Using geo statistical analysis to detect similarities in emotional responses of urban walkers to urban spac

Bielik, Martin

Donath, Dirk

Hijazi, Ihab Hamzi

König, Reinhard

Li, Xin

Schmitt, Gerhard

Schneider, Sven

Digitale Bibliothek Thüringen

USING GEO STATISTICAL ANALYSIS TO DETECT SIMILARITIES IN EMOTIONAL RESPONSES OF URBAN WALKERS TO URBAN SPACEREINHARD KÖNIG1, SVEN SCHNEIDER2andIHAB HAMZI3, XIN LI4, MARTIN BIELIK2, GERHARD SCHMITT1, DIRK DONATH21ETH Zürich Switzerland, 2Bauhaus-University Weimar Germany, 3An-Najah National University Palestine, 4Wuhan University China,AcknowledgmentWe are indebted to Dr. Georgios Papastefanou from Bodymonitor for his advice during our discussions and his technical assistance in the use of the Smartband and interpreting the data.This study was carried out as part of the research project ESUM - Analysing trade-offs between the energy and social performance of urban morphologiesfunded bySUMMARY• Empirical pre-study on measuring emotional arousal in public space• Study time: 14 - 22 October 2013, Place: Oerlikon, Zurich, Sitzerland• N= 14 Participants• Method of measurement: sensor-wristband Smartband• Signiﬁ cant similarities of emotional response to same spatial situation   among different participantsIntroductionIn the following we investigate the impact of urban form on emotional response. The presented examination is a preliminary-study aimed at developing a method which allows us to measure human emotions in various spatial conﬁ gurations and to deﬁ ne the spatial statistical methods needed to analyze the collected data.Data CollectionTo directly measure human emotional responses to real world environmental stimuli  we use a sensor-wristband (Smartband), developed by Bodymonitor in combination with a GPS-tracker. The Smartband [Papastefanou 2013] offers a mobile means of measuring skin conductivity (SC) as well as skin temperature. The collected data are analyzed with respect to four activations: Negative arousal, positive arousal, balanced, and retraction.Statistical AnalysisThe aim of the analysis is to answer following questions: What is the probability that the distribution of the emotion values occurs by chance? If the emotion values occur not by chance, where are the areas of clustering? Where is the clustering of high and low values?Based on Geospatial analysis methods (Getis-Ord General G, and Getis-Ord hot-spot analysis Gi* (Getis 1991, Mitchell 2005) signiﬁ cant clustering of positive and negative arousal values at speciﬁ c distances was proved and the null rejectedOutlookAssuming that further studies validate our ﬁ ndings, we will then search for correlations between emotional response and spatial measures derived from computational spatial analysis like isovists (2D and 3D Fig 2), visibility graphs (Hillier 1996), daylight and street-network analysis as well as combinations of these.As the results of this pre-study suggest, changes in a sequence of spaces along a path may be a reason for positive or negative arousal (Fig 3). Consequently, it would be valuable not only to test static values for certain points of view but to investigate how the measured responses change along the path.ConclusionThe statistical analysis has proven that different participants had similar emotional response to the same spatial situation regardless factors such as weather or the individual’s respective mood or personal background.The allocation of positive and negative arousal clusters could by characterized by sequence of spaces with high emotional arousal (positive, negative or both) followed be spaces with balanced emotional activation. A possible explanation is that the emotional response is not caused primarily by a certain spatial situation but rather by a changing sequence of spaces. If there are no changes in the environment our emotions are more or less in a kind of balanced or retrieving mode.Fig 1 Locations of  emotional arousal are colour-coded by Z-scores. Locations surrounded by locations with similarly positive or negative values are shown in red or blue.Starting point and direction of the walkPositive arousalNegative arousal 50m 100m 200mArea with prevailing:P     positive emotional arousalN     negative emotional arousalPN   mixed high emotional arousalB     balanced emotional arousalB1B2B3B4P1N1N2N3PN1PN2ReferencesGetis, A: 1991, Spatial interaction and spatial autocorrelation: a cross product approach, Environment and Planning A 23:1269 – 1277.Hillier, B: 1996, Space is the machine: a confi gurational theory of architecture, Cambridge University PressMitchell, A: 2005, The ESRI Guide to GIS Analysis, Volume 2, ESRI Press, Redlands, Ca, USAPapastefanou J Bodymonitor: Sensing emotions. http://www.bodymonitor.de/Projekte %28neu%29. Accessed 26 Nov 2013Emotional arousalIsovist2d occlusivityIsovist3d volume0m 200mminmaxFig 2 (left) 3d model of study environment + walk path and analysis point (every 5 meter), (middle) 2d Isovist set on analysis points looking in walk directioin at restricted angle 60°, (right) 3d isovistFig 3shows a sequence of changes of measures for simple 2D, 3D-Isovists and emotional arousal

Using geo statistical analysis to detect similarities in emotional responses of urban walkers to urban space

König, Reinhard (Jun.Prof. Dr.)

Online-Publikationssystem der Bauhaus-Universität Weimar

https://e-pub.uni-weimar.de/opus4/files/2514/Koenig_Schneider_Bielik+et+al_2014_Using+geo+statistical+analysis+to+detect+similarities+in+emotional+responses+or+urban+walkers+to+urban+space.pdf

Using geo statistical analysis to detect similarities in emotional responses of urban walkers to urban space

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