To The National Map and Beyond

Scientific understanding, technology, and social, economic, and environmental conditions have driven a rapidly changing demand for geographic information, both digital and analog. For more than a decade, the U.S. Geological Survey (USGS) has been developing innovative partnerships with other government agencies and private industry to produce and distribute geographic information efficiently; increase activities in remote sensing to ensure ongoing monitoring of the land surface; and develop new understanding of the causes and consequences of land surface change. These activities are now contributing to a more robust set of geographic information called The National Map (TNM). The National Map is designed to provide an up-to-date, seamless, horizontally and vertically integrated set of basic digital geographic data, a frequent monitoring of changes on the land surface, and an understanding of the condition of the Earth's surface and many of the processes that shape it. The USGS has reorganized its National Mapping Program into three programs to address the continuum of scientific activities—describing (mapping), monitoring, understanding, modeling, and predicting. The Cooperative Topographic Mapping Program focuses primarily on the mapping and revision aspects of TNM. The National Map also includes results from the Land Remote Sensing and Geographic Analysis and Monitoring Programs that provide continual updates, new insights, and analytical tools. The National Map is valuable as a framework for current research, management, and operational activities. It also provides a critical framework for the development of distributed, spatially enabled decision support systems.     

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.     

虚拟地理环境的地理学语言特征

在比较分析传统地图和GIS的地理学语言功能基础上,指出了虚拟地理环境对现实世界抽象表达的多维特征、多视点和多重细节的多模态可视表现、多种自然交互方式和跨时间、空间与尺度的地理协同以及多感知的空间认知能力等。说明虚拟地理环境提供了一种综合表意系统和更接近自然的多感知的空间认知能力,使得从现实世界到人类大脑的信息带宽最大化,提供了超越现实的抽象表示与解析理解能力,达到了增强现实的目的。虚拟地理环境作为继GIS之后新一代地理学语言的显著特征是以用户为中心、提供最接近人类自然的交流方式与表达形式。     

基于EPS平台的基础地理信息数据重要要素的更新

EPS地理信息工作站集信息化测绘生产技术体系、工艺流程、生产工具、数据管理于一体,是GIS技术与CAD技术高度融合的产品,通过该平台可以实现数据航测立体采集、数据采编、入库一体化作业。本文通过探讨基础地理信息数据重要要素的更新,提出了应用EPS地理信息工作站进行重要要素更新的技术方法。     

国土资源信息核心元数据的研究

国土资源信息核心元数据是建立运行在国土资源数据交换网络上的国土资源信息目录的基础和重要组成部分，也是目前数字国土工程中实现国土资源数据共享的重要途径。分析了国内外地理信息元数据标准的发展状况，根据国土资源信息核心元数据确定的原则和描述要求，确定了国土资源信息核心元数据的主要内容，提出了以通用建模语言UML类图作为国土资源信息核心元数据的结构设计，用数据字典详细定义核心元数据的组成，以构成国土资源信息核心元数据的完整描述的设计方法。     

Representing Complex Geographic Phenomena in GIS

Conventionally, spatial data models have been designed according to object- or field-based conceptualizations of reality. Conceptualization of complex geographic phenomena that have both object- and field-like properties, such as wildfire and precipitation, has not yet been incorporated into GIS data models. To this end, a new conceptual framework is proposed in this research for organizing data about such complex geographic phenomena in a GIS as a hierarchy of events, processes, and states. In this framework, discrete objects are used to show how events and processes progress in space and time, and fields are used to model how states of geographic themes vary in a space-time frame. Precipitation is used to demonstrate the construction and application of the proposed framework with digital precipitation data from April 15 to May 22, 1998, for the state of Oklahoma, U.S.A. With the proposed framework, two sets of algorithms have been developed. One set automatically assembles precipitation events and processes from the data and stores the precipitation data in the hierarchy of events, processes, and states, so that attributes about events, processes, and states are readily available for information query. The other set of algorithms computes information about the spatio-temporal behavior and interaction of events and processes. The proposed approach greatly enhances support for complex spatio-temporal queries on the behavior and relationships of events and processes.     

Deeply integrating Linked Data with Geographic Information Systems

The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.     

The Role of Knowledge Representation in Geographic Knowledge Discovery: A Case Study

With the advent of massive, heterogeneous geographic datasets, data mining and knowledge discovery in databases (KDD) have become important tools in deriving meaningful information from these data. In this paper, we discuss how knowledge representation can be employed to significantly enhance the power of the knowledge discovery process to uncover patterns and relationships. We suggest that geographic data models that support knowledge discovery must represent both observational data and derived knowledge. In addition, knowledge representation in the context of KDD must support the iterative and interactive nature of the knowledge discovery process to enable the analyst to iteratively apply, and revise the parameters of, specific analytical techniques. Our approach to knowledge representation and discovery is demonstrated through a case study that focuses on the identification and analysis of storms and other related climate phenomena embedded within a spatio‐temporal data set of meteorological observations.     

Environmental public health applications using remotely sensed data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.     

