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Common definitions of quality of life
There are different definitions of quality of life. The Oxford English Dictionary defines quality of life as “The standard of health, comfort, and happiness experienced by an individual or group” (Quality of life, n.d.). This definition focuses on an individual or group’s subjective judgement.
In the review portion of his work, Marans (2003) summarizes from the book Well-Being: The Foundation of Hedonic Psychology (Kahneman, Diener & Schwarz, 2003) that “the quality of life experience is embedded in the cultural and social context of both the subject and the evaluator” (p.73), and asserts that objective characteristics of a society, such as pollution, crime rates, and income level, also contribute to people’s quality of life.
Dowell Myers’ (1988) defines quality of life as “a potent political concept often used to describe citizen satisfaction with different residential locations.” (p.347) Myers also believes that community characteristics contribute to quality of life experience, either negatively or positively. Addressing these complex aspects of quality of life corresponds with the goals and concerns of comprehensive planning (Myers, 1988).
El Din et al. (2013) indicate that instead of describing certain physical features, quality of life defines the entire relationship facilitated by those features.
The World Health Organization Quality of Life (WHOQOL) Group is devoted to research on the measurement and assessment of quality of life (The WHOQOL Group, 1998). In the report WHOQOL-BREF, the domains of quality of life are addressed as six main categories: physical, psychological, level of independence, social relationship, environment, and religion beliefs.
Relationship between quality of life and built environment
Despite the different emphases of the quality of life definitions mentioned above, the impact of environmental factors has been considered in proposed definitions across disciplines. In urban planning fields, improving quality of life for individuals and groups has become “one of the most important dimensions for sustaining any urban development” (El Din et al., 2013, p.86). El Din et al. (2013) also recognized the tight relationship between quality of life improvements and various urban design approaches, such as new urbanism, smart growth, and urban village.
The Quality in Town and County (QTC) initiative drafted by the U.K. Department of the Environment aims to “promote sustainable development and resource utilization, develop the capacities of local communities and explore the role of planning process in promoting change” (Chapman & Larkham, 1999, p. 214). The City of London’s urban environmental quality has been examined through eight factors proposed in QTC: public spaces and special places, activity and mixed use, visual richness, urban management, cleanness and safety, structure legibility and identity, human scale and compactness, and moving about and pedestrian friendliness (Chapman & Larkham, 1999). These eight measurable factors bridge the use of quality of life measurements in urban design.
In order to reflect the physical quality of life measurement, datasets below are considered in the analysis process. These datasets are used to measure quality of life from three aspects: built environment, socioeconomic status, and accessibility to public transit.
- Behavioral Risk Factor Surveillance (BRFSS)
- BRFSS is a nation-wide telephone survey regarding participants’ health conditions.
- Census data contains information regarding socioeconomic status of the measurement unit.
- Smart location dataset (SLD)
- SLD contains information about employment density which is broken down to different employment categories, and basic transportation information such as intersection density per census block group.
- Parcel data provides information regarding the physical conditions of the parcel, and the buildings on the parcel, such as just value, total living area, etc.
- Building footprint
- Building footprint dataset is useful regarding the calculation of floor area ratio. Besides, building footprints data can be also used to calculate the betweenness centrality from residential buildings to other types of buildings. The results to this calculation can contribute to the quality of life measurement process.
- Street network
- Street network dataset can be used to measure accessibility to public transit.
- Locational data
- Locational datasets refer to datasets that provide the location information of features, such as location of schools, location of parks, etc.
Improve quality of life through future growth
Increasing evidence suggests that population increase is related to various challenges human character. Ehrlich and Holdren (1971) stated the tight relationship between population growth and environmental issues, including “disproportionate negative impact on the environment” (p.1212), resources allocation challenges, economic status of the region, etc. Responding to this issue, various urban allocation models have been proposed to assist with better decision making.
The wide variation between urban allocation models stems from the differences in the problems that the models are focusing on, the scale of the geographical study area that the models have the ability to handle, and the time range of the allocation scenarios that the models are compatible with. The table below summarizes the widely acknowledged urban allocation models:
Table 1. List of urban allocation models
|Urban allocation model||Aims||Author(s)||Year|
|gravity model||measure attractiveness between locations||Lowry||1964|
|optimization model||linear programming||Campbell et al.||1992|
|multinomial logit model||aggregate statistical and econometric models||Kitamura et al.||1997|
|CUF||simulating urban activities||Landis & Zhang||1998|
|TRANUS||simulating urban activities||De La Barra||1989|
|Cellular Automata||simulate land use changes||Batty & Xie||1997|
|ILUTE||simulate human behaviour||Miller & Salvini||1998|
|UrbanSim||urban growth simulation||Waddall||2002|
|LUCIS||urban growth simulation||Carr & Zwick||2007|
Several of the allocation models above were driven by agent-based modeling (ABS) theory. Agent-based modeling is one type of stochastic models, which reflects individual variation and influence. ABS was developed with a “bottom-up” nature. It focuses on how the behaviors are generated from each agent, and how those behaviors impact the general phenomenon, instead of analyzing the overall phenomenon directly (Kliigl & Bazzan, 2012).
Agent-based modeling is widely used in social sciences and natural sciences where social economic behavior needs to be understood; it has also been applied in both qualitative and quantitative studies (Helbing, 2012). The reasons that ABS is widely adopted include the complexity of the situations to be simulated, and the needs to relax certain assumptions to make the model realistic (Macal & North, 2009). Considering the nature of quality of life research, which emphasizes on individual’s well-beings, agent-based modeling is a proper method to use to conduct quality of life research.
Principal Author: Leilei Duan
Chapman, D. W., & Larkham, P. J. (1999). Urban design, urban quality and the quality of life: Reviewing the department of the environments urban design campaign. Journal of Urban Design, 4(2), 211-232. doi:10.1080/13574809908724447
Quality of life. (n.d.) In Oxford dictionary. Retrieved from https://en.oxforddictionaries.com/definition/quality_of_life
Marans, R. W. (2003). Understanding environmental quality through quality of life studies: the 2001 DAS and its use of subjective and objective indicators. Landscape and Urban Planning, 65(1-2), 73-83. doi:10.1016/s0169-2046(02)00239-6
Kahneman, D., Diener, E., & Schwarz, N. (2003). Well-being: the foundations of hedonic psychology. New York: Russell Sage Foundation
Myers, D. (1988). Building Knowledge about Quality of Life for Urban Planning. Journal of the American Planning Association, 54(3), 347-358. doi:10.1080/01944368808976495
El Din, H. S., Shalaby, A., Farouh, H. E., & Elariane, S. A. (2013). Principles of Urban Quality of Life for a Neighborhood. Housing and Building National Research Center Journal, 9, 86-92.
Ehrlich, P. R., & Holdren, J. P. (1971). Impact of Population Growth. Science, 171(3977), 1212-1217.
World Urbanization Prospects. (2014). Retrieved from https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf