visualizing hot spot analysis result based on mashup
Driven by travel demand, the distribution and density of taxi passenger pick-up and drop-off points reflect the attractiveness of an area and thus, can be used to find out hot spots and the movement of human flow, to benefit location-based services (LBS) and transport planning, etc. There exist some point pattern analysis (PPA) methods can facilitate the analysis. But most of them lack of the ability to integrate with location-based data in geo-visualization environment. We build an interactive visualization system based on mashup technique to contain diverse analysis data and applications under one framework. Two PPA methods--Kernel Density Estimation (KDE) and Agglomerative Hierarchical Clustering (AHC) are used to discover the hot spots. Microsoft Virtual Earth is used as data integration and visualization platform by combining with some other web techniques, to display analysis results in both static and dynamic effect. This study on one hand represents a novel application of vehicle trajectory data, reveals urban hot spots and traffic pattern, and addresses data integration and geo-visualization issues on the other hand. Preliminary attempt can benefit LBS and LBSN (Location-based Social Network) related web applications.
location-based service (lbs) point pattern analysis (ppa) geo-visualization microsoft virtual earth lbsn (location-based social network) taxi trajectory data