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Urban modelling is an important branch of land use science. It integrates geography, surveying and mapping, information science, system science, economics, sociology and other disciplines to establish urban models, which have been used to provide support for urban policymaking or analyses. Urban models are used to understand, analyse, evaluate and reproduce the process of urban development, and predict the consequence of urban planning scenarios. In this paper, we provide a systematic review of urban models, including the evaluation, classification, application of urban models, and the timeline of urban models’ development. According to their modelling styles and applications, urban models can be classified into three categories: aggregate static models of economic and spatial interaction, urban dynamics models, and behavioural models of individual agents which linked to spatial location. According to the different modelling methods, urban models can be classified into two categories: top-down and bottom-up. Nowadays, emerging technologies, especially Information and Communication Technologies (ICT), are gradually but significantly changing the organization form of urban economic activities. It enables regions to break the location limitation and join in the national even global industry division, and that triggers a new bottom-up rural urbanization process, which formed a significant challenge for urban models. Based on above discussion, we proposed two perspectives for improvements of urban models, inculding the integration of ICT with tradtional urban models and integaration of top-down and bottom-up models.

ABSTRACTThe simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models.

The simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models. [ABSTRACT FROM AUTHOR]

The urbanization processes with growing vehicle numbers cause heavy metal pollution in street dust, and high populations in metropolitan cities are exposed to pollutants. This paper aims to monitor the spatial distribution of heavy metals and evaluate the concentrations via health risk assessment of HMs (Cu, Ni, Cd, Co, Pb, and Zn) that expose the inhabitants to health hazards. According to the results of the current study, sixty street dust samples were applied to the acid digestion technique and quantification by inductively coupled plasma-mass spectrometry (ICP-MS). The spatial distribution of the selected heavy metals in the street dust was investigated using the spatial analysis tool in ArcGIS 10.0 according to population density and land use. In the present study, we used hazard index and cancer risk methods to estimate the public health risk of the pollutants exposed to street dust in Ankara. The concentrations range of the elements in street dust over the study area ranged from 3.34–4.50, 31.69–42.87, 16.09–21.54, 42.85–57.55, 0.00–3.51, and 23.03–30.79, respectively. The overall decreasing order of mean concentration of metals was observed as follows: Pb > Cu > Ni > Co > Cd > Zn. Vehicle traffic and industrial activities seem to be the most critical anthropogenic sources responsible for dust pollution in the study area. The risk assessment of Pb and Ni exposure was the highest, and the hazard index values were 2.42E + 00 and 2.28E + 00 mg/kg/day for children. However, the effect on adults was 2.62E-01 and 2.37E-02 mg/kg/day, followed by inhalation and dermal contact with street dust was almost negligible. The decreasing concentration is modeled spatially along the western development corridor of the city. The risk to public health is high in areas with high densities close to the city center and the main artery. [ABSTRACT FROM AUTHOR]

In cases of accidental or deliberate incidents involving a harmful agent in urban areas, a detailed modelling approach is required to include the building shapes and spatial locations. Simultaneously, when applied to crisis management, a simulation tool must meet strict time constraints. This work presents a Lagrangian particle model (LPM) for computing atmospheric dispersion. The model is implemented in the nuclear decision support system ESTE CBRN, a software tool developed to calculate the atmospheric dispersion of airborne hazardous materials and radiological impacts in the built-up area. The implemented LPM is based on Thomson's solution for the nonstationary, three-dimensional Langevin equation model for turbulent diffusion. The simulation results are successfully analyzed by testing compatibility with Briggs sigma functions in the case of continuous release. The implemented LPM is compared with the Joint Urban 2003 Street Canyon Experiment for instantaneous puff releases. We compare the maximum concentrations and peak times measured during two intensive operational periods. The modeled peak times are mostly 10–20% smaller than the measured. Except for a few detector locations, the maximum concentrations are reproduced consistently. In the end, we demonstrate via calculation on single computers utilizing general-purpose computing on graphics processing units (GPGPU) that the implementation is well suited for an actual emergency response since the computational times (including dispersion and dose calculation) for an acceptable level of result accuracy are similar to the modeled event duration itself. [ABSTRACT FROM AUTHOR]

