A loosely integrated data configuration strategy for web-based participatory modeling

Participatory modeling is an important approach for solving complex geo-problems from a comprehensive and holistic viewpoint, and it brings together stakeholders from multiple disciplines to provide diverse resources, including modeling, data fields and computational assets. Data configuration work (e.g., preparing appropriate input data for model execution, connecting a model’s output to the input data of another model) is important for constructing and executing a participatory modeling task. Most current data configuration methods depend on the model integration logic, which presents a challenge when adding new modeling resources into a model to dynamically create and execute new modeling tasks. To support the construction of participatory modeling tasks in a web environment, this article proposes a loosely integrated data configuration strategy for decoupling data configuration work from the execution process of a participatory modeling task. A model service controller is designed for model input/output (I/O) configuration, and a data service controller is designed for data access configuration. These two controllers can help modelers link the data I/O demands of a model-service with the appropriate data-services; thus, different modeling instances can be dynamically joined to a participatory modeling task and executed without reconstructing the original data configuration settings. A prototype participatory modeling system is proposed to demonstrate the flexibility and feasibility of the proposed method using an experimental modeling case. The results show that the proposed data configuration strategy supports the integration of different model-services based on the data dependency relationships and that the complexity and difficulty in configuring data for a participatory modeling tasks in the web environment are minimized.     

A comprehensive optimization strategy for real-time spatial feature sharing and visual analytics in cyberinfrastructure

For geospatial cyberinfrastructure-enabled web services, the ability of rapidly transmitting and sharing spatial data over the Internet plays a critical role to meet the demands of real-time change detection, response and decision-making. Especially for vector datasets which serve as irreplaceable and concrete material in data-driven geospatial applications, their rich geometry and property information facilitates the development of interactive, efficient and intelligent data analysis and visualization applications. However, the big-data issues of vector datasets have hindered their wide adoption in web services. In this research, we propose a comprehensive optimization strategy to enhance the performance of vector data transmitting and processing. This strategy combines: (1) pre- and on-the-fly generalization, which automatically determines proper simplification level through the introduction of appropriate distance tolerance speed up simplification efficiency; (2) a progressive attribute transmission method to reduce data size and, therefore, the service response time; (3) compressed data transmission and dynamic adoption of a compression method to maximize the service efficiency under different computing and network environments. A cyberinfrastructure web portal was developed for implementing the proposed technologies. After applying our optimization strategies, substantial performance enhancement is achieved. We expect this work to facilitate real-time spatial feature sharing, visual analytics and decision-making.     

露天矿采场时空数据模型的构建与应用

以地理信息系统技术为核心,建立露天矿采场时空数据库,通过构建采场的时空数据库,动态描述露天矿生产的剥、采、排过程,为露天开采的规划、设计、生产调度管理的优化预控决策提供全方位的空间信息支持。提出考虑台阶特征的不规则三角网（TIN）的露天矿采场三维建模理论与方法,采用基于基态修正和时间快照模型构建露天矿采场时空数据库,通过地理信息系统和虚拟现实技术,实现开采过程的回放和生产计划的超前演示,为数字露天矿建设构建基础的地理空间信息框架与平台。     

面向蓝藻的洱海气象监测站布局方法

针对洱海流域蓝藻预警研究所需的气象数据不足的问题,提出洱海气象监测站优化布局方法,采用环境影响统计概念模型分析了对环境产生影响的主要经济因素,根据水体污染指标将洱海各个行政区分为高污染区和低污染区,再考虑汇入洱海支流是否流经高污染区,认为在高污染区支流汇入口是蓝藻发生概率较高的区域。依据2个主要参数:区域污染程度和支流入口,部署气象监测站,该监测站能为洱海蓝藻预警研究提供必要、准确和可靠的水面气温、降雨量以及日照长度等气象参数,将对水环境的监测和研究具有重大意义和广泛应用前景。     

补偿海空重力测量动态效应剩余影响的通用模型

针对目前作业过程中普遍存在的测量动态环境效应剩余影响问题,在深入分析海空重力测量误差源形成机理及其变化特性基础上,提出了一种适用于补偿各类海空重力仪动态效应剩余影响的通用模型;研究探讨了通用模型形式优选和模型参数估计问题,将基于信息论的Akaike信息量准则引入通用模型表达式的优选过程,提出应用互相关分析方法对模型参数进行估计,在双重约束下构建了补偿动态效应剩余影响的优化模型。使用典型动态环境下的海面重力观测数据对该方法的有效性进行了验证,结果显示,海洋重力测量成果内符合精度从原先的±9.35×10^-5 m/s²大幅提升到±1.01×10^-5 m/s²,充分体现了本文方法和模型对消除高动态测量环境效应影响的优良特性。     

Evaluation of digital elevation models for delineation of hydrological response units in a Himalayan watershed

This study reports results from evaluation of the quality of digital elevation model (DEM) from four sources viz. topographic map (1:50,000), Shuttle Radar Topographic Mission (SRTM) (90 m), optical stereo pair from ASTER (15 m) and CARTOSAT (2.5 m) and their use in derivation of hydrological response units (HRUs) in Sitla Rao watershed (North India). The HRUs were derived using water storage capacity and slope to produce surface runoff zones. The DEMs were evaluated on elevation accuracy and representation of morphometric features. The DEM derived from optical stereo pairs (ASTER and CARTOSAT) provided higher vertical accuracies than the SRTM and topographic map-based DEM. The SRTM with a coarse resolution of 90 m provided vertical accuracy but better morphometry compared to topographic map. The HRU maps derived from the fine resolution DEM (ASTER and CARTOSAT) were more detailed but did not provide much advantage for hydrological studies at the scale of Sitla Rao watershed (5800 ha).     

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.     

The habitation Model Trend Calculation (MTC): A new effective tool for predictive modeling in archaeology

ABSTRACT

The aim of this paper is to create and present a new archaeological predictive model via GIS, incorporating what archaeologists consider the most important criterion absent of similar past models, that of critical thinking. The new model suggested in this paper is named habitation Model Trend Calculation (MTC) and is not only integrates the archaeological questions with a critical view, but it can be easily adjusted, according to the conditions or the questions concerning the archaeological community. Furthermore, it uses new topographical and geomorphological indexes such as Topographical Index (TPI), Hillslope and Landform Classification that give a new sense of the topographical and geomorphological characteristics of the examined area; therefore this model is a more powerful tool compared to older models that did not use new topographical and geomorphological indexes. The success of the created model is checked as a case study in the region of Messenia, Greece during the Mycenaean era. The region of Messenia is considered as one of the most important Mycenaean regions of Greece due to the great number and the importance of Mycenaean sites identified. For the present paper, 140 habitation sites were divided into four hierarchical categories (centers, large villages, villages, and farms) based on the extent and the plurality of the tholos tombs that exist in the broader region and according to the hierarchical categorization used by the archaeologists who have studied the area. The new predictive model presented in this work can assist in solving a series of criticisms that have been expressed in the previous studies regarding such models. Additionally, in the case of Mycenaean Messenia, the model shows excellent results in relation to the habitats of the time.