Examining the Relationship Between Spatial Configurations of Urban Impervious Surfaces and Land Surface Temperature

The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.

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.

Spatial Data Process Using Python Script and Visual Programming Language in ArcGIS

Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geographic Information System） software in GIS sector. However, those table data contain much unnecessary information that do not need for a certain project. Using the raw data can increase processing times and reduce performance of geoprocessing tools. This study shows steps of how the raw data are being processed using ArcGIS ModelBuilder and Python script.

Spatial analysis of tuberculosis in four main ethnic communities in Taiwan during 2005 to 2009

The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determination (such as global version of Moran’s test and local version of Gi*(d) statistic), using logistic regression calculations to identify spatial distributions over a contiguous five years and identify significant similarities, discriminant analysis to classify variables, and geographically weighted regression (GWR) to determine the strength of relationships between tuberculosis prevalence and ethnic variables in spatial features. Tuberculosis demonstrated decreasing trends in prevalence in both genders during 2005 to 2009. All results of the global Moran’s tests indicated spatial heterogeneity and clusters in the plain and mountainous Aboriginal townships. The Gi*(d) statistic calculated z-score outcomes, categorized as clusters or non-clusters, at at 5% significance level. According to the stepwise Wilks’ lambda discriminant analysis, in the Aborigines and Hoklo communities townships with clusters of tuberculosis cases differentiated from townships without cluster cases, to a greater extent than in the other communities. In the GWR models, the explanatory variables demonstrated significant and positive signs of parameter estimates in clusters occurring in plain and mountainous aboriginal townships. The explanatory variables of both the Hoklo and Hakka communities demonstrated significant, but negative, signs of parameter estimates. The Mainlander community did not significantly associate with cluster patterns of tuberculosis in Taiwan. Results indicated that locations of high tuberculosis prevalence closely related to areas containing higher proportions of the Aboriginal community in Taiwan. This information is relevant for assessment of spatial risk factors, which, in turn, can facilitate the planning of the most advantageous types of health care policies, and implementation of effective health care services.

基于网络核密度估计的交通事故黑点识别

为了对交通事故多发点进行分析,识别事故黑点并进行针对性治理,以深圳市龙华区2018—2020年3年间的1 105起道路交通事故数据及道路网络数据作为研究对象,采用高斯函数作为内核函数的道路网络核密度估计,通过K-means聚类进行路段密度等级划分。为进一步考虑路段之间的相关性,提高事故多发点识别精度,采用Local Moran′s I对事故多发点进行识别。对比平面核密度估计结果表明,所采用的基于网络核密度的Local Moran′s I的方法体现了事故多发点在路段及交叉口的分布特征。研究结论对识别事故黑点、进一步提高交通安全水平具有现实意义。

What control the spatial patterns and predictions of runoff response over the contiguous USA?

Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook several steps to explore the relationship.Initially,we partitioned runoff response change(RRC)into multiple components associated with climate and catchment properties,and examined the spatial patterns and smoothness indicated by the Moran's Index of RRC across the contiguous United States(CONUS).Subsequently,we employed a machine learning model to predict RRC using catchment attribute predictors encompassing climate,topography,hydrology,soil,land use/cover,and geology.Additionally,we identified the primary factors influencing RRC and quantified how these key factors control RRC by employing the accumulated local effect,which allows for the representation of not only dominant but also secondary effects.Finally,we explored the relationship between ecoregion patterns,climate gradients,and the distribution of RRC across CONUS.Our findings indicate that:(1)RRC demonstrating significant connections between catchments tends to be well predicted by catchment attributes in space;(2)climate,hydrology,and topography emerge as the top three key attributes nonlinearly influencing the RRC patterns,with their second-order effects determining the heterogeneous patterns of RRC;and(3)local Moran's I signifies a collaborative relationship between the patterns of RRC and their spatial smoothness,climate space,and ecoregions.

中国城市体系的空间分布格局及其演变

采用全国县级以上城市数据,运用最近邻与空间自相关分析,定量分析了1985—2010年中国城市体系的空间分布格局的时空特征与演变规律。结果表明:在1985—2010年间,中国城市的整体分布主要呈集聚型,其集聚程度有着逐渐增强的趋势。不同规模的城市在空间上的分布类型存在很大差异。集聚强度随着城市规模的变大而降低,表明随着城市规模的扩大,互斥作用不断加强。超大型、特大型城市由均匀分布转变为随机分布说明随着经济的发展,城市化总体水平的提高,城市发展对自然条件的依赖相对降低。从局部区域上城市的空间集聚或空间关联性质来看,其集聚特征的反差十分鲜明。东部地带不同规模等级城市都较为发达,形成了高水平均衡模式。没有通过显著性检验的城市主要集中分布在中部地区。西部地区城市分布表现出较为强烈的空间极化特征。

福建省马尾松毛虫害空间自相关分析

马尾松毛虫是危害最严重的森林害虫,掌握该虫害的空间分布规律有助于实现其有效监测与预防。以福建省67个县域单元为例,对马尾松毛虫害全局和局部空间自相关性进行分析。结果表明:1)福建省马尾松毛虫害Moran's I的值为0.613 5,Z值为13.464 2,达到极显著水平,表明虫害存在极为显著的全局正空间自相关性,集聚效应明显;2)从局部空间自相关结果看,空间差异小的县域单元数量远大于差异大的数量,"高-高"、"低-高"及"高-低"相关均位于森林资源丰富的闽中、闽北各县,呈"3横2纵"状分布格局,"低-低"正相关均位于沿海一线,呈带状分布格局,沙县、永安、邵武是马尾松毛虫害分布的"冷点",建瓯则是虫害分布的"热点";3)在大部分县(市),马尾松毛虫害空间自相关性不明显,呈随机型分布。研究表明,空间自相关分析可在马尾松毛虫害分布特征识别与规律研究方面得到较好的应用。

济南市商业地价的数字地价模型解译

利用通行成本量化河流、山体等阻隔性因子的影响,采用协克里金空间插值离散化方法建立格网数字地价表面,并采用标准差椭圆、局部变异系数、窗口分析算法分析地价的空间分布特征。结果表明:①基于通行成本的协克里金算法比克里金算法的拟合精度高;②地价呈椭圆状分布,分布中心位于素有济南市"金街"之称的泉城路中段的贵和商场及其邻近区域,椭圆长短半轴之比为1.75,旋转角为13.29°,在东北-西南方向上表现出更明显的分布趋势;③由中心向外,呈圈层状依次分布着大于1500平均地价值的高值集聚类型、异常集聚类型和低于平均值的低值集聚类型,聚集强度随聚集类型及距离的不同而不同;④具有点轴式空间分布结构,由1个一级中心、5个二级中心4、条地价脊线构成了地价空间分布的基本骨架,与商服中心及交通轴线的分布格局密切相关。

基于实时交通出行数据的居民生活便利性评价——以武汉主城区为例

居民生活便利性是评价宜居城市最核心的方面。本文基于实时交通出行数据，将居民的现实出行状况纳入研究，并整合生活圈理论、可达性方法和大数据手段，以期完善城市物质空间环境的评价体系。本文以武汉市主城区居民生活便利性的空间差异为研究对象，着重揭示其整体特征和基础设施布局存在的短板，并基于街道划分对各个街道的生活便利性进行评价。研究结果表明，武汉市主城区居民生活便利性呈现显著的高值聚类、低值分散的空间特征；片区生活便利性与其经济发展水平存在显著一致性，不同类型生活便利度高值区的空间分布基本一致；交通因子存在显著的低值聚类。居民生活便利性内在地反映了一定片区各类设施的分布状况，本文指出武汉市主城区居民生活便利度的高值区域和低值区域，进而揭示武汉市主城区基础设施空间分布不均等的结构现实。

基于公共自行车租赁点用地特征的租还潮汐性模型构建

以杭州主城区为例,利用ArcGIS软件中的莫兰指数和安瑟伦局部莫兰指数工具,在宏观和微观两个层次上分析租赁点早高峰租还潮汐比的空间自相关性和分布形态。以"潮汐显著"和"租还平衡"的空间聚类租赁点为典型样本,使用多变量相关分析方法考察租赁点服务区的加权居住用地比例和用地多样性指数对早高峰潮汐比的影响,并构建回归模型。实验结果验证了模型的正确性。

东北森林带森林生态系统固碳服务空间特征及其影响因素

东北森林带作为国家主体生态区划"两屏三带"国家生态安全格局中的重要组成部分,在全球碳平衡中发挥着重要的碳汇作用。以东北森林带为研究区域,采用净生态系统生产力(NEP)评估其森林固碳服务,通过Anselin Local Moran's Ⅰ算法识别固碳服务的"热点"、"冷点"和"异常点",并分析探讨其空间格局与影响因素。结果表明:(1)东北森林带森林生态系统整体上是碳汇。2014年东北森林带森林固碳总量为36.41 Tg C/a,单位面积固碳量为89.57 g C m^(-2)a^(-1)。(2)固碳服务的热点区主要分布在大兴安岭北部和长白山中北部,冷点区主要分布在大兴安岭东部、小兴安岭和长白山南部,固碳服务的高值异常区域主要分布在森林边缘的农林交错带,低值异常区域主要分布在人为干扰严重的城市蔓延区。(3)东北森林带森林生态系统整体上受人为因素的影响小,其固碳服务与NDVI显著正相关。(4)城市扩张等人为干扰是固碳服务异常降低的根本原因,植被本身生长状况不佳和较高的温度是导致固碳服务的异常降低的重要影响因素。

局部空间自相关及其在勘探地球化学中的空间聚集研究

运用局部空间自相关Moran的Ii和G*i指数,研究8 130 km2区域内采集的1∶20万水系沉积物1 482个组合样点的空间聚集情况,与基于稳健统计学的地球化学异常下限确定方法所圈定的异常范围对空间聚集范围进行对比。结果表明,局部Ii指数能够很好地探测出程度较弱和范围较小的空间聚集,局部G*i指数适合于程度较强和范围大的高高聚集探测,所探测出的空间聚集范围较大。使用该方法所发现的地球化学空间聚集范围和程度符合客观实际,与已知矿床(点)吻合很好。

地理信息产业分布相关性和空间相关性研究

地理信息产业已经上升为国家战略性新兴产业地位,地理信息产业的核心是地理信息企业,本文通过研究中国大陆31个省级区域甲级地理信息单位业务情况,应用皮尔逊相关系数、Moran’s I全局空间相关指标和Local Moran’s I局域空间相关指标,揭示地理信息产业分布的相关性和空间相关性,结果表明甲级业务存在一定的相关性和局域空间相关性。

基于Moran统计量的空间自相关理论发展和方法改进

本文旨在发展基于Moran指数的空间自相关分析理论和方法。首先,利用线性代数知识对基于Moran统计量的空间自相关过程的数学表示进行规范化整理;其次,基于变换中的不变性思想给出Moran指数的理论解释;第三,对空间权重矩阵的数理性质、建设方法和应用范围提出新的见解。总结并发展了Moran指数的三种计算方法——三步求值法、矩阵标度法和回归分析法,将空间权重矩阵划分为四种基本类型——局域关联型、准局域关联型、准长程关联型和长程关联型。以河南省鹤壁市乡镇体系为实证对象,以本文改进的理论和方法为依据,提供了一个空间自相关分析的简明案例。

Does the spatial arrangement of urban landscape matter?Examples of urban warming and cooling in Phoenix and Las Vegas

This study examines the impact of spatial landscape configuration(e.g.,clustered,dispersed)on land-surface temperatures(LST)over Phoenix,Arizona,and Las Vegas,Nevada,USA.We classified detailed land-cover types via object-based image analysis(OBIA)using Geoeye-1 at 3-m resolution(Las Vegas)and QuickBird at 2.4-m resolution(Phoenix).Spatial autocorrelation(local Moran’s I)was then used to test for spatial dependence and to determine how clustered or dispersed points were arranged.Next,we used Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)data acquired over Phoenix(daytime on 10 June and nighttime on 17 October 2011)and Las Vegas(daytime on 6 July and nighttime on 27 August 2005)to examine day-and nighttime LST with regard to the spatial arrangement of anthropogenic and vegetation features.Local Moran’s I values of each land-cover type were spatially correlated to surface temperature.The spatial configuration of grass and trees shows strong negative correlations with LST,implying that clustered vegetation lowers surface temperatures more effectively.In contrast,clustered spatial arrangements of anthropogenic land-cover types,especially impervious surfaces and open soil,elevate LST.These findings suggest that city planners and managers should,where possible,incorporate clustered grass and trees to disperse unmanaged soil and paved surfaces,and fill open unmanaged soil with vegetation.Our findings are in line with national efforts to augment and strengthen green infrastructure,complete streets,parking management,and transit-oriented development practices,and reduce sprawling,unwalkable housing development.