Urban analyses demand simplifications that balance modelling level of detail and scope broadness. Thus, classification by archetypes is a promising methodological approach. Such an approach is common for energy studies but rarely applied for Life Cycle Assessment (LCA) purposes. When archetypes are used in urban LCA, they generally result from previous studies for classification and characterization according to parameters that directly affect the operational energy performance of buildings. This paper tackles two research questions: i) Is it appropriate to aggregate building stocks based on operational energy (OE) variables when life cycle impacts are investigated? ii) When integrated LCA (OE + embodied impacts) is pursued, would variables describing both interests simultaneously result in better representation than using operational energy-based clustering to predict embodied impacts and vice versa? Thus, we aim to confirm that, combining variables that govern OE and embodied impacts offers a better result than using OE to predict materials groupings, even if some adherence is lost relatively to single-objective clustering. Clustering experiments were carried out for the campus of the University of Campinas, Brazil. After unsupervised k-medoid (PAM) grouping, the data were submitted to a supervised learning (neural networks) classification method. Generated confusion matrices demonstrate how adherent the clustering is when considering one interest to predict the other in three situations. Results indicate that an operational energy-driven archetype fails to represent buildings from the embodied impacts viewpoint, and that merging operational energy and embodied impact variables would better support integrated life cycle impact predictions.

Abstract Climate change and sustainability are among the most widely used terms among policymakers and the scientific community in recent times. However, climate action or steps to sustainable growth in cities in the global south are mostly borrowed from general studies at a few large urban agglomerations in the developed world. There are very few modeling studies over south Asia to understand and quantify the impact of climate change and urbanization on even the most primary meteorological variable, such as temperature. Such quantifications are difficult to estimate due to the non-availability of relevant long-term observational datasets. In this modeling study, an attempt is made to understand the urban heat island (UHI), its transition, and the segregation of regional climate change effects and urbanization over the rapidly growing tier 2 tropical smart city Bhubaneswar in India. The model is able to simulate the UHI for both land surface temperature, called the SUHI, and 2-m air temperature, called UHI, reasonably well. Their magnitudes were ~ 5 and 2.5°C, respectively. It is estimated that nearly 60–70% of the overall air and 70–80% of the land surface temperature increase during nighttime over the city between the period 2004 and 2015 is due to urbanization, with the remaining due to the regional/non-local effects.

Urban morphology is important in a broad range of investigations across the fields of city planning, transportation, climate, energy, and urban data science. Characterising buildings with a set of numerical metrics is fundamental to studying the urban form. Despite the rapid developments in 3D geoinformation science, and the growing 3D data availability, most studies simplify buildings to their 2D footprint, and when taking their height into account, they at most assume one height value per building, i.e. simple 3D. We take the first step in elevating building metrics into full/true 3D, uncovering the use of higher levels of detail, and taking into account the detailed shape of a building. We set the foundation of the new research line on 3D urban morphology by providing a comprehensive set of 3D metrics, implementing them in openly released software, generating an open dataset containing 2D and 3D metrics for 823,000 buildings in the Netherlands, and demonstrating a use case where clusters and architectural patterns are analysed through time. Our experiments suggest the added value of 3D metrics to complement existing counterparts, reducing ambiguity, and providing advanced insights. Furthermore, we provide a comparative analysis using different levels of detail of 3D building models. [ABSTRACT FROM AUTHOR]

Urban analyses demand simplifications that balance modelling level of detail and scope broadness. Thus, classification by archetypes is a promising methodological approach. Such an approach is common for energy studies but rarely applied for Life Cycle Assessment (LCA) purposes. When archetypes are used in urban LCA, they generally result from previous studies for classification and characterization according to parameters that directly affect the operational energy performance of buildings. This paper tackles two research questions: i) Is it appropriate to aggregate building stocks based on operational energy (OE) variables when life cycle impacts are investigated? ii) When integrated LCA (OE + embodied impacts) is pursued, would variables describing both interests simultaneously result in better representation than using operational energy-based clustering to predict embodied impacts and vice versa? Thus, we aim to confirm that, combining variables that govern OE and embodied impacts offers a better result than using OE to predict materials groupings, even if some adherence is lost relatively to single-objective clustering. Clustering experiments were carried out for the campus of the University of Campinas, Brazil. After unsupervised k-medoid (PAM) grouping, the data were submitted to a supervised learning (neural networks) classification method. Generated confusion matrices demonstrate how adherent the clustering is when considering one interest to predict the other in three situations. Results indicate that an operational energy-driven archetype fails to represent buildings from the embodied impacts viewpoint, and that merging operational energy and embodied impact variables would better support integrated life cycle impact predictions. [ABSTRACT FROM AUTHOR]

