collaborative virtual geographic environments: a case study of air pollution simulation

Information Sciences 181 (2011) 2231–2246

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Information Sciences

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Collaborative virtual geographic environments: A case study of airpollution simulation

Bingli Xu a,b, Hui Lin a,⇑, Longsang Chiu a, Ya Hu a,c, Jun Zhu a,c, Mingyuan Hu a, Weining Cui b

a Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, PR Chinab Department of Information Engineering, The Academy of Armored Forces Engineering, Beijing 100072, PR Chinac Surveying Engineering Department, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, PR China

a r t i c l e i n f o

Article history:Received 25 January 2010Received in revised form 7 January 2011Accepted 12 January 2011Available online 27 January 2011

Keywords:Collaborative virtual geographicenvironmentsGeo-collaborationGeo-visualizationGeo-modelingAir pollution simulation

0020-0255/$ - see front matter � 2011 Elsevier Incdoi:10.1016/j.ins.2011.01.017

⇑ Corresponding author.E-mail address: [email protected] (H. Lin).

a b s t r a c t

The integration of high dimensional geo-visualization, geo-data management, geo-processmodeling and computation, geospatial analysis, and geo-collaboration is a trend inGIScience. The technical platform that matches the trend forms a new framework unlikethat of GIS and is conceptualized in this paper as a collaborative virtual geographic envi-ronment (CVGE). This paper focuses on two key issues. One is scientific research on CVGEincluding the concept definition and the conceptual and system framework development.The other is a prototype system development according to CVGE frameworks for air pollu-tion simulation in the Pearl River Delta. The prototype system integrates air pollutionsource data, air pollution dispersion models, air pollution distribution/dispersion visualiza-tion in geographically referenced environments, geospatial analysis, and geo-collaboration.Using the prototype system, participants from geographically distributed locations can joinin the shared virtual geographic environment to conduct collaborative simulation of airpollution dispersion. The collaborations supporting this simulation happen on air pollutionsource editing, air pollution dispersion modeling, geo-visualization of the output of themodeling, and geo-analysis.

� 2011 Elsevier Inc. All rights reserved.

1. Introduction

GIScience has shifted from representation and analysis of the form of the Earth’s surface to processes that define itsdynamics [10]. The research foci of geo-processes include geo-process modeling, geo-computation, geo-visualization, andgeospatial decision making support. Meanwhile, geographic information is expected to be operated by multiple users, suchas in public participant geographic information systems (PPGIS) [31] and collaborative GIS [14]. These issues are the grandchallenges of GIScience, and can be summarized by several terms such as representation, modeling, computation, simulation,prediction, and collaboration.

GIScience is promoted based on GIS [9]. The framework of GIS has three key pillars: geo-data, geo-visualization, and geo-spatial analysis. Within this framework, GIS can be viewed as a geo-data management system, which has a core of geo-datasurrounded by geo-visualization and geospatial analysis. This geo-data centered framework provides GIS with the ability towell represent the form of the Earth surface. But unfortunately, GIS is inadequate in dealing with time based geo-models andgeo-processes. Also, GIS is designed for use by individuals, and does not support a collaborative operation conducted by mul-tiple participants.

Thus, to meet the grand challenges of GIScience, the technique platform should integrate not only all thecomponents of GIS, which are geo-data, geo-visualization, and geospatial analysis, but also two others, which are

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2232 B. Xu et al. / Information Sciences 181 (2011) 2231–2246

geo-modeling and geo-collaboration. This kind of platform has broken the traditional GIS framework and proposes anew type. In this research, this new type of framework is conceptualized as collaborative virtual geographic environ-ments (CVGE).

