Fast and Accurate Approximation to Kriging Using Common Data Neighborhoods

Unknown values of a random field can be predicted from observed data using kriging. As data sets grow in size, the computation times become large. To facilitate kriging with large data sets, an approximation where the kriging is performed in sub-segments with common data neighborhoods has been developed. It is shown how the accuracy of the approximation can be controlled by increasing the common data neighborhood. For four different variograms, it is shown how large the data neighborhoods must be to get an accuracy below a chosen threshold, and how much faster these calculations are compared to the kriging where all data are used. Provided that variogram ranges are small compared to the domain of interest, kriging with common data neighborhoods provides excellent speed-ups (2–40) while maintaining high numerical accuracy. Results are presented both for data neighborhoods where the neighborhoods are the same for all sub-segments, and data neighborhoods where the neighborhoods are adapted to fit the data densities around the sub-segments. Kriging in sub-segments with common data neighborhoods is well suited for parallelization and the speed-up is almost linear in the number of threads. A comparison is made to the widely used moving neighborhood approach. It is demonstrated that the accuracy of the moving neighborhood approach can be poor and that computational speed can be slow compared to kriging with common data neighborhoods.     

Complexity theory, spatial simulation models, and land use dynamics in the Northern Ecuadorian Amazon

Our research questions and analytical approaches are used to examine coupled human-natural systems in the Northern Ecuadorian Amazon. They are based on complexity theory and extend from our earlier work in Cellular Automata (CA) in which land use/land cover (LULC) change patterns were spatially simulated to examine deforestation and agricultural extensification on household farms. The basic intent is to understand linkages between people and the environment by explicitly considering pattern-process relationships and the nature of feedback mechanisms among social, biophysical, and geographical factors that influence LULC dynamics within the study area. In this research, we describe how our CA modeling approach emphasizes the human dimensions of LULC change by including socio-economic and demographic characteristics at the household-level along with biophysical data that describe the resource endowments of farms, geographic accessibility of farms to roads and communities, and the evolving nature of human-environment interactions over time and space in response to exogenous and endogenous factors.A LULC change scenario is examined by comparing model outcomes generated for a base CA model and an alternative CA model to explore the effects of increases in household income on land use change patterns at the farm level, achieved as a consequence of improved geographic accessibility to roads and communities and increased off-farm employment as a household livelihood strategy. Growth or transitions rules in our CA model, as well as neighborhood associations are sensitive to socio-economic and demographic factors of households, resource endowments of farms, geographic accessibility, and the uncertainty associated with peasant farming in a frontier setting. Model outcomes indicate that increases in household income are associated with more land in pasture and more land being cultivated for crops as a result of greater access to agricultural markets. In addition, more land in secondary forest succession occurs as a consequence of greater access to roads and communities, thereby, affording a better opportunity for off-farm employment and greater levels of household income.     

基于物理模型的被动微波遥感反演土壤水分

利用土壤水分和海洋盐度(SMOS)卫星进行土壤水分反演的算法中,对地表发射率的描述仍采用半经验Q/H模型,该模型描述地表粗糙度对有效发射率在V和H极化下影响相同.基于微波散射理论模型-高级积分方程模型(AIEM)建立了一个针对SMOS传感器的参数配置,包含各种地表粗糙度和介电特性的裸露地表辐射模拟数据库,发展了L波段多角度地表辐射参数化模型.在此基础上,利用SMOS多角度双极化特点,建立了土壤水分反演算法.该算法可以消除粗糙度对土壤水分反演的影响,同时最小化反演过程中辅助信息引入带来影响.反演算法通过美国农业部提供的L波段多角度地基辐射计数据(BARC)进行验证,在20°~50°入射角,土壤水分反演精度在4%左右.     

