Research on the Variation of Population Distribution and Its Characteristics Based on Spatial Autocorrelation Method: A Case Study of Poyang Lake Region in Jiangxi Province

Abstract

According to Statistical Yearbook of Jiangxi Province (2001~2006), We analyze the time-space variation of population distribution of Poyang Lake region from the two points of view. The former is quality of population, which involves culture structure, occupational structure, age structure and sex structure of population. The latter is quantity of population, which only involves the amount of population. Furthermore, we can reveal the internal relations and action mechanism of variation of population distribution by analyzing the regional economic development, population urbanization, land use and ecological landscape of Poyang Lake region. It is important to provide help for region planning, ecological landscape planning and environmental protection by correct understanding the man-land relationship of natural-human ecosystem in Poyang Lake region.     

Spatial Considerations in Wet and Dry Periods for Phosphorus in Streams of the Fort Cobb Watershed,United States

The Fort Cobb Watershed in Oklahoma has diverse biogeophysical settings and provides an opportunity to explore the association of water quality with a diverse set of landscapes during both wet (April 2007‐December 2009) and dry (January 2005‐March 2007) periods. The objective of this work was to identify spatial patterns in phosphorus (P) (soluble reactive P [SRP] and bioavailable P [BAP]) associated with landscape metrics for two distinct streamflow regimes. Spatial autocorrelation of P was evaluated using contiguous (side‐by‐side) and upstream (upstream:downstream) connectivity matrices. Biogeophysical metrics were compiled for each contributing area, and were partitioned based on association to P concentrations. Results for both SRP and BAP indicated that spatial autocorrelation was present (p < 0.05). There was more spatial autocorrelation and stream P concentrations were three to five times higher in the Wet phase than in the Dry phase (p < 0.05). Analysis with recursive partitioning resulted in higher R² with spatial autocorrelation than without spatial autocorrelation and indicated that lateral metrics (topography, soil, geology, management) were better predictors for SRP than instream metrics. During Wet phase, lateral metrics indicative of rapid surface and subsurface water movement were associated with higher P stream concentrations. This research demonstrated that we can detect landscapes more vulnerable to P losses and/or contaminations in either drought or very wet periods.     

环空带压气井液位测试方法改进研究及应用

为了实时有效监测环空带压气井的液面位置,评价环空带压气井的风险,在传统回波定位技术基础上,提出了1种基于自相关分析的气井液位声波测试方法;建立了环空液位声波测试实验系统,开展了不同起爆压力下环空液位测试实验;对检测到声波信号进行平滑降噪和自相关分析,计算液面位置,并与真实液位进行对比分析;将该方法在现场含噪声的环空带压气井液位测试中进行了应用。结果表明:该方法能够对环空液位声波测试信号中的液面反射波进行有效提取;相比较现有在测试信号平滑降噪基础上提取的液面位置,降低了计算误差;同时,该方法能够在井下噪声环境中实现微弱液面反射声波的有效提取,其处理后的信号峰值指标更为突出,提高了测试系统对液位的识别能力,便于自动识别。     

环空带压气井保护液液位测试方法研究

为了有效检测环空保护液位,评估气井环空带压情况,在传统回声法检测的基础上,提出了基于频谱分析及自相关分析的2种液位检测方法;搭建了液位检测试验系统,开展了不同环空压力下的液位检测试验,计算了不同压力下的环空声速;设计了FIR低通滤波器对液面回波信号进行滤波处理,同时利用了上述2种方法计算液位值并与实际值及传统液位计算方法对比,得出了声衰减系数并评判了衰减过程对液面回波信号频谱分析的影响程度。研究结果表明:设计的FIR低通滤波器适用于所建液位检测试验的滤波处理过程;频谱分析及自相关分析都能够有效检测液位高度,最大误差分别为1.65%和0.61%,自相关分析方法具有更高的精确度;声衰减系数整体较小,对液面回波信号频谱分析结果影响较小。     

基于ESDA的省域空气污染空间特征研究

运用探索性空间数据分析(ESDA)方法,对中国31个省份的空气质量指数和PM2.5进行了空间分布和时空演化分析,结果显示,中国省域空气综合污染存在较强的空间自相关性,可对相邻区域空气质量造成影响.PM2.5污染与空气综合污染的空间相关趋势保持了高度的一致性,体现主导污染物的地位.辽宁与华北省份北京、天津、河北、山东,以及部分华中省份河南、湖北及江苏形成了较为稳定的高污染集聚区,较轻污染的集聚大多分布在西部、西南和东南沿海区域.基于空间特征的污染治理措施,可实现功效发挥的最大化.     

