基于Oracle Spatial的多元重力场信息数据库的设计与应用

随着地球重力场信息多元化的发展及其应用领域越来越广,科学高效的管理和应用重力场信息数据就显得尤为重要。文中介绍了基于Oracle Spatial技术设计与构建的重力场信息数据库的结构框架,分析了存储数据信息的种类、属性及特点,研究了数据的存储方式。在多元化重力场数据与基础地图数据统一管理的基础上,初步实现了多元化重力场信息的存储、查询、计算、绘图及显示等功能。     

基于GIS与SDM技术的可视化空间数据分类研究

提出将GIS与可视化空间数据挖掘技术之集成的基本框架。在此基础上,基于VisualC++6.0和Ma-pObject2.0组件技术设计和开发了一个可视化交互空间数据挖掘分类系统,系统采用决策树方法和贝叶斯网络作为数据挖掘方法的基本算法,采用训练与学习相结合实现空间数据的分类。文中用实例数据对系统性能、算法和规则有效性进行了验证。结果表明,该系统是一个适用的、可扩展的可视化交互空间数据挖掘工具,系统能够实现数据挖掘实时动态的交互控制,实现了数据挖掘过程的可视化、挖掘模型的可视化和结果的可视化显示、可视化思考、可视化分析与评价。     

Holistic approach of GIS based Multi-Criteria Decision Analysis (MCDA) and WetSpass models to evaluate groundwater potential in Gelana watershed of Ethiopia

Appropriate quantification and identification of the groundwater distribution in a hydrological basin may provide necessary information for effective management, planning and development of groundwater resources. Groundwater potential assessment and delineation in a highly heterogeneous environment with limited Spatiotemporal data derived from Gelana watershed of Abaya Chamo lake basin is performed, using integrated multi-criteria decision analysis (MCDA), water and energy transfer between soil and plant and atmosphere under quasi-steady state (WetSpass) models. The outputs of the WetSpass model reveal a favorable structure of water balance in the basin studied, mainly using surface runoff. The simulated total flow and groundwater recharge are validated using river measurements and estimated baseflow at two gauging stations located in the study area, which yields a good agreement. The WetSpass model effectively integrates a water balance assessment in a geographical information system (GIS) environment. The WetSpass model is shown to be computationally reputable for such a remote complex setting as the African rift, with a correlation coefficient of 0.99 and 0.99 for total flow and baseflow at a significant level of p-value<0.05, respectively. The simulated annual water budget reveals that 77.22% of annual precipitation loses through evapotranspiration, of which 16.54% is lost via surface runoff while 6.24% is recharged to the groundwater. The calibrated groundwater recharge from the WetSpass model is then considered when determining the controlling factors of groundwater occurrence and formation, together with other multi-thematic layers such as lithology, geomorphology, lineament density and drainage density. The selected five thematic layers through MCDA are incorporated by employing the analytical hierarchy process (AHP) method to identify the relative dominance in groundwater potential zoning. The weighted factors in the AHP are procedurally aggregated, based on weighted linear combinations to provide the groundwater potential index. Based on the potential indexes, the area then is demarcated into low, moderate, and high groundwater potential zones (GWPZ). The identified GWPZs are finally examined using the existing groundwater inventory data (static water level and springs) in the region. About 70.7% of groundwater inventory points are coinciding with the delineated GWPZs. The weighting comparison shows that lithology, geomorphology, and groundwater recharge appear to be the dominant factors influence on the resources potential. The assessment of groundwater potential index values identify 45.88% as high, 39.38% moderate, and 14.73% as low groundwater potential zones. WetSpass model analysis is more preferable in the area like Gelana watershed when the topography is rugged, inaccessible and having limited gauging stations.     

Land quality index assessment for agricultural purpose using multi-criteria decision analysis (MCDA)

In this study, an attempt has been made to apply Remote Sensing (RS) and geographic information system (GIS) to determine land quality for agriculture purpose using analytic hierarchy process technique. In this study, various thematic layers were used like organic matter content, soil texture, soil depth, soil pH, soil P, soil K, geomorphology, run-off potential, slope and land use/land cover to assess the land quality index of the study area for the agriculture purpose which were generated in the RS and GIS environment. The study area can be divided into four zones, viz. high quality, moderately quality, marginally quality and low quality according to their suitability of land quality for agriculture purpose. It was found that about 39.09, 31.24, 20.41 and 9.26% of the study area falls under high quality zone, moderately quality, marginally quality and low quality zone, respectively, for agricultural purpose.     

