Abstract:

Measuring and analyzing the time-space compression phenomenon caused by improved road network accessibility in geographical space have been a major challenge for transport researchers. Current attempts of comparing accessibility pattern maps only describe centrality of nodes, and the isochrone approach is capable of analyzing individual cities but fails to provide a complete picture of regions of concern. This study developed an analytical model using time-space map to visualize time-space compression phenomenon, based on a synergy of current big-data available on the internet and statistical and 3D-visualization technologies. OD (Origin and Destination) data matrix (travel distance and duration by cities) was generated from web-map-server's direction API using web-crawling programs. Multidimensional scaling method was then applied to find the best-fit configuration space, which redraw the map by replacing the euclidean distance with network distance or duration. The scale and distribution of errors were statistically tested to verify the validity and reliability of the results. Finally, 3D time-space maps were visualized and overlapped with the geographical map to observe stretching, shrinking, and wrinkling effects of space caused by uneven transportation accessibility improvements. Using Hunan Province as a case, this study produced two time-space maps of travel distance and duration between cities, and analyzed the errors on these maps. The case study supports the view that time-space analysis is a valid method to visualize time-space compression and a useful tool to generate policy recommendations for building a more balanced road network with higher accessibility of regions.

Key words: time-space map, multidimensional scaling, web big data, accessibility, time-space compression, visualization, Hunan Province