美国失踪人口的时空格局演变与影响机制

基于Doe Network平台数据,综合运用数理统计、空间分析和地理探测器等方法,探讨了美国1996―2021年失踪人口的基本特征、时空格局与影响机制。结果表明：1）美国失踪多发生在青少年（13～18岁）与成年时期（19～59岁）,且失踪人口数量随年龄增长呈先增后减的趋势;男性失踪人数多于女性,但失踪高发年龄略滞后于女性;各种族中黑人面临着最大的失踪风险。2）时间上,自1996年以来,失踪人口的年际变化数量先呈现波浪式上升趋势,2017年达到峰值后大幅下降;受气温和节假日影响,夏季6―8月和冬季12月为失踪高发期,2―4月为失踪的低谷期。3）空间上,失踪人口在州尺度上呈由沿海边境地区向内陆递减的特征,失踪高发区域随时间推移,自东、西沿海地区与南部美墨边境同时向美国内陆推进;县尺度上呈边缘集中成片,内部零星分散的特征。4）失踪人口数量变化是多因素共同作用的结果,主要受地区人口流动性、人均GDP、生育率以及易失踪人群基数影响,人口环境因子与经济、社会因子结合后对美国失踪人口空间分异的解释力增强,达到80%以上。5）人口失踪可用“社会失范理论”解释,社会目标和手段的脱节导致社会失范,进而诱发...     

Kernel distributed residual function in a revised multiple order autoregressive model and its applications in hydrology

ABSTRACT

This paper describes a new approach to fill missing data in hydrologic series. Based on a multiple-order autoregressive model, our algorithm represents the random term with an empirical distribution function that includes different parameters for the low, medium and high ranges of the modelled hydrologic variable. The algorithm involves a corrective mechanism that preserves the original statistical distribution of the series that are filled, while also eliminating the possibility of obtaining negative values for low flows. The algorithm requires multiple correlated hydrologic time series with sufficient data to permit accurate calculation of their statistical properties. It ensures that both the original statistical dependence among the data series and the statistical distribution functions will be preserved after the missing data had been filled. The model has been tested using 15 streamflow series in the Upper Bow River watershed in Alberta, Canada.     

Hydrological Sciences Journal Contents 2002

Abstract

Flood frequency estimation is crucial in both engineering practice and hydrological research. Regional analysis of flood peak discharges is used for more accurate estimates of flood quantiles in ungauged or poorly gauged catchments. This is based on the identification of homogeneous zones, where the probability distribution of annual maximum peak flows is invariant, except for a scale factor represented by an index flood. The numerous applications of this method have highlighted obtaining accurate estimates of index flood as a critical step, especially in ungauged or poorly gauged sections, where direct estimation by sample mean of annual flood series (AFS) is not possible, or inaccurate. Therein indirect methods have to be used. Most indirect methods are based upon empirical relationships that link index flood to hydrological, climatological and morphological catchment characteristics, developed by means of multi-regression analysis, or simplified lumped representation of rainfall–runoff processes. The limits of these approaches are increasingly evident as the size and spatial variability of the catchment increases. In these cases, the use of a spatially-distributed, physically-based hydrological model, and time continuous simulation of discharge can improve estimation of the index flood. This work presents an application of the FEST-WB model for the reconstruction of 29 years of hourly streamflows for an Alpine snow-fed catchment in northern Italy, to be used for index flood estimation. To extend the length of the simulated discharge time series, meteorological forcings given by daily precipitation and temperature at ground automatic weather stations are disaggregated hourly, and then fed to FEST-WB. The accuracy of the method in estimating index flood depending upon length of the simulated series is discussed, and suggestions for use of the methodology provided.

Editor D. Koutsoyiannis     

Analysis of cross-correlated chaotic streamflows

Abstract

A trial is made to explore the applicability of chaos analysis outside the commonly reported analysis of a single chaotic time series. Two cross-correlated streamflows, the Little River and the Reed Creek, Virginia, USA, are analysed with regard to the chaotic behaviour. Segments of missing data are assumed in one of the time series and estimated using the other complete time series. Linear regression and artificial neural network models are employed. Two experiments are conducted in the analysis: (a) fitting one global model and (b) fitting multiple local models. Each local model is in the direct vicinity of the missing data. A nonlinear noise reduction method is used to reduce the noise in both time series and the two experiments are repeated. It is found that using multiple local models to estimate the missing data is superior to fitting one global model with regard to the mean squared error and the mean relative error of the estimated values. This result is attributed to the chaotic behaviour of the streamflows under consideration.     

