, observational and modelling studies (N = 17). We developed an empirical model with the
irrigation cooling effect as the dependent variable and background air temperature and rainfall of the study area
as the independent variables. The model was subsequently used to estimate the cooling potential of irrigating
green spaces in 100 global cities with arid, temperate and continental climates. We predict that 91 of the 100
cities will receive a cooling benefit from irrigating urban green space (mean = −1.09◦C), whereas the remaining
nine cities will experience a slight warming effect (mean = +0.76◦C). The cooling potential of irrigating urban
green space is greatest in arid cities (mean = − 1.65◦C).
2020
Jasper S. Wijnands, Haifeng Zhao, Kerry A. Nice, Jason Thompson, Katherine Scully, Jingqiu Guo, Mark Stevenson, Identifying safe intersection design through unsupervised feature extraction from satellite imagery, Computer-Aided Civil and Infrastructure Engineering, 2020. DOI:10.1111/mice.12623, Journal article | PDF |
The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection’s type, size, shape, lane
markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving behaviors captured during 66 million kilometers of driving. This showed more frequent hard acceleration events (per vehicle) at four- than three-way intersections, relatively low hard deceleration frequencies at T-intersections, and consistently low average speeds on roundabouts. Overall, domain-specific feature extraction enabled the identification of infrastructure improvements that could result in safer driving behaviors, potentially reducing road trauma.
K.A. Nice , J. Thompson, J. S. Wijnands, G.D.P.A. Aschwanden, M. Stevenson, The “Paris-end” of town? Deriving urban typologies using three imagery types, Urban Sci., 2020. Journal article | arXiv Pre-print | PDF |
Urban typologies allow areas to be categorised according to form and the social,
demographic, and political uses of the areas. The use of these typologies and finding similarities and
dissimilarities between cities enables better targeted interventions for improved health, transport,
and environmental outcomes in urban areas. A better understanding of local contexts can also
assist in applying lessons learned from other cities. Constructing urban typologies at a global scale
through traditional methods, such as functional or network analysis, requires the collection of data
across multiple political districts, which can be inconsistent and then require a level of subjective
classification. To overcome these limitations, we use neural networks to analyse millions of images of
urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics
between the largest 1692 cities in the world. The comparison city of Paris is used as an exemplar
and we perform a case study using two Australian cities, Melbourne and Sydney, to determine if
a “Paris-end” of town exists or can be found in these cities using these three big data imagery sets.
The results show specific advantages and disadvantages of each type of imagery in constructing urban
typologies. Neural networks trained with map imagery will be highly influenced by the structural
mix of roads, public transport, and green and blue space. Satellite imagery captures a combination of
both urban form and decorative and natural details. The use of street view imagery emphasises the
features of a human-scaled visual geography of streetscapes. However, for both satellite and street
view imagery to be highly effective, a reduction in scale and more aggressive pre-processing might
be required in order to reduce detail and create greater abstraction in the imagery.
J. Thompson, M. Stevenson, J. S. Wijnands, K. Nice, G.D.P.A. Aschwanden, J. Silver, M. Nieuwenhuijsen, P. Rayner, R. Schofield, R. Harihara, and C. N. Morrison, A global analysis of urban design types and road transport injury: an image processing study, The Lancet Planetary Health, 2020. DOI:10.1016/S2542-5196(19)30263-3, Journal article | SSRN Pre-print | PDF | Supplement PDF |
Background Death and injury due to motor vehicle crashes is the world’s fifth leading cause of mortality and morbidity.
City and urban designs might play a role in mitigating the global burden of road transport injury to an extent that has
not been captured by traditional safe system approaches. We aimed to determine the relationship between urban
design and road trauma across the globe.
Methods Applying a combined convolutional neural network and graph-based approach, 1692 cities capturing one
third of the world’s population were classified into types based on urban design characteristics represented in sample
maps. Associations between identified city types, characteristics contained within sample maps, and the burden of
road transport injury as measured by disability adjusted life-years were estimated through univariate and multivariate
analyses, controlling for the influence of economic activity.