From the perspective of GIScience, air pollution dispersion is a geographic process that closely relates to geographical fac-tors. The data and models to support air pollution simulation are geographically referenced. The visualization in air pollutionsimulation is always presented in a virtual geographic environment, such as a 2D map or 3D virtual environment. Thus, thedata can be viewed as geo-data, the models as geo-models, and the visualization as geo-visualization. However, air pollutionis complicated. To conduct comprehensive simulation of air pollution, multi-disciplinary knowledge should be shared, suchas atmospheric science, environmental science, geography, and computer technologies as a minimum. In this research, thecollaboration conducted on air pollution simulation is facilitated by geographic technology, which is called geo-collabora-tion. Thus, the collaborative air pollution simulation can well reflect the key components of CVGE. That is the reason whywe make the combined research of CVGE and air pollution simulation.

This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the concep-tual and system framework development. The other is a prototype system, developed according to the CVGE framework, forair pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dis-persion models, air pollution distribution/dispersion visualization in a geographically referenced environment, geospatialanalysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can joinin the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collabora-tions supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualizationof the output of the modeling, and geo-analysis.

In the remaining parts of this paper, we will first give the definition of CVGE. Then the conceptual framework and systemframework of CVGE will be designed and discussed. There follows a case study of collaborative simulation on air pollutiondispersion for the Pearl River Delta based on CVGE. The case study will also test the rationality of the methodology. Finally,the conclusion and some remarks are given.

2. The state-of-the-art of the CVGE

2.1. CVGE evolvement

Gong and Lin [6] were the first to systematically discuss virtual geographic environments (VGE). Research and applica-tions on VGE have since flourished. In China, VGE has attracted many scholars. Lin argues that VGE can be viewed as thenew generation knowledge of geography [18,21], and the core has extended from geo-data to geo-data and geo-models com-pared with GIS [20]. Lin also argues that VGE can become one of the scientific methods and advanced technologies of modernexperimental geographical study [22]. Gao carried out research on VGE based virtual battle terrain and VGE modeling [5].Sun leads a group to perform research on ‘‘Innovative virtual environments for resource environment’’ and ‘‘Virtual multipledimension information generation system for earth system science’’ [32]. Gong applies VGE to facilitate research in publichealth and digital drainage areas [8,38]. Li and Zhu focus their research on the Cyber-City with the feature of virtual reality[16]. Lv established the first lab of virtual geographic environments to study cognitive theory in virtual environments, thefast way to collect and assimilate 3D data, geography analysis modeling and integration into VGE, distributed cluster com-puters based spatial collaboration and decision making (http://vgekl.njnu.edu.cn/Yanjiu.aspx). Chen carries out research onforest modeling, generation and real time rendering for VGE [30]. Outside of China, there are also many scholars conductingresearch related to VGE. MacEachren and Cai researched and developed a collaborative geo-visualization environment [26].Dykes from the University of Leicester established a virtual environment for student fieldwork using networked components[3]. The electronic visualization laboratory at the University of Illinois has created two famous immersion systems: CAVE andGeoWall, which have been used in geographical research and education [15]. Batty from University College London performsmodeling in virtual environments and applies it in urban planning [12]. At the same time, more and more commercial com-panies are turning to virtual environment research. The most famous one is Google Earth, which was launched in 2005. Tocompete with the Google company, Microsoft put forth their product – Virtual Earth. There are also other companies show-ing their virtual environments, such as Leica Virtual Explorer from the Leica company, TerraExplorer from Skyline, Terrain-View from ViewTec, and so on.

Collaboration has been widely addressed in the domain of information science [11,17,29,40]. Collaborative virtual geo-graphic environments (CVGE) are little tackled, even though they belong to information science to some extent. The CGVE(collaborative geographic visualization environments) coined by MacEachren and Brewer [23] can be regarded as indirectdiscussions on some of the issues of CVGE. Gong and Lin examined CVGE by developing a CVGE framework to facilitatethe collaborative planning of silt dam systems in watersheds [7]. They also defined CVGE as ‘‘a 3D, distributed, and graphicalworld representing and simulating geographic phenomena and processes to enable geographically distributed users toexplore geo-problems and theories and generate hypotheses, and to support geo-model building and validation andcollaborative ecological planning.’’ As for system developing, Zhu et al. tried a technique of CVGE construction with P2P(Peer-to-Peer) and grid technologies [41]. Zhang used the technology of web services to establish the Distributed VirtualGeographic Environment [39].

http://vgekl.njnu.edu.cn/Yanjiu.aspx

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2.2. CVGE definition

CVGE can be simply regarded as VGE with the extended function of geo-collaboration. Before defining CVGE, VGE will beexplored first.

The definition of VGE given by Lin argued that VGE consists of the avatar-based human society and the surrounding objectiveenvironments, such as the hardware of the computer network and sensors, and the software of data and culture environments[19]. In particular, the avatar-based humans here are a combination of the humans in the real word and the 3D avatar in thevirtual world. From this point of view, users become part of the data set, part of the VGE, where they can explore and interactwith the virtual world. Compared with the data-centered GIS, VGE is, therefore, also defined as a human-centered environmentthat represents and simulates geographic environments (physical and human environments) and allows distributed multi-users to implement exploratory geospatial analysis, geo-computation, and geo-visualization, and to control collaborative workfor supporting design and decision-making. In our view, we re-define VGE as follows: Virtual Geographic Environments, whichare virtual representations of physical geography, culture geography and imaginary geography, have two cores (geo-data andgeo-models) centered by virtual environments established with the support of modern technologies of information collection(such as remote sensing, global navigation satellite systems and photogrammetry), huge volume data storage, wide band net-works, high performance computation, high fidelity graphics rendering and multiple channel human–computer interaction forthe representation, computation, simulation and analysis of geo- phenomena and geo-processes, not only to explain ‘‘what’’ thegeographic phenomena are, but also to discover ‘‘why’’ and ‘‘how’’ the phenomena act. This definition emphasizes the impor-tance of the management of geo-models and the representation, simulation and prediction of geo-models based geographicphenomena. This definition weakens the features of high dimension visualization and human centeredness because we thinkthose are not the core elements of VGE. Visualization is the only method for geo-knowledge representation in VGE, and thedimension should be multiple dimensional, not only high. Under some conditions, the transfer of data and information andthe running of the models only take place within a virtual system, while a human is outside the system. This system could alsobe called VGE, but without a human at the centre.

With the extended function of geo-collaboration, CVGE on the one hand inherits the characteristics of geo-data and geo-model integration and multi-dimensional visualization from VGE, while on the other hand extending this with new featuressuch as engaging with multiple participants, distributing functions and operations, sharing geo-knowledge and supportinggeo-collaboration. CVGE is still geo-data and geo-model centered, as is VGE. But the difference between CVGE and VGE is obvi-ous, it is the geo-collaboration. CVGE must be oriented towards multiple users, while VGE may not be. Consequently, CVGEshould contain the functions and operations needed to combine geographically dispersed participants, while VGE can be situ-ated on one desktop. Thus, we define CVGE as follows. CVGE, which is a VGE with the extended function of geo-collaborationand which is based on the two cores of geo-data and geo-models, is a geo-knowledge sharing and generation platform that en-gages multiple participants from geographically dispersed places to study and manage geo-processes and geo-phenomena inthe multi-dimension virtual environments. This definition not only supports and extends the framework of CGVE coined byMacEachren and Brewer [24], but also highlights the functions that were tackled by Gong and Lin [7].

3. Conceptual framework of CVGE

A conceptual framework is developed in this section according to the definition of CVGE, and the following discussionswill tackle the important, though not exhaustive, dimensions of CVGE.

3.1. The two cores of geo-data and geo-models

Geo-data still has a core position in CVGE, but the formats and contents have been broadly extended. Besides the widely-used raster and vector, which were developed by GIS, other types of data are also required to create high dimension CVGE,such as 3D objects, textures, video, audio and instant messaging. These extended data types improve the heterogeneity ofgeo-data and thus present more complex challenges for geo-data integration and management. On the flip side, the broadgeo-data provides abundant information to support spatial and temporal analysis.

Geo-data can be viewed as one way of presenting geographic phenomena. However, in geographic environments, a hugerange of geo-processes are more appropriately presented as geo-models than as geo-data, such as wind flow, air pollutiondispersion and human behaviors, because they are dynamic and the shapes are changing from time to time. If geo-data basedgeographic presentations can be used to show ‘‘what’’ the recorded phenomena are, geo-models based geographic presen-tations can reveal the mechanisms of ‘‘why’’ and ‘‘how’’ behind the phenomena.

Geo-data integration and management in CVGE can receive references from GIS and a huge number of database systems.However, the integration and management of geo-models in CVGE are lacking, and their challenges are obvious. Some ofthese challenges are listed below.

3.1.1. Unifying geo-modelsGeo-models come from many disciplines, for instance oceanography, atmospheric science, geology, biology, demography,

economics, mathematics and physics. A discipline creates its own professional models according to its own research


customs. In order to integrate multi-disciplinary models into CVGE, they must first be unified in format. This problem can beexplained by taking GIS as an example. GIS unifies geo-data by location ðx; y; zÞ and time (t). Given the location and time, allof the geographic data and information can be associated in GIS. Returning to geo-models in CVGE, what approach can beadopted to unify the huge number of heterogeneous geo-models that are created by multiple disciplines? This should bea topic for future research.

3.1.2. Matching geo-modelsThe relationship among models can be simplified as output–input, which means one model’s output serves as the other’s

input. The output–input relation between models can be classified as one–one, many–one, one–many, and many–many.

� One–one: If output of model A can only be accepted by model B, and model B only accepts model A’s output, we call it aone–one type relation.� One–many: If output of model A can be accepted by many models and these multiple models only accept the output of

model A, we call the relation a one–many relation.� Many–one: If outputs from multiple models can be accepted by model A and model A only accepts these models’ output,

we call the relation a many–one relation.� Many–many: If multiple models’ output can be accepted as inputs of other multiple models and vice versa, we call the

relation many–many.

Matching geo-models means searching a geo-model’s input from another geo-model’s output. The rules to check if twogeo-models are matching are based on parameter restrictions. Only if an input parameter has the same physical meaning, thesame unit, and the same scale as an output parameter can the two parameters match. If part or all of the parameters of twogeo-models are matched, the two geo-models are called partly matching or totally matching. Currently, automatic matchingamong geo-models has not been solved.

3.1.3. Efficient computation on geo-modelsFor synchronous geo-collaboration in CVGE, geo-models should respond to invoking as soon as possible. But unfortu-

nately, many geo-models are complex and computationally intensive. For instance, the Fifth-Generation NCAR/Penn StateMesoscale Mode (MM5), which is widely used for circulation modeling and computation, often takes several hours or evenseveral days for a two-week period computation. This long time delay will absolutely lower efficiency of geo-collaboration.Thus, special measures must be taken to minimize the computation time of geo-models.

3.1.4. Integrating and managing geo-modelsThe three types of modeling coupled with GIS (loose coupling, moderate integration, and tight integration) [27] can also

be applied to CVGE for coupling geo-models. Geo-models are in fact a set of program codes, and the management of geo-models can be referenced from commercial program development platforms, such as Visual Studio.Net 2005, Java, and Del-phi. In these platforms, models are coded as functions and stored in function libraries (.lib files) or dynamic link libraries(DLL). The platforms also supply description files (such as .h files for VC++ platform), which can be regarded as the metadataof models, to tell users which functions (models) and input and output variables are used.

3.1.5. Coupling geo-data and geo-modelsGeo-data and geo-models in CVGE should be coupled. Geo-data serves as the geo-model’s input parameters. The output of

geo-models will enrich geo-data.

3.2. Geo-visualization

Two issues are important for geo-visualization in CVGE.

3.2.1. Multi-dimension geo-visualizationGeo-visualization in CVGE must consider usability. Because of the different research backgrounds and different assigned

works, participants in CVGE may prefer different dimensions to be available in geographic visualization. This can be ex-plained by examining collaborative air pollution simulation. Suppose a collaborative air pollution simulation is carriedout by a geographer, an environmental scientist and a computer scientist. During the geographic visualization, all threeare inclined to work according to their customs. The environmental scientist prefers a 2D display, such as GrADS. The geog-rapher will choose a 2D map, but with more interaction between human and the map. For the computer scientist, because ofhis lack of knowledge of air pollution, he would like the system to present pollution mass in a more natural way, in 3D oreven higher dimensions.

Geo-visualization aims to present geo-phenomena and geo-processes in an intuitive way, which will improve usabilityand benefit the overall geo-collaboration. Generally speaking, a higher dimension visualization will have higher usabilityand consequently lead to more efficient geo-collaboration. However, high dimension geo-visualization will cost more timein system development and require higher performance from the computer source and network communication. Thus, when


designing geo-visualization, the balance between dimension and cost should be borne in mind. Two rules can be used toachieve this balance: one is that the geo-visualization must satisfy the overall geo-collaboration; the other one, based onthe first rule, is to adopt the lowest dimension.

3.2.2. Geo-data based geo-visualization and geo-models based geo-visualizationThe geo-visualization should tackle both geo-data and geo-models.Geo-data is geo-information appropriate to a certain time point. Once geo-data has been generated, the resulting geo-

information is fixed. Using the geo-data, we can only perform geo-information exploration by means of geo-data analysis,mining and so on. Compared to geo-data, geo-models are more affluent in the geo-information provided and can thus becalled geo-information generation machines. Geo-models are geo-knowledge and can generate new geo-knowledge bychanging the input/initial parameters. Time dependent geo-models can also present dynamic geo-processes.

Geo-data based geo-visualization, which is a popular pattern for much virtual environment work, is composed of twosteps: modeling terrain and objects using model creation tools; and driving them using graphic engines such as OpenGLin virtual environments. Unlike geo-data based geo-visualization, geo-model based geo-visualization automatically modelsgeo-phenomena and geo-processes when the program is running. In this way, modeling and geo-visualization are seamlesslyintegrated and alternately working.

3.3. Geo-collaboration

MacEachren et al. defined geo-collaboration from two aspects of activity and field of research as: r ‘‘As an activity’’, geo-collaboration is ‘‘group work about geographic scale problems facilitated by geospatial information technologies’’; s ‘‘As afield of research’’, geo-collaboration is ‘‘the study of these group activities, together with the development of methods andtools to facilitate them’’ [25]. Geo-collaboration in CVGE supports six dimensions, which are summarized in Table 1.

In another view, geo-collaboration in CVGE can be classified as several levels as geo-data collaboration, geo-model col-laboration, geo-visualization collaboration, geo-analysis collaboration, and geospatial decision making collaboration.

4. A system framework of CVGE

Gong and Lin designed a system framework of CVGE with five levels: network level, data level, modeling level, graphicslevel, and user level [7]. These five levels are absolutely the key domains for CVGE system development. Based on these fivelevels, we developed a new framework of CVGE as in Fig. 1. In the new framework, the geo-collaboration is defined with fourlevels, which are geo-data collaboration, geo-modeling collaboration, geo-visualization and analysis collaboration, and deci-sion-making collaboration. In the following paragraphs, the four levels of geo-collaboration will be discussed.