基于决策树的洪泽湖湿地信息提取

湿地是地球的重要组成部分,快速而准确地提取湿地信息,是湿地动态监测和可持续发展的一项基础而重要的工作.以洪泽湖淡水湿地为研究对象,采用2006年8月19日的Landsat5 TM遥感影像为数据源,采用经K-T变换光谱增强后的数据及利用灰度共生矩阵分析影像第一主成分的纹理统计量作辅助分类变量,基于地物光谱特征、纹理特征和形状特征,运用决策树分类法提取洪泽湖湿地植被信息,将其分类结果与最大似然法的分类结果进行比较.结果表明:1)洪泽湖地区的湿地植被比较丰富,面积约占全湖的10.74％,其中以浮水植物为主.2)基于决策树的分类结果的精度有了明显的提高,总精度由77.33％提高到86.33％,Kappa系数由0.7292提高到0.8354,证明基于决策树分类方法是提取淡水湿地植被信息的有效手段.     

空间插值算法在GIS中的应用

在GIS的研究中,通常需要具有一定规则的数据,而多数可获得的数据都是呈现离散状态并且是不规则分布的,这就需要应用空间插值的方法把离散的不规则数据转换成规则数据.本文介绍了几种常用的空间插值模型的原理与方法,并通过规则格网DEM的建立,说明空间插值算法在GIS中的应用.     

Spatial patterns of adolescent drug use

Adolescent drug use is individually and socially harmful in terms of disrupting adolescent development and social cohesion. Prior research has identified populations at risk and risk factors for adolescent drug use. This research sought to contextualize adolescent drug use by examining this behavior from a geographic perspective. The specific objectives were to identify patterns, local clusters and excess spatial risk for 5-digit zip codes within the 5-county Cincinnati, Ohio region. Adolescents (n = 57,241) were recruited within local schools by the Coalition for a Drug-free Greater Cincinnati. Results of this research show spatial clusters for perceived safety of marijuana; peer approval of alcohol, tobacco and marijuana; and age of onset for other drugs. The location and nature of these clusters are discussed and displayed in-text. Further, zip codes that were in excess risk compared to the 5-county region were identified. The utility of this research is two-fold: (1) It identifies the geographic variability in adolescent drug use and correlated factors of use, and (2) It provides a methodological framework for future research in spatial epidemiology of drug use.     

Using simulated data to investigate the spatial patterns of obesity prevalence at the census tract level in metropolitan Detroit

Obesity is a serious public health problem in the United States. It is important to estimate obesity prevalence at the local level to target programmatic and policy interventions. It is challenging, however, to obtain local estimates of obesity prevalence because national health surveys such as the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) are not designed to produce direct estimates at the local levels (e.g. census tracts) due to small population samples and the need to preserve individual confidentiality. In this study we address the problem of estimating local obesity prevalence rates by implementing a spatial microsimulation modeling technique to proportionally replicate the demographic characteristics of BRFSS respondents to census tract populations in metropolitan Detroit. Obesity prevalence rates are examined for high and low spatial clusters and studied in relation to the U.S. Department of Agriculture's (USDA) measures of low-income neighborhoods and local food deserts and CDC's measure of healthy and less healthy food environments currently used to target obesity reduction initiatives. This study found that obesity prevalence was largely clustered in the City of Detroit extending north into contiguous suburbs. The spatial patterns of highest obesity prevalence tracts were most similarly aligned with USDA-defined low-income tracts and CDC's less healthy food tracts. The locations of USDA's food desert tracts rarely overlapped with the highest obesity prevalence tracts. This study demonstrated a new methodology by which to assess local areas in need of future obesity interventions.     

A spatio-temporal analysis of academic performance at the Basic Education Certificate Examination in Ghana

Over the last decade, Ghana has more than tripled investment in its basic education system. Consequently, the country has made huge educational gains, primarily in providing universal access to basic education. However, many stakeholders are worried that academic performance is lagging because of disproportional attention to accessing basic education. Discussion of these concerns is hampered by ongoing disagreement about the true trajectory of academic performance at the basic education level and the widespread nature of students' lagging academic performance. In part, this disagreement stems from the failure of empirical studies to comprehensively examine trends in academic performance standards at the basic education level by concurrently considering a geographical and longitudinal perspective. Thus, this study examines the spatio-temporal trends of academic performance at the junior high school level since 2009 by using multilevel growth curve modeling, spatial statistics, and district-level longitudinal data. Results reveal 3 statistically distinct trajectories of academic performance: erratic, accelerating, and decelerating changes. Results also show that rural–urban gaps explain 31% of the performance trajectories, a trend which is expected to persist in the long term. In addition, we find extreme variations in academic performance within rural areas. Given the varying trajectories and geographical variability in academic performance, we suggest a localized approach to addressing challenges of low academic achievement at the basic education level in Ghana.     

Multitemporal settlement and population mapping from Landsat using Google Earth Engine

As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.