Findings Between Mar 1, 2017, and Dec 24, 2018, nine global city types based on a final sample of 1632 cities were
identified. Burden of road transport injury was an estimated two-times higher (risk ratio 2·05, 95% CI 1·84–2·27) for
the poorest performing city type compared with the best performing city type, culminating in an estimated loss of
8·71 (8·08–9·25) million disability-adjusted life-years per year attributable to suboptimal urban design. City types
that featured a greater proportion of railed public transport networks combined with dense road networks characterised
by smaller blocks showed the lowest rates of road traffic injury.
Interpretation This study highlights the important role that city and urban design plays in mitigating road transport
injury burden at a global scale. It is recommended that road and transport safety efforts promote urban design that
features characteristics inherent in identified high-performance city types including higher density road infrastructure
and high rates of public transit.
C. V Gál and K. A. Nice ‘Mean radiant temperature modeling outdoors: A comparison of three approaches’, in 100th Annual Meeting of the American Meteorological Society (AMS) jointly with the 15th Symposium on the Urban Environment, 2020. Conference Paper | PDF |
The thermal environment of cities is deteriorating steadily owing to both urbanization and climate change. As a consequence, urban planners and city officials are increasingly under pressure to maintain livable urban environments. In this respect, numerical models offer
the quickest and most economic means to assessing the performance and viability of various urban heat
mitigation strategies. Owing to these advantages, the numerical approach gained popularity over the past
decade—as indicated by the profusion of such studies and the proliferation of microclimate models.
Naika Meili, Gabriele Manoli, Paolo Burlando, Elie Bou-Zeid, Winston T.L. Chow, Andrew M. Coutts, Edoardo Daly, Kerry A. Nice, Matthias Roth, Nigel J. Tapper, Erik Velasco, Enrique R. Vivoni, and Simone Fatichi, An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0), Geosci. Model Dev., 2020. Journal article | PDF |
Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort,
and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these
adverse effects, and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment,
vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&C),
that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation, and urban trees. UT&C is a fully coupled energy
and water balance model that calculates 2 m air temperature, 2m humidity, and surface temperatures based on the infinite
urban canyon approach. It further calculates the urban hydrological fluxes in the absence of snow, including transpiration as a function of plant photosynthesis. Hence, UT&C accounts for the effects of different plant types on the urban
climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C
performs well when compared against energy flux measurements of eddy-covariance towers located in three cities in
different climates (Singapore, Melbourne, and Phoenix). A sensitivity analysis, performed as a proof of concept for the
city of Singapore, shows a mean decrease in 2m air temperature of 1.1C for fully grass-covered ground, 0.2C for high
values of leaf area index (LAI), and 0.3◦C for high values of Vc,max (an expression of photosynthetic capacity). These
reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5%, 2.1%, and 1.6%,
for fully grass-covered ground, high values of LAI, and high values of Vc,max , respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff.
Tatjana Todorovic, Geoffrey London, Nigel Bertram, Oscar Sainsbury, Marguerite A. Renouf, Kerry A. Nice, and Steven J. Kenway, 2019. 'Models for water sensitive middle suburban infill development', in 9th State of Australian Cities National Conference, 30 November - 5 December 2019, Perth, Western Australia. DOI: 10.25916/5efa774bda643. Conference Paper | PDF |
Infill development in Australian cities over the coming decades is expected to have
considerable negative influence on the hydrology, resource efficiency, liveability and amenity of our
cities. This project aims to develop and apply a performance evaluation framework to understand infill
impacts, create design options and processes for improved outcomes through case studies, and identify
improved governance options and arrangements. A 'typologies catalogue' of spatial configurations and
architectural models relevant to high amenity medium density infill development has been prepared,
with different arrangements and combinations of buildings and open spaces applied on a case study
development site in Adelaide, SA. Design scenarios from the catalogue are evaluated against a range
of qualitative and quantitative performative criteria, developed in consultation with industry partners,
including water and urban heat performance assessment. The case study site designs offer practical
models and methods for achieving infill development and densification in a manner that improves
amenity within the dwelling, across the site and for the surrounding precinct – while maintaining or
improving water and urban heat performance. During this process, a set of key design principles for
water sensitive infill development is defined, with prospects to further inform infill development practice
and related policies.
