顾及手机基站分布的核密度估计城市人群时空停留分布

为了减小人群在连续空间上停留分布的估计误差，结合手机基站的空间的分布特点，根据基站间的邻近性来计算带宽控制参数，使搜索带宽随着基站的分布而变化；利用最小二乘交叉验证和对数概率两种方法来评价其估计效果，结果表明变化带宽比固定带宽的核密度估计效果更优。以深圳市手机位置数据为例，利用改进方法估计了几个典型时段城市人群停留的时空分布差异，反映了城市人群对城市不同区域的使用情况及其随时间变化情况。

Analyzing Space-Time Variation of Urban Human Stay Using Kernel Density Estimation by Considering Spatial Distribution of Mobile Phone Towers

In recent years, the availability of mobile phone location data provides an opportunity and challenge for studying human stay. Therefore, we only can extract human stay based on base stations from the dataset, it need estimate to produce a continuous population distribution. Kernel density estimate (KDE) could generate a continuous surface and has been widely used to estimate population distribution, but the traditional KDE assumes that the sample data points are homogeneous and use fixed bandwidth to estimate for all data points, however, the service area of base stations in the city varies with the distribution of population distribution, so fixed bandwidth will bring error. In order to eliminate the errors, this paper introduces a search bandwidth controlling parameter to make the bandwidth to vary with the spatial distribution of mobile phone towers. Least-squares cross validation (LSCV) and log-probability methods were used to test the proposed approach, and the result of experiment demonstrates that this improvement can make the estimation better than fixed bandwidth. Taking mobile location data of Shenzhen as an example, we extract urban human stay for five typical time intervals, and the improved KDE was used to analyze the distribution difference of the five time intervals, which make us have a deep understanding of condition of urban different areas are used by human and how it vary with time going.