Spatiotemporal Analysis of Human Sentiment and Travel Mode Choices

The surge of microscale geospatial media data is helping researchers understand several space-time phenomenon. To this end, my co-authors and I are currently examining Twitter data to understand the human sentiment of motorized and non-motorized travel modes, using Chicago and Washington D.C. as case studies. We found that spatiotemporal trends of sentiments vary widely for each city-indicating that contextual effects influence attitude while traveling. Additionally, using a global and local regression model, we found that walking and water travel elicited positive sentiments. Also noteworthy, we found that the time of the day and weather factors also significantly influenced sentiments. This research has recently been accepted for publication in the Journal of Location-Based Services.

#photo #BigData #spatiotemporal #GIS #humansentiment