This paper seeks to develop the core concepts of a model of urban evolution. It proceeds in four major sections. First, we review prior adumbrations of an evolutionary model in urban theory, noting their potential and their limitations. Second, we turn to the general sociocultural evolution literature to draw inspiration for a fresh and more complete application of evolutionary theory to the study of urban life. Third, building upon this background, we outline the main elements of our proposed model, with special attention to elaborating the value of its key conceptual innovation, the "formeme". Last, we conclude with a discussion of what types of research commitments the overall approach does or does not imply, and point toward the more formal elaboration of the model that we undertake in "Towards a Model of Urban Evolution, Part II" and "Towards a Model of Urban Evolution, Part III". In "Towards a Model of Urban Evolution, Part IV" we demonstrate the application of the model to Yelp data. [ABSTRACT FROM AUTHOR]

In flood area mapping studies, hydrological-hydraulic modeling has been successfully applied around the world. However, the object of study of most of the research developed in Brazil is medium to large channels that use topographical and hydrometeorological data of coarse spatial and temporal resolution. Thus, the aim of this study is to investigate coupled modeling in a small urban channel, using high-resolution data, in the simulation of flood events in a small urban channel, located in Campo Grande, Mato Grosso do Sul. In this study, we used the HEC-HMS and HEC-RAS programs, where topographic information, land use, land cover, and observed data from rain gauges, water level, and flow sensors from 2015 to 2018 were used as input data. To calibrate and validate the hydrological model, four events were used that occurred during the monitoring period, while in the hydraulic model we chose a historical event that caused great disturbances. We then generated flood scenarios with representative synthetic rainfall for a basin, with return times of 5, 10, 50, and 100 years. We observed a good fit in the calibration and validation of the HEC-HMS, with values of R2 = 0.93, RMSE = 1.29, and NSE = 0.92. In HEC-RAS, we obtained values of R2 = 0.93, RMSE = 1.29, and NSE = 0.92 for the calibration, and in the validation, real images of the event prove the computed flood spot sources. We observed that rain with a return time of less than five years provides areas of flooding in several regions of the channel, and in critical channeled sections, the elevation and speed of the flow reach 5 m and 3 m·s−1, respectively. Our results indicate that the channel already has a natural tendency towards flooding in certain stretches, which become more compromised due to land use and coverage and local conditions. We conclude that the high-resolution coupled modeling generated information that represents local conditions as well, showing how potential failures of drainage in extreme scenarios are possible, thus enabling the planning of adaptations and protection measures against floods. [ABSTRACT FROM AUTHOR]

Modelling and visualization of three-dimensional (3D) models for cities is a great challenge for computer software and graphics. Recently, 3D city modelling has grown due to advances in applications accompanying the information technology revolution. 3D Geographic Information Systems (3D-GIS) have evolved enormously due to the availability of large-scale 3D modelling techniques. These technologies have become very important in representing large cities and conducting various analyses in the city’s virtual environment to support urban decision-making. CityEngine is one of the most recent 3D-GIS modelling applications. CityEngine can be described as parametric modelling using Procedural Modelling (PM) to create 3D urban elements through macros and routines. This paper highlights the importance of 3D Procedural Modelling (PM) of cities in the GIS environment using ESRI CityEngine and presents a parametric concept for designing urban spaces. This issue has been addressed in three respects. First, discuss the concept and strength of parametric design. Second, the concept of procedural modelling and its power to generate complex 3D models using a set of rules is discussed. Finally, CityEngine was evaluated through a real-world case study of a neighbourhood in the new city of Beni-Suef, Egypt. The results confirm the effectiveness of CityEngine as a 3D-GIS modelling software that generates dynamic 3D models from 2D spatial data. While the results are promising, it is important to investigate more complex cases. The CityEngine modelling approach enables comprehensive urban analyses such as sequence vision, façade studies, urban fabric and character, and statistical operations based on attribute database.

