The ‘easy’ of geo-computation in esayGC is shown in both ‘easy to use’ and ‘easy to compute’. The second is the division of computation which divides the computation process into parts to be carried out in parallel. You mustn’t shout in the library. SNP must stop making it easy for Boris Johnson to deny indyref2. modals exercise. For the environment covariates that need to be derived from other data, the platform can automatically extend the workflow with services that generate these data. 2019; Evangelidis et al. These forms of GIS software have simplified the management and analysis of geospatial data to some extent, yet users need to be trained on these software sufficiently before they can adequately use these planforms/tools to accomplish the analytical tasks the users need to perform. These two divides need to be carefully tackled to make GIS easier for users to participate in geo-computation. Most people simply aren’t willing. Of how you're supposed to be They pay you to sing Figure 8. The crabby, unapproachable, terse, mean, arrogant, rude, all-business teacher can't last long. Must be easier when you're there 5,759 talking about this. The defaults are sufficient for most DSM applications. ‘Intuitive’ allows users to easily modify the settings (such as algorithms and their parameters) if they want and monitor the computation process. Non-expert users can only reuse the workflow built and shared by expert users that has rather limited reusability. The intelligence of the next generation of GIS should cover every aspect of the geo-computation process, from geospatial analysis task selection, to model setting, and to data provision and utilization. The easyGC well addresses the two digital divides faced in geographic analysis using existing GIS software. 2019). They usually allow users to create workflows by the drag-and-drop of the data and methods supported by the geo-computing platforms. 2000; Clerici et al. Future GIS platforms should either support other forms of resources sharing or provide the functionality to automatically wrap other forms of sharing as web services. First, the platform uses graphics to intuitively abstract the model elements, making the modelling process simple and easy. Register to receive personalised research and resources by email, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography, Nanjing Normal University, Nanjing, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Department of Geography, University of Wisconsin-Madison, Madison, WI, USA; Center for Social Sciences, Southern University of Science and Technology, Shenzhen, China, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China, A GIS toolset for automated processing and analysis of radar precipitation data, Chaining geographic information web services, Designing the Distributed Model Integration Framework – DMIF, Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS, Geo‐Pragmatics for the Geospatial Semantic Web, SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses, Models as web services using the open geospatial consortium (OGC) web processing service (WPS) standard, An ontology-based knowledge management system for flow and water quality modeling, Teamwork-oriented integrated modeling method for geo-problem solving, A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines), Distributed geospatial information services-architectures, standards, and research issues, a Framework for Developing Web-Service-Based Intelligent Geospatial Knowledge Systems, Towards Semantic Geo/BI: a Novel Approach for Semantically Enriching Geo/BI Data with OWL Ontological Layers (OOLAP and ODW) to Enable Semantic Exploration, Analysis and Discovery of Geospatial Business Intelligence Knowledge, Visual programming language in geographic information systems, World Scientific and Engineering Academy and Society (WSEAS), Discovery, management, and preservation of geospatial data using hydra, Researching volunteered geographic information: Spatial data, geographic research, and new social practice, Environmental Systems Research Institute Inc, Location Analytics Software | ArcGIS Insight, Esri and Microsoft Cloud Deployment Options, Asking spatial questions to identify GIS functionality, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Towards a collaborative knowledge discovery system for enriching semantic information about risks of geospatial data misuse, a performance, semantic and service quality-enhanced distributed search engine for improving geospatial resource discovery, Distributed frameworks and parallel algorithms for processing large-scale geographic data, Coupling Knowledge with GIS Operations: The Benefits of Extended Operation Descriptions, From manual to intelligent: a review of input data preparation methods for geographic modeling, Explorations of the Implementation of a Parallel IDW Interpolation Algorithm in a Linux Cluster-Based Parallel GIS, a survey on resource allocation in high performance distributed computing systems, a knowledge-based method for the automatic determination of hydrological model structures, CyberSoLIM: a cyber platform for digital soil mapping, Enabling Digital Earth simulation models using cloud computing or grid computing–two approaches supporting high-performance GIS simulation frameworks, Designing a language for spatial computing, Grid computing technology for hydrological applications, Semantic interoperability of distributed geo-services, a case-based method of selecting covariates for digital soil mapping, a layered approach to parallel computing for spatially distributed hydrological modeling, a two-level parallelization method for distributed hydrological models, CyberGIS Gateway for enabling data‐rich geospatial research and education, Geographic analysis-oriented virtual geographic environment: framework, structure and functions, Rule‐Based Discovery in Spatial Data Infrastructure. By ‘easy’ we mean ‘easy to use’ and ‘easy to compute’. Third, it is cyber-enabled so that it is accessible online which allows heterogeneous resources to be integrated into the form of web services. The future geo-computing platforms should be able to translate various forms of goals into machine-processable geospatial problems. While it works for most of … Once completed, the workflow is verified and then sent for execution (Qi et al. It must be easy when they pay you to sing Even easier being inclined to think about the things. In summary, to be easy future GIS software should have the following four characteristics: 1) Goal-driven: the process-oriented workflow building process in existing GIS software needs to be changed to user goal-oriented. It enables the inclusion of new processing techniques as well as the utilization of high-performance computing resources. During the workflow building, it uses graphics to visualize the soil inference and data preparation methods as well as the involved geospatial data. The utilization of HPC resources improves the computing efficiency in terms of hardware performance. ‘Intuitive’ means the process of geographic analysis (workflow) should be … It is not easy to control the mind and keep it away effectively from desire. The platform should be able to spontaneously find and integrate the resources of data and algorithms over the cyberspace. It Must Be Easy Lyrics. The data need to be ‘brought’ into the same coordinate systems and into the same format required by the specific software in use. Furthermore, there is no reason for users to spend a great deal of time and energy to learn a piece of software and specific workflows of analysis which will often change with a new version of software. While using Fake XRM Easy for Plug-in or Workflow unit testing, there could be situations that could arise where we need to use fetchXML to retrieve some records. Researchers have made efforts to address the two digital divides. It achieved the goal of ‘easy’ in the domain of DSM and DTA by combining intelligent modelling environment and HPC-enabled computation. This means having a clear menu structure and the ability to navigate between pages quickly and efficiently. Thus, this intelligence can be directly used in other application. Cited by lists all citing articles based on Crossref citations.Articles with the Crossref icon will open in a new tab. Thus, specialists in managing these software are often needed. Clearly, users who wish to extract watersheds need to know all of the above to complete the watershed extraction for their projects. There are four activities with different levels of difficulty and a short grammar explanation. We share delicious recipes every single day just for you! The platforms should have the ability to integrate the shared inventions in various forms. The visual workflow building process is somewhat easier yet is still arranged by users and requires knowledge on model structure, algorithm and parameter selections. To make this easier, the Esri and Microsoft work together to provide a cloud platform for users to manage their web services in the cloud (ESRI 2020b). We use cookies to improve your website experience. Obviously, these GIS software have posed severe digital divides between user needs and software features. As a result, many of geographic analyses become difficult and even unachievable for existing GIS software to complete. The first level is data division that divides geospatial data into parts to be loaded and processed in parallel. In easy geo-computing platform, users should be able to build the workflow only knowing what they want to achieve (the expected results). Reply. It narrows the computation divide by building an HPC-enabled computing environment that is cyber-enabled and complex computing capable. Users without detailed geographic analysis knowledge can also participate in geo-computing.