4.1. Geo-data collaboration

Geo-data collaboration is collaboration on geo-data reading, editing, updating, deleting, inserting, and backing-up. Thereare several key issues for geo-data collaboration. The first is data consistency. For a single user oriented data managementsystem, data consistency can be ensured by database restriction rules. But for multiple participants’ collaborative environ-ments, not only database restriction rules for a single computer but also restriction rules among multiple computers areneeded to keep data consistency. The second key issue concerns data storage. Three kinds of data storage frameworks fora collaboration system can be adopted, which are centered data storage frameworks (Fig. 2(A)), distributed data storageframeworks (Fig. 2(B)) and hybrid data storage frameworks (Fig. 2(C)). Framework A is easy for maintaining data consistencyand implementing data collaboration, but hard for synchronization when users need to transport data from a centered serverto user clients for data rendering. Framework B distributes data to users’ computers completely, which may allow easy datarendering synchronization because there is no data needing transported from server to client. But on the other hand,framework B makes data management, date consistency and collaboration more complex. Framework C tries to balance

Table 1Six dimensions of geo-collaboration supported by CVGE (according to MacEachren [29]).

Dimensions Supported by CVGE

Problem context r Knowledge construction and refinement; s Design; t Decision-support; u Training and education

Collaborative task r Generate; s Negotiate; t Choose; u Execute; v Explore; w Analyze; x Synthesize; y Present

Commonality ofperspective

r Shared understanding; s Resolution of disputes among competing points of view

Spatial and temporalcontext

r Same time same place; s Same time different place; t Different time same place; u Different time different place

Interactioncharacteristics

r Free group size; s Connection depends on requirements; t Two-way communication information (text, instantmessaging, video, audio, files, and so on)

Mediator r Table; s Diagram; t 2D digital map; u 3D environments

Same function as left

part but in

different place

Image base

Objects base

Collaboration rules

Spatio-temporal Analysis

Data processing

Geo-modelling

Geo-Visualization

Simulation

Decision Making Supporting Publishing

Geo-Collaboration

Geo-data Collaboration

Geo-modeling Collaboration

Geo-visualization and analysis Collaboration

Decision making Collaboration

User base Other Data

Geographic Database

Geo-computation Modelling Evaluation

2D 3D Table Cartography Others

Public Decision makers Domain Experts

Model-base

Fig. 1. System framework of CVGE.

Centre

database

User 1 User2

User3 User4

User1

database

User 1 User2

User3 User4

User1

database

User3

database

User4

database

User1

database

User 1 User2

User3 User4

User1

database

User3

database

User4

database

Centre

database

(A) (B) (C)

Fig. 2. Data storage frameworks for collaboration.


the disadvantages of A and B to approach an optimized data storage system. The third key issue for data collaboration is realtime and reliability of data transport for network communication. In synchronous collaboration, real time communicationcalls for fast transportation of data. When huge data volumes are involved, such as terrain data, more bandwidth will beoccupied, which causes data delay. At the same time, reliability of data transport should be assured.

There are two ways to implement data collaboration, which are copy/paste methodology (CPM) and lock/unlock meth-odology (LUM). If geographically distributed participants only use data for rendering and analysis, but not editing and updat-ing, they may copy data from a central database and paste it into their local database. During collaboration, participants onlyneed to communicate with each other by several bit messages, which can make collaboration smooth. But if participantsneed to edit or update data, the lock/unlock methodology will be preferred. For instance, if participant A wants to edit a dataset, whether the data is locked needs to be checked. If yes, A should wait until the data is unlocked. If A has the authority toedit the data, A can lock the data first and then do what is needed.

4.2. Geo-modeling collaboration

Geo-modeling collaboration includes two aspects: geo-model creation and geo-model operation. Geo-model creation col-laboration or collaborative geo-modeling is collaboration among multiple participants to create a shared geo-model (such as


building, road, and so on). Geo-model creation collaboration can make reference to widely used model creation collaborationin product design [1,28,34,37]. Geo-model operation collaboration includes collaboration on geo-model selection, geo-modelparameters setting, geo-model running, processing of geo-model output, and so on.

