Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.

Spatial Statistical Analysis and Comprehensive Evaluation of High-Tech Industry Development

After 30 years of economic development, the high-tech industry has played an important role in China’s national economy. The development of high-level technological industry plays a leading role in guiding the transformation of China’s economy from “investment-driven” to “technology-driven”. The high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.

STATISTICAL ANALYSIS ON THE INFLUENCE OF THE LANDFALLING STRONG TROPICAL CYCLONES IN THE CATASTROPHIC MIGRATIONS OF NILAPARVATA LUGENS(STL) IN CHINA

In order to clarify the statistical pattern by which landfalling strong tropical cyclones(LSTCs)influenced the catastrophic migrations of rice brown planthopper(BPH),Nilaparvata lugens(stl)in China,the data of the LSTCs in China and the lighting catches of BPH that covered the main Chinese rice-growing regions from 1979 to 2008 were collected and analyzed in this work with the assistance of ArcGIS9.3,a software of geographic information system.The results were as follows:(1)In China,there were 220 strong tropical cyclones that passed the main rice-growing regions and 466 great events of BPH’s immigration in the 30 years from 1979 to 2008.73 of them resulted in the occurrence of BPH’s catastrphic migration(CM)events directly and 147 of them produced indirect effect on the migrations.(2)The number of the LSTCs was variable in different years during 1979 to 2008 and their influence was not the same in the BPH’s northward and southward migrations in the years.In the 30 years,the LSTCs brought more obvious influence on the migrations in 1980,1981,2005,2006 and 2007.The influence was the most obvious in2007 and all of the 7 LSTCs produced remarkable impact on the CMs of BPH’s populations.The effect of the LSTCs on the northward immigration of BPH’s populations was the most serious in 2006 and the influence on the southward immigration was the most remarkable in 2005.(3)In these years,the most of LSTCs occurred in July,August and September and great events of BPH's immigration occurred most frequently in the same months.The LSTCs played a more important role on the CM of BPH’s populations in the three months than in other months.(4)The analysis on the spatial distribution of the LSTCs and BPH’s immigration events for the different provinces showed that the BPH’s migrations in the main rice-growing regions of the Southeastern China were influenced by the LSTCs and the impact was different with the change of their spatial probability distribution during their passages.The most serious influence of the LSTCs on the BPH’s migrations occurred in Guangdong and Fujian provinces.(5)The statistical results indicated that a suitable insect source is an indispensable condition of the CMs of BPH when a LSTC influenced a rice-growing region.

Combinatorial analysis on spatial information statistics for the karst water environment in Guiyang,China

The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the contamination,this paper employs the spatial information statistics analysis theory and method to analyze the karst groundwater environment in Guiyang City.Based on the karst ground water quality data detected in 61 detection points of the research area in the last three years,we made Kriging evaluation isoline map with some ions in the karst groundwater,such as SO4 2-,Fe 3+,Mn 2+and F -,analyzed and evaluated the spatial distribution,extension and variation of four types of ions on the basis of this isoline map.The results of the analysis show that the anomaly areas of SO4 2-,Fe 3+,Mn 2+,Fand other ions are mainly located in Baba’ao,Mawangmiao and Sanqiao in northwestern Gui- yang City as well as in its downtown area by reasons of the original non-point source pollution and the contamination caused by human activities(industrial and domestic pollution).

Spatial autocorrelation analysis of 13 leading malignant neoplasms in Taiwan: a comparison between the 1995-1998 and 2005-2008 periods

Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.

Space-Time Cluster Analysis of Tuberculosis Incidence in Beijing, China

Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and time with high relative risk locations for tuberculosis incidence in Beijing area. A retrospective space-time clustering analysis was conducted at the districts level in Beijing area based on reported cases of sputum smear-positive pulmonary tuberculosis (TB) from 2005 to 2014. Global and local Moran’s I, autocorrelation analysis along with Ord (Gi*) statistics was applied to detect spatial patterns and the hotspot of TB incidence. Furthermore, the Kuldorff’s scan statistics were used to analyze space-time clusters. A total of 40,878 TB cases were reported in Beijing from 2005 to 2014. The annual average incidence rate was 22.11 per 100,000 populations (ranged from 16.55 to 25.71). The seasonal incidence occurred from March to July until late autumn. A higher relative risk area for TB incidence was mainly detected in urban and some rural districts of Beijing. The significant most likely space-time clusters and secondary clusters of TB incidence were scattered diversely in Beijing districts in each study year. The risk population was mainly scattered in urban and dense populated districts, including in few rural districts.