基于ArcGIS的规划成果信息符号库的创建探讨

地图符号是地理信息系统表达空间信息的语言单位,通过符号参量传输表达不同的信息认知内容;符号库是符号描述信息的集合,本文结合实际工作提出一套基于ArcGIS规划数据成果信息符号库的创建方案。     

Use of a Three-Domain Repesentation to Enhance GIS Support for Complex Spatiotemporal Queries

As the development of Geographic Information Systems (GIS) proceeds to advanced scientific and societal applications, there is an emerging need to enhance GIS support for complex spatiotemporal queries. Dynamic GIS representations (as opposed to static, map-based representations) that can integrate proper data elements in the production of geographic information are required. This paper demonstrates the use of a three-domain representation that facilitates compilation of higher-level information (such as frequency and rate) from preliminary data records (such as time and location) stored in a database. The three-domain representation is compared with snapshot, space-time composite, and spatiotemporal object models using a sample data set for forest transitions. While the three-domain representation is a normalization of these data models, it offers a conceptual alternative that enables GIS to represent spatiotemporal behaviors of geographic entities, in addition to entities as well as histories at locations as emphasized in most GIS data models. The comparison shows that the three-domain representation has combined the strengths of the space-time composite and spatiotemporal object models. Moreover, it enables aggregations of analytical use along with dynamic mappings between geographic concepts and locations, a distinct capability that takes GIS query processing beyond the level of information support offered by static map-based data models.     

Extending F‐Perceptory to model fuzzy objects with composite geometries for GIS

When analyzing spatial issues, geographers are often confronted with many problems with regard to the imprecision of the available information. It is necessary to develop representation and design methods which are suited to imprecise spatiotemporal data. This led to the recent proposal of the F‐Perceptory approach. F‐Perceptory models fuzzy primitive geometries that are appropriate in representing homogeneous regions. However, the real world often contains cases that are much more complex, describing geographic features with composite structures such as a geometry aggregation or combination. From a conceptual point of view, these cases have not yet been managed with F‐Perceptory. This article proposes modeling fuzzy geographic objects with composite geometries, by extending the pictographic language of F‐Perceptory and its mapping to the Unified Modeling Language (UML) necessary to manage them in object/relational databases. Until now, the most commonly used object modeling tools have not considered imprecise data. The extended F‐Perceptory is implemented under a UML‐based modeling tool in order to support users in fuzzy conceptual data modeling. In addition, in order to properly define the related database design, an automatic derivation process is implemented to generate the fuzzy database model.     

应用描述词汇约简的OGC地理信息服务演绎推理

针对OGC地理信息服务在地理空间知识的有效组织和表达方面的能力比较弱,缺乏对服务信息的描述,数据丰富而知识缺乏,致使已有的数据在知识的表示和检索上存在缺陷,通过引入地理本体,按照基础地理信息要素分类,对地理信息服务的关键描述词汇进行提取,构建了地理信息服务本体库和实例数据库,应用粗糙集理论建立关键描述词汇约简模型,基于该模型实现了对地理信息服务实例数据库进行知识约简,形成最优实例数据库。最后应用地理信息服务本体库和实例数据库,基于演绎推理模型开发实例原型系统,实现了对地理信息服务对象的语义检索和推理,并通过试验从查全率和查准率两个指标验证了该方法的可行性、有效性和准确性。     

地理空间元数据理论体系研究

地理空间元数据是地理数据的描述性信息,它对地理数据的标识、空间范围、数据质量、参照系等特征进行描述与说明。地理空间元数据的理论问题,是研究制定地理空间元数据标准的重要依据,是对地理空间元数据进行深入研究和应用的基础。阐述地理空间元数据理论的研究现状,分析目前地理空间元数据理论体系研究存在的问题,提出对地理现象抽象过程的四层次空间划分,在四层次空间划分理论基础上建立一个地理空间元数据理论体系的研究框架。     

Assessing Similarity of Geographic Processes and Events

The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of geographic phenomena. Much of the new data includes regular snapshots of the environment. Comparison of these snapshots can provide information about changes to the phenomena of interest. However, conventional GIS data models and analytical tools lack capabilities to adequately handle massive multidimensional data. One of the fundamental tools necessary to meet such challenges is query support to retrieve and summarize data according to dynamic geographic phenomena, such as geographic events and processes, of interest. Such query support depends upon abilities to assess spatiotemporal similarity so that data representing geographic events that exhibit the spatiotemporal characteristics of interest can be identified in a GIS database. To this end, this paper introduces a method to assess similarity of geographic events and processes (such as storms) based on their spatiotemporal characteristics (such as distribution of precipitation). We developed six indices to capture static and dynamic characteristics of geographic events and applied the Dynamic Time Warping method to temporal sequences of the six indices to examine the similarity among these events. With a case study, we demonstrated the proposed indices and method capable of comparing spatiotemporal characteristics of events as recorded in a GIS database and categorizing spatiotemporal data into groups of events according to their behavior in space and time.     

A spatially explicit reinforcement learning model for geographic knowledge graph summarization

Web‐scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non‐spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned.