Linking affect and the built environment using mobile sensors and geospatial analysis

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dc.contributor.author Whitaker, Taylor Dyer
dc.date.accessioned 2018-04-23T14:54:42Z
dc.date.available 2018-04-23T14:54:42Z
dc.date.issued 2018-05-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/38895
dc.description.abstract As urban development continues, it is imperative we understand how infrastructural policies impact well-being in order to design functional and healthy cities. The growth in wearable sensors and real-time data offer a way to assess the day-to-day influence of built infrastructure on health. The aim of this research is to determine if and how much characteristics of the built environment affect individual physiological responses. The purpose of this research is two-fold: 1) quantify and understand the linkages between form and function of the built environment on human affect and 2) identify practices for collecting and mining sensor data that can be used by planners. Subjects (n = 24) were sent on a walk through downtown Manhattan, Kansas. The route was carefully designated to expose individuals to different architectural and environmental features such as: vegetation, infrastructure (broadly), building height and area, land use, trees and street conditions. The study explores the associations of nearly a dozen environmental characteristics with the real-time feedback from sensor data. The sensors used in this study measure electrodermal activity (EDA) and heart rate (HR) which were linked spatially using GPS. The results enable a spatio-temporal analysis to identify correlations between environmental characteristics and spatial representations of urban form. Differences of stress-related responses are identified through statistical analysis. The data and spatial analyses were also used by colleagues to develop a machine learning approach to explore methods for estimating stress. In addition to quantifying urban form additional subject information was collected, such as demographic information, fitness level, sense of place, feeling of community, and feeling of exposure in the built environment. This work builds upon a previous study by Parker Ruskamp (MLA 2016). His qualitative results indicate that areas with lower lighting (at night) and higher-density infrastructure caused increased stress reactions. The efforts in this report, added additional participants and worked to spatially quantify urban form in order to conduct quantitative assessments to characterize the influence of environmental features against stress. Through the analysis it was discovered there is a relationship to biophysical measures and relationship to vegetation presence, building façades, building area or envelope, zoning and parking lots. In particular, the most influential characteristics were the amount of parking in close proximity to participants at night and the quality of the sidewalks during the day. While effects were discovered, further work should be done to confirm and generalize these findings. These initial results demonstrate how using biophysical measures can help planners evaluate the effectiveness of policies and built-environments toward improving the well-being of citizens. Further, this study provides a basis on how designs can be better informed by geospatial analysis, enhanced through an extensive environmental characteristic literature review, and statistical analysis to promote health and well-being through urban design. en_US
dc.language.iso en_US en_US
dc.subject Affect en_US
dc.subject Built environment en_US
dc.subject Mobile sensors en_US
dc.subject Geospatial analysis en_US
dc.subject Regression analysis en_US
dc.subject Health en_US
dc.title Linking affect and the built environment using mobile sensors and geospatial analysis en_US
dc.type Report en_US
dc.description.degree Master of Regional and Community Planning en_US
dc.description.level Masters en_US
dc.description.department Department of Architecture en_US
dc.description.advisor Brent Chamberlain en_US
dc.date.published 2018 en_US
dc.date.graduationmonth May en_US


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