Urban Geometry Research Articles

An extended SW-TES algorithm for land surface temperature and emissivity retrieval from ECOSTRESS thermal infrared data over urban areas

Land surface temperature (LST) over urban areas is an essential parameter in the monitoring of urban thermal environment. Urbanization has resulted in a more complex geometric structure of urban surface than natural surface. However, in the conventional LST retrieval algorithms, the impact of urban geometric structure is usually neglected. In this study, an extended algorithm that combines the split-window algorithm (SW) and the temperature and emissivity separation algorithm (TES) was proposed to retrieve LST and land surface emissivity (LSE) over urban areas simultaneously from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared (TIR) data. In the extended SW-TES algorithm with considering urban geometry effect, the brightness temperature at ground level was obtained using the SW algorithm, and the LST and LSE were retrieved simultaneously by using the TES algorithm. The results indicate that LST (LSE) retrieved by the extended SW-TES algorithm with urban geometry effect correction has generally lower (higher) values than that without urban geometry effect correction. The LST differences mostly vary from 0.3 K to 1.2 K, whereas the LSE differences are mainly concentrated between −0.01 and − 0.06. Furthermore, the root mean squared error (RMSE) between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction and LST/LSE obtained from the ECOSTRESS level-2 LST/LSE product decrease with the increase of sky view factor (SVF). The largest RMSE is approximately 1.6 K for LST and 0.03 for LSE in the SVF of 0.1, which represents an urban pixel surrounded by high buildings within its adjacent pixels. There are no evident differences between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction using five and three ECOSTRESS TIR bands. The LST differences are mainly concentrated between −0.2 K and 0.2 K, whereas the LSE differences mostly range from 0.001 to 0.004. The findings of this study indicate that it is necessary to consider urban geometry effect for accurate LST and LSE retrieval over urban areas.

Relevance of Urban Geometry and Outdoor Thermal Comfort in Urban Heat Island Mitigation – A Review

UHIs’ environmental and energy implications are well-known, but their significance in aggravating heat stress in populations is only recently being recognised. Planners, health managers, and public administrators now prioritise UHI prevention. Urban geometry, material finishes, and green and blue infrastructure are prominent UHI mitigation options. This paper examines urban geometry as a key parameter and reviews UHI research. This report also identifies information gaps and emphasizes the need for this type of research in key Indian metropolitan centers. The anticipated study will help public health planners and administrators. Methods: Authors compiled extensive literature on UHI and related concepts. The outcome intent was to understand the relevance of urban geometry parameters in UHI intensity and how it impacts outdoor thermal comfort. Various electronic databases were searched for publications meeting the criteria of outdoor thermal comfort and urban planning characters (urban geometry) as a mitigation strategy, ranging from 2000 to 2020. Summary: The impact of UHI on human comfort conditions cannot be ignored. Appropriate planning measures at the initial stages of city planning via urban geometry parameters can certainly yield encouraging results to reduce the intensity of UHI and improve outdoor thermal comfort conditions.

Multivariate optimization towards energy balance with geometric constraints of building design and urban space intensity

Rapid urbanization has caused extensive energy shortage and excessive carbon emissions, thus creating a constant demand for advancing building energy performance. Such work often involves complex analysis of urban geometry and non-uniform irradiance conditions for dynamic shading. This paper presents a novel method for comprehensive evaluation and optimization of building design with integrated photovoltaics. Key parameters include building-height-to-street-width ratio (H/W), number-of-floors (Nf), aspect ratio (r), floor area (Af), window-wall ratio (WWR), and photovoltaics’ tilt angle on façades (β). The developed model has been validated experimentally. A baseline model was produced to further highlight the differences between EUI with and without surrounding buildings. Results showed that H/W and Nf were positively correlated with deviations with Af. Higher Af and Nfimproved energy efficiency especially for low-to-mid rises. Heating and cooling presented no correlation with Nf for r≥5, while both loads reached peaks at r=4. As H/W increased, heating proportionally increased by 2.1 kWh/m2 while cooling decreased by 1.8 kWh/m2. These rates dropped as H/W increased. Energy production from façades increased when r>2, with a slope of 10,000 kWh per unit, and a variance of 190,000 kWh. The effect of β showed higher significance in cooling, heating, and production, especially for low H/W.

