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The Baseline
Published in Karlheinz Spitz, John Trudinger, Mining and the Environment, 2019
Karlheinz Spitz, John Trudinger
One main challenge remains–to differentiate data from information. What do data say? Environmental and social data collected in the field, the primary data, or data from existing sources, the secondary data, can only capture a narrow aspect of the structure, state, and functioning of natural and built components in environment and social systems at a given time and space. Data on water composition reflect water chemistry. But what does water chemistry tell us? Demographic data reflect the composition of the population in such terms as gender or age. But what does the age pyramid indicate? Accordingly, we attempt to assess the data to derive information on the state or condition of the environment and on environmental and social processes taking place. This is not always simple. Converting data to information is explored in the concluding section of this chapter.
Product Service Systems Innovation and Design
Published in Shatha N. Samman, Human Factors and Ergonomics for the Gulf Cooperation Council, 2018
Girish Prabhu, Beena Prabhu, Atul Saraf
Government regulations and legal issues such as tax policies, employment laws, environmental regulations, trade laws, and political stability are some of the variables investigated in order to understand the political trends. Economic trends include buying power of potential users, economic growth, and interest or exchange/inflation rates. A combination of social (and the immediate community or society) and cultural factors covers the influence of religion and traditional beliefs on values and practices, the impact of community on behaviors, and changes due to globalization on behaviors, values, and beliefs. Technological factors such as the state of the art, research and development activities, and government incentives can influence barriers to entry, partnerships required, and other dependencies for the manufacturing/development of product and services. Demographic parameters include gender, age, ethnicity, languages, education, and income. For design trends, one looks at the new ways in which designers are thinking about and implementing design (web design, smart screen design, physical-virtual interaction). Figure 6.4 provides some sample questions that can be used for trend analysis.
Built Environment—Healthy Homes and Healthy Communities
Published in Herman Koren, Best Practices for Environmental Health, 2017
In the stable areas, there has been a dramatic demographic shift to an aging population. With the nation’s population of 65 or older predicted to double in the next several decades, this will be the fastest-growing age group in the rural population. Low-income seniors may be below the poverty level and also lack affordable service options and housing options. Many more rural people have arthritis, asthma, heart disease, diabetes, hypertension, and mental disorders than populations in urban settings. This means that there are more disabled people who in fact are increasingly vulnerable to environmental pollutants. The numbers of healthcare professionals in rural areas are well below the amount that are needed to help prevent disease and injury, treat existing disease, and promote good health. (See endnote 22.)
Social Integration Among Adults Aging With Spinal Cord Injury: The Role of Features in the Built and Natural Environment
Published in Journal of Aging and Environment, 2023
Nasya Tan, Martin Forchheimer, Denise G. Tate, Michelle A. Meade, Lisa Reber, Philippa J. Clarke
Demographic variables included age (years), sex (male/female), and race/ethnicity. Highest level of education was self-reported according to four categories (less than high school, high school graduate or GED, some college including trade school, and college degree or higher). Annual household income was assessed with five ordinal response categories (less than $25,000, $25,000–$39,999, $40,000–59,999, $60,000–$79,999, $80,000 or higher). Health insurance was self-reported as none or self-pay, public (Medicare, Medicaid, VA), or nonpublic (auto no-fault, other private, worker’s compensation). Living arrangements were categorized as living alone or living with others (spouse/partner, paid caregiver, children, roommates, parents, or other persons). Urbanicity was determined from participants’ geocoded residential address, which was classified as urban, large rural, or isolated rural, based on Rural Urban Commuting Area codes (Hart et al., 2005). Participants indicated how many years they had lived at their current address. Because the telephone survey was administered both before and since the COVID-19 pandemic, we included a binary variable in all analyses to indicate whether the data were collected before or on/after March 10, 2020, in order to account for differences in social integration due to the pandemic.
Zone-level traffic crash analysis with incorporated multi-sourced traffic exposure variables using Bayesian spatial model
Published in Journal of Transportation Safety & Security, 2023
Hao Zhang, Jie Bao, Qiong Hong, Lv Chang, Wei Yin
The crash records are collected from the New York State Department of Transportation (NYSDOT). Each record contains crash time, crash location with latitude and longitude, collision type and crash severity level. In total, 162,012 traffic collisions occur in the study area from February 1st, 2010 to January 31st, 2011, and their spatial distribution is shown in Figure 1(b). The road network attributes are provided by New York City Department of Transportation. The length of each road segment and its related speed limit value are obtained from the given GIS shapefile. The traffic volume information, including AADT on freeways and major arterials, are provided by NYSDOT. Then, the DVKT value in each ZCTA can be further computed. The land use features of each ZCTA are provided by New York City Department of City Planning (NYCDCP), which are grouped into residential areas, commercial areas, industrial areas, transportation areas, public institution areas and outdoor recreation areas. The demographic data is released from U.S. Census Bureau and the social economic data is released from the American Community Survey. The collected demographic and social economic data from the two agencies include the population across different ethnic groups and age groups, the unemployment population, the median household income, and the average spending time to workplace in each ZCTA.
Application of machine learning with a surrogate model to explore seniors’ daily activity patterns
Published in Transportation Letters, 2022
Population aging has become a notable and common demographic phenomenon in most countries. According to World Population Prospects 2019 released by the Population Division of the United Nations, the number of people aged 60 and above will double by 2050, rising from 1.0 billion in 2020 to 1.4 billion in 2030 and 2.1 billion in 2050 globally (World Population Prospect 2019). As the most populous country, China has been witnessing prominent demographic aging because of increased longevity and declining birth rates. The share of the aging population in China is 17.9% in 2018 and is projected to reach 25% in 2030 (China’s Population Aging Trend Forecast Report 2018). The rapid increase of the aging population is supposed to dramatically influence urban and transportation systems. Considering the special activity-travel needs of seniors is important for urban planners and transportation operators to create a better travel environment for seniors.