Authors: Fangye Du*, , Jiaoe Wang, advisor
Topics: Transportation Geography, Urban Geography, Behavioral Geography
Keywords: accessibility, hospital, transportation, behavior
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Truman, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Recently, many attempts have been made to improve the spatial and temporal accuracy of measuring accessibility from the perspective of supplement and latent demand. And yet, the research about hospital accessibility from the perspective of transport behavior has received little attention for lack of data. Let alone comparing the hospital accessibility in the daytime and nighttime with taxi data. The emergence of big data in recent time, such as GPS data and smart card data, provides us an opportunity to track the actual behavior of individuals and actual demands. Using taxi data, we explore the characteristics of hospitalizing behavior, and compare hospital accessibility in the daytime and nighttime with the assistance of K-means clustering and spatial visualization. The results show that: (1) there are hotspots of hospitalizing demands both in the daytime and nighttime, showing that the distribution of demands is uneven; (2) In general, travel time to hospital in the daytime is longer than the counterpart at night, even though the travel distance at night is usually farther than daytime; (3) Five types of regions can be identified according to the travel time and distance in the daytime and nighttime, which can reflect the similarities and discrepancies of hospital accessibility in the daytime and nighttime. This paper contributes to the research of interactive between hospitalizing and transportation behavior. And the findings are significant in optimizing the spatial configuration of medical facilities and allocating public resources in the daytime and nighttime reasonably.