基于核密度函数的多尺度北京市休闲农业空间分布分析

核密度函数估计法是常用的分析城市点要素空间分布模式的方法。使用核密度函数估计法的关键是最佳带宽的确定。目前,大多数研究使用单纯基于数学的方法或目视判读法确定核密度函数的最适带宽,但是针对同一地理实体在不同分析尺度上所对应的核密度函数适用带宽确定方法问题研究相对不足。该文考虑了北京市休闲农业本身所具有的尺度特征,基于北京市休闲农业POI数据,使用核密度函数评估方法,识别并分析其多尺度空间分布模式,并使用文献求证法进行了验证。使用Moran’I、HH个数和Comprehensive I指数曲线相结合的方法确定了适合分析北京市休闲农业区域尺度和局部尺度空间分布模式的核密度函数所对应的带宽,分别为9 km和3 km。进而分析了北京市休闲农业多尺度空间分布模式。北京市休闲农业空间分布模式在区域尺度上形成了2个圈层及多个聚集中心的结构。距离城市中心约30～50 km的圈层为1日游圈层。距离城市中心约50～90 km的圈层为2日游或多日游圈层。1个主中心位于昌平区东部、怀柔区东南部、密云区西南部的山前平原地区。2个副中心分别位于密云区东北部和房山区西南部;在局部尺度上形成了3个等级的26个小的聚集中心,第一等级2个,第二等级3个,第三等级21个,主要位于中北部的山前平原地区,东南部城乡交错带的平原地区及西部山区分布相对较少,中心城区分布最少。该研究可为北京市休闲农业空间规划提供重要参考依据。

Multi-scale spatial distribution analysis of leisure agriculture in Beijing based on kernel density estimation

Abstract: With the improvement of urban economic development, leisure agriculture becomes one of the important forms of the integration development of rural and urban region. The distributions pattern of the leisure agriculture could be studied in multi-scale (from local scale to regional scale). Based on regional economic statistical data, recent researches exploring the spatial distribution pattern of leisure agriculture were difficult to accurately describe because the analyzing scale was too large. Recently, point of interest (POI) data has been widely applied to analyze features of the urban to show the spatial distribution patterns with advantages of large data volume, high position accuracy and the clear classification of the leisure agriculture format. While the researches on the distribution pattern of the leisure agriculture based on POI were rather inadequate. The key to apply kernel density estimation(KDE) for spatial distribution pattern of urban point element was exploring the suitable bandwidth. So far, most of researches on bandwidth determination of KDE were based on mathematical or visual interpretation methods. Considering the scale features of spatial distribution pattern, basing on POI data, applying the method of spatial autocorrelation to determine different bandwidths of KDE which suited to analyze the regional-scale and local-scale spatial distribution patterns, this paper has recognized and analyzed the multi-scale features of spatial distribution patterns on Beijing leisure agriculture. The spatial autocorrelation method contained 2 indices, Moran''I and Local Moran''I. With increasing of KDE bandwidth, the value of the normalized Moran''I gradually increased from 0 to 1 and the value of the normalized number of HH (a statistically significant cluster of high values) region from the result of local Moran''I decreased from 1 to 0. At this time, the result of KDE could express the regional scale distribution pattern of leisure agriculture and the detail features were covered. On the contrary, the result of KDE could express the small scale pattern of Beijing leisure agriculture and the detail features were obvious. Considering the variable characteristics of Moran''I, the number of HH and comprehensive I curves, this paper determined different bandwidths of KDE which suited to analyze the regional-scale and local-scale spatial distribution pattern of the leisure agriculture were 9 km and 3 km. Using the method of natural breaks, the results of KDE with 9 km or 3 km bandwidths were divided into three levels, high value, middle value and low value respectively. Then spatial distribution patterns with different scales were identified. Under the regional-scale, the spatial distribution pattern showed two rings and multi-centers. The interior ring away from the city center from 30 km to 50 km was one day tour zone. The exterior ring away from the city center from 50 km to 90 km was two days and up tour zone. It was found that a primary distribution center was lied in the sub-montane area in the east of Changping County, the southeast of Huairou County, the southwest of Miyun County. Two sub-centers were lied in the northeast of Miyun County and the southwest of Fangshan County. Under the local-scale, the spatial distribution pattern showed 26 centers divided into 3 classes by area size. Most centers lied in the sub-montane area in the north to the middle part of Beijing. Fewer lied in the rural-urban fringe area in the southeast and hills in the west of Beijing. The least lied in the central city area. Finally the literature analysis method was applied to certificate the multi-scale features of the spatial distribution pattern on Beijing leisure agriculture. The method proposed in this paper is effective to express the different scales of spatial distribution pattern on Beijing leisure agriculture.