Natural forest regrowth as a proxy variable for agricultural land abandonment in the Swiss mountains: a spatial statistical model based on geophysical and socio-economic variables

In many European mountain regions, natural forest regrowth on abandoned agricultural land and the related consequences for the environment are issues of increasing concern. We developed a spatial statistical model based on multiple geophysical and socio-economic variables to investigate the pattern of natural forest regrowth in the Swiss mountain area between the 1980s and 1990s. Results show that forest regrowth occurred primarily in areas with low temperature sum, intermediate steepness and soil stoniness as well as close to forest edges and relatively close to roads. Model results suggest that regions with weak labor markets are favored in terms of land abandonment and forest regrowth. We could not find an effect of population change on land abandonment and forest regrowth. Therefore, we conclude that decision makers should consider non-linearities in the pattern of forest regrowth and the fact that labor markets have an effect on land abandonment and forest regrowth when designing measures to prevent agricultural land abandonment and natural forest regrowth in the Swiss mountains.     

基于微空间单元的岩溶峡谷区土地利用结构演变

采用2004年、2010年和2015年分辨率均为2.5 m的SPOT等遥感影像和社会经济数据,创建200 m×200 m网格的微空间单元,计算单元网格内地类面积占比、土地利用程度综合指数,并结合双变量局部空间自相关和热点分析法,深入研究岩溶峡谷区土地利用结构演变特征。结果表明：（1）2004年林地、耕地、聚落和裸岩裸土网格内平均占比分别为6.46%、11.76%、0.87%、28.86%,2010年分别为10.36%、8.77%、1.27%、27.13%,2015年分别为9.13%、10.30%、26.18%、1.29%;（2）各种土地利用类型在空间分布上具有明显的空间差异特征;（3）耕地热点区呈减少趋势,且后期主要集中在研究区西南部,林地热点区总体增加,裸岩裸土极热点区逐渐向研究区东北部收缩。而近几年土地利用程度热点区逐渐向研究区西北部延伸,花椒林则主要分布于北部干热河谷地带。简而言之,花江峡谷区林地面积增加,且逐渐向集中连片演变,而裸岩地逐渐向峡谷区北部以及东北部收缩,研究区农户生计多样化发展及交通条件的改善,使得土地压力减小,生态环境逐渐转好。本文以典型岩溶地貌单元为代表,揭示中国西南岩溶山地的土地利用演变规律及内部差异性,对研究更大尺度土地利用演变特征与规律具有重要意义。     

中国臭氧时空分布特征及与社会经济因素影响分析

本文根据2015～2017年中国大陆338个城市空气质量监测站臭氧(O_3)浓度数据,综合利用空间插值法、全局自相关法和地理加权回归模型(GWR),探讨了O_3浓度的时空变化特征及其与社会经济因素的关系。结果表明,2015～2017年中国大陆338个城市的O_3日最大8小时浓度为2～300μg/m~3,其中超标天数比例为5. 9%,323个城市达标率在85%以上; O_3月均值变化曲线基本呈"单峰状",5月达到峰值,12月最低; O_3浓度季节变化为夏季春季秋季冬季; O_3日变化特征为夜间到清晨O_3浓度很低,上午8∶00左右开始升高,下午16∶00达到峰值;中国华北地区、华东地区和华中地区O_3污染严重,华南地区、西南地区、西北地区和东北地区整体污染较低。O_3浓度在全国尺度上的集聚性呈上升趋势,GWR表明,人口密度、人均私家车保有量与O_3浓度显著正相关,第一产业占比与O_3浓度显著负相关。     

Spatial structure effects on the detection of patches boundaries using local operators

Landscapes exhibit various degrees of spatial heterogeneity according to the differential intensity and interactions among processes and disturbances that they are subjected to. The management of these spatially dynamical landscapes requires that we can accurately map them and monitor the evolution of their spatial arrangement through time. Such a mapping requires first the delineation of various spatial features present in the landscape such as patches and their boundaries. However, there are several environmental (spatial variability) as well as technical (spatial resolution) factors that impair our ability to accurately delineate patches and their boundaries as polygons. Here, we investigate how the spatial structure and spatial resolution of the data affect the accuracy of detecting patches and their boundaries over simulated landscapes and real data. Simulated landscapes consisted of two patches with parameterized spatial properties (patches’ level of spatial autocorrelation, mean value and variance) separated by a boundary of known location. Real data allowed the investigation of a more complex landscape where there is a known transition between two forest domains with unknown spatial properties. Boundary locations are defined using the lattice-wombling edge detector at various aggregation levels and the degree of patch homogeneity is determined using Getis-Ord’s G*. Results show that boundary detection using a local edge detector is greatly affected by the spatial conditions of the data, namely variance, abruptness of the spatial gradient between two patches and patches’ level of spatial autocorrelation. They also suggest that data aggregation is not a panacea for bringing out the ecological process creating the patches and that indicators derived from local measures of spatial association can be complementary tools for analysing spatial structures affecting boundary delineation.

Spatial smoothing techniques for the assessment of habitat suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of observations is often violated in practice since the points at which the observations are collected are spatially aligned. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations.