Visual exploration of urban data: A study of riyadh taxi data

Abstract

In this paper, we describe one approach of land classification through linking taxi drop-off cost to traffic analysis zones (TAZs). We visually explore the number and costs of taxi drop-off points in the city of Riyadh, Saudi Arabia, to identify social dynamics and urban behavioral patterns. After analyzing the data with regard to gender, we identify some expected gender biases in the data set for taxi traces of trips since female mobility is constrained in Saudi Arabia and public transportation options are limited. We present a series of case studies of gendered mobility analysis that show how our model enables domain experts to visually explore data sets that were previously unattainable for them. Finally, we visualize the number and cost of drop-offs per TAZ for males and females and identify potential areas for future research in visual analytics for taxi traces.