A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

Evaluation of Groundwater Quality in the Deep Maastrichtian Aquifer of Senegal Using Multivariate Statistics and Water Quality Index-Based GIS

A regional groundwater quality evaluation was conducted in the deep Maastrichtian aquifer of Senegal through multivariate statistical analysis and a GIS-based water quality index using physicochemical data from 232 boreholes distributed over the whole country. The aim was to 1) identify the water types and likely factors influencing the hydrochemistry, and 2) determine the suitability of groundwater for drinking and irrigation. Results showed that sodium, chloride, and fluoride are highly correlated with electrical conductivity (EC) reflecting the significant contribution of these elements to groundwater mineralization. The principal component analysis evidenced: 1) salinization processes (loaded by Na+, K+, EC, Cl-, F- and HCO3-) controlled by water/rock interaction, seawater intrusion and cation exchange reactions;2) dolomite dissolution loaded by the couple Ca2+ and Mg2+ and 3) localized mixing with upper aquifers and gypsum dissolution respectively loaded by NO3- and SO42-. The hierarchical clustering analysis distinguished four clusters: 1) freshwater (EC = 594 μs/cm) with mixed-HCO3 water type and ionic contents below WHO standard;2) brackish (Na-mixed) water type with moderate mineralization content (1310 μs/cm), 3) brackish (Na-Cl) water type depicted by high EC values (3292 μs/cm) and ionic contents above WHO and 4) saline water with Na-Cl water type and very high mineralization contents (5953 μs/cm). The mapping of the groundwater quality index indicated suitable zones for drinking accounting for 54% of the entire area. The occurrence of a central brackish band and its vicinity, which were characterized by high mineralization, yielded unsuitable groundwater for drinking and agricultural uses. The approach used in this study was valuable for assessing groundwater quality for drinking and irrigation, and it can be used for regional studies in other locations, particularly in shallow and vulnerable aquifers.

Application of Statistical Methods and GIS for Downscaling and Mapping Crop Statistics Using Hypertemporal Remote Sensing

To sustain the management of natural resources, land use and land cover （LULC） should be spatially mapped and temporally monitored using GIS. For large areas, conventional methods are laborious. Alternatively, remote sensing can be used for LULC mapping and monitoring. Normalized differential vegetation index （NDVI） is the most used vegetation index for crop identification and phenology. For agricultural areas, crop statistics are estimated yearly at regional level following administrative units. However, these statistics are not informing about spatial extent of these crops within administrative units; such information is crucial for crop monitoring. The main objective of this research was to fill the gap, based on statistical methods and GIS, by adding spatial information to crop statistics by analyzing temporal NDVI profiles. The study area covers 1300 km2. Data consist of 147 decadal Spot Vegetation NDVI images. Crop statistics were compiled on seasonal basis and aggregated to different administrative levels. Images were processed using an unsupervised classification method. A series of classification runs corresponding to different numbers of clusters were used. Using stepwise multiple linear regression, cropped areas from agricultural statistics were related to areas of each NDVI profile cluster. Estimated regression coefficients were used to generate maps showing cropped fractions by map units. The optimal number of clusters was 18. Similar profiles were merged leading to eight clusters. The results show that, for example, rice was grown, in autumn, on 50% of the area of map-units represented by NDVI-profile group 4 and 75% of the area of group 7 while it was grown, in spring, on 2, 69 and 25% of areas of NDVI-profile groups 2, 61 and 7, respectively. Regression coefficients were used to generate map of crops. This research illustrates the benefit of integrating statistical methods, GIS, remote sensing and crop statistics to delineate NDVI profile clusters with their corresponding agricultural land cover map units and to link these statistics to geographical locations. These map units can be used as a reference for future monitoring of natural resources, in particular crop growth and development and for forecasting crop production and/or yield and stresses like drought.

Impacts of changing scale on Getis-Ord Gi* hotspots of CPUE:a case study of the neon flying squid (Ommastrephes bartramii)in the northwest Pacific Ocean

We examined the scale impacts on spatial hot and cold spots of CPUE for Ommastrephes bartramii in the northwest Pacific Ocean. The original fishery data were tessellated to 18 spatial scales from 5′×5′ to 90′×90′ with a scale interval of 5′ to identify the local clusters. The changes in location, boundaries, and statistics regarding the Getis-Ord Gi＊ hot and cold spots in response to the spatial scales were analyzed in detail. Several statistics including Min, mean, Max, SD, CV, skewness, kurtosis, first quartile（Q1）, median, third quartile（Q3）, area and centroid were calculated for spatial hot and cold spots. Scaling impacts were examined for the selected statistics using linear, logarithmic, exponential, power law and polynomial functions. Clear scaling relations were identified for Max, SD and kurtosis for both hot and cold spots. For the remaining statistics, either a difference of scale impacts was found between the two clusters, or no clear scaling relation was identified. Spatial scales coarser than 30′ are not recommended to identify the local spatial patterns of fisheries because the boundary and locations of hot and cold spots at a coarser scale are significantly different from those at the original scale.