This present study aims to determine the performance of using the Weather Research and Forecasting (WRF) Model, coupled with the urban canopy models (UCMs), in simulating the 2 m air temperature and 2 m relative humidity in Metro Manila. The simulation was performed during a high heat event on 22–29 April 2018, which coincided with the dry season in the Philippines. The four urban canopy model options that were used in this study include, the bulk (no urban), SLUCM, BEP, and BEM. The results of the simulations were compared with the hourly observations from three weather stations over Metro Manila from the National Oceanic and Atmospheric Administration Integrated Surface Dataset (ISD) and one agrometeorological station in Naic, Cavite. After model validation, the urban heat island (UHI) was then characterized to determine the spatial-temporal variations in the cities of Metro Manila. Statistical results show that the WRF simulation for 2 m air temperature agrees with measurements with an RMSE of <3.0 °C, mean bias error of <2.0 °C, and index of agreement of >0.80. WRF simulation for relative humidity still presents a challenge where simulation errors are higher than the acceptable range. The addition of UCMs does not necessarily improve the simulation for 2 m air temperature, while the use of BEP improved the 2 m relative humidity simulation. The results suggest the importance of using actual urban morphology values in WRF to accurately simulate near-surface variables. On the other hand, WRF simulation shows the presence of urban heat islands, notably in the northwest and central area of Metro Manila during daytime, extending throughout Metro Manila during nighttime. Lower air temperature was consistently observed in areas near Laguna Lake, while higher air temperature due to stagnant winds was observed in the northwest area of Metro Manila. High heat index was also observed throughout Metro Manila from daytime until nighttime, especially in areas near bodies of water like Manila Bay and Laguna Lake due to high humidity. [ABSTRACT FROM AUTHOR]

Modelling and visualization of three-dimensional (3D) models for cities is a great challenge for computer software and graphics. Recently, 3D city modelling has grown due to advances in applications accompanying the information technology revolution. 3D Geographic Information Systems (3D-GIS) have evolved enormously due to the availability of large-scale 3D modelling techniques. These technologies have become very important in representing large cities and conducting various analyses in the city's virtual environment to support urban decision-making. CityEngine is one of the most recent 3D-GIS modelling applications. CityEngine can be described as parametric modelling using Procedural Modelling (PM) to create 3D urban elements through macros and routines. This paper highlights the importance of 3D Procedural Modelling (PM) of cities in the GIS environment using ESRI CityEngine and presents a parametric concept for designing urban spaces. This issue has been addressed in three respects. First, discuss the concept and strength of parametric design. Second, the concept of procedural modelling and its power to generate complex 3D models using a set of rules is discussed. Finally, CityEngine was evaluated through a real-world case study of a neighbourhood in the new city of Beni-Suef, Egypt. The results confirm the effectiveness of CityEngine as a 3D-GIS modelling software that generates dynamic 3D models from 2D spatial data. While the results are promising, it is important to investigate more complex cases. The CityEngine modelling approach enables comprehensive urban analyses such as sequence vision, façade studies, urban fabric and character, and statistical operations based on attribute database. [ABSTRACT FROM AUTHOR]

In the past decades, various Earth observation-based time series products have emerged, which have enabled studies and analysis of global change processes. Besides their contribution to understanding past processes, time series datasets hold enormous potential for predictive modeling and thereby meet the demands of decision makers on future scenarios. In order to further exploit these data, a novel pixel-based approach has been introduced, which is the spatio-temporal matrix (STM). The approach integrates the historical characteristics of a specific land cover at a high temporal frequency in order to interpret the spatial and temporal information for the neighborhood of a given target pixel. The provided information can be exploited with common predictive models and algorithms. In this study, this approach was utilized and evaluated for the prediction of future urban/built-settlement growth. Random forest and multi-layer perceptron were employed for the prediction. The tests have been carried out with training strategies based on a one-year and a ten-year time span for the urban agglomerations of Surat (India), Ho-Chi-Minh City (Vietnam), and Abidjan (Ivory Coast). The slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) model was selected as a baseline indicator for the performance evaluation. The statistical results from the receiver operating characteristic curve (ROC) demonstrate a good ability of the STM to facilitate the prediction of future settlement growth and its transferability to different cities, with area under the curve (AUC) values greater than 0.85. Compared with SLEUTH, the STM-based model achieved higher AUC in all of the test cases, while being independent of the additional datasets for the restricted and the preferential development areas. [ABSTRACT FROM AUTHOR]