A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Accurate wind modeling is important for wind resources assessment and wind power forecasting.To improve the WRF model configuration for the offshore wind modeling over the Baltic Sea,this study per-formed a sensitivity study of the WRF model to multiple model configurations,including domain setup,grid resolution,sea surface temperature,land surface data,and atmosphere-wave coupling.The simu-lated offshore wind was evaluated against LiDAR observations under different wind directions,atmo-spheric stabilities,and sea status.Generally,the simulated wind profiles matched observations,despite systematic underestimations.Strengthening the forcing from the reanalysis data through reducing the number of nested domains played the largest role in improving wind modeling.Atmosphere-wave cou-pling further improved the simulated wind,especially under the growing and mature sea conditions.Increasing the vertical resolution,and updating the sea surface temperature and the land surface infor-mation only had a slight impact,mainly visible during very stable conditions.Increasing the horizontal resolution also only had a slight impact,most visible during unstable conditions.Our study can help to improve the wind resources assessment and wind power forecasting over the Baltic Sea.     

Addressing issues of spatial spillover effects and non-stationarity in analysis of residential burglary crime

The aim of this paper is to address two critical but largely neglected issues in the spatial analysis of urban crime which are spatial spillover effects of crime penetrating neighborhood boundaries and non-stationarity regarding the relationships between contextual factors and neighborhood crime. We use a GIS-based spatial approach to normalize the estimate of burglary crime at block group level and use the geographically weighted regression (GWR) to investigate the correlates of neighborhood crime. Results suggest that the use of normalized measure of neighborhood crime helps better reveal the spatial patterns of burglary crime and the use of GWR accounts for the spatial variations of relationships between contextual factors and crime. In particular, the normalized measure of crime has implications for improving the measurement accuracy of the risk of crime across urban neighborhoods and can be applied to the spatial analysis of other socioeconomic issues such as housing foreclosures and environmental hazards which are also plagued by the spatial spillover issue when geographically contiguous data are analyzed.     

Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS

The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied to simulate urban land use changes and to predict their spatial patterns in Tripoli metropolitan area, Libya. It is worth mentioning that there is not much research has been done about land use/cover change in Libyan cities. In this study, the performance of integrated CA–Markov model was assessed. Firstly, the Markov chain model was used to simulate and predict the land use change quantitatively; then, the CA model was applied to simulate the dynamic spatial patterns of changes explicitly. The urban land use change from 1984 to 2010 was modelled using the CA–Markov model for calibration to compute optimal transition rules and to predict future land use change. In validation process, the model was validated using Kappa index statistics which resulted in overall accuracy more than 85 %. Finally, based on transition rules and transition area matrix produced from calibration process, the future land use changes of 2020 and 2025 were predicted and mapped. The findings of this research showed reasonably good performance of employed model. The model results demonstrate that the study area is growing very rapidly especially in the recent decade. Furthermore, this rapid urban expansion results in remarkable continuous decrease of agriculture lands.     

黄河流域蒸散量分布式模拟

针对传统区域蒸散计算模型中净辐射及其各组成要素的计算直接采用国外经验公式或点上观测资料空间内插的不足,利用基于中国气象站观测资料建立的净辐射及其各组成要素的计算公式和遥感反演的地表反照率,以黄河流域作为研究区域,在考虑地形起伏和下垫面多样性等地表非均匀性因素的基础上,实现了黄河流域净辐射及其各组成要素的分布式模拟;在印证流域尺度存在蒸散互补相关关系的基础上,将净辐射、气温、水汽压等系列要素分布式拟合结果与基于区域蒸散互补理论的AA（Advection-Aridity model）模型耦合,实现了黄河流域蒸散量的分布式模拟。与基于水量平衡的黄河流域多年平均蒸散量空间分布图对照表明,二者趋势分布吻合很好,分区验证的最小相对偏差为1.14%,最大为26.80%,全流域平均相对偏差为1.50%,且分布式蒸散拟合结果更加细致地体现了蒸散量的空间变化情况。集成的区域蒸散分布式模型,以蒸散互补理论为基础,考虑了区域蒸散对近地层大气的反馈作用,仅以数字高程模型和常规气象站观测数据为输入项,应用方便。     

基于多源数据融合方法的中国1 km土地覆盖分类制图

精确的全球及区域土地覆盖数据是陆地表层过程研究的重要基础。在集成研究兴起和多种数据并存的背景下,利用多源信息融合技术进行大尺度土地覆盖制图具有重要的现实意义。证据理论清楚地表达了由于不确定和不完全信息所带来的对命题认识的“无知”,能够确定相应的假设在目前的认知与知识状态下的确定、不确定和“无知”程度,是多源数据决策融合的重要方法。基于证据理论,将2000年中国1∶10万土地利用数据、中国植被图集（1∶100万）的植被型分类、中国1∶10万冰川图、中国1∶[KG-*2]100万沼泽湿地图和MODIS 2001年土地覆盖产品（MOD12Q1）进行了融合,最终基于最大信任度原则进行决策,产生了新的、IGBP分类系统的2000年中国土地覆盖数据。新的土地覆盖数据在保持了中国土地利用数据的总体精度的同时,补充了中国植被图中对植被类型及植被季相的信息,更新了中国湿地图,增加了中国冰川图最新信息,使分类系统更加通用。     

上海旧里保护改造模式与产权处置路径

保护性改造里弄,维护建筑历史和城市文脉是当前城市更新思潮的主流,但改造过程将面临土地制度的诸多约束。本文以静安别墅、田子坊、建业里、179街坊、思南公馆等五个较具代表性的改造案例分析入手,结合产权变动与建筑改造解构与组合,提出里弄的保护改造四种模式,并分析不同模式下土地产权处置差别化方式、路径和土地利益分配形式,为城市日居住保护改造实施提供理论和实践借鉴。     

快速模拟退火算法在含水层参数识别中的应用

针对一般模拟退火算法收敛速度慢的缺陷,本文提出了含水层参数识别的快速模拟退火算法。它以广义Boltzmann-Gibbs统计理论为基础,采用依赖于温度的似Cauchy分布产生新的扰动模型,通过改变模型扰动、接收概率和降温方式来加快模拟退火算法的收敛速度。实例的计算结果表明,快速模拟退火算法不仅可行,而且求解精度高,收敛速度比其他算法快得多,是一种值得在实际中推广应用的高效的含水层参数识别方法。      

基于FLUS的长春市土地利用动态变化与预测分析

研究城乡土地利用变化规律与驱动机制,有利于实现区域土地资源可持续发展。本文以长春市为例,以监督分类与人工解译相结合的方式对1997、2007和2017年Landsat卫星影像进行分类,总体精度分别为93.06%,90.70%和94.12%。1997-2017年,草地、耕地和其他土地面积分别减少354.74、922.11和55.35 km²,建设用地、水域和林地面积分别增加1 154.14、70.38和107.54 km²,整体表现为建设用地向周边扩张,侵占其他用地类型面积。利用未来土地利用模拟（future land use simulation,FLUS）模型,以2007年分类数据为基础,结合地形、交通区位和社会经济等土地利用变化驱动因子,仿真2017年土地利用格局,仿真结果与真实情况吻合较好,仿真精度达85.10%,Kappa系数为0.821 2,验证了模型和驱动因子精度可靠,符合土地利用变化趋势。以此模型因子预测2027年土地利用格局,结果表明：在城镇周围,建设用地将持续侵占耕地、林地、草地和其他土地的面积,但趋势减缓,同时林地面积和水域面积增加。     

The differential impact of physical disorder and collective efficacy: a geographically weighted regression on violent crime

While much of the research on neighborhood crime considers the neighborhood as a whole, this study utilizes spatial analysis techniques to examine how the presence of disorder and collective efficacy create unique pockets of opportunities for criminal behavior within neighborhoods. This spatial perspective reveals how the effect of disorder and efficacy upon crime patterns itself varies across a neighborhood. Physical disorder is measured through systematic social observations and the level of collective efficacy is evaluated through survey responses of neighborhood residents. The indices of disorder and efficacy are compared to crime data from police call logs using geographically weighted regression. Our findings demonstrate a complex spatial relationship between disorder and efficacy. The effects of disorder and efficacy are not consistent across an entire neighborhood, but rather display local variations in small geographical areas within neighborhoods, including some pockets of the neighborhood where the relationships between disorder, efficacy, and crime were contrary to the expected relationships. Based upon these findings, we conclude opportunity is central to understanding crime, and emphasize the role of informal social control in neighborhoods.     

