Publication

Neighbourhood morphology and solar irradiance in relation to urban climate

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni, Govinda Upadhyay
2015
Conference Papers

Abstract

Assessing the relationship between neighbourhood morphology and urban climate is becoming increasingly important as cities continue to grow and the climate continues to change. Here we focus on the impact of (1) shapes and sizes of buildings, (2) street layouts, and (3) spatial distributions of buildings and streets on the urban climate in the 16 neighbourhoods (zones) within the city of Geneva, Switzerland. The results show that the size distributions of the areas, perimeters, and volumes of the buildings follow approximately power laws, whereas the heights of the buildings follow a bimodal (two-peak) distribution. Using the Gibbs-Shannon entropy formula as a measure of dispersion (spreading), we calculate the area, perimeter, volume, and height entropies for the 16 neighbourhoods in Geneva and show that the entropies have strong positive correlations (R2 = 0.43-0.84) with the average values of these parameters. By contrast, there are negative correlations (R2 = 0.39-0.54) between building density or urban compactness (site coverage and volume-area ratio) and the entropies of building areas, perimeters, and volumes. The calculated length-size distributions of the streets show negative correlations (R2 = 0.70-0.76) with the number of streets per unit area as well as with the total street length per unit area. We compare the neighborhood morphologies with the annual and monthly solar irradiance for each of the 16 neighborhoods using the simulation tool CitySim. The results show a negative correlation between building densities and solar irradiation as well as between street densities and solar irradiation. By contrast there is a positive correlation between entropy of the street lengths and solar irradiation, indicating that the greater the variation in street length the greater the solar irradiation. Further work will include comparison of these results with the average surface temperatures, sky view factors, and daylight factors in the 16 neighborhoods so as to explore the relations between neighborhood morphology and the local climate and, thereby, providing quantitative information to help urban designers in the decision making processes.

Official source

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni, Govinda Upadhyay
2015
Conference Papers

Abstract

Official source

Related concepts (32)

Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.

Urban design

Urban design is an approach to the design of buildings and the spaces between them that focuses on specific design processes and outcomes. In addition to designing and shaping the physical features of towns, cities, and regional spaces, urban design considers 'bigger picture' issues of economic, social and environmental value and social design. The scope of a project can range from a local street or public space to an entire city and surrounding areas.

Pearson correlation coefficient

In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.

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