2019
K.A. Nice, J. S. Wijnands, A. Middel, J. Wang, Y. Qiu, N. Zhao, J. Thompson, G.D.P.A. Aschwanden, H. Zhao, and M. Stevenson, Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning, Urban Climate, 2020. Pre-print | Journal article | Dataset | PDF |
Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In
urban climate, sky detection is an important first step in gathering information about urban
morphology and sky view factors. However, obtaining accurate results remains challenging and
becomes even more complex using imagery captured under a variety of lighting and weather
conditions.
To address this problem, we present a new sky pixel detection system demonstrated to
produce accurate results using a wide range of outdoor imagery types. Images are processed using a
selection of mean-shift segmentation, K-means clustering, and Sobel filters to mark sky pixels in
the scene. The algorithm for a specific image is chosen by a convolutional neural network,
trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy for the top three
classes. This selection step allows the sky marking to follow an adaptive process and to use
different techniques and parameters to best suit a particular image. An evaluation of fourteen
different techniques and parameter sets shows that no single technique can perform with high
accuracy across varied Skyfinder and Google Street View data sets. However, by using our
adaptive process, large increases in accuracy are observed. The resulting system is shown to
perform better than other published techniques.
Mark Stevenson, Jason Thompson, Jasper Wijnands, Kerry Nice, Gideon Aschwanden, Haifeng Zhao, Opportunities to reduce road traffic injury: new insights from the study of urban areas. International Journal of Injury Control and Safety Promotion, 2019. Journal article | PDF |
Over the past four decades considerable efforts have been taken to mitigate the growing burden of
road injury. With increasing urbanisation along with global mobility that demands not only safe but
equitable, efficient and clean (reduced carbon footprint) transport, the responses to dealing with the
burgeoning road traffic injury in low- and middle-income countries has become increasingly complex.
In this paper, we apply unique methods to identify important strategies that could be implemented to
reduce road traffic injury in the Asia-Pacific region; a region comprising large middle-income countries
(China and India) that are currently in the throes of rapid motorisation. Using a convolutional neural
network approach, we clustered countries containing a total of 1632 cities from around the world into
groups based on urban characteristics related to road and public transport infrastructure. We then
analysed 20 countries (containing 689 cities) from the Asia-Pacific region and assessed the global burden of disease attributed to road traffic injury and these various urban characteristics. This study demonstrates the utility of employing image recognition methods to discover new insights that afford
urban and transport planning opportunities to mitigate road traffic injury at a regional and global scale.
J. S. Wijnands, J. Thompson, K. Nice, G.D.P.A. Aschwanden, and M. Stevenson, Real-time monitoring of driver drowsiness on mobile platforms using 3-D neural networks. Neural Computing and Applications, 2019. Journal article | Preprint | PDF |
Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have
received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile
phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the
technology across the driving population. While it has been shown that three-dimensional (3D) operations are more
suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition
benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on
mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and
temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In
particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses
conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches
on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a
drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver
Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain
robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially
preventing micro-sleeps and reducing road trauma.
Haifeng Zhao, Jasper Wijnands, Kerry Nice, Jason Thompson, Gideon Aschwanden, Jingqiu Guo, and Mark Stevenson, Reducing Cyclist Crashes by Assessing the Road Environment: An Application of Google Imagery and Machine Learning. Journal of Transport & Health, 2019. Journal article | PDF |
Background: Cycling is an active and sustainable transportation mode, and is associated with health, environmental and societal benefits. Therefore,
increasing the use of bicycles is being supported as a transport policy in many countries. However, despite these benefits, cyclists are vulnerable road
users and are over-represented in traffic crash casualties compared to other modes of transport. The injury concern can discourage people from
adopting cycling as a main transportation mode. Urban infrastructure that caters to cyclists' safety can potentially reduce crashes and therefore, injury morbidity and mortality.