In cases of accidental or deliberate incidents involving a harmful agent in urban areas, a detailed modelling approach is required to include the building shapes and spatial locations. Simultaneously, when applied to crisis management, a simulation tool must meet strict time constraints. This work presents a Lagrangian particle model (LPM) for computing atmospheric dispersion. The model is implemented in the nuclear decision support system ESTE CBRN, a software tool developed to calculate the atmospheric dispersion of airborne hazardous materials and radiological impacts in the built-up area. The implemented LPM is based on Thomson’s solution for the nonstationary, three-dimensional Langevin equation model for turbulent diffusion. The simulation results are successfully analyzed by testing compatibility with Briggs sigma functions in the case of continuous release. The implemented LPM is compared with the Joint Urban 2003 Street Canyon Experiment for instantaneous puff releases. We compare the maximum concentrations and peak times measured during two intensive operational periods. The modeled peak times are mostly 10–20% smaller than the measured. Except for a few detector locations, the maximum concentrations are reproduced consistently. In the end, we demonstrate via calculation on single computers utilizing general-purpose computing on graphics processing units (GPGPU) that the implementation is well suited for an actual emergency response since the computational times (including dispersion and dose calculation) for an acceptable level of result accuracy are similar to the modeled event duration itself.

Modelling a 3D city poses an interesting challenge. To create a virtual city, a road pattern has to be designed and a large number of buildings need to be generated. Every urban place has a road network, often a superimposed pattern plan that serves a population density and buildings which follow statutory rules. This patterned behaviour of the city is why it is possible to develop rules or “computational instructions,” to generate city models. In this article, we are going to discuss how to use procedural modelling and CityEngine, a rule-based application commonly used in the movie industry and gaming to produce vast realistic cityscapes, for regional and urban planning via an urban analytics approach. Unlike cinema’s imaginary worlds, cities have real-life population dependencies that need to be modelled for the development of planning scenarios. The goal is then to use the generative properties of the procedural modelling approach, along with population prediction models, to create informed 3D city scenarios. Instead of designing solutions, the user can use interactive parameters to affect the 3D model globally, thus enabling virtual cities to become active simulators for planning. Using urban analytics and generative environments, procedural cities may be able to create a “teaser” of different versions of how the city would look like in the future.

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. Increasing access to large datasets collected in real time could make certain mathematical models developed in research fields related to the management of urban systems obsolete. These ongoing evolutions are the subject of numerous works whose main angle of reflection is the future of cities rather than the transformations at work in the academic field. Our article proposes grasp the scientific dynamics in areas of research related to two urban systems: transportation and water. The article demonstrates the importance of grasping these dynamics if we want to be able to apprehend what the urban management of tomorrow's cities will be like. To analyse these research areas' dynamics, we use two complementary materials: bibliometric data and interviews. The interviews conducted in 2018 with academics and higher education officials in Paris and Edinburgh suggest avenues for hybridization between traditional modelling approaches and research in machine learning, artificial intelligence and Big Data. The bibliometric analysis highlight the trends at work: it shows that traffic flow as well as transportation studies are focussing more and more on AI and Big Data and that traffic flow studies are arousing a growing interest among computer scientists, while, so far, this interest is less pronounced in the water research area, and more especially regarding water quality. The differences observed between research on transportation and that on water confirm the multifaceted nature of the developments at work and encourage us to reject overly hasty and simplistic generalisations about the transformations underway. [ABSTRACT FROM AUTHOR]

Abstract A long-standing question for urban and regional planners pertains to the ability to describe urban patterns quantitatively. Cities’ transport infrastructure, particularly street networks, provides an invaluable source of information about the urban patterns generated by peoples’ movements and their interactions. With the increasing availability of street network datasets and the advancements in deep learning methods, we are presented with an unprecedented opportunity to push the frontiers of urban modelling towards more data-driven and accurate models of urban forms.In this study, we present our initial work on applying deep generative models to urban street network data to create spatially explicit urban models. We based our work on Variational Autoencoders (VAEs) which are deep generative models that have recently gained their popularity due to the ability to generate realistic images. Initial results show that VAEs are capable of capturing key high-level urban network metrics using low-dimensional vectors and generating new urban forms of complexity matching the cities captured in the street network data.

