中国城市流动人口社会融合的空间格局与影响机制

城市流动人口的社会融合是新型城镇化建设的重点任务。针对现有研究在社会融合的空间特征及流入地作用方面的研究不足,本文基于2017年全国流动人口动态监测调查数据,引入空间分析和分层线性模型,揭示了中国地级以上城市流动人口社会融合的空间格局与影响机制。研究发现：① 中国城市流动人口的总体社会融合指数为0.43,存在行为融合（0.71）>心理融合（0.61）>经济融合（0.33）的维度特征。② 流动人口的社会融合存在空间自相关性和集聚性,高水平社会融合区主要分布在东北地区、山东半岛和川渝地区,低水平社会融合区主要分布在东南沿海、关中平原和华北平原地区。在这些地区中,总体上存在人口50万以下的小城市的流动人口社会融合水平高于大城市的基本特征。③ 流入地因素和个体因素共同影响和形塑了流动人口社会融合的水平和空间格局,且流入地的人口规模、住房支出（房价水平）和公共服务等因素对个体因素的影响具有调节作用,包含正向极化、负向极化、正向均化、负向均化等4种调节机制。本文呼吁从流入地—个体因素关联的人地关系视角开展流动人口社会融合研究,重视流入地因素对个体因素的影响,为基于流入地的政策调控来推进新型城镇化建设提供科学依据。     

Generalized Square Conservative Multistep Finite Difference Scheme Incorporating Historical Observations

In this paper,a multistep finite difference scheme has been proposed,whose coefficients are determined taking into consideration compatibility and generalized quadratic conservation,as well as incorporating historical observation data.The schemes have three advantages:high-order accuracy in time,generalized square conservation,and smart use of historical observations.Numerical tests based on the one-dimensional linear advection equations suggest that reasonable consideration of accuracy,square conservation,and inclusion of historical observations is critical for good performance of a finite difference scheme.     

双线偏振雷达差分反射率因子系统误差订正

差分反射率因子ZDR易受雷达系统自身的影响而产生明显偏差,这种偏差是不随空间积累而改变的.为了保证雷达资料及其产品的质量,根据中国气象科学研究院灾害天气国家重点实验室车载C波段双线偏振多普勒雷达(C-band Polarimetric Doppler Radar on Wheel,CPDRW)的外场试验,对观测的ZDR进行了分析,并以垂直扫描数据的订正结果为参考,重点讨论了以体扫模式最高仰角下的小雨和干雪这两种自然目标物为订正对象得到的订正结果.结果表明,高仰角体扫资料的这两种自然目标物都能较好地订正ZDR的系统误差.但相较于小雨,干雪的订正效果更加稳定,是进行ZDR系统误差订正的最优气象目标物,而利用高仰角体扫资料的干雪粒子进行ZDR系统误差订正也就成为垂直扫描订正法的最佳替代方法.     

非量测数码相机内方位元素测量研究

阐述了非量测数码相机内方位元素的概念、测定的原理和方法,以及在成像过程中产生误差的来源。使用共线条件方程的基本原理,选取合适的模型,利用澳大利亚墨尔本大学的Australis软件解算得到内方位元素,并分析结果的可靠性。     

Three-image matching. For 3-D linear object tracking

This paper will discuss strategies for trinocular image rectification and matching for linear object tracking. It is well known that a pair of stereo images generates two epipolar images. Three overlapped images can yield six epipolar images in situations where any two are required to be rectified for the purpose of image matching. In this case, the search for feature correspondences is computationally intensive and matching complexity increases. A special epipolar image rectification for three stereo images, which simplifies the image matching process, is therefore proposed. This method generates only three rectified images, with the result that the search for matching features becomes more straightforward. With the three rectified images, a particular line-segment-based correspondence strategy is suggested. The primary characteristics of the feature correspondence strategy include application of specific epipolar geometric constraints and reference to three-ray triangulation residuals in object space.     

Applications of Lodrigues matrix in 3D coordinate transformation

Three transformation models (Bursa-Wolf, Molodensky, and WTUSM) are generally used between two data systems transformation. The linear models are used when the rotation angles are small; however, when the rotation angles get bigger, model errors will be produced. In this paper, we present a method with three main terms: the traditional ? rotation angles θ, φ, ψ are substituted with a,b,c which are three respective values in the anti-symmetrical or Lodrigues matrix; ? directly and accurately calculating the formula of seven parameters in any value of rotation angles; and ? a corresponding adjustment model is established. This method does not use the triangle function. Instead it uses addition, subtraction, multiplication and division, and the complexity of the equation is reduced, making the calculation easy and quick.     

Estimation of impervious surfaces of Beijing,China, with spectral normalized images using linear spectral mixture analysis and artificial neural network

The objective of this article is to evaluate the effectiveness of various algorithms for estimating impervious surfaces. Linear spectral mixture analysis (LSMA) and multi-layer perceptron (MLP) network using original and spectral normalized images were applied to two ASTER images acquired on 31 August and 9 April 2004, respectively. Accuracy assessment was performed with a Quickbird image. Root-mean-square errors (RMSEs) were calculated and compared. Results indicated that LSMA with original images provided the poorest results. RMSE was 14.8% for the August image and 22.4% for the April image. Results from LSMA with normalized images improved significantly with RMSE of 12.6% for the August image and 18.9% for the April image. The MLP modelling with original images generated slightly better results with RMSE of 12.2% and 18.4% for each image. The MLP modelling of normalized images provided the best estimation, yielding a RMSE of 12.1% for the August image and 18.2% for the April image.     

Multiple linear regression,multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

ABSTRACT

Nowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

Simulation and spatio-temporal disaggregation of multi-site rainfall data for urban drainage applications

Abstract

For urban drainage and urban flood modelling applications, fine spatial and temporal rainfall resolution is required. Simulation methods are developed to overcome the problem of data limitations. Although temporal resolution higher than 10–20 minutes is not well suited for detailed rainfall—runoff modelling for urban drainage networks, in the absence of monitored data, longer time intervals can be used for master planning or similar purposes. A methodology is presented for temporal disaggregation and spatial distribution of hourly rainfall fields, tested on observations for a 10-year period at 16 raingauges in the urban catchment of Dalmuir (UK). Daily rainfall time series are simulated with a generalized linear model (GLM). Next, using a single-site disaggregation model, the daily data of the central gauge in the catchment are downscaled to an hourly time scale. This hourly pattern is then applied linearly in space to disaggregate the daily data into hourly rainfall at all sites. Finally, the spatial rainfall field is obtained using inverse distance weighting (IDW) to interpolate the data over the whole catchment. Results are satisfactory: at individual sites within the region the simulated data preserve properties that match the observed statistics to an acceptable level for practical purposes.     

Comparison of dew point temperature estimation methods in Southwestern Georgia

Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model.