Statistical evaluation of an agricultural land suitability model

The land evaluation and site assessment (LESA) method has been used extensively in the United States to assess agricultural land suitability for different localities. Despite widespread use, LESA models rarely have been evaluated in a systematic, comprehensive manner. This article discusses development of a LESA system for Hawaii, the first statewide application of the LESA methodology. The empirical model was implemented with a computerized geographic information system (GIS). The system's efficiency, ability to discriminate among land parcels, and robustness to subjective model parameter values are evaluated with statistical analyses and map overlays of GIS data. Results show great potential to simplify the original model specification, primarily through deletion of marginal site assessment factors. System output was generally insensitive to the numeric values selected for model parameters, with exception of the ratio used to combine the land evaluation (LE) and site assessment (SA) component scores. Relative supplies of the differing land attributes measured by the two components must be considered in determining an appropriate LE:SA ratio for a given area.     

COMPARING METHODS OF HYDROLOGIC REGIONALIZATION1

ABSTRACT: Data splitting is used to compare methods of determining “homogeneous” hydrologic regions. The methods compared use cluster analysis based on similarity of hydrologic characteristics or similarity of characteristics of a stream's drainage basin. Data for 221 stations in Arizona are used to show that the methods, which are a modification of DeCoursey's scheme for defining regions, improve the fit of estimation data to the model, but that is is necessary to have an independent measure of predictive accuracy, such as that provided by data splitting, to demonstrate improved predictive accuracy. The methods used the complete linkage algorithm for cluster analysis and computed weighted average estimates of hydrologic characteristics at ungaged sites.     

发展中国西部的生态旅游业

生态旅游作为“保护生态”和“发展经济”双赢理念的新兴旅游类型,被西部各省(区、市)所关注并将其定为本地区发展的主导产业.通过对西部地区的生态旅游资源优缺点、市场定位、产品转化及空间布局的分析,初步勾画了西部生态旅游业的发展前景,并提出相应地发展策略.     

Impact of Human Activities on Carbon Dioxide (CO<Subscript>2</Subscript>) Emissions: A Statistical Analysis

Summary The balance of evidence suggests a perceptible human influence on global ecosystems. Human activities are affecting the global ecosystem, some directly and some indirectly. If researchers could clarify the extent to which specific human activities affect global ecosystems, they would be in a much better position to suggest strategies for mitigating against the worst disturbances. Sophisticated statistical analysis can help in interpreting the influence of specific human activities on global ecosystems more carefully. This study aims at identifying significant or influential human activities (i.e. factors) on CO₂ emissions using statistical analyses. The study was conducted for two cases: (i) developed countries and (ii) developing countries. In developed countries, this study identified three influential human activities for CO₂ emissions: (i) combustion of fossil fuels, (ii) population pressure on natural and terrestrial ecosystems, and (iii) land use change. In developing countries, the significant human activities causing an upsurge of CO₂ emissions are: (i) combustion of fossil fuels, (ii) terrestrial ecosystem strength and (iii) land use change. Among these factors, combustion of fossil fuels is the most influential human activity for CO₂ emissions both in developed and developing countries. Regression analysis based on the factor scores indicated that combustion of fossil fuels has significant positive influence on CO₂ emissions in both developed and developing countries. Terrestrial ecosystem strength has a significant negative influence on CO₂ emissions. Land use change and CO₂ emissions are positively related, although regression analysis showed that the influence of land use change on CO₂ emissions was still insignificant. It is anticipated, from the findings of this study, that CO₂ emissions can be reduced by reducing fossil-fuel consumption and switching to alternative energy sources, preserving exiting forests, planting trees on abandoned and degraded forest lands, or by planting trees by social/agroforestry on agricultural lands.     

峨眉山旅游购物品满意度研究

通过问卷调查方式，对峨眉山的伏虎寺、善觉寺、游人中心站、五显岗停车场、清音阁、生态猴区、雷洞坪、金顶等景点的不同性别和不同年龄的游客进行了调查，采用SPSS11对峨眉山旅游购物品满意度综合指标进行了因子分析，提取出影响游客对峨眉山不同类别旅游购物品满意度的主因子。     

我国与发达国家的逆城市化现象对比分析

逆城市化作为城市化发展的趋势，已经在我国东部沿海地区出现，这一现象引起了国内很多学者的关注。在回顾发达国家逆城市化和我国城市化历程的基础上，分析了我国逆城市化和西方逆城市化的差异，对我国的逆城市化发展提出了一系列的建议。     

电厂袋式除尘器滤袋故障分析及处理

以北京高井热电厂为对象,运用故障树分析的方法,对袋式除尘器及滤袋失效进行了故障树分析,并将之演变为成功树,提出了防止滤袋失效的措施。     

GIS网络服务区分析在重庆市主城区夜间施工噪声投诉处置中应用

通过对重庆市主城区夜间施工噪声投诉处置现状分析,以主城区道路交通网络、环境监察机构分布等数据专题建模,采用G IS的网络服务区功能的分析结果,提出车辆调度合理化建议方案,为环境监察部门的夜间施工噪声投诉处置车辆部署调度提供辅助技术支持。     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.