基于GIS的海洋和大气数据管理与可视化研究(英文)

A GIS for ocean applications called “the Xiamen Atmospheric and Oceanographic Data Management and Display System (AODMDS)” has been designed and developed. The system is based on ArcObjects (AO), a component-based GIS development tool. The paper discusses in detail the storage and organization of the atmospheric and oceanographic data, the strategy and methods for the visualization and mapping of oceanographic and atmospheric data, and the implementation of the methods in AODMDS. It also discusses some advanced display control techniques that expand the functions of ArcObjects. One of the techniques is “gradient-fill-style color-map control,” which provides a feasible color-rich display control for all types of raster maps. As a stand-alone desktop GIS system built on AO, AODMDS provides effective data management and powerful mapping and visualization functions for atmospheric and oceanographic data.      

Graphically supported evaluation of mapping techniques used in presenting spatial accessibility

ABSTRACT

Given the rising availability of alternative ways of spatial data mapping, there is a growing need to study their usability. An important aspect here is the diversity of the cartographic visualizations that are used to draw conclusions about the usability of various mapping techniques. In our study we evaluated the usability of six various mapping techniques used in presenting spatial accessibility, taking into consideration four components: effectiveness and efficiency (objective criteria) and graphical attractiveness and user-perceived effectiveness (subjective criteria). Using Internet questionnaires, we conducted a graphical perception experiment with respondents performing tasks on a hypothetical monocentric road network model displayed using mapping techniques of spatial accessibility, differing in the applied parameters of graphical and non-graphical factors influencing effectiveness and efficiency. The results showed that non-graphical factors had a greater influence than the graphical factors. In total usability comparison the three-dimensional (3D) point technique was ranked the highest, while the worst result was achieved by the 3D area technique. The proposed graphical form of study results made it possible to compare the influence of various effectiveness factors, to analyze mapping techniques in terms of considered criteria and finally to present their total usability using a chart inspired by Uhorczak’s typogram.     

A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

ABSTRACT

Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.     

Multi‐sensor Image Maps from SPOT,ERS and JERS

Abstract

Image mapping using data from visible and infrared sensors has, as a major drawback, the frequent cloud cover experienced in many countries. This is one of the main reasons why topographic maps at 1:100,000 scale and larger are often outdated. The results of a study which investigated the possibilities of fusing up‐to‐date spaceborne microwave data with existing images from optical sensors for topographic map updating at a scale of 1:100, 000 are presented in this paper. A key issue researched was the influence of geometric distortions and corrections of remote sensing data on the results of pixel based digital image fusion. After having terrain‐geocoded and radiometrically enhanced imagery from the Landsat, SPOT, ERS‐1 and JERS‐1 satellites, the data were fused applying a variety of colour transformation techniques as well as statistical or arithmetic methods. Initially, the image fusion was implemented using images covering a test site in the north of The Netherlands in order to calibrate specified combinations and techniques in a rather flat area. With the experience gained, the remote sensing data acquired over the research site were processed. The research test site is located in a typical Developing Country in the humid Tropics, on the mountainous south‐west coast of Sumatra in Indonesia. The results of the various applied techniques and image combinations were evaluated with reference to their capability to overcome the cloud cover problem. New combinations of techniques and images were developed as result of an optimisation process. The research produced two prototypes of annotated 1:100,000 scale image maps containing fused, cloud‐free optical/microwave imagery.     

Detecting and mapping slums using open data: a case study in Kenya

ABSTRACT

The worldwide slum population currently stands at over one billion, with substantial growth expected in the coming decades. Traditionally, slums have been mapped using information derived mainly from either physical indicators using remote sensing data, or socio-economic indicators using census data. Each data source on its own provides only a partial view of slums, an issue further compounded by data poverty in less-developed countries. To overcome such issues, this paper explores the fusion of traditional with emerging open data sources and data mining tools to identify additional indicators that can be used to detect and map the presence of slums, map their footprint, and map their evolution. Towards this goal, we develop an indicator database for slums using open sources of physical and socio-economic data that can be used to characterize slum settlements. Using this database, we then leverage data mining techniques to identify the most suitable combination of these indicators for mapping slums. Using three cities in Kenya as test cases, results show that the fusion of these data can improve the mapping accuracy of slums. These results suggest that the proposed approach can provide a viable solution to the emerging challenge of monitoring the growth of slums.     

3D Visualization for the Analysis of Forest Cover Change

Abstract

Visualization techniques have been developed to recreate natural landscapes, but little has been done to investigate their potential for illustrating land cover change using spatio‐temporal data. In this work, remote sensing, geographic information systems (GIS) and visualization techniques were applied to generate realistic computer visualizations depicting the dynamic nature of forested environments. High resolution digital imagery and aerial photography were classified using object‐oriented methods. The resulting classifications, along with preexisting land cover datasets, were used to drive the correct placement of vegetation in the visualized landscape, providing an accurate representation of reality at various points in time. 3D Nature's Visual Nature Studio was used to construct a variety of realistic images and animations depicting forest cover change in two distinct ecological settings. Visualizations from Yellowstone National Park focused on the dramatic impact of the 1988 fire upon the lodgepole pine forest. For a study area in Kansas, visualization techniques were used to explore the continuous human‐land interactions impacting the eastern deciduous forest and tallgrass prairie ecotone between 1941 and 2002. The resulting products demonstrate the flexibility and effectiveness of visualizations for representing spatio‐temporal patterns such as changing forest cover. These geographic visualizations allow users to communicate findings and explore new hypotheses in a clear, concise and effective manner.     

