17538947%2E2013%2E769783

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Redefining the possibility of digitalEarth and geosciences with spatialcloud computingChaowei Yang a , Yan Xu b & Douglas Nebert ca Center of Intelligent Spatial Computing for Water/EnergySciences, George Mason University , Fairfax , VA , USAb Microsoft Research , Redmond , WA , USAc Federal Geographic Data Committee , Reston , VA , USAPublished online: 24 May 2013.

To cite this article: Chaowei Yang , Yan Xu & Douglas Nebert (2013) Redefining the possibility ofdigital Earth and geosciences with spatial cloud computing, International Journal of Digital Earth,6:4, 297-312, DOI: 10.1080/17538947.2013.769783

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Redefining the possibility of digital Earth and geoscienceswith spatial cloud computing

Chaowei Yanga*, Yan Xub and Douglas Nebertc

aCenter of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University,Fairfax, VA, USA; bMicrosoft Research, Redmond, WA, USA; cFederal Geographic

Data Committee, Reston, VA, USA

(Received 31 October 2012; final version received 29 December 2012)

Global challenges (such as economy and natural hazards) and technologyadvancements have triggered international leaders and organizations to rethinkgeosciences and Digital Earth in the new decade. The next generation visionspose grand challenges for infrastructure, especially computing infrastructure. Thegradual establishment of cloud computing as a primary infrastructure providesnew capabilities to meet the challenges. This paper reviews research conductedusing cloud computing to address geoscience and Digital Earth needs within thecontext of an integrated Earth system. We also introduce the five papers selectedthrough a rigorous review process as exemplar research in using cloud capabilitiesto address the challenges. The literature and research demonstrate that spatialcloud computing provides unprecedented new capabilities to enable Digital Earthand geosciences in the twenty-first century in several aspects: (1) virtually unlimitedcomputing power for addressing big data storage, sharing, processing, andknowledge discovering challenges, (2) elastic, flexible, and easy-to-use computinginfrastructure to facilitate the building of the next generation geospatial cyberin-frastructure, CyberGIS, CloudGIS, and Digital Earth, (3) seamless integrationenvironment that enables mashing up observation, data, models, problems, andcitizens, (4) research opportunities triggered by global challenges that may lead tobreakthroughs in relevant fields including infrastructure building, GIScience,computer science, and geosciences, and (5) collaboration supported by cloudcomputing and across science domains, agencies, countries to collectively addressglobal challenges from policy, management, system engineering, acquisition, andoperation aspects.

Keywords: EarthCube; CloudGIS; eScience; Earth science; interoperability

1. The challenges of geosciences and Digital Earth

Technology advancements and globalization require the integration of previously

separate fields, including geography, geochemistry, hydrology, environmental sciences,

climate science, soil science, and others in the twenty-first century (National Research

Council [NRC] 2012a) to (1) address international challenges, (2) obtain fresh

approaches for solving complex problems, and (3) protect local, regional, national,

and international interests in the context of an integrated Earth system. The integrated

system also contributes significantly to the human capability in answering many

previous unanswerable scientific enquires, such as those from climate and land use

*Corresponding author. Email: [email protected]

International Journal of Digital Earth, 2013

Vol. 6, No. 4, 297312, http://dx.doi.org/10.1080/17538947.2013.769783

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change, ecosystems, energy and minerals, environmental health, water, and natural

hazards. These existing and emerging international science opportunities as char-

acterized by US Geological Survey include (NRC 2012a) (u1) global natural hazards

planning and responses, (u2) global energy and mineral resource assessments,

(u3) enhanced water sustainability research in desert regions and tropical areas,

(u4) use of climate and land-cover science for decisions on climate adaptation and

natural resource management, (u5) understanding the influence of climate change onecosystems, populations, and disease emergence, (u6) clarification and development of

invasive species work using trade patterns, refugee situations, and changing climate

and environment for initial prioritization, (u7) quantitative health-based human

health risk assessment analysis based on contaminant exposure levels, (u8) ecological

and quantitative human health risk assessment analysis based on contaminant

exposure levels, (u9) research in water contamination and supply, (10) water and

ecological science in cold regions sensitive to climate change, and (u11) comprehensive

enhancement of, and accessibility to, essential topographic and geologic map

information through the topological and geological modeling and mapping.

