Modeling urban environmental quality in a tropical city

Nichol, Janet Elizabeth; Wong, Man Sing

doi:10.1016/j.landurbplan.2004.08.004

Cited by 143 publications

(65 citation statements)

References 11 publications

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“…The estimation of the insolation of buildings is also vital to estimate the thermal comfort, i.e., the detection of buildings that are exposed to too much sunlight, potentially resulting in overheating during summer [116,117]. This also allows us to design an urban layout to maximise the insolation of a neighbourhood [118], and to estimate the capacities of decentral energy sources in crisis management applications [119].…”

Section: Estimation Of the Solar Irradiationmentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

628

380

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In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

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Section: Estimation Of the Solar Irradiationmentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

628

380

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“…While surface temperatures can be both higher and more variable than concurrent air temperatures due to the complexity of the surface types in urban environments and variations in urban topography (e.g. Nichol, 1996;Streutker, 2002), they are more easily related to surface conditions themselves (Nichol & Wong, 2005;Owen et al, 1998;Voogt & Oke, 2003). Since surfaces heat and cool more rapidly than air, the greatest surface temperatures are observed during midday versus nighttime for air temperature (Roth et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of the urban heat island effect across biomes in the continental USA

Imhoff

Zhang

Wolfe

et al. 2010

Remote Sensing of Environment

1,218

686

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Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003)(2004)(2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the nonurban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8°C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9°C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3°C temperature difference in summer and only 1.3°C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic "U-shaped" horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.Published by Elsevier Inc.

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“…3D city models may be used for a variety of purposes, for instance, estimating the noise pollution at a location (Stoter, de Kluijver, & Kurakula, 2008), assessing visibility (Koltsova, Tunҫer, & Schmitt, 2013), and analyzing thermal comfort (Nichol & Wong, 2005). However, not all of the outcomes of these analyses result in a quantifiable result, which is a prerequisite for error propagation analyses as it provides a measure to compare results.…”

Section: Selection Of the Spatial Analysesmentioning

confidence: 99%

“…The solar irradiation of rooftops is calculated based on the orientation and inclination of roofs, among other factors, which involve spatial operations that are all prone to errors. This application has wide applicability, for example, assessing the suitability of installing solar panels (Szabó et al, 2016), preventing overheating (Nichol & Wong, 2005), and predicting house prices (Helbich, Jochem, Mücke, & Höfle, 2013). Typically the annual exposure to sun is estimated and quantified in kWh/m 2 (Nault, Peronato, Rey, & Andersen, 2015).…”

Section: Selection Of the Spatial Analysesmentioning

confidence: 99%

The effect of acquisition error and level of detail on the accuracy of spatial analyses

Biljecki

Heuvelink

Ledoux

et al. 2017

Cartography and Geographic Information Science

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There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data. ARTICLE HISTORY

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Modeling urban environmental quality in a tropical city

Cited by 143 publications

References 11 publications

Applications of 3D City Models: State of the Art Review

Applications of 3D City Models: State of the Art Review

Remote sensing of the urban heat island effect across biomes in the continental USA

The effect of acquisition error and level of detail on the accuracy of spatial analyses

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