Analysis and Visualization of Marketing, Statistical and Macroeconomic Data With GIS

The most common way to analyze economics data is to use statistics software and spreadsheets.The paper presents opportunities of modern Geographical Information System （GIS） for analysis of marketing, statistical, and macroeconomic data. It considers existing tools and models and their applications in various sectors. The advantage is that the statistical data could be combined with geographic views, maps and also additional data derived from the GIS. As a result, a programming system is developed, using GIS for analysis of marketing, statistical, macroeconomic data, and risk assessment in real time and prevention. The system has been successfully implemented as web-based software application designed for use with a variety of hardware platforms （mobile devices, laptops, and desktop computers）. The software is mainly written in the programming language Python, which offers a better structure and supports for the development of large applications. Optimization of the analysis, visualization of macroeconomic, and statistical data by region for different business research are achieved. The system is designed with Geographical Information System for settlements in their respective countries and regions. Information system integration with external software packages for statistical calculations and analysis is implemented in order to share data analyzing, processing, and forecasting. Technologies and processes for loading data from different sources and tools for data analysis are developed. The successfully developed system allows implementation of qualitative data analysis.

Groundwater Quality Evaluation Using GIS Based Geostatistical Algorithms

Groundwater quality is a major environmental aspect which needs to be analyzed and managed depending on its spatial distribution. Utilization of insufficient management of groundwater resources in Gaza Strip, Palestine, produces not only a reduction in quantity but also deterioration in quality of groundwater. The aim of this study is to provide an overview for evaluation of groundwater quality in the Gaza Strip area as a case study for applying spatially distributed by using Geographic Information System (GIS) and geostatistical algorithms. The groundwater quality parameters, pH, total dissolved solids, total hardness, alkalinity, chloride, nitrate, sulfate, calcium, magnesium, and fluoride, were sampled and analyzed from the existing municipal and agricultural wells in Gaza Strip;maps of each parameter were created using geostatistical (Kriging) approach. Experimental semivariogram values were tested for different ordinary Kriging models to identify the best fitted for the ten water quality parameters and the best models were selected on the basis of mean square error (MSE), root mean square error (RMSE), average standard error (ASE), and root mean square standardized error (RMSSE). Maps of 10 groundwater quality parameters were used to calculate the groundwater quality index (GWQI) map using the index method. In general, the results showed that this integrated method is a sufficient assessment tool for environmental spatially distributed parameters.

基于空间统计分析与GIS的人口空间分布模式研究——以甘肃省天水市为例

该文将空间统计分析与GIS相结合,探讨天水市人口空间分布模式。研究表明,天水市人口空间分布呈现"西北—东南"模式,存在显著的空间集聚现象,四种类型的空间关联在空间分布上具有明显的规律性。利用空间统计分析方法能够扩展和加强GIS的空间分析功能,从而更深入地探索、分析、处理和解释人口分布的空间模式与空间关联,为进一步研究空间模式的发生尺度和塑造该模式的空间过程奠定基础。

基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用

目前，ＧＩＳ技术已被广泛应用在野生动物生境研究中。但是，作为空间数据分析和处理工具，ＧＩＳ缺乏进行启发式推理的能力。因此，与擅长于此的贝叶斯统计推理技术相结合则是解决这一问题的重要途径。以西双版纳纳板河流域生物圈保护区为试验区，综合应用ＧＩＳ技术和多元统计技术建立印度野牛生境的两个逻辑斯蒂多元回归模型：趋势表面模型和环境模型，第一个模型的自变量是位置坐标，第二个模型的自变量是一组环境因子，然后应用贝叶斯统计合并这两个模型产生贝叶斯综合模型。结果表明，贝叶斯综合模型优于环境模型，可应用于野生动物生境概率评价。