To conduct more in-depth research, National Science Foundation (NSF) also

worked with NRC (2012b) to identify the new research opportunities in geosciences

for the next decade as (n1) early Earth, (n2) thermo-chemical internal dynamics and

volatile distribution, (n3) faulting and deformation processes, (n4) interactions among

climate, surface processes, tectonics, and deep Earth processes, (n5) co-evolution of

life, environment, and climate, (n6) coupled hydrogeomorphic-ecosystem responseto natural and anthropogenic change, (n7) biogeochemical and water cycles in

terrestrial environments and impacts of global change, and (n8) recent advances in

geochronology.

Among the new research frontiers identified for USGS and NSF, u1 and u11 are

unique to USGS and n1, n2, n3, and n8 are unique to NSF. The following are

complementing pairs: u2-u3 with n4, u4-u6 with n5, u7-u8 with n6, and u9-10

with n7. In this regard, although the two sets of geoscience challenges have different

focuses, they complement each other and both identify a priority for training the next

generation geoscientists. And this complementary is considered in Table 1.

While these national and international deep scientific inquiries being addressed,

international efforts are put on the advancements of the technologies to support the

scientific discoveries and applications driven by the Digital Earth concept (Gore

1998) and recently, by the NASA Earth Exchange (NASA 2012) and NSF (2011)

EarthCube effort at a US national level and the intergovernmental Group on Earth

Observation (GEO 2012) at the global level. Under the auspices of the InternationalSociety of Digital Earth, relevant activities defined the research directions and

challenges towards 2020 as a next generation Digital Earth by considering the recent

development and challenges (Craglia et al. 2012; Goodchild et al. 2012). Craglia

et al. (2012) defined five themes for next generation Digital Earth as (d1) a research

challenge, (d2) an information system, (d3) applications, (d4) organizational meta-

phor, and (d5) a strategic infrastructure.

All these visions call for the readiness of digital technologies for a computing

infrastructure to support the scientific quests and application needs (such as Gore

1998; NSF 2011; Craglia et al. 2012; NASA 2012). The development of sharing and

utilizing computing resources across geographic boundaries provided ideal meth-

odologies to solve the problem and construct such an infrastructure (Yang et al. 2010).

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The sharing of distributed computing has evolved from early High Performance

Computing (HPC), grid computing, peer-to-peer computing, and cyberinfrastruc-

ture to the recent cloud computing, which realizes access to distributed computing

for end users as a utility or ubiquitous service (Yang et al. 2010). Research for

adopting cloud computing to enable or solve the geoscience problems and Digital

Earth challenges have also attracted many computational scientists to investigate the

readiness of cloud computing (as reviewed in section 2 and section 3). Researches

were also conducted to explore how the spatiotemporal principles that govern the

geosciences and Digital Earth can be utilized to optimize the cloud computing and

provide better computing solutions via spatial cloud computing (Yang 2011).

CloudGIS was also brought up as the next generation GIS to provide GIS software

and functionalities through cloud computing platforms (Wu and Wu 2011).After the publication of the spatial cloud computing definition paper (Yang et al.

2011), we edit this spatial cloud computing special issue to capture the latest

investigations of global scientific communities in utilizing cloud computing to address

the geoscience and Digital Earth challenges and to identify the future research

directions in this emerging field. The next section summarizes, in examples, research

conducted to utilize cloud computing for enabling geoscience and Digital Earth.

Section 3 reviewed the technological advancements for addressing the scientific and

application problems. Section 4 introduces the selected papers, section 5 concludes the

paper with a recommended list of research agenda for spatial cloud computing to

support a new generation of geospatial information system (CloudGIS), and section 6

analyzes how the cloud computing research and capabilities can be utilized to redefine

the possibility of Digital Earth and geosciences.

2. Geosciences enabled by cloud computing

The geosciences enabled by cloud computing include almost all dimensions of the

new geosciences as defined by USGS (NRC 2012a) and NSF (NRC 2012b), for

example:

(1) Early Earth is one of the most challenging subdomains. Research has been

conducted to enable scientists access easily to astrophysics simulation

and visualization through science gateway supported by cloud computing

(Pajorova and Hluchy 2011a) and enables the processing of astronomy datato search for Earth-like planets orbiting other stars by integrating several

computational clouds including FutureGrid, NERSCs Magellan cloud, and

Amazon EC2 (Vockler et al. 2011). Early Earth research is generally

centralized but the computing is distributed in a SETI@Home fashion with

a relative simpler demand for computing infrastructure.