Acoustic sensing of wind generated vortices in urban environments

In this work, generated vortices within an urban microspaces are investigated numerically and experimentally. Of particular interest are vortices within an urban street canyon. Computational fluid dynamics simulations of wind vortices are compared to experimental observation of similar urban street geometries within the District of Columbia. Additionally, the vortex and ground interaction are investigated for acoustic propagation. Preliminary results are presented and discussed.

Spatial variation of exponentially modified Gaussian parameters in an urban setting

The distribution of urban acoustic noise levels varies spatially because of differences in traffic volume, distance to other sound sources (e.g., construction sites), urban canyon geometry, and several other factors. We want to connect these environmental variables to the parameters of a parametric probability density function of the acoustic levels. Previously, the exponentially modified Gaussian (EMG) distribution matched experimental observation more closely than other distributions for urban sound levels. Using the framework of common engineering methods (e.g., ISO 9613-2), the received pressure level is the source power minus several attenuation factors. The exponential distribution could be due to the attenuation from geometrical spreading because it is a good approximation for a source uniformly distributed on a line segment or within a circle. The Gaussian distribution could be due to multiple factors, including weak turbulent scattering, reflections and diffractions, multiple sources, and source power variability. We measured the sound levels at 37 locations in the North End of Boston (USA), plotted the spatial variation in the EMG parameters, and compared these maps to the environmental variables. The traffic volume, proximity to stationary sources, and scene geometry all seemed to impact the EMG parameters with the traffic volume being the most important.

Investigating pedestrian behaviour in urban environments: A Wi-Fi tracking and machine learning approach

Urban geometry plays a critical role in determining paths for pedestrian flow in urban areas. To improve the urban planning processes and to enhance quality of life for end-users in urban spaces, a better understanding of the factors influencing pedestrian movement is required by decision-makers within the urban design and planning industry. The aim of this study is to present a novel means to assess pedestrian routing in urban environments. As a unique contribution to knowledge and practice, this study: (a) enhances the body of knowledge by developing a conceptual model to assess and classify pedestrian movement behaviours, utilising machine learning algorithms and location data in conjunction with spatial attributes, and (b) extends previous research by revealing spatial visibility as a driver for pedestrian movement in urban environments. The importance of the findings lies in the perspective of revealing novel insights concerning individual preferences and behaviours of end-users and the utilisation of urban spaces. The approaches developed can be utilised for observations in large-scale contexts, as an addition to traditional methods. Application of the model in a high pedestrian traffic-dense retail urban area in London reveals clear and consistent relationships amongst spatial visibility, individuals’ motivation, and knowledge of the area. Key behaviours established in the study area are grouped into two activity categories: (i) Utilitarian walking (with motivation - expert and novice striders) and (ii) Leisure walking (no motivation - expert and novice strollers). The approach offers an insightful and automated means to understand pedestrian flow in urban contexts and informs wider wayfinding, walkability, and transportation knowledge.

The effect of urban geometry on outdoor thermal comfort. Application of the UTCI index in hot and arid climates.

The object of our study is to evaluate the effect of urban geometry on microclimate and outdoor thermal comfort. In this context, “the RayMan pro 2.1” software was adopted to define UTCI index applying in hot and arid climates, exactly, in the urban fabric of the Ksar of The Red Village, El Kantara in Biskra city, Algeria, to define the most efficient urban geometry in term of summer thermal comfort by studying the real impacts of the urban form on the solar control and microclimatic conditions. studying the correlation between the geometry of the street estimated by the ratio H/L, the sky view factor (SVF), its orientation and the evolution of the physical variables (Ta, MRT, Ws) and the values of UTCI index. Significant relationships were found between UTCI index, urban geometry and heat stress in outdoor environments. The results of this research have shown the effect of urban design strategies on modifying the microclimatic conditions in hot summer for outdoor spaces in hot-arid climate.

Modeling the impact of traffic noise: analysis of lightning geometry and its contribution to direct, specular and diffuse path propagation.