In flood area mapping studies, hydrological-hydraulic modeling has been successfully applied around the world. However, the object of study of most of the research developed in Brazil is medium to large channels that use topographical and hydrometeorological data of coarse spatial and temporal resolution. Thus, the aim of this study is to investigate coupled modeling in a small urban channel, using high-resolution data, in the simulation of flood events in a small urban channel, located in Campo Grande, Mato Grosso do Sul. In this study, we used the HEC-HMS and HEC-RAS programs, where topographic information, land use, land cover, and observed data from rain gauges, water level, and flow sensors from 2015 to 2018 were used as input data. To calibrate and validate the hydrological model, four events were used that occurred during the monitoring period, while in the hydraulic model we chose a historical event that caused great disturbances. We then generated flood scenarios with representative synthetic rainfall for a basin, with return times of 5, 10, 50, and 100 years. We observed a good fit in the calibration and validation of the HEC-HMS, with values of R2 = 0.93, RMSE = 1.29, and NSE = 0.92. In HEC-RAS, we obtained values of R2 = 0.93, RMSE = 1.29, and NSE = 0.92 for the calibration, and in the validation, real images of the event prove the computed flood spot sources. We observed that rain with a return time of less than five years provides areas of flooding in several regions of the channel, and in critical channeled sections, the elevation and speed of the flow reach 5 m and 3 m·s−1, respectively. Our results indicate that the channel already has a natural tendency towards flooding in certain stretches, which become more compromised due to land use and coverage and local conditions. We conclude that the high-resolution coupled modeling generated information that represents local conditions as well, showing how potential failures of drainage in extreme scenarios are possible, thus enabling the planning of adaptations and protection measures against floods.

This paper seeks to develop the core concepts of a model of urban evolution. It proceeds in four major sections. First, we review prior adumbrations of an evolutionary model in urban theory, noting their potential and their limitations. Second, we turn to the general sociocultural evolution literature to draw inspiration for a fresh and more complete application of evolutionary theory to the study of urban life. Third, building upon this background, we outline the main elements of our proposed model, with special attention to elaborating the value of its key conceptual innovation, the “formeme”. Last, we conclude with a discussion of what types of research commitments the overall approach does or does not imply, and point toward the more formal elaboration of the model that we undertake in “Towards a Model of Urban Evolution, Part II” and “Towards a Model of Urban Evolution, Part III”. In “Towards a Model of Urban Evolution, Part IV” we demonstrate the application of the model to Yelp data.

This present study aims to determine the performance of using the Weather Research and Forecasting (WRF) Model, coupled with the urban canopy models (UCMs), in simulating the 2 m air temperature and 2 m relative humidity in Metro Manila. The simulation was performed during a high heat event on 22–29 April 2018, which coincided with the dry season in the Philippines. The four urban canopy model options that were used in this study include, the bulk (no urban), SLUCM, BEP, and BEM. The results of the simulations were compared with the hourly observations from three weather stations over Metro Manila from the National Oceanic and Atmospheric Administration Integrated Surface Dataset (ISD) and one agrometeorological station in Naic, Cavite. After model validation, the urban heat island (UHI) was then characterized to determine the spatial-temporal variations in the cities of Metro Manila. Statistical results show that the WRF simulation for 2 m air temperature agrees with measurements with an RMSE of 0.80. WRF simulation for relative humidity still presents a challenge where simulation errors are higher than the acceptable range. The addition of UCMs does not necessarily improve the simulation for 2 m air temperature, while the use of BEP improved the 2 m relative humidity simulation. The results suggest the importance of using actual urban morphology values in WRF to accurately simulate near-surface variables. On the other hand, WRF simulation shows the presence of urban heat islands, notably in the northwest and central area of Metro Manila during daytime, extending throughout Metro Manila during nighttime. Lower air temperature was consistently observed in areas near Laguna Lake, while higher air temperature due to stagnant winds was observed in the northwest area of Metro Manila. High heat index was also observed throughout Metro Manila from daytime until nighttime, especially in areas near bodies of water like Manila Bay and Laguna Lake due to high humidity.

In the past decades, various Earth observation-based time series products have emerged, which have enabled studies and analysis of global change processes. Besides their contribution to understanding past processes, time series datasets hold enormous potential for predictive modeling and thereby meet the demands of decision makers on future scenarios. In order to further exploit these data, a novel pixel-based approach has been introduced, which is the spatio-temporal matrix (STM). The approach integrates the historical characteristics of a specific land cover at a high temporal frequency in order to interpret the spatial and temporal information for the neighborhood of a given target pixel. The provided information can be exploited with common predictive models and algorithms. In this study, this approach was utilized and evaluated for the prediction of future urban/built-settlement growth. Random forest and multi-layer perceptron were employed for the prediction. The tests have been carried out with training strategies based on a one-year and a ten-year time span for the urban agglomerations of Surat (India), Ho-Chi-Minh City (Vietnam), and Abidjan (Ivory Coast). The slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) model was selected as a baseline indicator for the performance evaluation. The statistical results from the receiver operating characteristic curve (ROC) demonstrate a good ability of the STM to facilitate the prediction of future settlement growth and its transferability to different cities, with area under the curve (AUC) values greater than 0.85. Compared with SLEUTH, the STM-based model achieved higher AUC in all of the test cases, while being independent of the additional datasets for the restricted and the preferential development areas.

In recent years, DAda Laboratory of University of Pavia has conducted numerous research activities in Holy Land aimed at defining operational strategies for the testing of documenting systems useful for the development of tools for the management and enhancement of historical heritage. The translation of the urban image into complex drawings is accomplished to replicate the characteristics of the real space, in order to entrust the virtual quality useful for configuring management tools and manipulating information. The synthesis of the data acquired from digital survey procedures, from the integration and post-production of a wide variety of instrumental out-puts, is expressed in models that reproduce morphological complexity and dimensional aspects in the desire to replicate the urban space of Middle Eastern cities, enhanced in their information content on the levels of decomposition and analysis of which the urban context is composed, to encourage the fruition of research results through the drawing-model. In particular, the paper will present the model of a portion of San Giovanni of Acre, relating to the area of Al-Jazzar Mosque, for the analysis of the historical stratification of religious and urban environments; a portion of Jerusalem adjacent to the monumental walls, for which it was built an interactive platform for the fruition in AR of the survey database; and a portion of the historical center of Bethlehem, for which an analysis of the characteristics of the ahwash quarters and buildings is addressed, useful for a semantic decomposition of the urban structure functional to the creation of an intelligent model for the monitoring of structural risk.DOI: https://doi.org/10.20365/disegnarecon.21.2018.5

These paper reports applications using satellite images to the identification of vegetation types in the Campo Grande city. This identification allows studies of urban vegetation, palynology and environmental changes. Images from Landsat 8 and Rapideye satellites from the Campo Grande urban area were used. A soil coverage map was done for each one of the seven sub-regions. The Normalized Difference Vegetation Index was applied. In addition, a field survey was carried out to confirm the vegetation types sites through satellite images. Satellite images and in situ data validation allowed the distinction of the following features: water, urban structure, herbaceous, open and dense vegetation. For the identification of urban vegetation, Rapideye images were the most suitable for this type of study. The Rapideye satellite sensor detected 6.55% more dense vegetation area than Landsat 8 images.

The goal of urban building mesh simplification is to generate a compact representation of a building from a given mesh. Local smoothness and sharp contours of urban buildings are important features for converting unstructured data into solid models, which should be preserved during the simplification. In this paper, we present a general method to filter and simplify 3D building mesh models, capable of preserving piecewise planar structures and sharp features. Given a building mesh model, a mesh filtering technique is firstly designed to yield piecewise planar regions and extract crease contours. The planar regions are used to constrain the simplification of the mesh. Mesh decimation is achieved through a series of edge collapse operations, which uses regional structural constraints and local geometric error metrics to handle planar and non-planar areas respectively. The proposed method preserves the mesh structure with meaningful levels of detail while reducing the number of faces. The effectiveness of this method is evaluated on various building models generated from different observation scales, and the performance is validated by extensive comparisons to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Dymium is a microsimulation framework written in R for integrated urban modelling. It aims to solve technical challenges faced by microsimulation modellers and make the approach more accessible to a broad range of users. With numerous packages that are available for free in R, including Dymium, all the stages – data preparation, microsimulation, calibration, and model validation – in building a microsimulation model can be done effectively under one programming environment. Although we developed the framework for modelling urban systems, i.e., demography, firmography and transport modelling, it has all necessary functionalities that can be readily adopted for other contexts.

Climate predictions indicate a strong likelihood of more frequent, intense heat events. Resource-vulnerable, low-income neighbourhood populations are likely to be strongly impacted by future climate change, especially with respect to an energy burden. In order to identify existing and new vulnerabilities to climate change, local authorities need to understand the dynamics of extreme heat events at the neighbourhood level, particularly to identify those people who are adversely affected. A new comprehensive framework is presented that integrates human and biophysical data: occupancy/behaviour, building energy use, future climate scenarios and near-building microclimate projections. The framework is used to create an urban energy model for a low-resource neighbourhood in Des Moines, Iowa, US. Data were integrated into urban modelling interface ('umi') software simulations, based on detailed surveys of residents’ practices, their buildings and near-building microclimates (tree canopy effects, etc.). The simulations predict annual and seasonal building energy use in response to different climate scenarios. Preliminary results, based on 50 simulation runs with different variable combinations, indicate the importance of using locally derived building occupant schedules and point toward increased summer cooling demand and increased vulnerability for parts of the population. 'Practice relevance' To support planning responses to increased heat, local authorities need to ascertain which neighbourhoods will be negatively impacted in order to develop appropriate strategies. Localised data can provide good insights into the impacts of human decisions and climate variability in low-resource, vulnerable urban neighbourhoods. A new detailed modelling framework synthesises data on occupant–building interactions with present and future urban climate characteristics. This identifies the areas most vulnerable to extreme heat using future climate projections and community demographics. Cities can use this framework to support decisions and climate-adaptation responses, especially for low-resource neighbourhoods. Fine-grained and locally collected data influence the outcome of combined urban energy simulations that integrate human–building interactions and occupancy schedules as well as microclimate characteristics influenced by nearby vegetation.

Small cities in developing countries are under a set of transitions that imperil their sustainability and whose repercussions can reach the international level. Therefore, local decision-makers need specific geoprospective urban models to seek alternative pathways. This paper presents a housing allocation model, designed to meet the context of small cities in developing countries. It is a multi-level Cellular Automata that relies on a new apprehension of urbanisation process, implemented through raster irregular grid with time-step variation. The model application to reconstruct housing dynamics in Kasba-Tadla city in Morocco, between 1999 and 2016, confirmed its ability to mimic well the spatio-temporal allocation of housing settlements. The obtained results showed that the spatial allocation of informal settlements is not anarchic, but driven by flexible rules, and also revealed Kasba-Tadla city could have better negotiated the after ‘Arab Spring’ urban transformations if local decision-makers had foreknowledge.

Two urban schemes within the Joint UK Land Environment Simulator (JULES) are evaluated offline against multi-year flux observations in the densely built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab model, used in climate simulations; (ii) the 2-tile canopy model MORUSES (Met Office–Reading Urban Surface Exchange Scheme), used for numerical weather prediction over the UK. Offline, both models perform better at the suburban site, where differences between the urban schemes are less pronounced due to larger vegetation fractions. At both sites, the outgoing short- and longwave radiation is more accurately represented than the turbulent heat fluxes. The seasonal variations of model skill are large in London, where the sensible heat flux in autumn and winter is strongly under-predicted if the large city centre magnitudes of anthropogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1-tile model in London results in large negative bias in the morning. The partitioning of the urban surface into canyon and roof in MORUSES improves this as the roof tile is modelled with a very low thermal inertia, but phase and amplitude of the grid box-averaged flux critically depend on accurate knowledge of the plan-area fractions of streets and buildings. Not representing non-urban land cover (e.g. vegetation, inland water) in London results in severely under-predicted latent heat fluxes. Control runs demonstrate that the skill of both models can be greatly improved by providing accurate land cover and morphology information and using representative anthropogenic heat emissions, which is essential if the model output is intended to inform integrated urban services. [ABSTRACT FROM AUTHOR]

Inspired by the fast take-up of data analytics and modelling in urban planning and design in Chinese cities, this paper aims to address a serious knowledge gap in terms of using data to deliver better policy outcomes rather than technical outputs. Such a knowledge gap is discussed in the wider context of smart city development where technology deployment failed to deliver the expected policy benefits. Lessons thus can be, and should be, learnt from early experiments to prevent the data revolution in planning and design in Chinese cities from repeating the same failure. One of the key arguments is that, in order to leverage the potential power of data and analytics for the urban planning and design industry, a wider theoretical framework is required for rethinking the core role as well as core competence of the planning profession in China. It entails a diversion from the purely technical discourse and the disciplinary / professional silos, towards a socio-technical perspective. A series of propositions are proposed to evoke more critical discussion about the digital agenda for urban planning and design. [ABSTRACT FROM AUTHOR]