(2) Energy and mineral science requires a good data management to support

modeling of the generation and distribution of energy. Liu, Wang, and Liu

(2012) used cloud computing to address the data, storage, and processing

demands for energy information management. Energy and mineral science is

relatively complex with very broad spatiotemporal distribution of producers,

managers, and consumers of material/information, and demands a relative

complex computing infrastructure.

International Journal of Digital Earth 299

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(3) Climate science faces the grand challenges of big data management and

analysis. Cloud computing has been used to enable the effective management

of large-scale collections of observational data and model output data for

community-defined services such as the Earth System Grid (Schnase et al.2011). Huang, Gangl, and Bingham (2011) used cloud computing for

climatology services of storing and analyzing spatiotemporal characteristics

of scatterometer data over Antarctica. Tran et al (2011) used cloud

computing and Apache Object Oriented Data Technology synergistically to

form an effective, efficient, and extensible combination to the challenges of

NASA science missions data management at reduced costs. The integration

of climate science and environment, ecological, and health sciences will make

it increasing complex in the next decades.(4) Traffic management and simulation systems require the (near) real-time

capabilities enabled by cloud computing to (1) solve data-intensive geospatial

problems in urban traffic systems for traffic surveillance management

(Li, Zhang, and Yu 2011), (2) integrate the management of International

Traffic Database and support project communication and publishing (Miska

and Kuwahara 2010), and (3) provide the most environmentally friendly

transport solution with intuitive and individualized services for a dynamic

number of end users (Di Martino, Giorio, and Galioro 2011).(5) Ecology faces the challenges of storage, scalability, and platform integration

in a global context. Cloud computing and open source has been utilized to

(1) enable storage, scalability, and deployment flexibility for global marine

biogeography data and analyses (Fujioka et al. 2012), (2) setup a platform for

forest pest control to handle the huge amount of pest data (Jiang et al. 2010),

(3) provide worldwide integrated monitoring of the environment and its

inhabitants, understand their interrelationships, improve our ability to

protect the planet and its people by integrating hundreds of thousands ofdata sources (Montgomery and Mundt 2010), and (4) support sustainability

research (Mobilia et al. 2009).

(6) Civil engineering and water management is critical for human being, and

Behzad et al. (2011) used both HPC and cloud computing in a hybrid

cyberinfrastructure to support groundwater ensemble runs for forecasting the

availability of fresh water.

(7) Disaster/waste management, disaster monitoring, forecasting, warning, pre-

paration, and response can be supported efficiently by cloud computing(Liang, Lii, and Chang 2011). For example, Bessis, Asimakopoulou, and

Xhafa (2011) use the next generation emerging technologies for enabling

collective computational intelligence in managing disaster situations.

Ishikawa, Sugiyama, and Sasaki (2011) investigated using cloud computing

and satellite images to monitor and simulate the dispersion of industrial

waste to reduce waste impact.

(8) Human and environment health is another example needing global computa-

tional flexibility and extensibility that can be provided by cloud computingfor prediction analyses (Bohm, Mehler-Bicher, and Fenchel 2011). Shen et al.

(2012) used service-oriented architecture and cloud computing to support

analysis and visualization of medical data highlighting the global variation of

health data by geography, living habits, and cultures. Eriksson et al. (2011)

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used a hybrid cloud-based simulation architecture for pandemic influenza

simulation and found that it is possible to develop a scalable simulation

environment using cloud computing.

Among these examples, the most popular domains (such as energy, transportation,

civil engineering, disaster/waste management, and human and environment health)

will benefit the most from cloud computing to support the massively distributed and

concurrent end-user requests with elasticity, on-demand, and pay-as-you-go features

(Yang et al. 2011).

3. Key technology advancements for cloud computing

Besides the virtualization, web services, and service-oriented architecture technolo-

gies, the driving fundamental technologies of cloud computing include (1) parallel

computing, (2) multitasking supported by multiple computers and cores, and

(3) distributed or collaborative processing through the natural distribution of data,

problems, computations, and users. There are many efforts to explore the paralleliza-

tion aspect of cloud computing. For example, Tilevich and Eugster (2010) organized a

workshop to explore programming support innovations to address the emerging

distributed applications and the state-of-the-art of their programming support.Akdogan et al. (2010) studied the problem of parallel geospatial query processing

using the MapReduce programming model with spatial index and Voronoi diagrams.