Developments in digital map production and data integration within the British Geological Survey

Abstract

Digital techniques for cartographic data capture and high quality map production have been developed and applied over some 18 years to the mapping of the geoscientific datasets of the British Geological Survey, in particular to the geochemical dataset. Over this period, technological advances and developments in both vector and raster data processing techniques have facilitated high quality map production and the integration and display of multiple datasets. This paper reviews the developments in high quality map production and interpretation of survey information, with particular regard to the Regional Geochemical Atlas Series, through the application of image processing techniques on the display and analysis of multiple datasets.     

Exploratory Analysis of Spatial Data Using Interactive Maps and Data Mining

We present new methods for analyzing geo-referenced statistical data. These methods combine visualization and direct manipulation techniques of exploratory data analysis and algorithms for data mining. The methods have been implemented by integrating two hitherto separate software tools: Descartes for interactive thematic mapping, and the data mining toolbox Kepler. In using these tools, data analysis may proceed as a steady interaction between visual inspiration and insights gained from mathematical–statistical calculations. After introducing the various components of the methods and tools, the paper guides the reader through in-depth examples of using the tools in the context of analysis of urban demographic data. In particular, it is shown how geography-based classifications of urban districts can be related to available thematic characteristics by applying the data mining algorithms classification tree derivation, attribute weighting, and subgroup discovery.     

BinSq: visualizing geographic dot density patterns with gridded maps

ABSTRACT

Dot maps have become a popular way to visualize discrete geographic data. Yet, beyond showing how the data are spatially distributed, dot maps are often visually cluttered in terms of consistency, overlap, and representativeness. Existing clutter reduction techniques like jittering, refinement, distortion, and aggregation also address this issue, but do so by arbitrarily displacing dots from their exact location, removing dots from the map, changing the spatial reference of the map, or reducing its level of detail, respectively. We present BinSq, a novel visualization technique to compare variations in dot density patterns without visual clutter. Based on a careful synthesis of existing clutter reduction techniques, BinSq reduces the wide variety of dot density variations on the map to a representative subset of density intervals that are more distinguishable. The subset is derived from a nested binning operation that introduces order and regularity to the map. Thereafter, a dot prioritization operation improves the representativeness of the map by equalizing visible data values to correspond with the actual data. In this paper, we describe the algorithmic implementation of BinSq, explore its parametric design space, and discuss its capabilities in comparison to six existing clutter reduction techniques for dot maps.     

Blending Aggregation and Selection: Adapting Parallel Coordinates for the Visualization of Large Datasets

Abstract

Many of the traditional data visualization techniques, which proved to be supportive for exploratory analysis of datasets of moderate sizes, fail to fulfil their function when applied to large datasets. There are two approaches to coping with large amounts of data: data selection, when only a portion of data is displayed, and data aggregation, i.e. grouping data items and considering the groups instead of the original data. None of these approaches alone suits the needs of exploratory data analysis, which requires consideration of data on all levels: overall (considering a dataset as a whole), intermediate (viewing and comparing collective characteristics of arbitrary data subsets, or classes), and elementary (accessing individual data items). Therefore, it is necessary to combine these approaches, i.e. build a tool showing the whole set and arbitrarily defined subsets (object classes) in an aggregated way and superimposing this with a representation of arbitrarily selected individual data items.

We have achieved such a combination of approaches by modifying the technique of parallel coordinate plot. These modifications are described and analysed in the paper.     

Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service

Abstract

Currently, many soil erosion studies at local, regional, national or continental scale use models based on the USLE-family approaches. Applications of these models pay little attention to seasonal changes, despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology. This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing. The latter, together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000. This potential service has led to the establishment of a new modelling approach (called the G2 model) based on the inheritance of USLE-family models. The G2 model proposes innovative techniques for the estimation of vegetation and protection factors. The model has been applied in a 14,500 km² study area in SE Europe covering a major part of the basin of the cross-border river, Strymonas. Model results were verified with erosion and sedimentation figures from previous research. The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.     

电子地图可视化质量因素研究与实践

电子地图研究是现代地图学发展方向,它已成为数字制图成果表达主要形式,也是地图服务于社会经济的重要手段.电子地图的可视化效果已经成为评价电子地图产品或者系统的一个重要方面,但事实上电子地图距高质量显示还有很大的距离.本文首先对电子地图的高质量可视化问题进行讨论,进而分析电子地图可视化的质量因素,并对如何保证和提高电子地图...     