空间自相关分析在探究疾病分布热点区域中的应用

目的运用空间自相关分析探究疾病空间分布的热点区域,为疾病的防治提供科学依据。方法将ArcGIS 9.3软件作为数据管理和分析平台,利用软件空间统计模块进行全局空间自相关分析和热点分析,综合反映疾病空间分布特征。结果 2010年聊城市手足口病发病率存在热点区域。全局空间自相关Moran's I=0.69,u=11.46,P<0.01;广义G统计量G=0.01,u(G)=9.51,P<0.01。利用局部G统计量得出疾病分布聚集性,热点区域主要集中在东昌府区和高唐县。结论在空间统计中,利用空间自相关分析可以有效探究研究区域内的热点区域。

基于空间自相关的灾害影响因子分析

在进行灾害预测时,导致灾害产生的影响因子在空间上的关联程度对预测结果有较大影响。文章提出一种量化灾害影响因子间关联程度的方法,即测量灾害影响因子的空间自相关性;并以重庆市东北部五县区域内的森林覆盖率为例,进行森林覆盖率的空间自相关性分析。研究通过地理信息系统,对相关数据进行预处理,构建数值表面模型,并通过全局Moran’s I指数检验,得出该地区的森林覆盖呈现聚集式的分布,通过局部Moran’s I和局部G统计量得出属性值高低的聚类及其相互间关系。研究结果表明,该地区的森林覆盖情况呈现显著的空间自相关,且存在一定的聚集性,在利用该影响因子进行灾害分析与预测时,应将空间自相关同时作为一项影响因素。

中国上市公司的时空变化及机制研究

针对在中国地区发展不均衡的态势下,上市公司分布格局时空演变问题,该文对2009-2017年中国大陆各省上市公司的数量和总资产采用Local Moran’s I指数与Getis-Ord Gi*统计量进行研究,分析了中国大陆上市公司的空间分布格局和时间演变趋势,总结出中国上市公司相关指标的高值聚集和高低聚集分布格局、热点区域及其经济辐射能力。并且以北京、上海和广东为热点区域典型代表,从上市公司产业结构和区域经济发展两方面对时空变化机制的成因进行深入探讨。

夜光遥感支持下的城市人口核密度空间化及自相关分析

基于福建省福州市鼓楼区街道社区人口统计数据、夜光遥感影像、Landsat8影像,融合核密度与回归方程,绘制30 m栅格空间分辨率的人口密度图并进行空间自相关分析。方法:①采用核密度方法对69个社区人口计算生成人口密度分布图。建立786个居民小区点的人口密度与夜光遥感的常规QQ分位图,检测出人口密度存在较大的误差区域:五凤街道和洪山镇。②建立人口密度与夜光遥感、Landsat8线性分解的不可渗透表面影像之间的二元二次回归方程,修正两个区域的人口密度误差。③采用高/低聚类分析、热点分析、聚类和异常值分析,得到鼓楼区人口高聚类属性,显示了鼓楼区最大商业圈区域与人口密度最大居民点区域,展示了人口聚类的空间局部差异性格局。结果:①研究中所采用的人口空间化技术融合了2种空间化方法:核密度与回归方程。生成30 m栅格空间分辨率的人口密度图。②鼓楼区人口密度均值分为3种类型:11 000、25 000和50 000人/km2,人口密度分布近似正态分布。③当鼓楼区人口密度均值大于33 000人/km2,不可渗透表面灰度值与人口密度相关性更强。反之,夜光亮度值与人口密度相关性更强。