•A workflow is provided for understanding urban patterns based upon meaningful measurements of urban form elements.•Interpretable urban patterns are derived through publicly accessible data and tools ...ensuring workflow scientific validity.•EO has a shifted but important role for the potential of interpreting socioeconomic patterns through urban forms.
Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through image features, to the mapping of interpretable urban morphology of basic urban elements as an intermediate step, to which socioeconomic patterns can then be related. This shift is profound, in that, rather than abstract image features, it allows to capture the morphology of real urban objects, such as buildings and streets, and use this to then interpret other patterns, including socioeconomic ones. Because socioeconomic patterns are not derived from raw image data, the mapping of these patterns is less data demanding and more replicable. Specifically, we propose a 2-step approach: (1) extraction of fundamental urban elements from satellite imagery, and (2) derivation of meaningful urban morphological patterns from the extracted elements. We refer to this 2-step approach as “EO + Morphometrics”. Technically, EO consists of applying deep learning through a reengineered U-Net shaped convolutional neural network to publicly accessible Google Earth imagery for building extraction. Methods of urban morphometrics are then applied to these buildings to compute semantically explicit and interpretable metrics of urban form. Finally, clustering is applied to these metrics to obtain morphological patterns, or urban types. The “EO + Morphometrics” approach is applied to the city of Nairobi, Kenya, where 15 different urban types are identified. To test whether this outcome meaningfully describes current urbanization patterns, we verified whether selected types matched locally designated informal settlements. We observe that four urban types, characterized by compact and organic urban form, were recurrent in such settlements. The proposed “EO + Morphometrics” approach paves the way for the large-scale identification of interpretable urban form patterns and study of associated dynamics across any region in the world.
Urban growth and development caused by urbanization influence the urban heat island (UHI) phenomenon. With the rapid development of urbanization, China's major cities are facing more serious climate ...change problems, especially the UHI phenomenon. Proper planning and urban design of compact cities may improve the ventilation of street canyons and change the heat balance in the urban canopy and thus mitigate the UHI phenomenon. The aim of this study is to evaluate and discuss the mitigation of UHI with different types of land-use and land-cover (LUCC), as well as different development patterns for compact cities. To this end, we applied the weather research and forecasting model (WRF) with urban canopy model (WRF/UCM) in this study. To evaluate the impact of LUCC changes on the UHI, we set 2 cases based on land use and land cover statistical data from 1965 and 2008 of Wuhan. Also, to evaluate the impact of urban morphology changes on the UHI, we designed 2 hypothetical cases based on 2 different urban developing patterns, one is high rise case and another is high density case, to simulate the impact of urban morphology on the UHI. As for the results of this study, with different LUCC of 1965 and 2008, UHI intensity of Wuhan increased by 0.2 °C–0.4 °C in average. Moreover, the critical wind speed which can mitigate UHI of case 1965 is much lower than case 2008. With different urban morphology, the high-rise case may lead to lower UHI intensity at the pedestrian level due to the shading effects of high-rise buildings. However, the critical value of wind speed in the high-rise case was almost 1.5–2 times greater than that of the high-density case, which illustrates the reduced possibility of mitigating the UHI phenomenon for high-rise buildings in Wuhan City.
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•When the lake area decreased by 130 km2 in the built-up area of Wuhan, UHI intensity increased by 0.2 °C–0.4 °C.•When the lake area decreased by 130 km2, the critical value of wind speed needed to mitigate the UHI doubled at 04:00.•Air temperature differences between these two development patterns can reach 1 °C and that at 12:00 and 17:00, respectively.•The critical values of wind speed in the high-density case were 1.5 and 2 times higher than those of the high-rise case.•The T2 in the high-density case was 0.5 ºC higher, however, wind speed of high-rise case was 70% of high-density case.
•New spatial information systems, visualization tools, and workflows offer new possibilities for urban morphology research.•We present a computational workflow to model and visualize urban form and ...uncover planning patterns/histories using big data.•We illustrate across worldwide study sites how urban planning/evolution organize space and produce different spatial logics.•Data-driven and narrative/interpretative approaches reveal physical form that results from complex sociotechnical processes.
Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, conceptualize proposed designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.
Previous studies of the effects of regional climate conditions on urban heat islands (UHIs) focused mostly on surface UHIs, whereas few considered canopy layer UHIs. In the present study, a numerical ...modeling method is used to investigate the impacts of regional climate conditions on canopy layer UHIs at the district scale while controlling for the urban morphology. The urban morphology is classified according to the local climate zone (LCZ) system as LCZ1-LCZ6. Analysis of the spatial distribution of the urban heat island intensity (UHII) show that the nighttime and daytime UHII are most significantly correlated with the air temperature and wind speed, respectively. In five typical cities, LCZ1 has the most obvious urban heat island (UHI) effect, with an average annual UHII of 1–2.3 °C, which is about 1.5 times that for LCZ4. Reducing the building density has more significant influence on mitigating the UHI effect, where reducing the building height and building density reduce the heat island degree-hours (HIdh) by about 20% and 30%, respectively. The relationships between the UHII and meteorological conditions vary among different periods. For example, the correlation between UHII and average wind speed is more significant in the winter and at night. Our results help to understand the relationships between regional climate conditions and the canopy layer UHI at the district scale.
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•Canopy layer UHIs is studied while controlling for urban morphology.•Regional climate conditions significantly affect canopy layer UHIs.•Reducing building density more conducive to alleviating UHIs.•Relationships between UHII and meteorological conditions vary among periods.
