Large Geospatial Datasets Research Articles

Graphically Hearing: Enhancing Understanding of Geospatial Data through an Integrated Auditory and Visual Experience.

Effective presentation of data is critical to a users understanding of it. In this manuscript, we explore research challenges associated with presenting large geospatial datasets through a multimodal experience. We also suggest an interaction schema that enhances users cognition of geographic information through a user-driven display that visualizes and sonifies geospatial data.

Architecture of a spatial data service system for statistical analysis and visualization of regional climate changes

The use of large geospatial datasets in climate change studies requires the development of a set of Spatial Data Infrastructure (SDI) elements, including geoprocessing and cartographical visualization web services. This paper presents the architecture of a geospatial OGC web service system as an integral part of a virtual research environment (VRE) general architecture for statistical processing and visualization of meteorological and climatic data. The architecture is a set of interconnected standalone SDI nodes with corresponding data storage systems. Each node runs a specialized software, such as a geoportal, cartographical web services (WMS/WFS), a metadata catalog, and a MySQL database of technical metadata describing geospatial datasets available for the node. It also contains geospatial data processing services (WPS) based on a modular computing backend realizing statistical processing functionality and, thus, providing analysis of large datasets with the results of visualization and export into files of standard formats (XML, binary, etc.). Some cartographical web services have been developed in a system’s prototype to provide capabilities to work with raster and vector geospatial data based on OGC web services. The distributed architecture presented allows easy addition of new nodes, computing and data storage systems, and provides a solid computational infrastructure for regional climate change studies based on modern Web and GIS technologies.

The VI-Suite: a set of environmental analysis tools with geospatial data applications

BackgroundThe VI-Suite is a free and open-source addon for the 3D content creation application Blender, developed primarily as a tool for the contextual and performative analysis of buildings. Its functionality has grown from simple, static lighting analysis to fully parametric lighting, shadowing, and building energy analyses. It adopts a flexible, mesh geometry based approach to the specification of calculation points and this has made it suitable for certain types of 3D geospatial analyses and data visualisation.ResultsAs this is the first academic paper to discuss the VI-Suite, a history of its development is presented along with a review of its capabilities of relevance to geospatial analysis. As the VI-Suite combines the functionality of 3D design software with performance simulation, some of the benefits of this combination are discussed including aspects that make it suitable for the processing and analysis of potentially large geospatial datasets. Example use cases with a 3D city model of the Hague are used to demonstrate some of the geospatial workflows possible and some of the result visualisation options.ConclusionsThe free and open-source nature of the VI-Suite, combined with the use of Blender mesh geometry to define calculation points, has encouraged usage scenarios not originally intended by the authors, for example large scale urban shadow and radiation analyses. The flexibility inherent in this mesh based approach enables the analysis of large geospatial datasets by giving the user refined control over the distribution of calculation points within the model. The integration of GIS analysis into a digital design package such as Blender offers advanced geometry/material editing and specification, provides tools such as ray casting and BVH tree generation to speed up the simulation of large datasets, and enhanced visualisation of GIS simulation data including animated city fly-throughs and high quality image production. The VI-Suite is part of a completely open-source tool chain and contributions from the community are welcome to further enhance its current geospatial data capabilities.

Development of Distributed Research Center for analysis of regional climatic and environmental changes

We present an approach and first results of a collaborative project being carried out by a joint team of researchers from the Institute of Monitoring of Climatic and Ecological Systems, Russia and Earth Systems Research Center UNH, USA. Its main objective is development of a hardware and software platform prototype of a Distributed Research Center (DRC) for monitoring and projecting of regional climatic and environmental changes in the Northern extratropical areas. The DRC should provide the specialists working in climate related sciences and decision-makers with accurate and detailed climatic characteristics for the selected area and reliable and affordable tools for their in-depth statistical analysis and studies of the effects of climate change. Within the framework of the project, new approaches to cloud processing and analysis of large geospatial datasets (big geospatial data) inherent to climate change studies are developed and deployed on technical platforms of both institutions. We discuss here the state of the art in this domain, describe web based information-computational systems developed by the partners, justify the methods chosen to reach the project goal, and briefly list the results obtained so far.

Interactive visual cluster detection in large geospatial datasets based on dynamic density volume visualization

The emerging spatial big data (e.g. detailed spatial trajectories, geo-referenced social media data) provide tremendous opportunities for GIScientists and geographers. However, their large volume also poses challenges to existing spatial data analytical techniques (including visual analytical techniques). This article presents an interactive visual approach to detect clusters from those emerging data sets based on dynamic density volume visualization in a three-dimensional space (two spatial dimensions plus a third temporal or thematic dimension of interest). Cluster can be visually discovered through dynamic adjustment of density to colour/opacity mapping and extracted through flexible selection tools. The approach was tested on a large simulated data-set and a spatial trajectory data-set. The results show that the approach can overcome the visual clotting problem in traditional visualization tools caused by large data volume and facilitate the involvement of domain knowledge in analysis. It can effectively support visual cluster detection in the emerging large geospatial data sets.

