Hello Ismo & ThomasI am looking at peculiar scenario where I am forcing entire fabric metro cluster to shutdown for both sites maintenance on same day/time. In entire scenario if DWDM link was not active and ISL was not available to nodes (both fabrics).
Fabric Metro Area Rare
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Probably the most unheard of subway and newest system in the US is the Los Angeles Metro. It started off with just two lines and has expanded to many regional districts, making it easier to travel around the metropolitan areas of LA without having to deal with the crazy driving and freeway situation.
A pest control company can create a barrier of pesticides near your outdoor furniture to discourage grasshoppers from going into the area. The barrier can also keep other bugs off your property and away from your outdoor furniture. For the most protection, remove any cushions and soft fabric pieces when they are not in use outdoors.
Dense, compact, connected, diverse, safe, and both physically and socio-economically accessible urban areas and transportation networks provide the basis for a shift towards sustainable and active mobility, and the increased use of sustainable modes of transportation as alternatives to the private car (Sallis et al. 2016; Nieuwenhuijsen and Khreis 2016). A carless lifestyle can be used as an indicator of accessible and well-connected areas where an active, equitable and sustainable alternative to the use of private car is provided for daily travel (Nieuwenhuijsen and Khreis 2016; Brown 2017; Banister 2018). Newman et al. (2016) have advanced this linkage between the features of the physical urban environment and different mobility lifestyles by defining three urban environment types, urban fabrics, that support either a carless lifestyle (walking urban fabric, and transit fabric) or a car-dependent lifestyle (automobile fabric). In other words, some urban environments enable and induce carlessness, while others support or even require car use.
The Urban fabrics approach divides the city into walking, transit, and automobile fabrics, based on the primary transportation mode the local urban structure supports (Newman et al. 2016). Fabric thus refers to the physical attributes of the urban environment, that are applied to classify the urban area into three different transportation environments. The areas of urban fabrics are not defined by the actual use of different travel modes but by the qualities of the physical urban environment that enable certain mobility choices. Walking urban fabric is compact, connected, and dense, with short blocks, narrow streets, high share of walking, public spaces, and diverse local services. The Transit urban fabric is more arterial in its coverage, as it spans across the city according to the bus and rail-based routes. The densities are medium and the environment accommodative specifically for public transportation users, but also somewhat to pedestrians and car users. The Automobile urban fabric is represented by low densities, long distances, wide roads, abundant parking facilities, high car use, large but dispersed shopping facilities, and a low number of functions accessible without a car in general (Newman et al. 2016). This division of urban areas into three different fabrics is a generalization of a more fine-grained range of physical environments with different densities and urban features. Newman et al. 2016 also recognize different subtypes of the fabrics based on distance from the city center (e.g. inner walking fabric, outer walking fabric, inner transit fabric, and outer transit fabric). Each urban fabric has its characteristic qualities, but the fabrics can partially overlap. These qualities reflect the functions and lifestyles of different areas and enable certain types of mobility choices while restricting others.
However, living in a specific urban fabric does not automatically mean adhering to the mobility choices that the fabric primarily enables (Ettema and Nieuwenhuis 2017). For example, dense areas with mixed land-use do not only accommodate carless households, and respectively, outer suburbs also harbor households that rely on poor transit connections instead of owning a car, often due to economic constraints (Bhat and Guo 2007). This phenomenon is also referred to as residential self-selection, meaning that people opt for residential locations matching their desired travel behavior, but that personal attitudes and preferences towards travel modes also play a role (Ettema and Nieuwenhuis 2017). The urban fabric typologies create the basis for our analysis of the HMA and a classification for the voluntary, involuntary (and vulnerable) groups of the carless population, as the fabrics dictate what type of mobility lifestyles are supported or challenging in the different urban environments. Essential to our analysis of carlessness as a choice or a constraint is that a carless lifestyle in the automobile urban fabric is categorized as difficult (Newman et al. 2016).