There are two key issues for geo-model collaboration. One is conflict detection and resolution. The other is geo-modelmanagement. Conflict of geo-model collaboration happens in geo-model selection, geo-model parameter setting, geo-modelcontrolling and geo-model output processing. For example, participant A (PA) wants to use geo-model A (MA) to present ageographic problem. At the same time in the collaborative group, participant B (PB) wants to use another geo-model B (MB)to present the same problem. So, the two participants trying to model the same object with different models will result inconflict. Thus, to achieve smooth collaboration, a mechanism of conflict detection and resolution must be included. The sec-ond key issue is geo-model management. Geo-model collaboration must be established on the shared geo-model pool. Whena huge number of geo-models fills the pool, an efficient mechanism for geo-model management is pivotal. In GIS, all the geo-data can be queried by spatial temporal indices ðx; y; z; tÞ. With a spatial temporal index, GIS data is standardized as raster orvector, which can be stored and managed in a geo-database management system. Like spatial and temporal data in GIS, geo-models in CVGE should be indexed by some features, such as a model code.

4.3. Geo-visualization and analysis collaboration

If classified by contents, visualizations in CVGE include geo-data visualization, geo-model operation visualization, andcomputation output visualization. Visualization in CVGE can also be classified in dimensional space as 2D visualization,3D visualization, and higher dimension visualization. So, visualization collaboration in CVGE is collaboration on geo-datavisualization, geo-model operation visualization and visualization in 2D, 3D or even higher dimension environments.

Currently, some visualization collaborations, such as scene visualization collaborations, have been tackled for virtualscene and geo-data [13,33,36]. Scene visualization collaboration means editing and updating of a shared scene by one par-ticipant that can be detected and viewed by other participants. During editing and updating, the other participants can givetheir suggestions or even prevent the operation. View point navigation is one common technique used in scene visualizationcollaboration.

4.4. Spatial decision making collaboration

Scientific research supports decision making; so does CVGE. Spatial decision making (SDM) collaboration is based on geo-data collaboration, geo-model collaboration, geo-visualization collaboration, and geospatial analysis collaboration. SDM canbe structured as a step-by-step work flow. Some of the steps may involve parts of, or the entire, geo-data collaboration, geo-model collaboration, geo-visualization collaboration, and geospatial analysis collaboration. Collaboration for SDM may in-volve comparison of multiple similar scenarios on the same objectives or collaboration on each step of one shared scenario,which is schematically shown in Fig. 3.

5. A prototype system of CVGE for air pollution simulation in the Pearl River Delta

5.1. Background

The economic development and population expansion of the Pearl River Delta (PRD) bring negative impacts on this re-gion’s air quality. Currently, scientific research on and governmental management of air pollution in PRD are both underway. Computer supported air pollution simulation is a good method not only of facilitating scientific research into the lawsof air pollution, but also of conducting predictions that can influence decision making on air quality management. Air pol-lution is a complex phenomenon. To achieve a comprehensive simulation of air pollution, the most reasonable method is toshare multi-disciplinary knowledge and engage multi-disciplinary experts in order to conduct a collaborative simulation.The collaboration in air quality management that is conducted by decision makers is also required in daily life. If a city gov-ernment wants to improve the local air quality, the most effective way is to seek collaborative control with all the neighbor-ing cities, and also engage decision makers, multi-disciplinary experts and the public to present their advice, suggestions andcomments.

Existing software or tools supporting air pollution simulation, such as GrADS, Vis5D, have many limitations in modeling,visualization, analysis, and collaboration. The modeling is of low efficiency and not user-friendly. The visualization is viewscale fixed, which does not support information query. The analysis is mainly conducted manually or by third party statis-tical tools (such as SPSS) on correlation analysis among pollution concentration and geographic factors. The analysis seldomincludes spatial and temporal analysis such as that in GIS. As the most obvious problem, almost all of the tools/software tosupport air pollution simulation are designed for use by individual discipline analysts and nominally do not support collab-oration among multiple participants.