Wang and Liu (2008) researched parallel computing architecture structure based on

cloud computing for parallelizing data-mining algorithms. Zhang (2010) developed a

parallel spatial statistics module for visual explorations on top of Personal HPC-G in

combination with cloud computing. van Zyl et al. (2012) extended multitasking and

distributed processing capabilities for Earth observation scientific workflows in

a distributed computing environment to allow these geospatial processes to beseamlessly executed across distributed resources. Karimi, Roongpiboonsopit, and

Wang (2011) studied distributed algorithms for geospatial data processing on clouds

and compared their experimentation with an existing cloud platform to evaluate its

performance for real-time geoprocessing. Panchul, Akopian, and Jamshidi (2011)

reported that depending on the applications, the best possible results are produced

by different parallelization approaches from hardware-implemented parallelism to

software multithreading. Considering the driving principle and the requirements of

geosciences and Digital Earth, the enablement by cloud computing lies in theadvancements of several key techniques:

(1) System architecture is the key to a successful computing platform. Cloud

computing is the latest success of the distributed computing architectural

paradigm after HPC and Grid Computing (Mateescu, Gentzsch, and

Ribbens 2011). Current solutions utilizing cloud computing may benefit

from the integration of a hybrid framework of HPC, grid, cloud, and cluster

computing to solve problems of large-scale sciences such as Earth science,astronomy, and related sciences (Pajorova and Hluchy 2011b). An optimiza-

tion strategy is also proposed (Cui et al. 2011) to unify the management of

spatial data from multiple sources using cloud computing and existing legacy

systems. Rothenberg (2010) reviewed developments that have taken place in


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architecting data center networks to meet the requirements of the cloud

and speculated on the potential impacts of such computing developments

in shaping the future Internet by driving incentives of adoption of new

protocols and architectural changes.(2) Visualization is a key to the success of cloud computing by providing easy-to-

use interfaces to domain specialists without advanced technical knowledge

(i.e. meteorologists, geography specialists, hydrologists, etc.) to manipulate

Big Data in Earth observation and geosciences (Stefanut, Popescu, and

Gorgan 2011). Many research efforts, such as that by Pitcher (2009), have

been conducted to investigate cloud computing in combination with virtual

Earth and computer graphics to provide easy-to-use visualization interfaces

for end users.(3) Big Data is a popular challenge that cuts across all geoscience and Digital

Earth subdomains. The advancement of managing and processing Big Data

can be greatly enhanced by using cloud computing (Karimi, Roongpiboon-

sopit, and Wang, 2011) to support geoprocessing for real-time navigation

applications. On the other hand, current cloud computing platforms require

improvements and special tools for handling efficiently real-time geoproces-

sing, such as iGNSS QoS prediction (Karimi, Roongpiboonsopit, and Wang,

2011).(4) Real-time data processing is required by many geoscience and Digital Earth

applications. Cloud computing could enable the real-time response with

virtually unlimited resources and on demand services within minutes for

applications, such as geo-streaming (Kazemitabar, Banaei-Kashani, and

McLeod 2011) and the GEOSS clearinghouse (Huang et al. 2010).

(5) Data storage is another challenge related to Big Data support. Cloud

computing can provide inexpensive and simple solution for users to post

and share data on the web (Bunzel, Ager, and Schrader-Patton 2010). Ji`cekand Di Massimo (2011) reported users can easily upload and store public

data into the Microsoft Cloud, while leveraging the Windows Azure Platform

and environment for processing. However, data storage and management

need additional research to reduce the hosting cost for inherently large

volumes of Earth Science data and maintaining an easy-to-use experience.

(6) Data and process co-location is a key optimization approach that may

improve cloud throughput. This means the data processing can be conducted

at different places according to scheduling strategy by either shipping thedata or the processing modules to address the significant latency arising from

frequent access to large datasets and corresponding data movement between

distributed data centers (Deng et al. 2011). Liu et al. (2011) argued that the

most related datasets can be placed into the same data center based on

the data dependence at workflow build-time; the tasks are then scheduled to

their most closely related data centers for execution and the newly generated

data-sets are put into the data center that has the most dependency at

workflow runtime.(7) Key spatial methods must be implemented in the cloud to provide basic

support to Digital Earth and geosciences (Goodchild et al. 2012). Key

methods, including spatial data storage, spatial indexing, and spatial

operations, should be researched systematically with respect to optimal use

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of cloud computing (Wang and Wang 2010). The combination of cloud

computing with geoscience and Digital Earth problems could also generate

new key spatial methods, such as optimal spanning tree for securing

distributed data replicas in geographically dispersed clouds (He et al. 2012)and spatiotemporal indexing to improve cloud application efficiency in a

global or regional scale.