The spatial distribution of different urban fabrics was identified in the case area using the methodology introduced by Helminen et al. (2020). The classification is based on an overlay analysis of combined density of residents and jobs per hectare, prevalence of a grocery store, and public transportation supply. The following thresholds (identified according to available literature and in cooperation with local planners) were applied: Walking and transit fabrics have (1) a combined density of residents and jobs of at least 20/ha, (2) maximum distance to a grocery store of 500 m, and (3) a maximum distance of 250 m to a public transportation stop with a headway of 15 min for bus or 700 m for railway stations. The difference between the walking and transit fabrics is their distance to the city center: the urban fabrics are further identified according to different distance thresholds. The Euclidean distance of 2 km from the city center signifies the border of the walking fabrics and the inner transit and automobile fabrics. The remaining areas were classified as automobile fabric. Then, the classifications were finalized by dimensional circles (1, 2, and 8 km) from the city center (Fig. 1). At this point the walking, transit, and automobile fabrics were treated as inner and outer categories based on their distance from the city center point, and the spatial representation includes inner and outer sub-centers, as well. 8 km marks the border between the inner transit and automobile fabrics and the outer transit and automobile fabrics. Additionally, inner and outer sub-centers were identified according to the Travel-related Urban Zones database (Finnish Environment Institute 2015).
The classification of urban fabrics presented in Fig. 1 was generalized (from 8 spatial classes to 3 classes) for improved legibility of the results and to ensure a statistically adequate number of NTS respondents with varying car ownership in each spatial class, i.e. urban fabric. The data were thus organized into three subsets by combining similar spatial classes with similar car ownership rates in a way that we could form three subsets with adequate and as equal number of respondents as possible. First, Walking urban fabric covering the inner and outer walking fabrics, the inner transit fabric, and the inner sub-centers consisted of 895 observations. Second, Transit urban fabric consisting of the outer sub-centers and the outer transit fabric had 1181 observations. Third, Automobile urban fabric composed of the inner and outer automobile fabrics had 845 observations in total. NTS data were weighted to avoid biases related to age and sex distributions between the survey respondents and the actual distribution in the study area. The weighting was carried out individually for each of the three subsets by calculating the distributions for sex (male/female) and age (in five age groups). The age and sex distributions of the survey respondents were matched with the actual distribution in HMA core area using YKR data as a reference.
Car ownership significantly varies across the HMA core area. Figure 2 and Fig. 3 display the distribution of car ownership in relation to the three urban fabrics. As expected, carless households represent the majority of households in the walking urban fabric. Carlessness is slightly more common in the transit urban fabric than in the automobile urban fabric, particularly compared to the outer automobile fabric. The automobile fabric also clearly demonstrates an increase in multiple-car ownership.
Car ownership illustrated based on the areas of generalized urban fabrics in the HMA core area. The urban fabrics in this map are generalized from 8 spatial classes to 3 classes from the classification of urban fabrics presented in Fig. 1 for improved legibility of the results and to ensure a statistically adequate number of NTS respondents in each spatial class
The distribution of carless, one-car and multiple-car households within the different urban fabrics in the HMA core area. Due to rounding, some columns may total 99% or 101%. The vertical lines represent the generalized reclassification of the urban fabrics (walking, transit, automobile) used in the statistical analyses to ensure a statistically adequate number of NTS respondents in each spatial class. (Sources: SYKE/YKR and Statistics Finland 2014, 2016, and 2017, and A. C. Nielsen retail register 2016)
FA for each urban fabric produced corresponding results to the ones described above, yet a few interesting differences reflecting variation between the fabrics were discovered. In all fabrics, the first factor indicated increasing distances and travel times to the city center (Supplementary Tables S1-S3), but only in the walking urban fabric the decreasing density played a role while density was unimportant in the transit and automobile urban fabrics. In terms of Factor 2, the walking urban fabric did not show any association with the socioeconomics of the area, contrary to the transit and the automobile urban fabrics. Moreover, the third factor in transit and automobile urban fabrics matched with the overall results, while in the walking urban fabric only the increasing proportion of row houses was slightly influential. Due to association with just one variable, Factor 3 should not be over-interpreted regarding the walking urban fabric. 2ff7e9595c
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