Methods: This research uses cyclist crashes recorded by the state road authority from 2010 to 2013 in Greater Melbourne. Exposure data used
anonymised bicycle trips recorded by volunteer users of RiderLog smartphone application from 2010 to 2013. Crash locations and control sites were
sampled from areas with high cycling exposure. Google Street View maps and satellite images at crash locations and control sites were downloaded to
capture information of the road environments where cyclists crash and never crash. Deep learning methods using generative adversarial networks were
applied to explore features of road environments associated with cyclist crashes.
Results: A number of unique observations were identified namely, that locations that have low crash risk had more green space (trees or grass), and
median strips (that separate traffic from opposing lanes on divided roadways) also decreased a cyclist’s crash risk. Road environments with high-rise
buildings casting shadows on the roadside are mostly seen in the environment in which crashes occurred. The experiments also identified factors that
have been reported previously in the literature and statistical analysis, providing confidence in the presented methods. Such factors include tram
tracks, intersections, on-road parking and off-road bicycle paths. Statistical analysis showed 52.6% of crash locations were within 5 metres of a tram
line, while this percentage for control sites was 5.6%.
Conclusions: This research presents a method that takes advantage of the increasing availability of big datasets, computing power and the advances of
deep learning techniques, to analyse the road environments of locations where cyclists crash from a new perspective. The findings give urban planners
insights on how streetscapes might be reconstructed to improve safety situations for cyclists. The results also provide transportation engineers and
cyclists with visual indications about what kind of streetscapes are safer.
G.D.P.A. Aschwanden, J. S. Wijnands, J. Thompson, K.A. Nice, and M. Stevenson, Learning To Walk: modelling transportation mode choice distribution through neural networks. Environment and Planning B: Urban Analytics and City Science, 2019. Journal article | PDF |
Transportation mode distribution has a large implication on the resilience, economic output,
social cost of cities and the health of urban residents. Recent advances in artificial intelligence and
the availability of remote sensing data have opened up opportunities for bottom-up modeling techniques that allow understanding of how subtle differences in the urban fabric can impact transportation mode share distribution. This project presents a novel neural network-based modeling technique capable of predicting transportation mode distribution. Trained with millions of images labeled with information from a georeferenced transportation survey, the resulting model is able to infer transportation mode share with high accuracy (R2 1⁄4 0.58) from satellite images alone. Additionally, this method can disaggregate data in areas where only aggregated information is available and infer transportation mode share in areas without underlying information. This work demonstrates a new and objective method to evaluate the impact of the urban fabric on transportation mode share. The methodology is robust and can be adapted for cases around the world as well as deployed to evaluate the impact of new developments on the
transportation mode choice.
J. Wijnands, K. Nice, J. Thompson, H. Zhao, and M. Stevenson, Streetscape augmentation using generative adversarial networks: optimising health and wellbeing., Sustainable Cities and Society, 2019. Journal article | Preprint | Dataset | PDF |
Deep learning using neural networks has provided advances in image style transfer, merging the content of one
image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended
to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Australian population
health survey (n = 34,000) was used to identify the spatial distribution of health and wellbeing outcomes,
including general health and social capital. For each outcome, the most and least desirable locations formed two
domains. Streetscape design was sampled using around 80,000 Google Street View images per domain.
Generative adversarial networks translated these images from one domain to the other, preserving the main
structure of the input image, but transforming the ‘style’ from locations where self-reported health was bad to
locations where it was good. These translations indicate that areas in Melbourne with good general health are
characterised by sufficient green space and compactness of the urban environment, whilst streetscape imagery
related to high social capital contained more and wider footpaths, fewer fences and more grass. Beyond identifying relationships, the method is a first step towards computer-generated design interventions that have the potential to improve population health and wellbeing.