The evaluation of urban noise is an important tool in noise control. From existing sources of noise, vehicular traffic is considered the main cause of discomfort in the population. The intensity of the noise that reaches the receiver depends on the level of sound power emitted by the source and the attenuation that occurred in the propagation medium. The analysis of the dispersion of rays in different urban geometries allows the evaluation of parameters that influence the trajectory of noise. The objective of this study is to analyze the contribution of the energy fraction of each path in different urban settings in the global noise, to contribute to the analysis and adoption of mitigating measures. For this, the mathematical model of prediction of noise levels was adopted, which calculates the direct and specular (source-image method) and diffuse (radiosity method) paths. The study was applied in two sectors of the city of Maringá, Brazil. In total, 73200 paths were evaluated in 14640 readings of source positions in 54 measured points. The results showed the part of contribution that each path represents in the acoustic field, and the sound decay caused by obstacles, urban geometry, distance and material properties.

Numerical simulations of sonic boom propagation over urban areas

Acceptability of supersonic transportation by population requires an accurate prediction of ground noise levels generated by sonic boom. This study aims at predicting sonic boom propagation over urban areas. For this purpose, numerical simulations are performed; the full 2D Euler equations are solved using high-order finite-difference time-domain techniques. First, the case of an isolated building is considered. From a geometrical analysis, two characteristic zones are highlighted: an illumated region in front of the building and a shadow zone at its rear. The sonic boom waveforms at the ground are composed of several arrivals, related to reflection at the building facades and diffraction at the building corners. The evolution of the noise levels is then shown to follow closely the geometrical analysis, with an amplification in the illuminated region and a large reduction in the shadow zone. Second, the case of two identical buildings is investigated. The acoustic field inside the street canyon is examined. In particular, the boom waveforms exhibit low-frequency oscillations, in addition to the geometrical arrivals. They are related to resonant modes of the canyon. Finally, an urban geometry representative of European city centres is considered. The variability of the boom waveforms and the noise levels is shown.

Analyzing the effect of view factors on surface heat flux, surface temperature, and vegetation cover.

With the increase in population in cities, economic, social, and environmental problems continue to increase, and it is thought that the microclimatic conditions created by these problems will cause more environmental problems. One of the factors affecting the urban climate in urban areas is urban geometry. The climate of a region changes due to the shape of the land surface, human activities, atmospheric movements, and latent and sensible heat fluxes. Sky view factor (SVF), building view factor (BVF), and tree view factor (TVF) are actively used in the determination of urban geometry. However, studies on how these factors affect the thermal state of the urban environment are insufficient. Determining the land surface changes and thermal condition characteristics (LST, NDVI, SHF, and LHF) depending on urban growth and examining how these properties affect thermal conditions are very important in the construction of sustainable urban planning. In the study, SVF, BVF, TVF, LST, NDVI, SHF, and LHF values of 55 points determined for three different areas with different urban geometries were calculated. How these values affect each other and their situation on urban outdoor thermal comfort is evaluated. In the study, statistical analysis was performed to evaluate the relationship between surface temperature, surface heat fluxes, different view factors, and vegetation. As a result of ANOVA analysis, it was determined as very significant (p > 0.01) in all regions. Both SHF and LHF values differ with SVF. The SHF value has a direct relationship with the SVF value. LHF is inversely proportional to the SVF value. The situation is reversed for SHF variation concerning vegetation. LHF and NDVI are directly related. SHF and NDVI are inversely proportional. SVF and NDVI values also vary according to the LST value. It has been observed that LST has a direct relationship with SVF and an inverse relationship with NDVI.

A predictive analysis of thermal stress in a densifying urban business district under summer daytime conditions in a Mediterranean City

A new urban master plan in the metropolitan area of Tel Aviv, Israel calls for the extreme densification of an already intensively-built business district. This study assesses the existing daytime thermal stress experienced by pedestrians under the Mediterranean climatic conditions of the region during the summer season, and predicts future changes under various development scenarios. Microclimatic measurements were used to calculate the Index of Thermal Stress (ITS) under present conditions, and possible future scenarios were modeled by hypothetically altering the urban geometry and subjecting the results to a series of sensitivity analyses. Findings reveal that the net impact of taller buildings is a reduction in pedestrian thermal stress on clear summer days, since the deep shading of narrow street canyons lowers the radiant heat exposure – outweighing the weaker convective cooling that is expected due to higher air temperature and lower wind speed. Nonetheless, outdoor summer conditions remain thermally stressful during daytime hours, and there is a need for appropriate urban design strategies to further mitigate urban warming. Additional shading improved thermal comfort at certain times and street orientations, but available space for street trees is limited. This emphasizes the need for creative solutions, such as building-integrated green infrastructure.