基于地统计学和GIS的江西省森林土壤养分空间分布特征

土壤作为一种形态和演化过程都十分复杂的自然综合体,在成土过程中,受到不同的自然、环境条件因素的影响,使得土壤养分的分布具有高度的差异性。研究基于江西省340个森林土壤剖面5个土层(0—10cm,10—20cm,20—30cm,30—50cm和50—100cm)土壤养分数据,运用地统计学方法和GIS技术对江西省森林土壤有机质(SOM)、全氮(TN)及全磷(TP)空间变异特征进行研究,并分析了三种土壤养分在江西省的空间分布特点。结果表明:(1)垂直方向上看,江西省森林土壤SOM,TN均随土层深度增加而减少,而TP变化趋势不明显;水平方向上看,土壤SOM总体丰富,高值区主要在东北部和西部;TN具有明显的南北分异,赣北地区森林土壤TN含量较高,赣南地区较为缺乏;全省各地磷素较为缺乏,各方向无明显分异;(2)江西省森林土壤养分受植被类型、土壤类型影响大,土壤养分含量总体表现为棕壤>黄壤>红壤,竹林>阔叶林>针叶林;(3)SOM具有强烈变异性,各土层变异系数为77.88%~123.73%,产生了严重的"贫富差距",TN和TP具有中等程度的变异;(4)球状模型、指数模型、高斯模型能较好地描述江西森林土壤养分空间变异特征,TN和TP的空间自相关性在垂直方向上具有"上下强,中间弱"的规律,SOM表现为"上强下弱"的规律,植被因素和取样方式均为造成较大的随机性变异的主因;表层土壤中SOM和TN变程在100km以内,深层土壤变程均大于200km,TP自相关距离相对较小,为19~75km;(5)江西省山地森林土壤养分含量显著高于丘陵森林,土壤养分形成由江西省边界山区向吉泰盆地等中部地区递减的趋势。

基于GPS和GIS的田间土壤特性空间变异性的研究

以一块面积约为 13 .3 hm2 的冬小麦田为研究区 ,利用 GPS接收机定位 ,按 5 0 m× 5 0 m设置网格 ,共取 63个采样点 ,测定土壤表层 (2 0 cm)内的土壤有机质、全氮、碱解氮、速效磷、速效钾、容重、田间土壤含水率和电导率 ,研究麦田土壤特性的空间变异规律。采用传统统计学和地统计学相结合的方法对所取的数据进行了分析 ,利用 Arcview3 .2软件的空间分析功能 ,绘制了表达这些土壤特性随机性和结构性的半方差图和空间分布图。研究结果表明 :所有土壤特性均服从正态分布 ;土壤容重具有弱变异强度 ,其它土壤特性具有中等变异强度 ;土壤有机质、全氮、碱解氮、速效钾和电导率具有很强的相关性 ,土壤容重、速效磷和含水率具有中等强度的空间相关性 ,土壤特性的相关距变化范围为 2 46.8～ 42 6.8m。该成果可为农田的定位施肥、灌溉以及其它的农田精细管理提供依据。

基于遥感和GIS的流域社会经济数据空间化方法研究

人口和社会经济数据及其空间分布,在资源、环境及自然灾害评估中的重要性已被广泛认知。把遥感和GIS技术相结合,是探讨解决社会经济统计数据空间化的重要思路。以东江流域为例,以多期人口、GDP、土地利用数据为基础,建立东江流域人口与土地利用、GDP和土地利用的多元线性回归模型;以土地利用数据和100 m×100 m网格数据为基础,构建东江流域人口和GDP空间分布约束力指标图层;然后结合统计模型和面积内插,实现了东江流域2009年人口、GDP统计数据的空间化。在县域空间尺度上对模拟结果进行了验证,与同尺度研究工作进行了对比,结果显示模拟得到的人口和GDP空间分布数据,与同尺度的研究工作处于同一精度或者略高的精度水平,表明该方法是一种进行流域社会经济数据空间化处理的有效方法。

GIS支持下的马龙县植烟土壤有效态微量元素评价

采用GPS定位技术,对马龙县基本烟田进行大规模的土壤取样,研究了区域植烟土壤养分状况的总体特征、空间变异特征,并采用普通Kriging插值法绘制了微量元素空间分布图。结果表明:马龙县植烟土壤微量元素均表现为各向异性分布,7种有效态微量元素的空间变异均是由结构性因素和随机性因素共同作用的结果;土壤微量元素空间分布图显示,马龙县植烟土壤有效硼普遍极度缺乏,有效锌含量中等,有效铜、有效铁和有效锰含量十分丰富,土壤有效钼区域差异较大,占研究区28.02%的区域有效钼缺乏,土壤水溶性氯含量超过临界值(30mg·kg^(-1))的面积占研究区13.88%。