D. Dommenget, K. Nice, T. Bayr, D. Kasang, C. Stassen, and M. Rezny, The Monash Simple Climate Model Experiments (MSCM-DB v1.0): An interactive database of mean climate, climate change and scenario simulations, Geosci. Model Dev., 2019. DOI:10.5194/gmd-12-2155-2019 Journal article | PDF |
This study introduces the Monash Simple Climate Model (MSCM) experiment database. The simulations are based on the Globally Resolved Energy Balance (GREB) model to study three different aspects of climate model simulations: (1) understanding processes that control the mean
climate, (2) the response of the climate to a doubling of the CO2 concentration, and (3) scenarios of external forcing
(CO2 concentration and solar radiation). A series of sensitivity experiments in which elements of the climate system
are turned off in various combinations are used to address (1) and (2). This database currently provides more than 1300
experiments and has an online web interface for fast analysis and free access to the data. We briefly outline the design of all experiments, give a discussion of some results, put the findings into the context of previously published results from similar experiments, discuss the quality and limitations of the MSCM experiments, and also give an outlook on possible further developments. The GREB model simulation is quite realistic, but the model without flux corrections has a root mean square error in the mean state of the surface temperature of about 10 ◦C, which is larger than those of
general circulation models (2◦C). It needs to be noted here that the GREB model does not simulate circulation changes
or changes in cloud cover (feedbacks). However, the MSCM experiments show good agreement to previously published
studies. Although GREB is a very simple model, it delivers good first-order estimates, is very fast, highly accessible, and
can be used to quickly try many different sensitivity experiments or scenarios. It builds a basis on which conceptual
ideas can be tested to first order and it provides a null hypothesis for understanding complex climate interactions in
the context of response to external forcing or interactions in the climate subsystems.
A. Broadbent, A. Coutts, K. Nice, M. Demuzere, E. Krayenhoff, N. Tapper, and H. Wouters, The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET v1.0): an efficient and user-friendly model of city cooling. Geosci. Model Dev., 2019. Journal article | PDF |
The adverse impacts of urban heat and global climate change are leading policymakers to consider green
and blue infrastructure (GBI) for heat mitigation benefits. Though many models exist to evaluate the cooling impacts
of GBI, their complexity and computational demand leaves most of them largely inaccessible to those without specialist
expertise and computing facilities. Here a new model called The Air-temperature Response to Green/blue-infrastructure
Evaluation Tool (TARGET) is presented. TARGET is designed to be efficient and easy to use, with fewer user-defined parameters and less model input data required than other urban climate models. TARGET can be used to model average street-level air temperature at canyon-to-block scales
(e.g. 100 m resolution), meaning it can be used to assess temperature impacts of suburb-to-city-scale GBI proposals. The
model aims to balance realistic representation of physical processes and computation efficiency. An evaluation against two different datasets shows that TARGET can reproduce the magnitude and patterns of both air temperature and surface temperature within suburban environments. To demonstrate the utility of the model for planners and policymakers, the results from two precinct-scale heat mitigation scenarios are
presented. TARGET is available to the public, and ongoing development, including a graphical user interface, is planned for future work.
2018
K.A. Nice, A. Coutts, and N.J. Tapper, Development of the VTUF-3D v1.0 urban micro-climate model to support assessment of urban vegetation influences on human thermal comfort. Urban Climate, 2018. DOI:10.1016/j.uclim.2017.12.008. Journal article | PDF |
With urban areas facing longer duration heat-waves and temperature extremes from climate change and growing
urban development, adaptation strategies are needed to protect city residents. Examining the role that increased tree
cover and water availability can have on human thermal comfort (HTC) is needed to help guide the development of
thermally comfortable cities. To inform planning, modelling tools are needed that provide sufficient resolution to
resolve urban influences on HTC and the ability to model important physiological processes of vegetation. To achieve
this, a new micro-scale model, VTUF-3D (Vegetated Temperatures of Urban Facets) has been developed. In it, offline
modelling of individual items of vegetation is performed using the MAESPA process-based tree model (Duursma and
Medlyn, 2012) (a model that can model individual trees, vegetation, and soil components), and integrated into the
TUF-3D (Krayenhoff and Voogt, 2007) urban micro-climate surface energy balance (SEB) model. This innovative
approach allows the new model to account for important vegetative physiological processes and shading effects, using
configurable templates to allow representation of any type of vegetation or water sensitive design feature. This work
enables detailed calculations of surface temperatures (Tsfc), mean radiant temperature (Tmrt), and a HTC index, the
universal thermal climate index (UTCI), across urban canyons. This study presents an overview of VTUF-3D. Also
presented are two evaluations of VTUF-3D. The first evaluation compares modelled surface energy balance fluxes
to observations in Preston, Australia (Coutts et al., 2007). The second evaluation compares spatial and temporal
predictions of Tmrt and UTCI to two observed street canyons in the City of Melbourne (Coutts et al., 2015b). The
VTUF-3D model is shown to perform well and is suitable for use to examine critical questions relating to the role of
vegetation and water in the urban environment in support of HTC.
