基于STARMA模型的城市暴雨积水点积水短时预测

近年来城市暴雨出现突发和多发态势,导致城市内涝灾害频繁发生,威胁着城市居民的生命和财产安全。随着城市降雨积水监测网的建立,获得分钟尺度的降雨和积水时序监测数据成为可能,实现了城市内涝的实时监控。但目前对监测数据的利用仍显不足,缺乏对其深度分析挖掘,造成监测系统“只监不控”的局面。本文基于城市降雨积水监测网的监测数据,根据积水时间相关性、降雨空间相关性以及降雨积水序列相关性,构建降雨积水的时空自相关移动平均模型(STARMA),对城市暴雨积水点积水过程进行短时预测。STARMA模型已被广泛应用于交通预测、环境变量预测以及社会经济领域,特别是在时空过程机理不清楚、多因素时空变量影响的情况下效果较好。本文首次将该模型应用到降水积水过程拟合和积水短时预测上,同时在方法上改进了传统单变量的STARMA模型,建立降雨和积水双变量的STARMA模型模拟降雨积水过程。并以北京市2012年“7.21”事件降雨积水过程为研究对象,以丰北桥、花乡桥、马家楼桥和六里桥4个积水监测点为例,建立降雨积水的STARMA模型,以5 min为步长作积水5、10、15 min三步预测。验证结果表明,该模型在降雨积水过程中拟合效果较好,模型短时预测精度较高。该项研究能够有效地利用监测数据,提高信息预警和应急指挥能力,为市政防汛或交通等部门提供决策支持。     

空间分析方法在房地产市场研究中的应用--以北京市为例

基于北京市2003年普通住宅数据,利用空间分析中点模式分析、空间自相关分析和空间插值方法等,对北京市房地产,尤其是普通住宅的空间格局进行分析。研究表明,北京市房地产发展在空间上具有强烈的集聚特点,而房价的空间自相关特性也非常明显。空间分析方法提供了准确认识、评价和综合理解空间位置和空间相互作用的手段,为定量研究空间格局提供了支持。在房地产发展等社会经济现象研究中,空间分析方法强调了“位置”因素的重要性,是刻画房地产空间格局的理想工具。     

广州市土地利用程度的空间自相关分析

分析了1990-2004年广州市土地利用结构的变化和基于Moran's I的土地利用程度空间自相关变化.广州市土地利用结构中,耕地和林地面积占较大比例,但是面积比例在减少;而建设用地和水域用地的面积比例较小,但有所增加.广州市土地利用程度呈高度的正全局空间自相关,各局部空间自相关类型呈同心圆分布,中间为高高集聚,外围为低低集聚,两者之间为非集聚和零散的高低、低高集聚,呈现出单中心结构.1995年以后,Moran's I有所下降,说明这种单中心结构在弱化.单中心结构的优点在于增强中心地区土地集约利用的空间集聚,但同时也说明外围土地利用程度较低地区的空间集聚性,中心和外围的土地利用存在空间不平衡.     

美国马萨诸塞州华人空间分布自相关研究

基于美国10年一度的人口普查资料,文章采用空间自相关理论对美国马萨诸塞州华人的空间分布进行了研究。研究表明:马萨诸塞州的华人存在空间聚集现象;波士顿地区和汉普郡境内某地是华人显著聚集的区域。研究了聚集区域的分布特征并给出了相应的解释和推断。     

Measurements of visual double stars with PISCO2 at the Nice 76-cm refractor in 2013–2014 (new data) and in 2015

We present relative astrometric and photometric measurements of visual double stars made in 2013–2015, with PISCO2 installed at the 76-cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as double stars whose motion is still uncertain. Most of the observations were done in 2015, but some other observations of wide couples were done in 2013–2014 (0.3% of the total of the observations). Three different techniques were used for obtaining measurements: lucky imaging, speckle interferometry and the direct vector autocorrelation method. From our observations of 2837 multiple stars, we obtained 5182 new measurements with angular separations in the range of 0 ${}^{″}$.1–32 ${}^{″}$ and an average accuracy of $0^{″}.018$. The mean error on the position angles is 0°.8. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems ( $m_V\approx 9-11$) with large magnitude difference (up to $\mathrm{\Delta }{m}_V\approx 4.5$). We have thus been able to measure 21 systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 1079 double stars with an estimated error of 0.2 mag. Except for a few objects that are discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. Thanks to good seeing images and with the use of high-contrast numerical filters, we have also been able to obtain 196 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. We also report measurements of the 164 new double stars that we found in the files obtained during the observations. Finally, from a study of the DR3 Gaia release, it is shown that all the objects of the Tycho Double Star Catalog that we did not resolve in 2015 are probably false detections by Tycho.