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An application of ‘Big Data’ in flood risk management
Published in Wim Uijttewaal, Mário J. Franca, Daniel Valero, Victor Chavarrias, Clàudia Ylla Arbós, Ralph Schielen, Alessandra Crosato, River Flow 2020, 2020
D. Morrison, G. Aitken, L. Beevers, G. Wright
Susceptibility is analysed by adopting a previously established method, the Social Flood Vulnerability Index (SFVI) developed by (Tapsell et al., 2002). This approach enables broad scale analysis using indicators taken from Census 2011 data to quantify the susceptibility of output areas (geospatial statistical unit consisting of approximately 125 households). We expand this approach by exploring the potential application of Big Data by using Experian Mosaic to quantify susceptibility of Mosaic groups at household/postcode level. By following the same method as Tapsell et al. (2002), Test 3 compares the impact spatial aggregation has on susceptibility quantification by comparing susceptibility scores for a specific Mosaic group and two output areas (S00092730; S0092724) that lie within the 1 in 30 year floodplain. Since Mosaic includes Census 2011 variables that match like-for-like with the variables contained in the SFVI method, it is an appropriate approach to examine the effect of spatial aggregation on susceptibility without focusing on indicator selection or method of calculation. The description of indicators are provided in Section 3.3, however, we refer the read to Tapsell et al. (2002) for full details of how data are processed and susceptibility scores are calculated.
Project management practice
Published in Riadh Habash, Professional Practice in Engineering and Computing, 2019
Sigma (σ) is a Greek letter that is a statistical unit of measurement used to define the standard deviation of a population. As process variation decreases, so does the standard deviation. A σ level is defined as the number of standard deviations that fit between the process mean and the customer specification limit. As the process “σ level” increases, more process outputs, products, and services meet customer requirements, reducing production defects.
Continuous improvement
Published in John Oakland, Marton Marosszeky, Total Quality in the Construction Supply Chain, 2006
John Oakland, Marton Marosszeky
Sigma is a statistical unit of measurement that describes the distribution about the mean of any process or procedure. A process or procedure that can achieve plus or minus six-sigma capability can be expected to have a defect rate of no more than a few parts per million, even allowing for some shift in the mean. In statistical terms, this approaches zero defects.
A hybrid framework for synchronized passenger and train traffic simulation in an urban rail transit network
Published in International Journal of Rail Transportation, 2022
Hongxiang Zhang, Gongyuan Lu, Yuanzheng Lei, Guangyuan Zhang, Irene Niyitanga
Based on the proposed passenger aggregation method, some variables and parameters are included as shown in Table 2 to complete the design of the passenger batch agent. Noted, though the parameters are designed for passenger batch agent, the statistical unit is still each passenger rather than a passenger batch agent, since the evaluation indicators in Section 3.5 are calculated by each individual’s data. That is, some passengers have completely identical total travel times when calculating the evaluation indicators, but which actually have differences in the real world. However, the difference of travel time is less than 30 s due to the condition (3) above, which is acceptable compared to the total travel time that may be up to more than 1 h. Therefore, to guarantee the accuracy of the simulation model and cooperate with the aggregation of successive entering passengers, the passenger loading frequency of the SPTTSM should be set to the value that equals the short time period of aggregation processing, i.e. 30 s.
A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data
Published in International Journal of Digital Earth, 2020
Lun Wu, Ximeng Cheng, Chaogui Kang, Di Zhu, Zhou Huang, Yu Liu
TAS extraction: The first step consists of extracting the TAS of each zone in the study area based on big geo-data, such as mobile phone data, taxi trajectory data, and social media check-in data. The spatial–temporal statistics is applicable to acquire the activity volume in each statistical unit, based on the human activity information. The definitions of spatial units and time intervals are important and depend on the data characteristics and the research objective. If the total number of spatial units is N and the dimension of the TAS is T, the TASs of all zones can be constructed as a matrix. The volume of human activities in zone i during the t-th time slot is denoted by , and the TAS of zone i is represented by a vector . To avoid differences between zones with similar activity patterns but different sizes, the vector S is normalized further (Pei et al. 2014; Liu et al. 2016).
A fine-grained evaluation of social vulnerability to COVID-19, healthcare access and built environmental disparities in Tokyo, Japan
Published in International Journal of Digital Earth, 2022
The primary goal of our study is to construct the grained-level measure of vulnerability and healthcare accessibility in Tokyo Metropolis, Japan as the most populated metropolitan region in the world (United Nations 2022) – in order to further identify the vulnerable neighbourhoods with low healthcare access and to evaluate the disparity in the built environment of vulnerable neighbourhoods. Tokyo Metropolis has a total area of 2191 square kilometres and a total population of 13.98 million in 2022 (Statistics Bureau of Japan 2022). It consists of the 23-special-ward (ku in Japanese) region towards the coast of Tokyo Bay as well as the inland Tama region that is made up of 26 cities (shi), 3 towns (machi), and 1 village (mura) (Figure 1). Drawing on multiple data sources, including the latest census data in 2022 at the level of census blocks (chome in Japanese) as the smallest statistical unit of the national census, as well as transport network, medical data, digital cadastral data, land use maps, and points of interest data retrieved from Open Street Map, our study has three research objectives to achieve: (1) measuring social vulnerability to COVID-19; (2) measuring multi-modal healthcare accessibility; and (3) evaluating the disparity in healthcare access and built environment of neighbourhoods at different levels of vulnerability. In doing so, our outcome datasets and findings provide nuanced and timely evidence for place-based health planning and policy making in the post-COVID era and beyond. Our analytical framework can be employed in other geographic contexts to help the nation better prepare for future public health crises.