A modular software architecture for processing of big geospatial data in the cloud

In this paper we propose a software architecture that allows for processing of large geospatial data sets in the cloud. Our system is modular and flexible and supports multiple algorithm design paradigms such as MapReduce, in-memory computing or agent-based programming. It contains a web-based user interface where domain experts (e.g. GIS analysts or urban planners) can define high-level processing workflows using a domain-specific language (DSL). The workflows are passed through a number of components including a parser, interpreter, and a service called job manager. These components use declarative and procedural knowledge encoded in rules to generate a processing chain specifying the execution of the workflows on a given cloud infrastructure according to the constraints defined by the user. The job manager evaluates this chain, spawns processing services in the cloud and monitors them. The services communicate with each other through a distributed file system that is scalable and fault-tolerant. Compared to previous work describing cloud infrastructures and architectures we focus on the processing of big heterogeneous geospatial data. In addition to that, we do not rely on only one specific programming model or a certain cloud infrastructure but support several ones. Combined with the possibility to control the processing through DSL-based workflows, this makes our architecture very flexible and configurable. We do not only see the cloud as a means to store and distribute large data sets but also as a way to harness the processing power of distributed computing environments for large-volume geospatial data sets. The proposed architecture design has been developed for the IQmulus research project funded by the European Commission. The paper concludes with the evaluation results from applying our solution to two example workflows from this project.

Transmission of Vector Mapped Data over the World Wide Web

Within distributed computing environments, access to very large geospatial datasets often suffers from slow or unreliable network connections. To allow users to start working with a partially delivered dataset, progressive transmission methods are a viable solution. While incremental and progressive methods have been applied successfully to the transmission of raster images over the World Wide Web, and, in the form of prototypes, of triangular meshes, the transmission of vector map datasets has lacked a similar attention. This paper introduces a solution to the progressive transmission of vector map data that allows users to apply analytical GIS methods to partially transmitted data sets. The architecture follows a client-server model with multiple map representations at the server side, and a thin client that compiles transmitted increments into a topologically consistent format. This paper describes the concepts, develops an architecture, and discusses implementation concerns.

Pattern-based Regionalization of Large Geospatial Datasets Using Complex Object-based Image Analysis

Abstract Pattern-based regionalization – spatial classification of an image into sub-regions characterized by relatively stationary patterns of pixel values – is of significant interest for conservation, planing, as well as for academic research. A technique called the complex object-based image analysis (COBIA) is particularly well-suited for pattern-based regionalization of very large spatial datasets. In COBIA image is subdivided into a regular grid of local blocks of pixels (complex objects) at minimal computational cost. Further analysis is performed on those blocks which represent local patterns of pixel-based variable. A variant of COBIA presented here works on pixel-classified images, uses a histogram of co-occurrence pattern features as block attribute, and utilizes the Jensen-Shannon divergence to measure a distance between any two local patterns. In this paper the COBIA concept is utilized for unsupervised regionalization of land cover dataset (pixel-classified Landsat images) into landscape types – characteristic patterns of different land covers. This exploratory technique identifies and delineates landscape types using a combination of segmentation of a grid of local patterns with clustering of the segments. A test site with 3 . 5 × 10 8 pixels is regionalized in just few minutes using a standard desktop computer. Computational efficiency of presented approach allows for carrying out regionalizations of various high resolution spatial datasets on continental or global scales.

A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations

A novel geospatial tool box named Geospatial Simulation (GeoSim) has been developed, which can be used to manage point-based model simulations at multiple locations using geospatial data within a geographic information system (GIS). The objectives of this paper were to describe GeoSim and demonstrate its use. GeoSim has been developed as a plug-in for Quantum GIS, and both of these software programs are open-source and freely available. An important feature of GeoSim is its model-independent nature, meaning any point-based simulation model that uses ASCII files for input and output can be managed spatially. GeoSim facilitates the transfer of geospatial data from the GIS database to the model input files and from the model output files back to the GIS database. GeoSim presently includes six software tools, each with a graphical user interface. A case study demonstrates the use of GeoSim for processing geospatial data layers at a field site, conducting spatial model simulations, and optimizing model parameters for site-specific conditions. Two cropping system models, AquaCrop and the DSSAT Cropping System Model, were implemented to simulate seed cotton yield in response to irrigation management, nitrogen management, and soil texture variability for a 14 ha study area near Lamesa, Texas. Geoprocessing tools within GeoSim were able to summarize 5592 data points within 405 polygon features in 3.8 s. Simulation tools were able to swap 33,316 and 44,550 parameters values to complete 405 spatial simulations with the AquaCrop and DSSAT models in 112 s and 398 s, respectively. These results demonstrate the utility of GeoSim for summarizing large geospatial data sets and transferring the data to the file formats of multiple models. Simulation duration was increased as compared to stand-alone model simulations without parameter swapping, which may be problematic for applications requiring large numbers of simulations. The flexible design of GeoSim is intended to support spatial modeling exercises for a variety of models and environmental applications.