水稻、小麦与土壤中重金属Cd含量的关系模拟研究

土壤污染防治工作已成为提升耕地质量、保护国土生态安全的重要任务之一.为了科学预测我国大宗农作物(如水稻、小麦)与土壤重金属含量的关系,减少安全利用类农用地的大量农产品与土壤的协同监测,实现重金属污染农用地的安全利用,本研究以重金属Cd为例,选取对水稻、小麦Cd含量影响较大的土壤Cd含量、土壤pH值、土壤阳离子交换量(C...     

基于水文模型的雷达监测降雨量误差传递研究

基于多普勒雷达和相应的雨量计资料,利用卡尔曼滤波校准法对分组Z~I关系估算的雷达降雨进行同化,结合新安江模型,提出采用增长模繁殖法对雷达监测降雨量资料进行扰动,定量分析了模型输入误差对径流模拟的影响。经过湖北省白莲河流域的实际应用研究表明,卡尔曼滤波校准法估算降水量的精度比Z~I关系法有了明显的提高,其相应的洪水预报效果也都优于Z~I关系;提出的增长模繁殖法对雷达监测降雨量资料产生的扰动输入误差在经过模型的传递后有增大的趋势。     

The impact of neighborhood services on land values: an estimation through the hedonic pricing model

A strong need exists to increase the knowledge and recognize the values of neighborhood services. This paper makes an attempt to examine the impact of neighborhood services on land values, through analysis based on results obtained from multi linear regression analysis. This case study was done in area of dense urban settlement to check the consequence of neighborhood services through the hedonic pricing model base variables which include the structural, locational, community and neighborhood services. The regression coefficient was checked at (p?<?0.05) level of significance for each variable. The primary data was collect through the questionnaire filling by random sampling and Punjab Urban Gazette was used for the verification of land values in study area. The results found a valuable potential on land and property values of neighborhood services through the regression analysis results express through R (0.926), R² (0.856) and coefficient tables to represent the effect of each individual variable on property and land values. Every individual variable play role in defining the values of land and property based upon its utilization such as larger the structure of a property, more the covered area, larger size of a property more the land area higher price. Similarly, for locational factor study in geography, a piece of land adjacent to the road have high land values 50–70% compare to those located at distance. Very less attention has been paid on such issues in the world due to lack of effective urban planning and research on such crucial issues. If empirical studies on such issue has been done, effective way can be obtained for urban planning.

     

不同噪声水平高密度电法的分辨率和勘探深度研究

电位噪声是影响高密度数据采集质量进而影响反演成像解释可靠性的重要因素。通过模拟四种不同水平电位噪声并获得噪声数据,对比分析四种装置(温纳装置、三极装置、偶极装置、温纳-施伦贝谢装置),在噪声条件下的反演模型电阻率差异,并评估各装置对噪声响应的灵敏程度以及不同噪声水平对反演模型分辨率和各装置的勘探深度的影响。将反演模型分辨率和各装置勘探深度进行了量化,定量分析模型分辨率和不同装置勘探深度在各噪声水平下的变化规律,综合评估噪声对四种装置反演模型分辨率和勘探深度的影响得出:①偶极和三极装置对噪声灵敏程度较高,而温纳和温施装置对噪声灵敏程度相对较低;②噪声对深层地电模型分辨率影响较大,在有效分辨地电模型的深度范围内,温纳和温施装置的模型分辨率降低2/5,三极装置降低一半,偶极装置降低3/5左右;③勘探深度在噪声影响下均有所减小,温纳和温施装置减小约1/4,偶极和三极装置减小约1/3。     

基于管道流量监测数据的城市雨洪模型实用性研究

在总结过去多年管道流量监测经验教训的基础上,创新使用多声道多普勒流量计,成功获取了5min频次的雨水管网流量过程,提升了监测的稳定性和监测结果的可靠性,使得利用多场次资料进行城市内涝模型参数率定以及验证成为可能。选取城市典型区域,分别建立了MIKE、SWMM、InfoWorks一维管网模型,并按照有关规范要求,在对降雨资料和水位、流量资料进行科学分析后,利用经过筛选的有效数据进行模型的参数校核和率定,经过验证符合《内涝防治系统数学模型应用技术规程》要求。通过误差分析,率定后的模型,对典型区域的管网汇流模拟较好,在内涝分析预测预报中精度较高,其流量峰值预测值与实测值的误差在10%以内。     

工程渣土场回填土的力学性能及扩容分析

工程渣土填埋场由于缺乏合理的库容设计,导致场地库容利用率较低,当前所采用的简单倾倒措施也会引发崩塌、滑坡等安全隐患。为探索合理填埋方法,增大填埋容量,防止安全事故发生。本文对梧州市龙圩区李家坳堆填4年的渣土场库容利用情况进行了研究,通过原位试验、室内试验及测试结果分析,研究了工程渣土的力学性能指标特征;通过三维地质建模并结合LANDFILL程序对填埋过程中进行了仿真模拟,采用LANDFILL软件与Sowers固结模型计算对场地沉降与容量变化进行了对比分析。结果表明:(1)工程渣土由于扰动性大,导致其组分不均,土体多处于半饱和-饱和状态,主要以含砂细粒土(CLS)、低液限黏土(CL)两种土为主;(2)工程渣土多处于流塑状态、软塑状态,压实度低,平均地基承载力较低;(3)填埋过程仿真分析结果表明填坑在完善排水措施、改进填埋工艺后,可使扩容率由8.9%增加到25.1%,能有效提高填埋场的容量;(4)LANDFILL软件沉降计算结果与模型相比,误差值较小,平均误差小于4.5%,表明LANDFILL软件计算填埋场沉降更符合实际。研究结果为工程渣土场的扩容设计、安全运营、填埋施工等提供了数据参考及工程借鉴。     

基于ABC-GRNN组合模型的露天矿边坡变形预测

准确预测露天矿边坡变形是有效实现边坡临灾预警的重要保证,针对传统边坡变形预测方法无法表征和综合分析边坡变形受多种因素的影响,提出一种露天矿边坡变形的人工蜂群(ABC)算法优化广义回归网络(GRNN)组合预测模型(ABC-GRNN)。在此预测模型中,综合考虑了影响露天矿边坡变形的5个因素：开采扰动、降雨量、降雨持续时间、温度以及湿度。以山西中煤平朔安家岭露天矿为例,通过遗传算法改进BP神经网络(GA-BPNN)、支持向量机(SVM)等人工智能算法与实测变形数据进行预测效果对比分析。结果表明：ABC算法能够快速帮助GRNN寻优获取合适的传递参数,并对变形进行有效的预测。ABC-GRNN组合预测模型,将预测结果的平均绝对误差292.9 mm、平均绝对百分比误差0.691 3%及均方根误差338.9 mm分别降低到25 mm、0.043 3%和29.5 mm,说明该模型具有更高的预测精度;ABC-GRNN模型比其他模型收敛速度快,只经过7步的迭代,即可得到最小的均方误差。与其他预测模型相比较,本文模型的预测精度更高、泛化能力更强、收敛速度更快,有较高的实用价值。     

基于SoilGrids的栅格新安江模型参数空间分布估算

为实现中小流域降雨径流过程精细化模拟, 合理估算水文模型参数的空间分布具有重要意义。基于新版全球数字土壤制图系统(SoilGrids)构建栅格新安江模型(GXM)参数化方案, 对陕西省陈河流域2003—2012年16场洪水进行模拟, 与新安江模型计算结果进行对比, 开展基于洪水过程划分的自由水蓄水容量敏感性及空间分布特征量化分析。结果表明: GXM模拟的峰现时间误差水平降低约0.31 h, 洪峰和洪量模拟精度较高, 模型能够对土壤水饱和度等水文要素的动态空间分布进行较合理的模拟; 自由水蓄水容量参数对洪峰和涨洪过程的确定性系数以及涨洪段的洪量相对误差影响较大, 对退水过程影响小; 自由水蓄水容量在陈河流域河谷和山脊附近较大, 坡段中部较小。