淮安市不透水面时空演变及其对生态环境影响

为了探究中小城市不透水面演变对城市生态环境质量的影响,基于2000-2020年Landsat遥感影影像,采用不透水面指数(NDISI)和遥感生态评价指数(RSEI)监测与分析淮安市不透水面对生态环境的影响.结果表明,2000-2020年淮安市不透水面面积显著增加,以蔓延式、跨越式发展模式由城市中心逐渐向周边扩张,年均增长率为8.5%,2008-2012年期间增长最快.不透水面丰度空间上由城市中心向周边逐渐递减,年均值从2000年的0.261增加至2020年的0.330.研究期生态环境质量有所改善,生态环境质量等级中较差和一般的面积减少,而良好和优等级分别增加了1677.01 km^(2)和672.1 km^(2).NDISI丰度与城市生态环境质量负相关,研究期内不透水面与生态环境质量的联系越来越密切.

空间统计法在复杂曲面几何偏差分析中的应用

通过高精度的三坐标测量机(CMM)对数控加工的复杂曲面进行检测,能得到曲面上的几何偏差分布。复杂曲面的几何偏差本质上属于空间数据,采用空间统计法进行研究分析曲面几何偏差的空间自相关性,并且采用热点分析(Getis-Ord Gi*)方法计算得到几何偏差的热点区域和冷点区域。对数控加工的复杂曲面检测获取529个点和1024个点两组几何偏差数据,结果表明两组数据反映的曲面上的几何偏差空间自相关性都为正相关;划分几何偏差热点和冷点区域的范围与测点数量有关。对数控加工的复杂曲面的几何偏差进行数据分析,将有助于对其加工质量进行分析,从而为进一步提高加工质量提供依据。

基于Voronoi图的空间点事件统计聚类方法

挖掘地理空间数据中点事件聚集模式对于揭示流行疾病、犯罪分布热点区域及城市基础设施空间分布格局等具有重要意义。针对不同形状、密度和大小的显著空间点聚集模式的识别,目前以空间扫描统计为代表的方法虽然可以对空间点聚类的显著性进行统计推断,减少虚假聚类结果,但其主要用于识别球形或椭圆形状的聚簇,对于沿着街道或河道分布的任意形状、不同密度的显著空间点聚簇识别还存在局限。因此,本研究提出一种基于Voronoi图的空间点聚集模式统计挖掘方法。首先,采用Voronoi图来度量空间点分布的聚集性,将空间点聚类问题转化为热点区域探测问题;其次,结合局部Gi*统计量探测统计上显著的空间点聚簇;最后,通过模拟数据和真实犯罪事件数据进行实验与对比分析。结果表明:本方法能够有效探测任意形状的空间点聚类,并对空间点簇的显著性进行统计判别,识别显著的空间点簇,减少随机噪声点的干扰;聚类识别结果优于现有代表性方法,如DBSCAN算法、空间扫描统计方法等。

物资集中招标采购成本控制方法研究

集中招标采购提高了大型企业集团的议价能力,有助于降低企业采购成本。文章基于多价值链视角挖掘采购成本的关键性影响因素,以某大型企业集团钢铁类物资集中招标采购为例,通过空间统计分析和构建空间面板模型,分析关键性影响因素对企业集团下属各项目单位采购成本的差异性,并提出该采购模式下的成本控制策略,有助于其他企业集团结合下属各项目单位的区域特性统筹资源调度,进行成本管控。

基于多尺度的吉林省城市国土空间监测数据统计分析方法研究

本研究主要将吉林省作为研究区域,从选取空间尺度、设计指标体系、探究统计方法3个方面,综合分析多尺度下的城市国土空间监测数据统计分析最适方法,深入反映城市国土空间格局优化发展关注的要素情况。结合城市区域间的多尺度统计单元,进行横向比较分析,不仅可以使国土空间资源布局可视化,而且能为城市国土空间多层级结构优化提供指导。

宁波市休闲渔业空间分布及影响因素分析

休闲渔业经营单位要实现效益最大化,需减少资源调配成本,发挥区位优势,同时要有科学合理的企业空间布局。该研究以宁波市284个休闲渔业经营单位为研究对象,综合运用最近邻指数、变异系数、核密度分析法、聚类和异常值分析法,借助Arcgis空间分析工具,从分布形态、分布均衡程度、分布密度等3个维度对宁波市休闲渔业经营单位的空间分布特征进行分析,并对其空间分布特征的影响因素进行探讨。结果表明,宁波市休闲渔业经营单位整体上呈现集聚型的分布形态,集聚程度由高到低排序依次是鄞州区、海曙区、象山县。宁波市休闲渔业经营单位的空间分布密度具有显著差异性,具有明显的“双中心、大集聚、区域分化明显”的密集性,并以双中心为核心,向周边辐射。宁波市休闲渔业经营单位的空间分布受交通、市场、景区区位因素的影响显著。研究结果可为优化宁波市休闲渔业企业选址,提高企业经营效率提供科学依据。

Spatial variation and soil nitrogen potential hotspots in a mixed land cover catchment on the Chinese Loess Plateau