The Urban heat island (UHI) effect is an increasingly serious problem in urban areas. Information on the driving forces of intra-urban temperature variation is crucial for ameliorating the urban ...thermal environment. Although prior studies have suggested that urban morphology (e.g., landscape pattern, land-use type) can significantly affect land surface temperature (LST), few studies have explored the comprehensive effect of 2D and 3D urban morphology on LST in different urban functional zones (UFZs), especially at a fine scale. Therefore, in this research, we investigated the relationship between 2D/3D urban morphology and summer daytime LST in Wuhan, a representative megacity in Central China, which is known for its extremely hot weather in summer, by adopting high-resolution remote sensing data and geographical information data. The “urban morphology” in this study consists of 2D urban morphological parameters, 3D urban morphological parameters, and UFZs. Our results show that: (1) The LST is significantly related to 2D and 3D urban morphological parameters, and the scattered distribution of buildings with high rise can facilitate the mitigation of LST. Although sky view factor (SVF) is an important measure of 3D urban geometry, its influence on LST is complicated and context-dependent. (2) Trees are the most influential factor in reducing LST, and the cooling efficiency mainly depends on their proportions. The fragmented and irregular distribution of grass/shrubs also plays a significant role in alleviating LST. (3) With respect to UFZs, the residential zone is the largest heat source, whereas the highest LST appears in commercial and industrial zones. (4) Results of the multivariate regression and variation partitioning indicate that the relative importance of 2D and 3D urban morphological parameters on LST varies among different UFZs and 2D morphology outperforms 3D morphology in LST modulation. The results are generally consistent in spring, summer and autumn. These findings can provide insights for urban planners and designers on how to mitigate the surface UHI (SUHI) effect via rational landscape design and urban management during summer daytime.
Urban morphology is one of the factors that influence the intensity of urban heat islands. Studying urban morphology means studying the factors that influence the formation of the urban environment, ...such as urban canyons and the ratio between building height and street width. The aim of this article is to verify the errors of heat island prediction models with respect to data obtained from measurements for the city of Goiânia/Brazil. Air temperature surveys were carried out in the field and the heat island intensity values were compared with the numerical model by Oke (1982) and by Nakata-Osaki et al. (2016). A simulation of an urban scenario was applied, represented by various types of urban canyons. The results showed that two numerical models correlated well with the values measured on site. The errors of the models used were determined using the Root Mean Square Error, which showed an error of around 5 °C for the Oke model and 2 °C for the Nakata-Osaki model. It is hoped that the research will be an analytical tool for public authorities, such Municipal Environment Agency, providing support for legislation and municipal plans based on the results obtained, contributing to the wellbeing of the population.
•Street-level photographs are now an omnipresent urban dataset.•Buildings are often imaged multiple times, but most photos are obstructed.•An approach to generate 3D models of buildings from their ...single views.•The method can be aided by building footprints.•The resulting models are usable for a variety of use cases.
3D building models are an established instance of geospatial information in the built environment, but their acquisition remains complex and topical. Approaches to reconstruct 3D building models often require existing building information (e.g.their footprints) and data such as point clouds, which are scarce and laborious to acquire, limiting their expansion. In parallel, street view imagery (SVI) has been gaining currency, driven by the rapid expansion in coverage and advances in computer vision (CV), but it has not been used much for generating 3D city models. Traditional approaches that can use SVI for reconstruction require multiple images, while in practice, often only few street-level images provide an unobstructed view of a building. We develop the reconstruction of 3D building models from a single street view image using image-to-mesh reconstruction techniques modified from the CV domain. We regard three scenarios: (1) standalone single-view reconstruction; (2) reconstruction aided by a top view delineating the footprint; and (3) refinement of existing 3D models, i.e.we examine the use of SVI to enhance the level of detail of block (LoD1) models, which are common. The results suggest that trained models supporting (2) and (3) are able to reconstruct the overall geometry of a building, while the first scenario may derive the approximate mass of the building, useful to infer the urban form of cities. We evaluate the results by demonstrating their usefulness for volume estimation, with mean errors of less than 10% for the last two scenarios. As SVI is now available in most countries worldwide, including many regions that do not have existing footprint and/or 3D building data, our method can derive rapidly and cost-effectively the 3D urban form from SVI without requiring any existing building information. Obtaining 3D building models in regions that hitherto did not have any, may enable a number of 3D geospatial analyses locally for the first time.
Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the ...national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 108 m3). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.
•A first country-wide 10 m building height map for China.•Shading index is the most important variable in estimating building height.•High degree of building height accuracy with RMSE of 6.1 m.
Urban morphology significantly affects the urban thermal environment. Seasonal impacts of two-dimensional (2D) and three-dimensional (3D) urban morphology on land surface temperature (LST) remain ...uncertain, and the impacts exist scale effects. Thus, taking Beijing as the study area, boosted regression tree (BRT) model was used to investigate the seasonal contributions of urban morphology to the thermal environment. Building density (BD), building height (BH), floor area ratio (FAR), sky view factor (SVF), and frontal area index (FAI), were used to comprehensively characterize urban morphology, and 13 scales ranging from 30 m to 600 m were used to investigate scale effects. The results showed that there are obvious spatial differences in LST and urban morphology indicators in the study area. 270 m was determined as the optimal scale for modeling in the study area. BH and BD are the domain indicators, which together contribute more than 75% of the variance of LST among four seasons, while the relative influences of SVF, FAR, and FAI are relatively low. Relationships between urban morphology indicators and LST are nonlinear among four seasons. The findings provide a scientific understanding for urban planners on mitigating the UHI effects through optimizing buildings.
•BRT model was applied to explore seasonal contributions of 2D/3D urban morphology to the thermal environment.•Scale effects for urban morphology analysis were explored.•Relative contribution and marginal effect of urban morphology indicators to LST across seasons were analyzed.•Rational allocation of BH and BD should be carried out to mitigate UHI effects.
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