2016
K.A. Nice, Development, validation, and demonstration of the VTUF-3D v1.0 urban micro-climate model to support assessments of urban vegetation influences on human thermal comfort, 2016. PhD Thesis. Monash University. Thesis | PDF |
With urban areas facing future longer duration heat-waves and temperature extremes from climate change and growing urban development, adaptation strategies are needed. Examining the role that increased tree cover and water availability can have on human thermal comfort (HTC) in urban areas as part of these strategies has been done using observations, but further work requires a modelling tool suited for this task. Sufficient model resolution is needed to resolve variables used to calculate HTC, as well as the ability to model the physiological processes of vegetation and their interaction with water and with the rest of the urban environment. The lack of such a model has been identified as a research gap in the urban climate area and has impaired the ability to fully examine the use of urban greenery and water for improved human thermal comfort.
A new model, VTUF-3D (Vegetated Temperatures Of Urban Facets), addresses this gap by embedding the functionality of the MAESPA tree process model (Duursma & Medlyn 2012), that can model individual trees, vegetation, and soil components, within the TUF-3D (Krayenhoff & Voogt 2007) urban micro-climate model. An innovative tiling approach, allows the new model to account for important vegetative physiological processes and shading effects using configurable templates to allow representation of any type of vegetation or water sensitive design feature. The high resolution of VTUF-3D is sufficient to examine the processes that drive human thermal comfort (HTC). This allows detailed calculations of surface temperatures, mean radiant temperature (Tmrt), and a HTC index, the universal thermal climate index (UTCI), across an urban canyon.
An extensive validation process, using three different observation data sets to validate a number of different and key aspects of the VTUF-3D model, has shown it performs well and is suitable for use to examine critical questions relating to the role of vegetation and water in the urban environment.
A demonstration of the model using modelling scenarios of varying canopy cover shows that average peak daytime HTC improvements of 1◦C UTCI (and 2.3◦C UTCI) are possible in doubling (and quadrupling) existing street cover canopies, with localised effects under canopy cover approaching 5◦C UTCI. These scenarios also show the value of the existing canopy cover, as reductions and elimination of existing cover can create reductions in HTC of 2◦C UTCI. In addition, reductions in average air temperature (Ta) across urban canyons can differ by 1◦C between streets with differing canopy cover.
After the development, validation, and demonstration of this new model, it is now possible to conduct further analysis to quantify the impact each individual tree can have on temperatures in urban canyons. Further, the model can help inform the optimal arrangement and quantity of trees to maximise temperature moderation effects and be used to generate best practices guidelines for urban greening.
2011
K.A. Nice, The micro-climate of a mixed urban parkland environment. Masters Thesis, 2011. Monash University. Thesis | PDF |
Progression of climate change, with its predicted intensification of temperature extremes and heat wave durations, combined with demographic trends towards increased urbanization makes the study of urban micro-climates desirable. Understanding of mixed urban parkland morphologies leads to insights into possible adaptation and mitigation strategies to minimize impacts due to temperature extremes and UHI (urban heat island) on human health. Observational methodologies to study these environments present difficulties in obtaining data of sufficient spatial and temporal resolution, and are expensive and time consuming as well. Modelling using mathematical computer simulations addresses some of these concerns. However, confidence in the results obtained from models requires verification of accuracy. Merely observing that the modelling output looks plausible isn't enough. Verification of underlying processes and their interactions are ultimately necessary for complete confidence.
