China
Africa
Explore chapters and articles related to this topic
Constructions and related matters relevant to environmental health
Published in Stephen Battersby, Clay's Handbook of Environmental Health, 2023
John Bryson, Stephen Battersby
Tower Blocks UK3 has suggested that the concerns are because the structural design of large panel system blocks is weak: they could collapse in an explosion, high wind or serious fire. There are frequently gaps between the floor and wall panels, and these gaps prevent the flats from containing a fire for one hour and lead to the risk of serious fire spread. Such gaps also allow the easy spread of infestations whether cockroaches, ants or mice.
Engineering Models
Published in Keith Attenborough, Timothy Van Renterghem, Predicting Outdoor Sound, 2021
Keith Attenborough, Timothy Van Renterghem
The lack of available land means that many cities contain high-rise residential and commercial tower blocks with heights of over 100 m (over 40 stories). Railway noise can be a particularly acute problem near such buildings which can be located very close to railway viaducts so the resulting noise levels in the adjacent residential areas are significant. Prediction schemes for railway noise [9,57,58] do not offer a way of predicting railway noise in high-rise cities. Neither do they offer a way of predicting railway noise from an elevated viaduct. Van Leeuwen [57] has identified the inherent differences between the various models including the assumption of source positions, the levels of noise emitted by pass-by trains, the characteristics of sound radiation and the correction factors adopted to account for the effect of reflection. Consequently, the noise levels predicted by the models for any given situation are different.
Dwelling size and usability in London: a study of floor plan data using machine learning
Published in Building Research & Information, 2022
The first selection criteria for LSOAs were property type (detached, semi-detached, terraced, flat), built period and bedroom numbers to reflect the variety of housing and urban developments in London. Due to housing being produced in England at scale using standardized layouts and construction methods, some building typologies (such as tower-block, slab-block, terraced house, or semi-detached houses) are dominant in specific periods. For example, terraced houses made up 87% of all housing in England in 1911 (Muthesius, 1982). Terraced and semi-detached houses were mostly built before 1939, housing estates with repetitive blocks of flats and maisonettes from 1945 to 1982, and flats in large mixed housing developments after 1983. Using council tax statistics (VOA, 2018), LSOAs with a minimum of 60% of their properties completed in the same built period were identified. However, while built periods and building typologies in principle correspond, many older houses have been converted or altered, changing their property type classification. According to the VOA (2010), 20% of dwellings in these boroughs were converted from dwellings built before 1939. To capture this change in property type and dwelling size in the sample, the number of bedrooms was used as an additional criterion. For every selected LSOA, the predominant building typology and level of repetition were verified using Google Maps satellite images and historical ordnance survey maps from the 1840s to the 1990s, while ensuring that the overall selection of LSOAs contained all typical London building typologies (Table S2).
On the effectiveness of residential involuntary load curtailment programs
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2021
Marcin Czupryna, Leszek Morawski, Jan Rączka
The variable Q1 (the type of premises) is a categorical variable and is recoded into a series of dichotomous variables. The first category “flat in a block of flats/tower block – several/several dozen flats in the entire building” is defined as the base level. The other categories are included as binary variables with parameters denoted as , and . Other Q-variables are dichotomous or continuous. The dummy controls for the compensation scheme with when participant belongs to the group RED II. Other control variables included the monthly mean energy consumption during peak hours () (over 15-min intervals) and d for the consecutive reduction day. The stochastic part includes a participant-related random affect and white-noise term . The variables are described in Tables 4 and 5. The regression results are shown in Tables 6 and 7. The p values were calculated using a test, see Winter (2013) for the details.
Assessing the accuracy of kernel smoothing population surface models for Northern Ireland using geographically weighted regression
Published in Journal of Spatial Science, 2019
Behnam Firoozi Nejad, Christopher Lloyd
The results suggest that all variables contribute to the OLS models. While the population variance has a small coefficient value (in Table 4), it is significant. An extreme example of a large population variance would be in a location with a large tower block; in such a case under-estimation may be likely at the building location and over-estimation will be likely in neighbouring cells. However, there are large coefficients in rural areas (Figure 6) where population variance may change rapidly from a small (sub)urban area to a nearby non-residential area. Therefore, another concern in applying Martin’s (1989) model is the spatial structure of the population and its variance in the study area. The model may indicate very low accuracy in areas with a dispersed population and locations where population variance is marked. In other words, the accuracy of the model will also (obviously) be likely to vary based on the homogeneity of the study area. This weakness of the model was expected as the kernel model is not able to identify the actual population variance across the area. This analysis also confirms the importance of ancillary data, such as land-use data, to redistribute the population within zones.