(8) Standardization and interoperability are keys to integrate systems across

diverse cloud platforms. For the geoscience and Digital Earth applications,

the standards and interoperability sit in the Web service interface level of

cloud computing architecture. Examples include investigation into the best

cloud deployment approach to support the Open Geospatial Consortium

(OGC) Web Coverage Service specification (Shao et al. 2011), how OGC Webservice standards are becoming ubiquitous in cloud computing environments

replacing old computing models for geoprocessing (Reichardt 2010), and how

the application interoperability bottleneck in a data-intensive application can

be solved by cloud computing (Wu, Wu, and Huang 2010).

(9) Event detection and computing is essential for best utilizing cloud computing

resource to support autoscaling for elasticity and utility characteristics.

Detecting events in cloud computing platforms (Helmer, Poulovassilis, and

Xhafa 2011) will require new spatiotemporal algorithms to exploit thescalability of cloud computers while working within the limits of state

synchronization across multiple servers in the cloud (Olson et al. 2011).

These technological advancements will pave routes for implementing cloud

computing and enabling geosciences and Digital Earth. The full implementation

of the five National Institute of Standards and Technology (NIST 2011) cloud

computing characteristics will further the advancement and foundation building of

the cloud computing for geosciences and Digital Earth.

4. Introduction to the papers

We called for and received 25 abstract submissions and invited 10 full paper

submissions. Five papers were selected for publication based on a rigorous peer

review process.

Liu et al. (2013) reported research using Microsoft Azure to support the

on-demand requirement of groundwater supply analyses using Texas and Arizonaexamples. Technology and license problems were solved by integrating Azure and

Dropbox for computing, integrating desktop and cloud for software license access,

and transferring files between the desktop and the cloud for data sharing. They

found that cloud computing could enhance groundwater analyses by providing

informed uncertainty analysis results that assist groundwater planning and sustain-

ability analysis.

Dust storms are a challenging natural hazard facing us in the twenty-first century

with an increasing frequency and broad geographic and socioeconomic impacts. Theprediction of dust storms involves modeling uncertainty and requires significant

computing resources. Taking the computing challenge and exploring spatiotemporal

optimization (Yang et al. 2011), Huang et al. (2013) utilized cloud computing to

provision large quantities of computing resources on-demand for a short time period


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to support high-resolution dust storm forecasting over large geographic region, while

reducing the cost of forecasting by exploiting the measured service and elastic

capabilities of the cloud environment.

Wen et al. (2013) reported using cloud computing to support an open environment

for sharing geographic analyses models. To solve the heterogeneity problem, they used

several strategies of model description, model encapsulation, model deployment, and

transparent access, and verified the strategies with an experimental environment

established on a cloud computing platform. They also identified several future

research challenges including interoperability, performance, system security, load

balancing, and quality of service assessment.Following the parallelization spirit, Kim and Tsou (2013) compares cloud

computing approaches to those of grid computing using WebGIS-based geoproces-

sing simulations. They found that with limited amount of parallelization, grid

computing has a better performance but with increased parallelization, cloud

computing performance is comparable. Their research proves cloud computing to

be a viable solution for enabling computation and data-intensive processing for

complex GIS models. From an accessibility aspect, the on-demand aspects of

commercial cloud computing allows timely access at low cost. They also commented

that different instances of cloud computing can satisfy different WebGIS computing

needs; therefore, assessments should be conducted to evaluate the best-fit cloud

instance for specific applications.

Yue et al. (2013) provide a comparative analysis of the design and implementa-

tion of geoprocessing services in Microsoft Azure and Google App Engine. The

research compares the running environment, programming language, application

framework, storage service, and platform application programming interfaces for

both cloud platforms for geoprocessing functions. The result provides the reference

on selectively utilizing cloud computing platforms in a hybrid cloud pattern. The

research shows that virtualization is the key for portable geoprocessing cloud

services. The performance tests demonstrate how the cloud computing can support

the on-demand geoprocessing and economic deployment of geoprocessing services.

