Multimer: validating multimodal, cognitive data in the city

Multimer: Modeling Neurophysiological Experience in Public Urban Space

“…Participants in this subgroup were selected based on two criteria: whether they recorded at least ten hours of data, and whether they participated in a sub-study conducted to collect heart rate data. Signal processing and analysis tools were used to compare a continuous, periodic biosensor signal (dependent variable) to other relevant (explanatory) variables, processes established by Ducao et al (2018). The results of the signal validation process show that the band powers associated with repose and relaxation (delta, theta, alpha) tended to predict meditation values ( Fig.…”

“…The y-axis indicates the predictor's coefficient value complex, street-based analysis predicted more true-to-reality results than grid-based analysis. Regression models were developed for EEG and heart rate data types, but models were not developed for GPS speed data due to inconsistent speed measurements yielded by GPS units (Ducao et al 2018). Following guidance from ESRI's documentation on exploratory regression (ArcGIS n.d.), the research team focused on the following regression outputs: p-values (probability) for each exogenous independent variable, and adjusted R-squared (effect size) values for each regression model.…”

“…During the study, the research team developed a basic system for data collection and analysis. Additional components of the MDCAS are detailed by Ducao et al (2018), as is the specification of data variants, which is summarized in Fig. 3.…”

“…4 Map of the Manhattan study area (epoching) as necessary, and creating temporal regressions or histograms. These steps were established by Ducao et al (2018).…”

“…The spatial validation methodology involves subdividing the study area into manageable partitions, processing spatial weights matrices to normalize distribution for each data type, using the spatial weights calculation to process spatial autocorrelation and lag, processing Local Indicators of Spatial Association (LISA) diagrams (Anselin 1995), and evaluating the results. The details of the signal validation process are detailed by Ducao et al (2018). Both signal and spatial validation indicate whether a given biosensor dataset is a useful component for training an AI model that includes exogenous data.…”