EM算法在广播星历计算卫星位置中的应用

在利用广播星历计算卫星坐标时,为了提高效率,经常会采用多项式来进行拟合.由于多方面的原因,可能会导致拟合点数据的精度不够或者拟合点的个数不够,继续用此数据进行拟合则会大大降低拟合结果的精度和可靠性.文中使用EM（expectation maximization）算法添加有益于多项式拟合的“潜在数据”,即缺失数据的条件期望,有效提高缺失数据条件下卫星坐标的拟合精度和可靠性.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

A watershed rainfall data recovery approach with application to distributed hydrological models

Watershed areal rainfall estimation, which is one of the most important and fundamental aspects in hydrological forecasting and various kinds of catchment‐scale hydrological models, is widely used in the analysis of hydrological regime change, and its precision has a direct influence on the accuracy of hydrological forecasting and hydrological simulation. In China, it is difficult to obtain the watershed areal rainfall estimate with reliable precision and avoid the phenomenon of ‘the same effect of different parameters’ because of the low density of the rain gauge network. Therefore, a watershed rainfall data recovery approach of improving the precision of watershed areal rainfall estimation is proposed here. This approach is to build new observatories, establish the time–space relations of rainfall between newly built observatories and previously built observatories in a relatively short interval and then recover the rainfall data of newly built observatories prior to their construction through simulating the relations over a longer time. As a result, watershed rainfall information could be elaborated to improve the precision of watershed areal rainfall estimate and avoid the phenomenon of ‘the same effect of different parameters’ to a certain degree in the process of hydrological simulation. The approach is used in the hydrological simulation of Hali River catchment. In combination with the Soil Water Assessment Tool model, a better result can be obtained in the hydrological simulation. Therefore, the approach can be used in other similar catchments. Copyright © 2011 John Wiley & Sons, Ltd.     

河南省上行气象数据信息传输率统计系统的设计与实现

为了实时监控上行气象信息传输是否及时,并能统计和分析各地市局气象资料的传输及时率,设计并实现了基于SQL Server数据库的上行气象数据传输率监控分析系统。该系统采用B/S模式,在PHP环境下开发完成,监控结果以表格形式显示,并可以采用Excel表格输出;实现了任意时间段、任意报类和地区的传输率统计。系统界面友好,具...     

地震目录的完整性对b值计算的影响

本文通过对华北地区和西南地区完整地震目录按照均匀抽取和非均匀抽取2种原则进行抽取地震,得到了地震目录遗漏率和b值变化量间的经验关系式.结果表明,在进行非均匀抽取地震时,得到的地震目录遗漏率和b值的减小量间呈正比关系;在进行均匀抽取地震时,利用最小二乘法和极大似然法计算得到的b值变化较复杂,b值随地震目录遗漏率的增加,或为增加,或为减少.同时,在进行非均匀抽取地震时,地震目录遗漏率对极大似然法计算的b值影响大于最小二乘法计算的b值影响.     

涉毒人员日常活动对盗窃警情空间格局影响的时间差异

根据日常活动理论,犯罪时空格局与受害者、犯罪者日常活动规律均存在较强关系。但受限于数据获取难度,较缺乏有关犯罪者日常活动与实际警情时空格局的研究。已有文献表明涉毒人员与盗窃等财产犯罪存在较大相关性。基于此,本研究通过分析涉毒人员日常活动对盗窃警情时空格局的影响,验证犯罪者日常活动在塑造盗窃警情时空格局中的作用。本文以中国南部大城市ZG市XT派出所为例,以150 m×150 m的格网为分析单元,采用盗窃警情数据、涉毒人员日常活动数据、POI数据以及巡逻盘查数据,划分不同时间段分别建立泊松回归模型。研究发现：① 相对于传统静态的抓获或警情数据,动态的潜在犯罪者、受害者日常活动数据可更有效地提高盗窃模型的拟合优度;② 相对于全天汇总的总人数,动态近实时的涉毒人员活动与居民活动能更好地解释盗窃的空间分布;③ 静态的土地利用混合度在不同时段对盗窃具有不同的影响作用。以上结果验证了涉毒人员日常活动与盗窃警情的时空格局的关系,研究结论验证和丰富了日常活动理论,可为实际犯罪预测与警力布置提供一定的参考。