CyberGIS software: a synthetic review and integration roadmap

Wang, Shaowen; Anselin, Luc; Bhaduri, Budhendra; Crosby, C. J.; Goodchild, Michael F.; Liu, Yan Y.; Nyerges, Timothy L.

doi:10.1080/13658816.2013.776049

Cited by 128 publications

(73 citation statements)

References 31 publications

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“…Generally, it involves two steps to parallelize a spatial algorithm: to decompose the data (spatial decomposition) and to schedule the tasks (task scheduling) [20][21][22].…”

Section: Methodsmentioning

confidence: 99%

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

et al. 2017

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Surface area estimation is a widely used tool for resource evaluation in the physical world. When processing large scale spatial data, the input/output (I/O) can easily become the bottleneck in parallelizing the algorithm due to the limited physical memory resources and the very slow disk transfer rate. In this paper, we proposed a stream tilling approach to surface area estimation that first decomposed a spatial data set into tiles with topological expansions. With these tiles, the one-to-one mapping relationship between the input and the computing process was broken. Then, we realized a streaming framework towards the scheduling of the I/O processes and computing units. Herein, each computing unit encapsulated a same copy of the estimation algorithm, and multiple asynchronous computing units could work individually in parallel. Finally, the performed experiment demonstrated that our stream tilling estimation can efficiently alleviate the heavy pressures from the I/O-bound work, and the measured speedup after being optimized have greatly outperformed the directly parallel versions in shared memory systems with multi-core processors.

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“…Generally, it involves two steps to parallelize a spatial algorithm: to decompose the data (spatial decomposition) and to schedule the tasks (task scheduling) [20][21][22].…”

Section: Methodsmentioning

confidence: 99%

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

et al. 2017

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“…The proliferation of cloud/web-based and FOSS4G tools also highlights the progression from the traditional desktop model of Geographic Information Science (Goodchild, 1992; abbreviated to GI Science per Hall, 2014) to an advancing geospatial cyberinfrastructure, or CyberGIS (Anselin, 2012;Wang et al, 2013;Wright & Wang, 2011;Yang, Raskin, Goodchild, & Gahegan, 2010). In particular, the CyberGIS community has promoted the integration of existing GI Science and spatial analysis tools with cyberinfrastructure tools that harness cloud and high performance computing technologies (i.e.…”

Section: The Evolution Of a Collaborative Spatial Data Science Workflowmentioning

confidence: 99%

“…272). In the quest to transform the technological infrastructure available for geospatial research, CyberGIS has also recognized the importance of support for shared problem-solving, distribution of geospatial data in flexible and secure ways, and community-driven solutions for wrangling and analyzing large and complex datasets (Wang et al, 2013;Wright & Wang, 2011;Yang et al, 2010).…”

Section: The Evolution Of a Collaborative Spatial Data Science Workflowmentioning

confidence: 99%

A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges

Palomino

Muellerklein

Kelly

2017

Computers, Environment and Urban Systems

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a b s t r a c t a r t i c l e i n f oTo solve current environmental challenges such as biodiversity loss, climate change, and rapid conversion of natural areas due to urbanization and agricultural expansion, researchers are increasingly leveraging large, multiscale, multi-temporal, and multi-dimensional geospatial data. In response, a rapidly expanding array of collaborative geospatial tools is being developed to help collaborators share data, code, and results. Successful navigation of these tools requires users to understand their strengths, synergies, and weaknesses. In this paper, we identify the key components of a collaborative Spatial Data Science workflow to develop a framework for evaluating the various functional aspects of collaborative geospatial tools. Using this framework, we then score thirty-one existing collaborative geospatial tools and apply a cluster analysis to create a typology of these tools. We present this typology as a map of the emergent ecosystem and functional niches of collaborative geospatial tools. We identify three primary clusters of tools composed of eight secondary clusters across which divergence is driven by required infrastructure and user involvement. Overall, our results highlight how environmental collaborations have benefitted from the use of these tools and propose key areas of future tool development for continued support of collaborative geospatial efforts.

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“…In the geospatial domain, some progress has been made towards implementing an operational big geo-data computing architecture. Part of the research has focused on web service-based geoprocessing models for distributed spatial data sharing and computing [1][2][3][4], and some studies have investigated CyberGIS-based [5,6] methods to address computationally intensive and collaborative geographic problems by exploiting HPC infrastructure, such as computational grids and parallel clusters [7][8][9], particularly focused upon the integration of particular CyberGIS components, spatial middleware and high-performance computing and communication resources for geospatial research [10,11].…”

Section: Cloud-based Big Geo-data Processingmentioning

confidence: 99%

An Effective NoSQL-Based Vector Map Tile Management Approach

Wan

Huang

Xia

2016

IJGI

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Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL) databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC) or relational databases. In this paper, we propose a flexible storage framework that provides feasible methods for tiled map data parallel clipping and retrieval operations within a distributed NoSQL database environment. We illustrate the parallel vector tile generation and querying algorithms with the MapReduce programming model. Three different processing approaches, including local caching, distributed file storage, and the NoSQL-based method, are compared by analyzing the concurrent load and calculation time. An online geological vector tile map service prototype was developed to embed our processing framework in the China Geological Survey Information Grid. Experimental results show that our NoSQL-based parallel tile management framework can support applications that process huge volumes of vector tile data and improve performance of the tiled map service.

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CyberGIS software: a synthetic review and integration roadmap

Cited by 128 publications

References 31 publications

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges

An Effective NoSQL-Based Vector Map Tile Management Approach

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