In addition to the five accepted papers, we added this field review paper, fully

reviewed by a broader expert base and the community, to capture the state-of-the-art

and to identify the future research directions.

5. Research directions and towards a research agenda

Cloud computing provides enabling capabilities for geosciences and Digital Earth in

the twenty-first century. The eventual success of the spatial cloud computing and the

next generation GIS CloudGIS will be determined in five aspects (Luftmanand Zadeh 2011): (1) improvements to research productivity and cost reduction;

(2) alignment to current information technology (IT) procedure; (3) agility and speed

in responding to computing needs; (4) ease of application in scientific research; and

(5) improvement in the reliability and efficiency of IT. As global collaboration

requires, cloud computing in support of geosciences and Digital Earth will transcend

organizations, jurisdictional boundaries, and continents (NRC 2011b). The potential

impediments and solutions can be addressed relative to policy, management, system

engineering, acquisition, and operations requirements to ensure the eventual success

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of CloudGIS. The following cloud computing research directions need additional

attention from the interdisciplinary domains:

(1) Continuing visions for geosciences and Digital Earth will provide drivingdemands for cloud computing advancements. These visions could include,

to name a few, smart/intelligent Earth (Liu et al. 2010) and campus (Liu,

Xie, and Peng 2009); virtual geographic environment (Lin et al. 2011);

global military conflict simulator (Tanase and Urzica 2009); global sharing

of Earth observation data (Huang et al. 2010); new cartography (Meng

2011); and security, data, and future of computing (IEEE 2011).

(2) Cloud interoperability has to rely on the standards developed by different

organizations, such as OGC, OGF, NIST, ISO, and IEEE, through asystematic architecture designed to support the sharing of distributed

computing resources at different levels. This has to be driven by large user

groups, application domains, vendors, and governments to achieve the

required level of interoperability (Lee 2010).

(3) New visualization and interactive systems for cloud computing will be

essential to support ease-of-use and straightforward access (Vogel 2011).

For example, regional arctic systems modeling (Roberts et al. 2010) and a

polar research cyberinfrastructure (Yang, Nebert, and Taylor, 2011) requirea computing infrastructure that is easily accessible, extensible, and usable.

(4) Reliability and availability is a challenge to achieve in a cloud platform

where most components are distributed and independently managed. This

includes being able to access the platform from different regions and the

efficiency of the access.

(5) Real-time simulation and access is essential for different types of decision

support from emergency response to individual decisions. Relevant research

should include theory-based simulation and multiscale, multicomponentmodeling, as well as data-intensive and interactive visualization capability

for both cloud computing platforms and applications (NRC 2011a).

(6) Cloud management needs significant improvement to meet the five character-

istics defined by NIST (2011). Systematic management at the enterprise level

to support semiautomatic engineering operations for the cloud and the entire

information system will be essential (Choi and Lee 2010).

(7) Cloud outreach should help convey the message to (potential) users properly

with its own strengths and shortcomings.(8) Security is a big concern when utilizing cloud computing to deploy a

distributed platform with certain information, for example, for labor and

social security applications (Lu 2010). Examples include a secure web

environment to develop phenologic matrices from linked media in real time

(OLeary and Kaufman 2011) and creation of international license agree-

ments or exceptions to ensure that export-controlled technical data stored on

the cloud is secure and protected (Schoorl 2012). It will be a continuing

challenge in how to ensure the protection of sensitive data, privacy, andsystems while maintaining the sharing spirit of cloud computing.

(9) Spatiotemporal optimization is key to fully realize the benefit of cloud

computing to geoscience, computing infrastructure, Digital Earth, and

education (Yang et al. 2011). Addressing fundamental science questions and


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application problems and optimizing the cloud computing platforms with

spatiotemporal principles will help lay the foundation to implement spatial

cloud computing (Yang et al. 2010).