Using Machine Learning to Predict Urban Canopy Flows for Land Surface Modeling

AbstractDeveloping urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations.

Reconstruction of the hourly fine-resolution apparent temperature (Humidex) with the aerodynamic parameters

Apparent temperature is the preferred measure of hotness or coldness expressed to depict the human sense. Spatially explicit measurement of the hourly apparent temperature is essential for capturing the threats to bioclimatic comfort and preventing potential mortality/morbidity risk from heat or cold. However, existing apparent temperature products only provide daily observations at the spatial resolution of several dozen kilometers, resulting in some substantial underestimations for some life-threatening thermal stresses highly localized in space and time. Furthermore, some data-driven models lack mechanical constraints on the turbulent exchange between the surface and the atmosphere, making some unsatisfactory accuracy. Here, we propose Humidex reconstruction model incorporating atmospheric dynamics theory and aerodynamic parameters (i.e., heat and momentum roughness lengths for natural surfaces and three urban canopy geometry parameters for artificial surfaces), capable of developing an hourly dataset at fine-grained spatial resolution (0.01° × 0.01°). In this study, a total of 2952 h in four seasons were selected to test the seasonal performance of this model, taking the Yangtze River Delta as an example. The results show that the Humidex products from this model generally outperform the existing comparable products, with the hourly population root mean square error (RMSE) ranging from 1 to 2 °C in winter and autumn and 2–3 °C in spring and summer. Moreover, the constraint of aerodynamic parameters can reduce RMSE with a significant margin for each season, up to 2 °C, especially in areas with dense woodlands or buildings. In addition, the results demonstrate the excellent performance of this model in capturing short-lived thermal health threats, which are easily overlooked when observed data only provides a daily variation. This indicates that the model can allow researchers and practitioners investigate the fine-grained spatial and temporal evolution of thermal stress and its impact on public health, tourism, learning, and work performance.

Конвективный перенос условной примеси в пределах городской геометрии

The paper studies the process of a heavy impurity propagation in urban geometry. As an object of re-search, a three-dimensional layout of a part of the city of Perm was used. It reproduces the height and lo-cation of buildings as well as terrain features on an area of about 5 km². The model was placed in a con-tainer with water; aqueous solutions of NaCl were used as an impurity. The experimental parameters were selected based on the approximation of turbulent characteristics and qualitatively describe the process of propagation of the carbon dioxide/air mixture. Experiments were performed for localized and extended impurity sources. It is shown that the terrain plays an essential role in the process of impurity propagation. The admixture tends to fill the lowest areas of the relief. The presence of buildings prevents the move-ment of impurities. In some cases, this can lead to the formation of stagnant zones with a high impurity content.

The Effects of Microclimate on the Behaviour of People in Urban Public Spaces: Insights from the Waterfront in Kandy, Sri Lanka

Human beings by nature are social beings. Therefore, the provision of public spaces for social activity is an integral part of urban design. It is known that public spaces have positive impacts on the health and well-being of people. However, the modern-day decline in the use of urban public spaces is due to comfortable thermal environments being created indoors through technologies and advanced design principles. In this context, this study examines the effect of microclimatic conditions on the behavior of people in outdoor urban public spaces, in order to identify design methods to create more conducive environments. This research employs a case study method: a plaza and waterfront in the city of Kandy as an urban public space. Data collection involved physical observations, activity mapping, a photographic survey, a thermal sensation questionnaire, 3D modelling, and simulations of the thermal environment. Data was subject to triangulation for establishing validity, to generate a better understanding and to show that tropical climate shade is not the only solution. Air temperature and solar radiation greatly affect the use of urban public spaces and people adapt to environmental conditions by experience. Wind, shade, vegetation cover, and surrounding urban geometry also contribute to thermal perception. It was inferred that there was no one primary factor but rather the culmination of all factors in different ratios that causes the environmental temperature to morph and change, affecting the thermal comfort of a space. This study provides a design-based recommendation in order to achieve an optimum level of thermal comfort and provide design efficacy for urban public spaces.

