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Ecological Modelling 319 (2016) 147–154

Contents lists available at ScienceDirect

Ecological Modelling

journa l h om epa ge: www.elsev ier .com/ locate /eco lmodel

hat’s going on about geo-process modeling in virtual geographicnvironments (VGEs)

hunxiao Zhanga,c,1, Min Chenb,d,e,1, Rongrong Li c, Chaoyang Fangf, Hui Linc,g,h,∗

School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, ChinaJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, ChinaInstitute of Space and Earth Information Science, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, ChinaKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, ChinaState Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, 210023, Jiangsu Province, ChinaKey Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Jiangxi 330022, ChinaDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, ChinaThe Chinese University of Hong Kong Shenzhen Research Institute, ShenZhen 518057, China

r t i c l e i n f o

rticle history:vailable online 18 May 2015

eywords:irtual geographic environments

a b s t r a c t

Geography investigates changes in physical structures and distributions of objects in spatiotemporalworld, which are shaped by geographic process (geo-process). With extensive simulation models used tostudy geo-process, this paper examines the status of geo-process modeling (namely model-based simu-lation) for multidisciplinary geo-processes across scales in virtual geographic environments (VGEs). The

eographic process modelingodel sharingodeling management

ollaborative modeling

conceptual framework of integrated modeling in VGEs is proposed with a review of specific issues, includ-ing model sharing and management, collaborative modeling and uncertainty analysis. The contributionof a model base in model reusability and modeling management, concerning input data, parameteriza-tion, and simulation output, is detailed. Finally, this paper concludes with a discussion of future research

-proc

directions for holistic geo

. Introduction

In the geographic world, a vast amount of information is ref-renced by space and time. Space, time and processes are closelynterconnected (Worboys, 1994), and changes in physical struc-ures and distributions of objects in space are shaped by dynamiceographic processes (geo-processes) (Hofer, 2009). In the realorld, these processes are three-dimensional, time-dependent and

omplex; they frequently involve nonlinearity, stochastic compo-ents, and feedback loops over multiple space-time scales (Bivandnd Lucas, 2000). For example, landscape patterns are producedy a succession of states that evolve over a period of time, withnormous ecological impacts (Soares-Filho et al., 2002). Hence, tomprove the understanding of the mechanisms and feedbacks ofeo-processes, geographers have strived to gain additional knowl-dge about geo-processes.

Due to the limitation in data acquisition and informationechnology in previous decades, geographers usually focused ontatic expressions and relationships among multiple geographic

∗ Corresponding author. Tel.: +852 39436010; fax: +852 26037470.E-mail address: huilin@cuhk.edu.hk (H. Lin).

1 Contributed equally to this work.

ttp://dx.doi.org/10.1016/j.ecolmodel.2015.04.023304-3800/© 2015 Elsevier B.V. All rights reserved.

ess modeling.© 2015 Elsevier B.V. All rights reserved.

variables by analyzing a series of snaps of geo-processes (Cho et al.,2011; Wu, 2004; Xu et al., 2011). Along with the accumulationof geographic knowledge and developments in Earth observation,database technology, and computer science, geographers and envi-ronmental modelers have recently designed massive models ofgeo-process to simulate dynamic geographic phenomena, such asland surface process modules for atmospheric models, water flowand contaminant transport modeling on a larger scale (Steyaert andGoodchild, 1994). As the next step in geographic data-based staticexpression of the geo-process, which is employed to explain the“what”, a model-based dynamic analysis of the geo-process can alsoreveal the mechanisms of the “why” and “how” behind geographicphenomena (Xu et al., 2011). Thus, the development of dynamicmodels is attracting the interest of geographers in their investi-gation of geo-processes (Albrecht, 2005; Gogu et al., 2001; Mark,2003).