Soil nitrogen(N) is critical to ecosystem services and environmental quality. Hotspots of soil N in areas with high soil moisture have been widely studied, however, their spatial distribution and their linkage with soil N variation have seldom been examined at a catchment scale in areas with low soil water content. We investigated the spatial variation of soil N and its hotspots in a mixed land cover catchment on the Chinese Loess Plateau and used multiple statistical methods to evaluate the effects of the critical environmental factors on soil N variation and potential hotspots. The results demonstrated that land cover, soil moisture, elevation, plan curvature and flow accumulation were the dominant factors affecting the spatial variation of soil nitrate(NN), while land cover and slope aspect were the most important factors impacting the spatial distribution of soil ammonium(AN) and total nitrogen(TN). In the studied catchment, the forestland, gully land and grassland were found to be the potential hotspots of soil NN, AN and TN accumulation, respectively. We concluded that land cover and slope aspect could be proxies to determine the potential hotspots of soil N at the catchment scale. Overall, land cover was the most important factor that resulted in the spatial variations of soil N. The findings may help us to better understand the environmental factors affecting soil N hotspots and their spatial variation at the catchment scale in terrestrial ecosystems.

Identifying Vehicular Crash High Risk Locations along Highways via Spatial Autocorrelation Indices and Kernel Density Estimation

Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.

RANDOM SYSTEMS OF HARD PARTICLES:MODELS AND STATISTICS

This paper surveys models and statistical properties of random systems of hard particles. Such systems appear frequently in materials science, biology and elsewhere. In mathematical-statistical investigations, simulations of such structures play an important role. In these simulations various methods and models are applied, namely the RSA model, sedimentation and collective rearrangement algorithms, molecular dynamics, and Monte Carlo methods such as the Metropolis-Hastings algorithm. The statistical description of real and simulated particle systems uses ideas of the mathematical theories of random sets and point processes. This leads to characteristics such as volume fraction or porosity, covariance, contact distribution functions, specific connectivity number from the random set approach and intensity, pair correlation function and mark correlation functions from the point process approach. Some of them can be determined stereologically using planar sections, while others can only be obtained using three-dimensional data and 3D image analysis. They are valuable tools for fitting models to empirical data and, consequently, for understanding various materials, biological structures, porous media and other practically important spatial structures.

Spatial Distribution of Hospitals in Handan City and Its Influencing Factors

Hospital is an important factor of people’s livelihood security,and the spatial layout of hospitals effectively ensures the medical convenience for residents.Location entropy and mathematical statistical analysis are used to study spatial distribution of hospitals.The results display that the distribution of medical facilities in Handan City is at a disadvantage level in Hebei Province,and medical facilities arr concentrated in the plain area.The layout of grade 3A hospitals in Hebei Province is characterized by urban centralization,and it is stronger in the east and weaker in the west.There is no medical facilities in Feixiang District of Handan City,and layout of medical facilities in Hanshan District and Congtai District is at advantage level of Handan City.The built-up area is the influencing factor for the distribution of medical resources.

西南喀斯特地区某典型铅锌选冶渣场影响区土壤重金属污染评价及其空间分布特征

掌握渣场影响区的土壤重金属空间分布特征并对其进行综合评价,可为其土壤污染区划和环境治理提供科学依据。在西南喀斯特地区某典型铅锌选冶渣场影响区农用地和林地采集了15个土壤样品(每个采样点均包含表、中、底层),测定了该土壤样品的重金属含量(Pb、As、Cu、Zn、Ni、Cr)和理化性质,采用多元统计-空间分析-土壤污染综合评价方法分析渣场影响区土壤重金属空间分布特征并进行污染风险评价,结果表明:Pb和As是该渣场影响区土壤的主要重金属污染物;表层土壤重金属污染主要与渣场位置有关,中、底层土壤重金属污染主要受土壤理化性质影响;土壤高污染区主要分布在研究区的中部和南部,低污染区分布在研究区的北部;渣场影响区土壤重金属污染的区划可识别风险区域,实现对渣场影响区土壤重金属污染的分区治理和区域管控。

近20年南充市土地利用与生态系统服务价值时空变化

在城镇化推进和退耕还林还草等政策影响下,南充市土地利用结构发生显著改变,基于2000-2019年3个时期土地利用数据,运用价值当量因子和空间统计分析等方法,探索南充市土地利用与生态系统服务价值(ESV)的时空变化。结果表明:(1)耕地是南充市主要土地利用类型,占82%以上,2000-2019年耕地和灌木呈减少趋势,而林地、水域、草地和建设用地呈增长趋势,建设用地(109.44%)和水域(40.62%)增长最为显著。(2)2000-2019年南充市ESV总量先急速增长后小幅回落,净增加73.8亿元,增长率为22.89%,耕地ESV占38%以上;单项ESV以水文调节和土壤保持为主,两者占总ESV的44%以上;各县(市、区)ESV总量和单位面积ESV呈北高南低的特点。(3)2000-2019年3个时期ESV及其变化的Moran's I指数均大于0,具有显著自相关性;ESV高值区和低值区不断扩展,空间聚集性逐步增强;ESV减损区和变化冷点区集中于城镇建设区域,增益区和变化热点区集中于水域和林地覆盖区域。研究结果可为南充市国土规划和生态文明建设提供参考。