基于空间统计学和GIS的农牧交错带土壤养分空间特征分析——以内蒙古翁牛特旗为例

土壤养分是土壤肥力的重要标志,是土地评价和管理的重要指标之一。通过空间统计分析和G IS分析了农牧交错带土壤养分的空间分布特征。结果表明,土壤有机质的M oran I系数对尺度响应敏感,随着尺度的增大,空间自相关性减少,而全氮、速效磷和速效钾的M oran I系数对空间的尺度响应不明显;土壤的有机质、全氮和速效磷在50 km的范围内表现出空间的正相关,而在60 km以外表现出空间的负相关;土壤表层速效钾变程最大(33 000 m),全氮次之(11 000 m),速效磷最小(3 000 m);土壤表层养分的变程与分维数呈明显的负相关关系;土壤表层有机质和速效钾的最佳拟合模型为指数模型,而全氮和速效磷的最佳拟合模型为球状模型。

基于GIS的长江三峡库区滑坡影响因子分析

利用GIS技术和统计方法,对三峡库区选定的研究区域(面积4539km2)滑坡空间分布和地形、地质等滑坡内部因子之间相关性进行统计计算。在建立地质、地形数据库等滑坡因子空间数据库和滑坡空间分布数据库(数据比例尺均为1∶10000)基础上,从地形数据库提取25m分辨率DEM,再派生出高程、高差、坡度、坡向、平面曲率、剖面曲率等地形影响因子;从地质数据库提取地层和岩性组合影响因子。将各个定性的因子按一定规则进行重分类、转换为25m分辨率的栅格数据格式,在GIS中进行地图代数运算、统计计算滑坡和各影响因子相关性。结果表明,滑坡分布和Q4、J1x,J1z、S岩性岩组;90m以下、90～135m和135～175m三个高程带;15～20m局部高差;10°～25°坡度;北、南和西北方向及-1～1曲率范围等影响因子相关性等级都大于1,为滑坡发生的主要影响因子类属。研究的结果是进行滑坡易发性评价的基础,可以指导库区滑坡灾害管理、土地利用等。

基于GIS的三峡库区滑坡坡形研究

借助GIS技术及统计分析方法,对三峡库区云阳—巫山段的205个滑坡点的坡形进行了定量化研究,得到了研究区内坡形因素对滑坡发育的影响规律,为进一步对研究区进行滑坡危险性评价和分区奠定了基础。在提取坡形时用了两种不同的方法,第一种方法是借助GIS软件用计算机自动生成的坡形,第二种方法是借助GIS软件和3Dmapper软件半自动生成的,经实践检验和理论证明发现,第二种提取坡形的方法要优于前者。

GIS叠置位置不确定度的统计估计方法

针对手工数字化地形图的GIS数据 ,利用多层叠置上的同名点坐标来估计位置数据的不确定性 ,提出了一套计算方法。主要内容包括运用成对子样序列的t检验法 ,对两层叠置的手工数字化数据进行了粗差检验 ,提出了单位权方差的综合估计方法。其特点是应用统计检验和统计估计原理 ,使得到的结果更合理和简捷。

基于GIS的统计数据可视化研究

阐述了地理空间数据可视化的含义、内容以及统计数据的时空特性和传统的统计数据可视化方法,结合统计数据和GIS的特点,提出了基于GIS的统计数据可视化体系和可视化功能流程模式。最后以国家社会经济统计地理信息系统为例,研究基于GIS的统计数据可视化的各种方法和应用,表明该可视化体系和功能流程模式具有很好的推广价值。

采用地统计学和GIS技术对东北地区不同时期降水的分析

采用地统计学与GIS技术相结合的方法对东北地区146个站1961—1990年与1971—2000年的年降水和夏季降水气候值进行分析，与传统统计方法的结果对比后认为传统的统计方法不能真实地反映不同时段数据的差异。通过在GIS平台上对两组气候值差异百分比的空间分布分析得出：黑龙江西南部以及东部部分地区、吉林东南部和辽宁中部地区夏季降水量增加3%～6%，其余地区减少，其中辽宁南部和西北部减少幅度最大，达6OA～9％；黑龙江大部、吉林东部以及辽宁中部地区年降水量增加，其余地区减少，辽宁南部地区减少幅度最大，达6％～9％。

GIS技术在三峡库区滑坡灾害研究中的应用

对三峡库区选定的研究区域,建立地质、地形等滑坡因子空间数据库和滑坡空间分布数据库(比例尺均为1:10000),利用GIS技术对滑坡和各影响因子相关性进行了统计分析。计算得出滑坡发生的主要影响因子类属:Q4、J1x,J1z、S岩性岩组;90m以下、90～135m和135～175m三个高程带;15～20m局部高差;10°～15°、15°～20°和20°～25°坡度;北向、南向和西北向方向和1～1曲率范围。研究的结果是进行滑坡易发性评价的基础,可以帮助指导库区滑坡灾害管理、土地利用等。