(10) Global collaboration is important to make a difference on several fronts:(1) the integration of multiple scientific domains through common informa-

tion science platforms will require global collaboration (Yang et al. 2010);

(2) GIScience advancement will depend on global collaboration from the

identified directions of (g1) position technology for knowing where every-

thing is, at all times, (g2) citizen science and crowd-sourcing, (g3) dynamic

or spatiotemporal events, (g4) the 3rd, 4th, and 5th dimensions, and (g5) the

challenge of education (Goodchild 2010); (3) Craglia et al (2012) also

emphasized the importance of global collaboration for the next generationDigital Earth in the light of the many developments from IT, data infras-

tructures, and Earth observation. It is essential to develop a series of

collaborations at the global level to turn the vision into reality.

In addition to this list, other research frontiers should be added when needed. For

example, cloud-based knowledge discovery will require data mining and knowledge

discovering across linked data distributed worldwide and without fully accessing the

actual data.

6. Enabling the visions with cloud technologies

This section provides an analysis of how the spatial cloud computing advancements

and future research would enable the visions. Two levels are identified as kernel

support (marked with x) or lightly dependent (without mark). This may serve as

Table 1. Geoscience vision and the spatial cloud computing research (u1u11 and n1n8 arein section 1 and adopted from NRC 2012a, 2012b).

USGS u1 u2 u3 u4 u5 u6 u7 u8 u9 u10 u11

Technologies\vision NSF n4 n5 n6 n7 n1 n2 n3 n8

Architecture X X X X X X

Visualization X X X X X X X X X X x X X

Big data X X X X X X X X X

Real-time X X X X X X

Data storage X X X X X X X X X X X

Co-location X X X X X X X X X

Spatial methods X X X X X X X X X X X X X X

Interoperability X X X X X X X X X X X

Event X X X X X X X X X X X X

Vision X X X

Reliability X X X X X X X

Cloud management X X X X X X

Communication X X X X X X X X

Security X X X X X X X X

Spatiotemporal X X X X X X X X X X X X X

Collaboration X X X X X X X x X X X X

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a reference in conducting research of spatial cloud computing to enable the visions.

The technologies are detailed in section 3 and section 5.

Table 1 illustrates that natural hazards, water sustainability, climate adaptation

and natural resource management, environmental and human health will be very

comprehensive and require advancements in all different technological aspects to

Table 2. Digital Earth vision and the spatial cloud computing research (d1d5 are in section 1adopted from Craglia et al. 2012).

Technologies\vision

d1

research

d2

system

d3

application

d4

metaphor

d5

infrastructure

Architecture X X X X

Visualization X X X

Big data X X X X X

Real-time X X X X

Data storage X X X

Co-location X X X X

gis methods X

Interoperability X X X

Event X X X X X

Vision X X

Reliability X X X

Cloud management X X X X X

Communication X X

Security X X X

Spatiotemporal X X X X

Collaboration X X X X

Table 3. GIScience vision and the spatial cloud computing research (g1g5 are in 10th item ofsection 5 adopted from Goodchild 2010).

Technologies\vision

g1.

position

g2. citizen

science

g3.

dynamics

g4. 3dimension

g5.

education

Architecture X X X

Geovisualization X X X X X

Big data X X X X

Real-time X X X X

Data storage X X X X

Co-location X X X X

gis methods X X X X

3Interoperability X X X X

Event research X X X X X

Vision X X X X

Reliability X X X

Cloud management X X X

Communication X X

Security X X X

Spatiotemporal X X X X X

Collaboration X X X


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address the problems. It also illustrates that visualization, spatial methods, and

spatiotemporal studies will be key areas for most geoscience domains.Table 2 illustrates that Digital Earth as an infrastructure will foster the

advancements of different technological aspects of cloud computing. Big Data,

event, and cloud management will be the key technologies for enabling Digital Earth

and geosciences.

Table 3 illustrates that education will be critical for cloud computing.

Geovisualization, events, and spatiotemporal studies will be the key areas of GIS

that enables the advancements of cloud computing.

Acknowledgements

Research is supported by State Administration of Foreign Experts Affairs (20120464001),NSF (IIP-1160979 and CNS-1117300), FGDC (GeoCloud and GEOSS Clearinghouse), andMicrosoft Research. Drs. Dennis Guo, Xiang Li, Ziyong Zhou, Yang Hong, Peng Yue, RickKim, Yong Liu, Qunying Huang, Min Chen, Xinyue Ye, and Santonu Goswami reviewed themanuscript. We sincerely thank Drs. Huadong Guo and Changlin Wang for inviting us toorganize the special issue and facilitating the process of developing this special issue.

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