Street Tree Planting Patterns to Modify the Sky View Factor for Outdoor Thermal Comfort Enhancement

Outdoor thermal conditions influence the quality of life in the urban commons. Urban vegetation is an effective way to improve pedestrian thermal comfort in the tropics as it provides shade, reduces urban surface heating, improves air quality, and human health. The primary objective of this study is to understand the correlation between the Sky View Factor (SVF) modifications and Outdoor Thermal Comfort (OTC) that could inform optimum street tree planting strategies in the warm, humid, context of Colombo, Sri Lanka. Common street tree planting patterns are identified and simulated, utilising the RayMan software. Results showed that there is a distinct advantage in utilising trees for SVF modifications and thereby increasing OTC. Tree planting patterns with less SVF yielded better results during the day, yet, showed little or no impact during the night. Conclusions discuss SVF-based design implications for the different urban canyon geometries, and orientations explored in the study.

Estimating Mean Wind Profiles Inside Realistic Urban Canopies

Mean wind profiles within a unit-aspect-ratio street canyon have been estimated by solving the three-dimensional Poisson equation for a set of discrete vortex sheets. The validity of this approach, which assumes inviscid vortex dynamics away from boundaries and a small nonlinear contribution to the growth of turbulent fluctuations, is tested for a series of idealised and realistic flows. In this paper, the effects of urban geometry on accuracy are examined with neutral flow over shallow, deep, asymmetric and realistic canyons, while thermal effects are investigated for a single street canyon and both bottom cooling and heating. The estimated mean profiles of the streamwise and spanwise velocity components show good agreement with reference profiles obtained from the large-eddy simulation: the canyon-averaged errors (e.g., normalised absolute errors around 1%) are of the same order of magnitude as those for the unit-aspect-ratio street canyon. It is argued that the approach generalises to more realistic flows because strong spatial localisation of the vorticity field is preserved. This work may be applied to high-resolution modelling of winds and pollutants, for which mean wind profiles are required, and fast statistical modelling, for which physically-based estimates can serve as initial guesses or substitutes for analytical models.

A novel machine learning-based framework for mapping outdoor thermal comfort

ABSTRACT Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learning-based framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS Machine learning use in outdoor thermal comfort assessment has been investigated. Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.

Cost-Optimal Deployment of Millimeter-Wave Base Stations Under Outage Requirement

Today’s growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave networks can carry a large volume of traffic, highly intermittent connectivity and the challenges related to installing many small-cell base stations (BSs) in urban geometry have impeded its progression into practical networks. To cope with these challenges, we present, in this paper, an approach to the mmWave BS deployment (site planning) problem, based on the minimum-deployment-cost criterion that is subject to user equipment (UE) outage constraints. Unlike the prior works, the proposed model captures the randomness of link blockage and signal-to-interference-plus-noise-ratio (SINR) statistics in mmWave networks. We formulate the minimum-cost deployment problem as large-scale integer nonlinear programming (INP). To deal with the coupled and combinatorial of the problem, the large-scale INP has approached to devise a suboptimal but efficient algorithm by decomposing it into two subproblems: (i) cell coverage optimization and (ii) minimum subset selection. We provide the solutions to each subproblem as well as theoretical justifications of them. Simulation results that illustrate UE outage guarantees of the proposed BS deployment method are presented. The results reveal that the proposed method uniquely distributes the macro-diversity orders that are distinct from other benchmarks.

Autonomous urban topology generation for urban flow modelling

• New framework is developed that integrates the autonomous urban topology generation for urban flow modeling. • A new deep learning model is developed for building footprints extraction from satellite imagery • Deep learning model was meshed with Light Detection and Ranging (LiDAR) data by meshing to generate 3D building models • An illustration pedestrian level wind simulation was carried out for city of London On. One of the challenges in realistic numerical urban micro-climate modelling for wind, heat transfer, and building energy simulation applications is the complexity of urban topology and complex building geometries. This paper presents a deep learning modelling for building footprint polygon extraction from satellite imagery that is integrated with Light Detection and Ranging (LiDAR) data to generate 3D building models. The deep learning model registered a mean squared error of 0.02. The trained deep learning model can then be applied to a new set of input image data to extract building footprint polygons for autonomous application, and it can also be incrementally retrained with good quality data when it becomes available. A framework is developed that integrates the autonomous urban topology generator with urban flow modelling. The modelling steps are explained through an application example of urban flow modelling encountered during a pedestrian level wind assessment for the city of London, Ontario.