Data collected in a mixed urban parkland study area was analysed for spatial and temporal temperature variations. Urban micro-climate drivers such as incoming shortwave radiation, wind, and humidity played a role in the variations across the area. Wind was found to be an important driver. It moderated afternoon maximums through mechanical mixing at solar exposed sites as effectively as tree cover shading did at other sites. At the same time, heat was allowed to build at wind sheltered sites. Calming winds also contributed to dropping temperatures after dusk and warming temperatures in pre-dawn hours coinciding with increasing wind speeds. On average, temperatures in parkland areas were found to be 2°C cooler than urban areas.
Modelling of this study area was carried out using ENVI-met, a urban micro-climate model. However, ENVI-met's ability to predict the temperature gradients seen in the observations was hampered by constant values, both spatial and temporal, in wind speed and humidity levels. As these were found to be important components in driving spatial temperature variability in the observations, these constant values are unable to drive variabilities as they would have in the observations. Temperature variations lag behind observed values and temperatures are also under-predicted during the day and over-predicted at night. This leads to low confidence levels about ENVI-met's accuracy in resolving temporal and spatial temperature variation and yields an inconclusive result as far as modelling predictions are concerned.
Book Chapters
2022
Lipson MJ, Nazarian N, Hart MA, Nice KA and Conroy B (2022) A Transformation in City-Descriptive Input Data for Urban Climate Models, p 196-213, In Middel, A., Bechtel, B., Demuzere, M., Nazarian, N., eds. (2023). Urban climate
informatics. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-83251-592-1. Book chapter | PDF
2019
Haifeng Zhao, Jasper S. Wijnands, Kerry A. Nice, Jason Thompson, Gideon D. P. A. Aschwanden, Mark Stevenson, Jingqiu Guo (2019) Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety. In: Qu X., Zhen L., Howlett R., Jain L. (eds) Smart Transportation Systems 2019. Smart Innovation, Systems and Technologies, vol 149. Springer, Singapore. Book chapter | PDF
Submitted / Preprints
Ellie Traill, Kerry A. Nice, Nigel Tapper, Julie Arblaster, Pavement watering as an urban heat mitigation technique, Urban Climate (1st revision under review).
Kerry A. Nice, Jason Thompson, Haifeng Zhao, Sachith Seneviratne, Belen Zapata-Diomedi, Leandro Garcia, Ruth F. Hunter, Rodrigo Siqueira Reis, Pedro C. Hallal, Christopher Millett, Ruoyu Wang, Mark Stevenson, How city design influences road transport mode choice and associated health risks during a crisis: a global observational study, Lancet Planetary Health. (1st revision under review)
Leandro Garcia, Mehdi Hafezi, Larissa Lima, Christopher Millett, Jason Thompson, Ruoyu Wanga, Selin Akaracia, Rahul Goel, Rodrigo Reis, Kerry A. Nice, Belen Zapata-Diomedi, Pedro Hallal, Esteban Moro, Clifford Amoako, Ruth Hunter, Future-proofing cities against negative city mobility and public health impacts of impending natural hazards, Lancet Planetary Health. (1st revision under review)
Ruth F. Hunter, Selin Akaraci, Ruoyu Wang, Rodrigo Reis, Pedro C. Hallal, Alex Pentland, Christopher Millett, Leandro Garcia, Jason Thompson, Kerry Nice, Belen Zapata-Diomedi, Esteban Moro, A global natural experiment on city mobility patterns during the COVID-19 pandemic, Lancet Public Health. (1st revision under review)
Debjit Bhowmick, Danyang Dai, Meead Saberi, Trisalyn Nelson, Mark Stevenson, Sachith Seneviratne, Kerry Nice, Christopher Pettit, Hai L. Vu and Ben Beck, Collecting population-representative bicycling GPS data to understand bicycling activity and patterns using smartphones and Bluetooth beacons, Travel Behaviour and Society (Under review).