China Digital Ocean Prototype System

Abstract

Digital Ocean is a new research domain of Digital Earth. Because of the spatio-temporal, three-dimensional (3D) and intrinsically dynamic nature of ocean data, it is more difficult to make a breakthrough in this domain. The construction of the China Digital Ocean Prototype System (CDOPS) pushes Digital Ocean a step forward from its operation as a mere concept to its achievement as a realistic system. In this paper, the technical framework of the CDOPS is discussed, including its data, function, and application layers. Then, two key technologies are studied in detail that will enable the construction of the 3D ocean environment and the visualization of the ocean model output data. Practical demonstrations show that the CDOPS provides a technical reference for the development of Digital Ocean. This paper is based on an ongoing research project of the development of CDOPS that aims at the facilitation, integration, sharing, accessing, visualization, and use of the ocean data and model computing data from the Digital Earth perspective.     

OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project

ABSTRACT

Nowadays, several research projects show interest in employing volunteered geographic information (VGI) to improve their systems through using up-to-date and detailed data. The European project CAP4Access is one of the successful examples of such international-wide research projects that aims to improve the accessibility of people with restricted mobility using crowdsourced data. In this project, OpenStreetMap (OSM) is used to extend OpenRouteService, a well-known routing platform. However, a basic challenge that this project tackled was the incompleteness of OSM data with regards to certain information that is required for wheelchair accessibility (e.g. sidewalk information, kerb data, etc.). In this article, we present the results of initial assessment of sidewalk data in OSM at the beginning of the project as well as our approach in awareness raising and using tools for tagging accessibility data into OSM database for enriching the sidewalk data completeness. Several experiments have been carried out in different European cities, and discussion on the results of the experiments as well as the lessons learned are provided. The lessons learned provide recommendations that help in organizing better mapping party events in the future. We conclude by reporting on how and to what extent the OSM sidewalk data completeness in these study areas have benefited from the mapping parties by the end of the project.     

Sonifying data uncertainty with sound dimensions

ABSTRACT

The communication of data uncertainty is a crucial problem in data science, information visualization, and geographic information science (GIScience). Effective ways to communicate the uncertainty of data enables data consumers to interpret the data as intended by the producer, reducing the possibilities of misinterpretation. In this article, we report on an empirical investigation of how sound can be used to convey information about data uncertainty in an intuitive way. To answer the research question How intuitive are sound dimensions to communicate uncertainty? we carry out a cognitive experiment, where participants were asked to interpret the certainty/uncertainty level in two sounds A and B (N = 33). We produce sound stimuli by varying sound dimensions, including loudness, duration, location, pitch, register, attack, decay, rate of change, noise, timbre, clarity, order, and harmony. In the stimuli, both synthetic and natural sounds are used to allow comparison. The experiment results identify three sound dimensions (loudness, order, and clarity) as significantly more intuitive to communicate uncertainty, providing guidelines for sonification and information visualization practitioners.     

Mapping solar energy potential of southern India through geospatial technology

Abstract

There is an urgent need for alternate energy resources throughout the world due to the decline in the accessibility of fossil fuels and other conventional resources. In this pursuit, solar energy offers a favourable solution and can easily be converted into energy. There are some solar radiation models which provide point and area-based computations based on the geometry of surface orientation and visible sky. The work comprises the mapping of the solar energy generation potential of southern India to balance the competing and upcoming energy demands. The current work explores and employs the geospatial technology to prepare solar energy potential map to deliver the valuable inputs to the energy sector. The research was supported with satellite-derived global solar radiation and temperature data over a span of one year. The values obtained from the spatial radiation models were processed to get the improved datasets. Suitable cartographic visualization of solar insolation parameters was presented for enhanced understanding of the spatial distribution of solar potential, PV output, air-temperature or other spatial characteristics. Twelve monthly maps, an annual map, and another map with the location of the measurement stations were created to unravel a number of unturned mystery about the understanding of solar resource diversity in a spatial context, which will support our decisions & arguments for any kind business.     

基于《测绘工程》期刊的测绘技术动态可视化分析

结合文献计量方法与可视化技术,以《测绘工程》期刊1996-2015年间发表的论文为数据源,进行关键词、摘要的统计分析,并将统计结果以统计图表和地图的形式展现,直观地反映工程技术实践类测绘研究的研究热点以及核心研究区的地理分布。从研究结果可以得出我国工程技术实践类测绘研究每年的研究热点都不相同,学科的发展地理分布较不平衡,一定程度上反映我国工程技术实践类测绘研究的水平。     

Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers

Abstract

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (ROC) curve and pair t-test. The results showed that the area under ROC of Random Subspace ensemble model performed better than the other models based on statistical indicators. Comparing pair t-test with Area Under Curve values showed a slight difference of about 1%. Therefore ensemble techniques had significantly improved the performance of the base classifier. However, the performances might vary according to geographic locations. It is concluded that the machine learning classifiers combined with remotely sensed data and GIS is promising for malaria vulnerability mapping, and the derived maps can be used as a fundamental basis for programmes on spatial disease control.