基于轨迹偏移算法的居民就医时空特征与空间格局分析

居民就医时空特征与空间格局反映了医疗设施的服务能力与布局合理性。本文以厦门岛为例,采用出租车轨迹数据,探讨了居民就医的时空特征和空间格局。论文提出了基于道路中心线的研究单元划分方法;提出OD轨迹偏移算法,更精细地提取出三级医院的就医OD数据,改善传统的缓冲区分析法中精确度较低的问题;对居民就医行为进行时空特征分析;基于就医意向与K-means聚类算法分析了居民的就医空间格局。结果表明：① 相对于传统的缓冲区分析,使用OD轨迹偏移算法提取医院的就医OD数据时,不要求OD数据具有较高的定位精度,仅通过偏移OD点坐标即可更加准确与完整地提取就医OD数据,精度提高约30%以上,并适用于所有浮动车轨迹数据;② 居民就医高峰期在7时与14时,休息日日均就医人次为工作日的2倍,当就医出行距离大于1 km时,就医出行人次随着出行距离的增加不断减少,整体符合韦伯分布函数;③ 居民就医时首要选择中山医院、第一医院或中医院,就医选择具有显著的区域性差异,反映居民临近就医的习惯,厦门岛西南部区域医疗资源十分充足,居民首要选择的就医最大出行距离在4 km以内,而西北部与东南部区域的居民首要选择的就医出行距离多在10 km左右,医疗资源较为匮乏,亟待加强;④ 厦门岛三级医院的吸引力具有明显的层次性,居民的强就医意向 （P_ij>33%）的医院皆为中山医院、第一医院与中医院中的1所,这3所医院的服务范围基本包括整个厦门岛,对居民有着较强的吸引力,居民对其余6所医院的就医意向值在0~33%之间,服务范围基本为医院临近的一些区域,吸引力与服务能力相对较弱。研究结果不仅为挖掘居民就医时空特征提供方法参考,还为后续医疗设施资源空间配置优化提供决策支持。

Analysis of Spatiotemporal Characteristics and Spatial Patterns of Residents' Medical Treatment based on Algorithm of Trajectory Drift

The spatial and temporal characteristics of residents' medical treatment reflect the service capacity and layout rationality of medical facilities. This study investigated the features and patterns of medical treatment using taxi trajectory data in Xiamen. We divided Xiamen Island into different research units based upon the central lines of roads. We presented a trajectory drift algorithm to extract the medical treatment OD data for tertiary hospitals. This algorithm deals with the positional error associated with trajectory data and can improve the extraction accuracy. Hospitalizing behavior was analyzed from the perspective of space and time. Finally, based on the residents' preference for hospitals, we discussed the spatial patterns of residents' medical treatment by K-means algorithm. The results show that: (1) Compared with traditional buffer analysis, the trajectory drift algorithm didn't require high positioning accuracy when extracting OD data for hospitals. OD data can be extracted more reasonably and completely only by shifting OD point's coordinates, with an accuracy increased by more than 30%. It was also applicable to all floating vehicle trajectory data; (2) The peak time of medical treatment occurred at 7 am and 2 pm, respectively. The number of medical visits was twice on weekends (including holidays) than working days. When the travel distance was greater than 1 km, the number of medical visits decreased with the increase of travel distance, following a Weibull function distribution; (3) Residents regarded the Zhongshan, the First Affiliated, and the Chinese Medicine Hospital as their first choice for medical treatment. There was a significant regional difference in choices of medical treatment, that is residents preferred nearby hospitals. The southwest of Xiamen Island had sufficient medical resources, and residents' average medical travel distance was less than 4 km. However, residents in northwest and southeast of Xiamen Island mostly had to travel about 10 km for medical treatment. The medical resources in these regions were relatively scarce and needed to be strengthened eagerly; (4) The service capacity of the nine tertiary hospitals in Xiamen Island was obviously different. The residents had a strong preference for the Zhongshan, the First Affiliated, and the Chinese Medicine hospitals, with evaluated preference values greater than 33%. The service scopes of these three hospitals basically covered the whole Xiamen Island, which indicated strong attraction and service capacity for the residents. The values of residents' preference for the other six hospitals ranged from 0 to 33%. These six hospitals mainly treated nearby residents, leading to weak attraction and service capacity. This study provides alternative methods to extract the spatiotemporal features of residents' medical treatment and supports the decision-making of optimizing the spatial configuration of medical facilities.