Jixuan Chen, Peter M. Bach, Kerry A. Nice, João P. Leitão, Modelling urban microclimate to bridge heat, greenery and walkablity in the built environment, Sustainable Cities and Society. (1st revision under review)
Sachith Seneviratne, Jasper Wijnands, Kerry Nice, Haifeng Zhao, Branislava Godic, Suzanne Mavoa, Rajith Vidanaarachchi, Mark Stevenson, Leandro Garcia, Ruth Hunter, Jason Thompson, Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision, Engineering Applications of Artificial Intelligence, (Under review) Pre-print | PDF
Ruth F. Hunter, Leandro Garcia, Mark Stevenson, Kerry Nice, Jasper S. Wijnands, Frank Kee, Geraint Ellis, Neil Anderson, Sachith Seneviratne, Mehdi Moeinaddini, Branislava Godic, Selin Akaraci, and Jason Thompson, Computer vision, causal inference and public health modelling approaches to generate evidence on the impacts of urban planning in non-communicable disease and health inequalities in UK and Australian cities: A proposed collaborative approach, (Under Review) Pre-print | PDF
K.A. Nice, G.D.P.A. Aschwanden, J. S. Wijnands, J. Thompson, H. Zhao, and M. Stevenson, The Nature of Human Settlement: Building an understanding of high performance city design. arXivPre-print | PDF
In Preparation
Blesson M. Varghese, Berhanu Wondmagegn, Matthew Borg, Michael Tong, Matthias Demuzere, Kerry Nice, Nigel Tapper, Peng Bi, Heat-attributable hospitalisations and associated costs: current risk, future projection, and intervention effectiveness: A Multi-site Study in Australia, 2010-2019, Medical Journal of Australia.
Haifeng Zhao, Jason Thompson, Kerry A. Nice, Sachith Seneviratne, Leandro Garcia, Ruth Hunter, Mark Stevenson, et al., Designing healthy urban environment using machine learning.
Valentina Marchionni, Christopher Szota, Claire Farrell, Stephen Livesley, Kerry A. Nice, Veljko Prodanovic, Sally Thompson, Edoardo Daly, Pui Kwan Cheung, Hamideh Nouri, Brandon Winfrey, The role of water in urban greening in a hotter and drier climate: benefits, costs, and future challenges for Australian cities, Journal of Hydrology.
Cristina E. Ramalho, Brenda Lin, Kerry A. Nice, Melanie Davern, Kate Lee, Steve Livesley, Luis Mata, Caragh G. Threlfall, Jason Byrne, Thomas Astell-Burt, Melanie Lowe, Leila Farahani, Casey Furlong, Kirsten Parris, Alison Haynes, At the nexus between urban green space attributes and functions – a framework to support planning and design, Landscape and Urban Planning.
Publication venues
2024
Australian and New Zealand Journal of Public Health
Frontiers in Sustainable Cities
International Journal of Sustainable Transportation
Landscape and Urban Planning
Urban Climate
2023
6th International Conference on Countermeasures to Urban Heat Islands (IC2UHI)
DICTA 2023
DICTA 2023
Quarterly Journal of the Royal Meteorological Society
Science of the Total Environment
2022
Atmospheric Pollution Research
Australian and New Zealand Journal of Public Health
Building and Environment
Environment and Planning B: Urban Analytics and City Science
Environmental Research: Climate
Frontiers in Environmental Science: Environmental Informatics and Remote Sensing
Transport Reviews
Urban Climate
2021
Digital Image Computing
Sustainable Cities and Society
2020
9th State of Australian Cities National Conference
100th Annual Meeting of the American Meteorological Society (AMS)
Computer-Aided Civil and Infrastructure Engineering
Geoscientific Model Development
The Lancet Planetary Health
Urban Science
2019
Environment and Planning B: Urban Analytics and City Science
Geoscientific Model Development
Geoscientific Model Development
International Journal of Injury Control and Safety Promotion
Journal of Transport & Health
Neural Computing and Applications
Sustainable Cities and Society
Urban Climate
2018
Urban Climate
Summary, SRJ in 2024
6th International Conference on Countermeasures to Urban Heat Islands (IC2UHI)
9th State of Australian Cities National Conference
100th Annual Meeting of the American Meteorological Society (AMS)