Pioneering GML Deployment for NSDI — Case Study of USTIGER/GML

The National Spatial Data Infrastructure (NSDI) is defined as the technologies, policies and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors and the academic community. The US Census Bureau is the federal agency lead for administrative units data, one of the seven data themes identified by the NSDI framework. The administrative unit is a unit with administrative responsibilities. These units are organized as nodes/lines/areas feature data. The OpenGIS Geography Markup Language (GML) is the XML grammar to express the geographic features. This study at the US Census Bureau investigates how the general-purpose GML standard could be leveraged and extended to describe the most comprehensive geographic dataset with national coverage in the US. Challenges and problems in dealing with data volume, GML document structure, GML schema design and GML document naming are analyzed, followed by proposed solutions proven for feasibility. Our results show that one key point in making a successful GML deployment for NSDI is to reflect the characteristics of the geographic data through a carefully designed GML schema, structure and organization. The lessons learned may be useful to others transforming NSDI framework data and other large geospatial datasets into GML structures.

Multicriteria Spatial Decision Analysis in Web GIS Environment

Internet, a client/server system, is a perfect means of GIS data accessing, analyzing and transmission. The World Wide Web, FTP (file transfer protocol) and HTTP programs make it convenient to access and transfer data files across the Internet. Using Internet for GIS makes it easy access to acquire GIS data from diverse data sources in the distributed environment. The geospatial multicriteria decision analysis in a client/server environment is an important and challenging task for the GIS community because of narrow Internet bandwidth for large geospatial data sets. In the present paper, we are developing a multicriteria decision analysis tool for spatial decision making in the web GIS environment. The developed system has been demonstrated for biodiversity conservation and priorities. An attempt has been made to generate the alternative decisions based on priority vectors. The multicriteria technique of Analytic Hierarchy Process (AHP) is used to derive the eigen vectors with the given multiple constraints of conflicting criteria and aims at selecting optimal alternative from the available sets. However, the evaluation recognizes the importance of expert knowledge when assigning the weights for the best spatial priorities. Comparing within classes and alternatives using judgment and decision matrix is based on Saaty's Pairwise Comparison. The Multicriteria Spatial Decision Support System (MC-SDSS) software development uses ASP, ArcIMS 9.0, ArcSDE9.0 and Oracle 9i data server in the web GIS environment. The database organization of spatial and non-spatial data is done in the RDBMS environment using ArcSDE and Oracle 9i data server.

Visual Data Mining in Large Geospatial Point Sets

Visual data-mining techniques have proven valuable in exploratory data analysis, and they have strong potential in the exploration of large databases. Detecting interesting local patterns in large data sets is a key research challenge. Particularly challenging today is finding and deploying efficient and scalable visualization strategies for exploring large geospatial data sets. One way is to share ideas from the statistics and machine-learning disciplines with ideas and methods from the information and geo-visualization disciplines. PixelMaps in the Waldo system demonstrates how data mining can be successfully integrated with interactive visualization. The increasing scale and complexity of data analysis problems require tighter integration of interactive geospatial data visualization with statistical data-mining algorithms.

Progressive Transmission of Vector Map Data over the World Wide Web

Within distributed computing environments, access to very large geospatial datasets often suffers from slow or unreliable network connections. To allow users to start working with a partially delivered dataset, progressive transmission methods are a viable solution. While incremental and progressive methods have been applied successfully to the transmission of raster images over the World Wide Web, and, in the form of prototypes, of triangular meshes, the transmission of vector map datasets has lacked a similar attention. This paper introduces a solution to the progressive transmission of vector map data that allows users to apply analytical GIS methods to partially transmitted data sets. The architecture follows a client-server model with multiple map representations at the server side, and a thin client that compiles transmitted increments into a topologically consistent format. This paper describes the concepts, develops an architecture, and discusses implementation concerns.