On Methods of Teaching Geography

This study investigates the location and varying spatiality of questions in geography textbooks. The results show that study questions posed in page margins address the three components of spatial thinking—concepts of space, using tools of representation, and processes of reasoning—more than questions in other locations within the text. Three practices are particularly recommended to help students develop spatial thinking skills through answering textbook questions: (1) utilize page-margin questions; (2) select questions around spatial representations; and (3) focus on higher-level cognitive processes.     

Spatial Thinking Concepts in Early Grade-Level Geography Standards

ABSTRACT

Research in the cognition and learning sciences has demonstrated that the human brain contains basic structures whose functions are to perform a variety of specific spatial reasoning tasks and that children are capable of learning basic spatial concepts at an early age. There has been a call from within geography to recognize research on spatial cognition in a meaningful way in primary school curriculum. This article utilizes the spatial thinking taxonomy proposed by Gersmehl and Gersmehl (2006) to examine to the extent to which spatial thinking concepts are being practiced in U.S. schools. The National Geography Standards and forty-nine state social studies or geography standards are examined. Using standards as a measure of geography content, it is concluded that while some of spatial thinking concepts appear often in curriculum, others are largely absent. Designing geography standards that address the findings of spatial cognition research may serve as a means of improving geography instruction.     

An Evaluation of University World Geography Textbook Questions for Components of Spatial Thinking

ABSTRACT

Geography textbooks contain chapter or review questions that may engage students in spatial thinking. This research used Jo and Bednarz’s (2009) Taxonomy of Spatial Thinking to evaluate the percentage of spatial thinking questions in four university-level world geography course textbooks. The results from this study were then compared to the findings in Jo and Bednarz’s (2009) analysis of high school geography textbooks. Thirty-five percent of university level textbook questions are related to spatial thinking compared to twenty-four percent in high school geography textbooks. The results provide information useful to stakeholders, such as teachers, administrators, and textbook writers and may help these stakeholders to consciously incorporate the three components of spatial thinking as defined by the National Research Council (2006). A simplified taxonomy for identifying spatial-thinking concepts in textbook review questions is also suggested.     

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.     

机载多光谱影像语义分割模型在农田防护林提取中的应用

农田防护林是农田生态系统的屏障,其健康状况的监测与评估在我国北方农田林网管理中尤为重要。本文以新疆生产建设兵团第三师51团为研究区,使用复合翼无人机CW-20搭载Micro MCA12 Snap多光谱相机获取农田防护林的多光谱影像,经辐射校正、裁剪等预处理,通过优选有效特征和模型比较,提出农田防护林提取的有效方法。首先,基于原始12波段,依据相关性系数矩阵和最佳指数因子（Optimum Index Factor,OIF）选取最优3波段和植被指数特征进行组合,构建8种农田防护林提取方案;然后,通过建立语义分割Deeplabv3+模型进行精度评价,得到最优3波段组合6（波长710 nm）、8（波长800 nm）、 11（波长900 nm）波段为最佳特征组合;最后,以最优3波段为基础,将Deeplabv3+模型与U-Net、ENVINet5模型进行对比分析。结果表明：Deeplabv3+模型能够更深层次的挖掘光谱中潜在的信息,相比其他模型,能够较好地处理正负样本不均衡问题,获得最高MIoU值85.54%,比U-Net、ENVINet5的MIoU值则分别高出21.21%、27.19%。该研究结果可为基于多光谱遥感影像的语义分割在农田防护林提取及健康状况监测的应用提供借鉴和参考。     

案例推理的制图综合应用背景与方法

从挖掘和利用经验数据的角度出发,提出了基于案例推理的智能化制图综合方法。首先探讨了基于算法和模型的自动综合实现方式所面临的瓶颈,并提出了采用制图综合案例推理辅助制图综合的思想;然后设计了制图综合专家案例的三元表示方法,以及获取、使用和管理流程;最后对实现该基于案例推理的智能化制图综合方法的五个关键性步骤进行了分析,并深入阐述该方法的原理和可行性,提出了基于案例推理的智能综合辅助系统架构。     

基于语义规则的高分辨率影像道路交叉口提取方法

为了准确提取遥感影像上道路交叉口目标,提出一种利用特征语义规则从高分辨率影像上提取道路交叉口的方法。该算法构建交叉口模型时将其视为由同质区域像素集合及区域轮廓边界构成的面对象,提取过程分为两步:1)利用辐射、纹理特征语义匹配提取交叉口候选区域;2)通过几何特征语义匹配筛选候选区域、识别交叉口属性。利用多源遥感影像对算法正确性及合理性进行验证,结果表明:算法能准确、完整地提取道路交叉口,可为影像道路网构建提供辅助信息。     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

地理空间元数据关联网络的构建

利用资源描述框架（RDF）设计地理空间元数据关联模型,根据地理空间元数据之间的语义关系和语义相关度的计算,以构建以元数据为节点、元数据之间的语义关系为边、语义相关度为权重的关联网络。在这一网络中,一个节点是一个地理空间元数据的资源描述图,包含属性特征（数据来源、空间特征、时间特征、内容）及其关系特征（元数据之间的语义关系、语义相关度）。实验及其分析表明,地理空间元数据关联网络可以有效地支持地理空间数据语义关联检索、推荐等应用,这与传统的基于关键词的元数据检索方式相比,具有更高的准确度。     

面向LOD的地质资料机构知识库语义扩展方法研究

针对地质资料信息服务过程中,存在信息孤岛和数据共享不够等问题,提出了面向开放关联数据LOD的地质资料机构知识库语义扩展方法,并对方法的框架和关键技术进行了研究。首先,基于DSpace构建地质资料机构知识库,自动实现资源描述框架RDF的存储与转化,与LOD形成统一的元数据描述标准。其次,构建地质资料数据的关联模型,明确数据间的语义关系。最后,采用D2RQ平台实现地质资料机构知识库与LOD数据集的语义关联。该方法将进一步加快语义化地质资料信息服务的步伐。