In the context of the varying interests of geographers, theresearch tools for geographic study have also changed. Until the1980s, geographic information system (GIS) has been extensivelyapplied to assemble and manage large spatial databases, to per-form spatial and statistical analyses, and to produce effective visual

representations of geographic data (Steyaert and Goodchild, 1994).Interdisciplinary and multiscale modeling approaches have creatednew applications for GIS technology by integrating GIS and various

1 Modelling 319 (2016) 147–154

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48 C. Zhang et al. / Ecological

ynamic models (Bivand and Lucas, 2000; Clarke and Gaydos, 1998;oodchild, 1993; Gualtieri and Tartaglia, 1998; Lü, 2011; Reboljnd Sturm, 1999), such as the contemporary links between GIS andydrological modeling (Maidment, 1993; Mccolm et al., 1990) andhe use of GIS technology in regional air quality and tropospherichemistry models (Novak and Dennis, 1993). Recently, simulationodels are increasingly recognized as sophisticated tools for inves-

igating and understanding geographic patterns and processes andstimating the effect of geographic change on local, regional, andlobal scales (Emery et al., 2012; Steyaert and Goodchild, 1994).n this context, with simulation models as a critical component inhe study of geo-process, virtual geographic environments (VGEs)ere proposed (Batty, 1997; Lin and Batty, 2009; Lin and Gong,

001) as a new generation of geographic analysis tools that evolvedrom GIS (Lin et al., 2013a; Zhang et al., 2014a). Concerning com-lex geographic systems in the physical world, dynamic models

n VGEs include not only air, land, water, animals and plants butlso their interactions with non-natural systems, including con-tructed infrastructure and economic and social systems (Argent,004; Chen et al., 2013a).

This paper considers the problems in the development andpplication of geo-process modeling in VGEs and examines thedvancements that have occurred over the last years. The remain-er of the paper is organized into four sections. We discuss theackground and scope of the paper concerning the theory of VGEsnd the technical components of VGEs for dynamic geo-processodeling in Section 2. In Section 3, we propose a conceptual

heoretical and technical framework that addresses geo-processodeling problems across a range of VGEs applications. Section

reviews integrated modeling in VGEs and corresponding criti-al issues, including model sharing and management, collaborativeodeling and uncertainty in geo-process modeling. This paper cul-inates with the conclusions and a discussion of directions for

uture development.

. Virtual geographic environments: Background

VGEs are constructed with the objective of providing open andigital windows into geographic environments based on two coresf geographic data and dynamic simulation models (Lin et al.,013a; Xu et al., 2011). These VGEs are expected to correspond tohe real world, in which human-environment interactions can beepresented, simulated and analyzed (Chen et al., 2013a; Hu et al.,011; Lin et al., 2013b), such as path planning with semantically-nhanced and geometrically-accurate VGEs (Mekni and Moulin,010a). In addition to replicating the real world, VGEs can helpesearchers reproduce the past and predict the future (Chen et al.,013b; Lin and Batty, 2009). For example, based on land cover

nformation about different stages of the urbanization process (forxample, at 1995, 2005 and 2015) with air pollutant emission,eteorological and air quality models, the effect of urbanization on

ir quality can be simulated and recognized by multidisciplinaryesearchers with corresponding visualization functions in VGEs.hus, VGEs make it possible to design and implement effective poli-ies, which need to be informed by a holistic understanding of theystems (social and economic processes), their complex interac-ions, and how they respond to various changes (Kelly et al., 2013).n the near future, VGEs are expected to produce user-friendly mod-ling with less dependency on model specialist knowledge, becauseoth VGEs software and aided hardware are expected to be moreowerful and graphical, less expensive, and easier to use (Argent,

004; Lin et al., 2013b).

According to the theories of VGEs, one completed VGEs con-ists of four types of components: data, modeling and simulation,nteractive, and collaborative components (Fig. 1) (Lin et al., 2013a;

Fig. 1. Four components of VGEs.

Lü, 2011). The modeling and simulation component is the maincomponent of VGEs as the fundamental goal of VGEs is to sim-ulate dynamic processes by integrated models (Lü, 2011). Basedon geo-process modeling theory and integration technologies, thiscomponent facilitates the exploration of occurrences in our realworld and why and how it is evolving (Lin et al., 2013b). Meanwhile,this modeling component is extremely relevant to the remainingthree components. First, diverse models (for processes of air, water,and land) require massive geographic data as an input to simu-late geo-processes; the output from modeling is managed in thedata component for additional modeling and application. Second,the interactive component provides multidimensional methods forboth external and involved interactions to set modeling (Lin et al.,2013b). In this context, the general scientific model is embeddedin a ‘user-friendly’ application to satisfy the needs of scientists,decision-makers and other participants who utilize VGEs. Third,users who conduct modeling may originate from different disci-plines and spatially distribute; for example, both meteorologistsand air quality experts need to work together to conduct air qualitymodeling. For a complex issue, participants of VGEs need to coop-erate with each other not only to set modeling but also to analyzemodeling results with visualization. Thus, the collaborative compo-nent is applied to support modeling and analysis. Considering thesignificance of the simulation component and its internal interac-tion with each component of VGEs, this paper examines the statusof geo-process modeling.

3. A conceptual framework of modeling

3.1. Theoretical framework

To construct the modeling and simulation component of VGEs, aconceptual framework is proposed in this paper (Fig. 2). The mainadvantage of VGEs is integrated modeling, which has been con-sidered to focus on multidisciplinary and cross-scale modeling,as demonstrated in the theoretical framework (Fig. 2 in green).The evolution of geographic processes is frequently and coher-ently influenced by interactions among several processes relatedto different fields. For example, the development of urbanizationmay affect river resources, air quality, landscape patterns, andpopulations (Chin, 2006; Wu et al., 2015). Thus, a comprehen-

sive analysis of a geographic system, such as a catchment (Argentet al., 1999), can be conducted by integrating multidisciplinarymodels in a visual and formalized mode in VGEs (Haddad andMoulin, 2010; Chen et al., 2011). Concerning these interactions, the

C. Zhang et al. / Ecological Mode

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be considered (Baklanov and Nuterman, 2009). Fig. 3 shows the

ig. 2. Conceptual framework of modeling in VGEs. (For interpretation of the refer-nces to color in this figure legend, the reader is referred to the web version of thisrticle.)

evelopment of an integrative view of the entire urban social-cological landscape system was proposed as an importanttrategy to support practical management (Borgstrom et al., 2006).eanwhile, cross-scale linkage is another factor that deserves

ur attention in integrated modeling (Arakawa and Jung, 2011;orstemeyer, 2009; Zhang et al., 2014b). Different geographic pro-esses may operate on different scales; thus, conclusions basedn one scale may not be applicable to another scale (Perveen andames, 2010). Processes examined on different scales may be closelyelated (Peterson and Parker, 1998).

In addition to cross-disciplinary and multiscale models, partici-ants comprise an inevitable component (the four arrows in Fig. 2).articipants with different backgrounds should also be consideredn integrated modeling because models vary based on the objectivede Lara et al., 2013; Lin et al., 2010; Lü, 2011). For example, if usersre public people with limited knowledge about air quality mech-nisms, they may apply the simplest modeling in VGEs to obtain

general idea of the air quality situation; otherwise, they willpply the most professional model to establish detailed parametersor modeling. Different stakeholders who use VGEs for decision-

aking should also be involved as participants in modeling to solveeographic problems (Satake et al., 2008).

.2. Technical framework

To support the implementation of the previously discussed the-retical framework, technical developments such as model sharingnd management, collaborative modeling, and uncertainty anal-sis are abstracted in Fig. 2 (the blue part). With the support ofhese computer-aided technologies, VGEs can achieve integrated

odeling and simulation.Due to an increasing number of applications of extensive mod-

ls to support integrated modeling, model sharing and reusabilityre considered with valuable development in technologies (Granellt al., 2013b). As an additional step from the archives of model codesRollins et al., 2014), the model base, which considers geographicnteractions and knowledge, becomes an important component in

modeling system to improve model reusability. In addition, mod-ling management is also considered in the model base to provide

raceable modeling by managing and linking input data, modelarameterizations and simulation output. Meanwhile, model basedimulation and analysis is supported in a collaborative mannero ensure that participants, including multidisciplinary experts,

lling 319 (2016) 147–154 149

officials, and public officials, across locations can efficiently col-laborate with each other (Lin et al., 2013a; Xu et al., 2011). Prior tothe application of simulation, uncertainty in modeling from modelcreation, model parameters and input data should be estimated(Goodchild, 2011; Schwanitz, 2013).

4. Integrated modeling and main issues

4.1. Integrated modeling

4.1.1. Cross-disciplinary modelingFrom the perspective of model development and by analyzing

the process of model development and application in four possiblelevels, the need for a model that can interact with other modelsand issues related to model integration, from level I to level IV,have increased (Argent, 2004; Guan et al., 2011). Hence, integratedmodeling is enabled by transdisciplinary science and computercapabilities that enable geographic processes to be considered ina holistic manner (Laniak et al., 2013).

In this context, VGEs can provide a platform for researchersto gain a systematic understanding that parallels the real worldbased on their common sense (Bainbridge, 2007; Batty, 1997). Fromthe perspective of the real world, multiple geographic processesare significantly interacted to shape patterns and phenomenon,such as emergency planning, which is conducted based on themodeling of hurricane forecasting, storm surge, overland flooding(Akbar et al., 2013). This complex interaction is the motivation forintegrated modeling, particularly for researchers in different disci-plines (Argent and Houghton, 2001; Parker et al., 2002; Randhir andRaposa, 2014). Multidisciplinary modeling for addressing grand sci-entific challenges, such as monitoring the environment for changedetection, forecasting environmental conditions and the conse-quences for society, is gaining momentum (Granell et al., 2013a).

4.1.2. Multiscale modelingIn the discipline of geography, scale has always been a major

issue (Meentemeyer, 1989; Openshaw, 1983) and geo-processesusually manifest themselves with multiscale characteristics andhierarchical structures (Brown et al., 2005; He and Tao, 2012; Hofer,2009; Yuan, 2007). Most processes of interest establish a numberof dominant frequencies and organize themselves more charac-teristically on some scales compared with others. Thus, they canonly be observed on corresponding scales and conclusions basedon one scale may not be applicable to another scale (Perveen andJames, 2010; Wilbanks and Kates, 1999). As a result, multiscaleintegrated simulation technology is developed to obtain holisticcognition (Akbar et al., 2013).

4.1.2.1. Cross-scale modeling. One focus of multiscale modeling iscross-scale modeling, which is technically developed by couplingmultiscale models (Store and Jokimaki, 2003; Zhang et al., 2014b).Concerning geographic analysis, Granell et al. (2013a) proposed thedevelopment of prototype systems for small and medium scales.After these proof-of-concept experiments have been conducted,scalability of the global dimension and the robustness of the pro-vided services should be illustrated in the environmental sector.Regarding the nesting of multiscale models to consider cross-scalelinkages, the required scale ratio (between the grid resolutions ofthe main models and the nested models) to maintain the numericalstability, suitable approximation and accuracy of the models should

framework of multiscale air quality modeling, in which cross-scaleinteractions from the global scale to the local scale and a rationalscale ratio are considered using a step-by-step approach instead ofthe direct linking of the regional and individual scales.

150 C. Zhang et al. / Ecological Mode

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Fig. 3. Multiscale air quality modeling concerning the scale ratio.

.1.2.2. Scale compatibility in modeling. In addition to cross-scaleinkages, scale compatibility (Zhang et al., 2014b) in modelingnteractive geo-processes is important with regard to researchbjectives, multiscale data, dynamic models, and geographicalnalysis and visualization (Lilburne et al., 2004). Due to mismatch-ng in scale in the study of a geographic process, the conclusions

ay be invalid, uncertainty may be imported and the derived policyay be inefficient (Adger et al., 2005; Cash and Moser, 2000). Thus,ismatching in scale may cause discrepancies in the model-based

rocess simulation, which can not only confuse simulation resultsut also hinder the study of the relationships between patterns androcesses. In this context, scale compatibility in modeling shoulde considered (Baklanov and Nuterman, 2009; Lilburne et al., 2004;hang et al., 2014b). On the one hand, concerning multiple inter-ctive processes, Satake et al. (2008) examined scale mismatchesnd their ecological and economic effect on landscapes using a spa-ially explicit model that integrates processes in multiple scaleshat range from the parcel level to the global scale. Borgstrom et al.2006) proposed two import strategies for modeling the urbaniza-ion process and investigating the effect of urbanization on multiplepatial, temporal and functional scales. On the other hand, regard-ng scale compatibility between geographic data and modeling in

single geographic process, Zhang et al. (2014c) investigated theoherent scale effect from multiscale DEM data and the Weatheresearch and Forecasting model (WRF); the results explain theignificance of scale matching for multiscale geographic data andynamic models. Given these typical applications concerning scaleompatibility, the development of a systematic mechanism thatonsiders this compatibility in cross-scale and multi-disciplinaryodeling remains challenging.

.2. Model sharing and management

Models are cores of geo-process modeling; extensive and het-

rogeneous models from many disciplines, for instance hydrologicodels, land surface and sub-surface models, ecological models,

tmospheric models, ocean models, geology, biology, demography,conomics, mathematics and physics, are available (Bivand and

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Lucas, 2000; Lin et al., 2013b; Lü, 2011; Xu et al., 2011). Concerninginteractive and complex geographic processes, model sharing andmanagement is calling for attention.

4.2.1. Model sharing and reusabilityGeoinformation science always goes hand in hand with com-

puter science. Geo-process modeling systems usually encompasssoftware engineering concepts and techniques to simplify the inte-gration and programing efforts required by scientists and modelersto properly combine geographic data and models. In this context,geo-process simulation has evolved from component-based mod-eling and service-based modeling to resource-oriented modeling(Granell et al., 2013a). In component-based modeling, the modelsare always developed and primarily employed within the (coopera-tive) research group, who locally deployed and executed interactivemodels (Argent, 2005; David et al., 2013). Some component-basedmodeling frameworks attempt to support remote web serviceaccess, such as the Actor concept of Kepler (Ludäscher et al., 2006),where users can select components via specialized Actors (e.g.,Web Service Actor) and add them to Kepler-compliant workflows.This design is similar to service-based modeling, which impliesthat data and processing capabilities are exposed as a network-accessible service via standard interfaces (Lee and Percivall, 2008).Service-oriented architectures (SOA), which are defined as “openand interoperable environments based on reusability and standard-ized components and services”, are utilized to develop collaborativeand distributed web applications (Granell et al., 2013a). Resource-oriented modeling aims to simplify the access and reuse ofpieces of functionality in integrated modeling and highlights theimportance of uniform interfaces for enhancing integration ofmulti-disciplinary resources at any granularity level (Granell et al.,2013b; Jakeman et al., 2006). The flexibility of model reusability andthe development from component-based modeling to resource-based modeling is approved in the domain of information science.However, the geographic interactions of the models, includingcross-scale linkages and geographic interactions, are limited, whichis significant to geographic problem solving.

4.2.2. Model managementFocusing on model sharing and reusability, model management

is increasingly considered in geo-process modeling in two aspects.The first perspective of model management focuses on modelsharing and reusability that have evolved from component-basedmodeling to resource-oriented modeling (Granell et al., 2013a; Linet al., 2013b; Lü, 2011; Xu et al., 2011). The second perspectivefocuses on the management of modeling process in VGEs, includ-ing geo-data, modeling parameterizations, and simulation outputwith quality assessment.

Meanwhile, model management is relevant to geographicknowledge sharing, which is gaining attention by informationresearchers, including geographers (Mennis and Guo, 2009).Because models are expressions of geographic knowledge, modelreusability and sharing is one type of knowledge sharing. Modelingoutput with detailed modeling information is usually employed byrelevant disciplines as a boundary or constraint condition, which isconsidered to be one type of knowledge. For example, the EuropeanForest Information System (EFFIS) provides models that reflect thespatial distribution of fires based on previous knowledge; theseresults, such as forest fire’s emissions impact on air quality and itsinfluence on the health of humans in the vicinity of a fire, maybe employed in other disciplines, such as environmental health(Granell et al., 2013a).

4.2.2.1. Construction of model base. The significance of a modelbase (i.e., model library) has been recognized in the businessworld, where process models are a well-established component in

C. Zhang et al. / Ecological Modelling 319 (2016) 147–154 151

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models is dependent not only on scientific models but also the

Fig. 4. Flowchart for construction of model base in VGEs.

perations research, and in the natural sciences, such as the Keplerystem, which is heavily funded by the National Science Foun-ation (Albrecht, 2005). In this context, geographic analysis iso exception, as model base is identified as one of two coresi.e., data and model cores) of VGEs in studying geo-process (Lint al., 2013a; Rollins et al., 2014; Xu et al., 2011). To constructhe model base, extensive models must be unified or redevel-ped, because models are derived from multiple disciplines andach discipline with different research groups may create its pro-essional models according to its research customs. Thus, modelsre usually described using domain-specific modeling languagesDSMLs), which provide primitives and constructs of the domainde Lara et al., 2013). Meanwhile, it deserves our attention in

odel base design that models are developed in different for-ats, such as agent-based, cellular automata, and computationalodels (Lagarias, 2012; Mekni and Moulin, 2010b; Rollins et al.,

014; Zhang et al., 2006). Furthermore, model base is interactiveeo-processes oriented with geographic knowledge as guidance torganize multidisciplinary models instead of a simple archive ofodes and documentations of models; for example, the networkor computational modeling in the social and ecological sciencesomputational model library (CML) (Rollins et al., 2014). Finally,odel base design should consider exiting standards and protocols

or access to models, such as successful standards-based interfaceso combine a limited set of models in a specific domain (Granellt al., 2013a; Kanpen et al., 2009).

The flowchart for constructing a model base in VGEs, accordingo the discussion by Lü (2011), is shown in Fig. 4. Defining a series oftandard formats for the metadata of models from the mechanismevel is the first step; based on this step, models are classified withematic abstraction (de Lara et al., 2013). The next step is to define aetadata standard of models from the model running level, includ-

ng the input/output (I/O) structure, the operation control structure,nd the running environment structure. Based on the metadatarom the analysis of models and running structures, the third step iso provide a model base and sharing environment, in which models

an be organized with methods and an algorithm to smoothly seg-ent, encapsulate, integrate (or construct), and upgrade models.

uch environment should also be extensible and open to introduce

Fig. 5. Workflow in VGEs for modeling management.

new models and tools (Torrens et al., 2012; Wen et al., 2013). At thesame time, in model base design, model standard specifications areemployed to obtain guidance and matching rules; some constraintengines have been designed to guide the model matching and datamatching processes (Lin et al., 2013b). Based on a well-establishedmodel base in VGEs, models for interactive geo-processes can beselected from the model base with a methods and algorithm library(Fig. 4) to create a new geographical analysis tool (Lin et al., 2013b).

4.2.2.2. Model base to manage modeling. Geographic simulation ishighly case dependent and different model settings may producevariations in model performance with different input data andphysical parameters (Beekhuizen et al., 2014; Wang et al., 2009;Zhang et al., 2014c). By focusing on the same problem, alternatemodeling settings can be created to estimate the potential resultsfrom different situations. For instance, five scenarios are employedto quantify various existing and proposed land use measures, bestmanagement practices, and combinations of these items (Randhirand Raposa, 2014). Fortunately, with the model base, integratedmodeling solutions, including input data, model parameterization,and simulation output, can be conveniently traced and reusedto address existing interconnected natural and social challenges(Granell et al., 2013b).

Fig. 5 shows an example of the workflow for the modeling man-agement of an air quality problem. In this case study, modelingparameterization and modeling output were linked for additionalvisualization and analysis. This design is helpful for three reasons.First, new users from different disciplines can learn from the expe-rienced and professional modeler by reviewing the model base formodeling setting. Second, the extent that the model can be repli-cated is improved with detailed information about the modeling,which is identified as one of the three main challenges of simu-lation, such as agent-based modeling for social behavior (Crookset al., 2008). Third, the sharing of simulation output can be extendedbecause the simulation results from extensive modeling informa-tion should be more reliable. For example, the simulation resultsfrom WRF would always be employed as initial and boundary con-ditions by a building designer who needs to examine the windfield for different building conditions. Thus, modeling informationenables designers to gain more confidence in the evaluation results.

4.3. Collaborative modeling

A model base provides geographers with ‘tools’ to simulatecomplex and interactive geographic processes. Because modelsare professional and field-oriented, the successful application of

knowledge to apply the models (de Lara et al., 2013). For instance,to conduct air quality simulations, experts from geographic, mete-orological and air quality fields should collaboratively construct

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odels and analyze the modeling results (Lin et al., 2013a). Inddition to multidisciplinary scientists running models in a group,ifferent stakeholders (including public people, officials, govern-ent, and businesses) are also included in collaborative modeling

Niswonger et al., 2014; Randhir and Raposa, 2014). Furthermore,hese participants may be geographically distributed throughouteterogeneous locations (Chen et al., 2012; Recker et al., 2013; Xut al., 2011). This collaboration about geo-process modeling and theevelopment of necessary methods and tools, such as the collabo-ative modeling tool based on the Google Wave (Recker et al., 2013),re identified as collaborative modeling in this paper (MacEachrent al., 2003).

Collaborative modeling tools are useful for many tasks foreographic problem solving but are usually limited to a specificomain. In response to this situation, Gallardo et al. (2012) pro-osed a model-driven method that consists of a methodological,onceptual, and technological framework to construct a domain-ndependent collaborative modeling environment. With guidancerom extensive frameworks of collaborative modeling, criticalechnologies are developed concerning information access andommunication in the stages of modeling, i.e., selection, editingodels, modeling and validation (Chen et al., 2011; Niswonger

t al., 2014; Recker et al., 2013; Xu et al., 2011) for a group ofsers instead of single-user oriented models (Recker et al., 2013;u et al., 2011). However, conflict in modeling collaboration occurs

n each stage. For example, participant A wants to use an agent-ased model to estimate land use changes. At the same time in theollaborative group, participant B wants to use a cellular automataodel. Thus, to achieve smooth collaboration, a mechanism of con-

ict detection and resolution, such as the object oriented conflictetection technology employed by Xu et al. (2011), or a predefinedriority sequence according to the access time or significance of thearticipant, must be included.

Given the complexity of models and the potential conflict inodeling, collaborative modeling is an iterative process to dissem-

nate knowledge from multiple disciplines (Niswonger et al., 2014).hus, communication, including conflict mitigation, is necessaryo achieve collaboration in each stage, and a variety of electronicechnologies can be employed, such as e-mail, the Intranet andn-line chats, to provide a type of negotiation platform (Xu et al.,011). To improve the communication and interaction efficiency,he participants in the collaborative group are expected to share theame screen in the modeling system (Nissen et al., 2014; Xu et al.,011). Given the participants with different backgrounds, visual-

zation with virtual reality technologies is proposed to improve thenderstanding and efficiency of communication (Lin et al., 2013b).

.4. Uncertainty in geo-process modeling

Uncertainty in modeling is derived from the model and the mod-ling process given the complexity of the geographic dynamics.odels are created based on our knowledge of fundamental laws

nd historical laws about geographic dynamics, which is usuallyncomplete (Schwanitz, 2013). Furthermore, by definition, modelsre a simplification of a specific reality based on our knowledge,hich involves distilling the essence of that reality to a lesser rep-

esentation with different abstract levels for a specific purposeBatty and Torrens, 2005; Lilburne et al., 2004). Thus, any modelill always contain explicit or implicit assumptions and limitations,hich requires researchers to identify the uncertainty in informa-

ion and ascertain the degree to which this uncertainty alters theutcome prediction (Argent et al., 1999).

In addition to the uncertainty in model creation, uncertaintylso exists in the stage of the modeling process, including theodel parameters and input data. Different model parameteri-

ations with simulation assumptions may cause uncertainty in

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the modeling results (Torrens and Nara, 2007). For instance, withdifferent surface-layer options, the meteorological field differsfrom the same WRF model (Zhang et al., 2014c). In addition tothe model parameters, the uncertainty in the simulation is alsoderived from the input data employed in the modeling, includingbasic geographic data, initial or boundary conditions, and the dataemployed to express the interactive processes (Goodchild, 2011;Zhang et al., 2014b), because the uncertainty inherent in the avail-able data is substantial, accounting methods differ and data seriesare incomplete. For instance, Machnick (2011) discusses uncer-tainty in emissions and energy data across various data sources.Beekhuizen et al. (2014) quantified the effect of input data uncer-tainty on the prediction accuracy of an environmental exposuremodel and proposed an approach to estimate the total uncertaintyproduced by potential errors in the input data. This uncertaintyanalysis can be performed to optimize the model and better inter-pret model output (Goodchild, 2011).

To quantify the uncertainty in comprehensive modeling, a cou-ple of frameworks and methods are increasing mentioned in therecent study (Schwanitz, 2013), particularly concerning the uncer-tainty from data input and model structure (Refsgaard et al.,2007). For instance, a set of methods were proposed to assess theuncertainty, such as data uncertainty engine, error propagationequations, expert elicitation, inverse modeling (parameter estima-tion), Monte Carlo analysis, and so on (Bastin et al., 2013; Refsgaardet al., 2007). However, given the complexity of interaction amongmodels and the increasing availability of data and models forholistic modeling, technologies for the integrated assessment ofuncertainty and expression of uncertainty in modeling are still sig-nificant challenges in geo-process modeling.

5. Concluding remarks

This review provides a conceptual framework for current geo-process modeling in VGEs. Theoretically, integrated modeling iscross-disciplinary and cross-scale with different participants. Themain issues for implementing this type of modeling include modelsharing and management, collaborative modeling, and uncertaintyanalysis. With the design of a model base, models can be con-veniently shared with any level of granularity based on modelsegmentation and encapsulation. Tracing and retrieving the modelsetting concerning input data, parameterization, and simulationoutput is also realizable. The design of modeling management withtraceable characteristics can also support collaborative modelingand the estimation of uncertainty in integrated and interac-tive modeling. This paper also discussed methods for improvinggeographic knowledge sharing in modeling concerning modelmanagement and collaborative modeling, which is the next step ingeo-data sharing (Mennis and Guo, 2009). It is a valuable directionof VGEs to conduct modeling based on geographic knowledge, andthe same is to explore and share geographic knowledge by apply-ing modeling. However, geographic knowledge sharing can also befostered across disciplines using visual support systems, which areidentified as a necessary component of VGEs (Chen et al., 2013a;Lin et al., 2013a).

Although improvements in model sharing and collaborativemodeling have been achieved, some areas warrant additionalinvestigation. Future studies should address the following issues:(1) the effective consideration of scale, such as cross-scale linkagesand scale compatibility, in all stages of modeling. (2) The creationof a geographic standard for constructing a geographic model base,

with the goal of sharing models and supporting collaborative mod-eling in the simulation of geographic systems. (3) The assessment ofthe comprehensive uncertainty in modeling to improve the validityof simulation output.

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cknowledgements

The paper was supported by National Key Basic Research Pro-ram of China, the Ministry of Science and Technology of theeople’s Republic of China (grant no. 2015CB954103), the Nationalatural Science Foundation of China (grant nos. 41171146 and1371424), the Innovation and Technology Fund of Hong Konggrant nos. ITS/042/12FP and 2012BAH32B03), the National Planor Science & Technology Support (grant no. 2012BAK10B07-01),nd PAPD.

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