We built a prototype system of CVGE for collaborative air pollution simulation by integrating air pollution source data,atmospheric circulation and air pollution dispersion model (MM5), geographic visualization of output of air pollution mod-eling, geospatial analysis, and collaboration facilitated by geo-information technologies. This prototype system presents air

Step 3

Step 2

Step 1

Results

Step 3

Step 2

Step 1

Results

Step 3

Step 2

Step 1

Results

Comparsion

(A) Close collaboration (B) Loose collaboration

Fig. 3. Two types of decision making collaboration. (A) is close collaboration, which happens in all decision making steps, while (B) is loose collaboration,which only compares each participant’s result.


pollution in a high dimensional virtual geographic environment, which makes the pollution distribution/dispersion easier tounderstand. The geospatial analysis supported by the prototype includes point profile, transection, overlay, animation, andso on. The collaboration is designed to engage geographically distributed governmental officials, discipline experts, and thepublic to conduct collaborative simulation of air pollution in CVGE.

Scientific research has revealed that sulphur dioxide (SO2) is one of the main pollutants in PRD [35] Thus, sulphur dioxideis selected as the simulated pollutant. The models, which are used to model the distribution and dispersion of SO2, are MM5(Fifth-Generation NCAR/Penn State Mesoscale Mode) [2,4] for atmospheric circulation and air pollution dispersion modeling.

5.2. Architecture

According to the framework of CVGE (Fig. 1), the prototype system architecture is designed as in Fig. 4. The architectureincludes a geo-data module (including data processing), a geo-modeling and computation module, a geo-visualization mod-ule, a network communication and geo-collaboration module, and an analysis and decision-making module. These are putinto five tiers. Each tier is restricted to suit the CVGE construction and air pollution simulation. The bottom tier is geo-data,including CVGE development data (DEM, texture, 3D objects, video and audio), air pollution source data, meteorological data(wind field, temperature, air pressure), and collaboration data (messages, files, rules and shared metadata). The geo-datacomes from different sources, and has different types, formats and scales. The geo-data processing tier is used to validateand transform the geo-data into a CVGE readable format. Above the geo-data processing tier is the geo-modeling and com-putation tier. The terrain models in this tier are chosen to optimize the algorithm to improve large-scale terrain data loading,rendering and editing. The numerical weather prediction models are used to update environmental conditions, and this out-put will be input to the air pollution dispersion algorithm. The collaboration models will be used to detect conflicts and openthe way for negotiation. The visualization models are algorithms dealing with coordinate conversion, finding the locations ofobjects in the virtual environment, level of detail, and so on. The analysis models integrate geographically related spatial andtemporal analysis algorithms with domain-specific (air pollution) analysis algorithms. The visualization tier is the interfacebetween computer and operators and includes the functions of geo-data representation, geo-model computation visualiza-tion, analysis visualization, air pollution distribution/dispersion visualization, collaboration visualization and simulationvisualization. At the top of the architecture is decision making. The collaboration will take place on each tier of geo-data,geo-models and visualization to aid decision making.

The prototype system is distributed as shown in Fig. 5. The whole system is structured as a star topology, with the systemmanagement node at the center surrounded by a computational grid node to support MM5 computation, and virtual geo-graphic environment (VGE) node to serve as the user interface. The system management node takes many roles in the overallsystem, such as: r middleware to connect users and modules in the computation environment; s security guard to preventattacks on the computational grid; t coordinator for detecting and negotiating conflicts among users; and u storage ofshared data (both pollution sources and model computation results). MM5 is installed on the computational grid of the Chi-nese University of Hong Kong (CUGrid). The environmental data to support the running of MM5 is also stored on the CUGrid.MM5 is remotely controlled by a windows style of model operation interface, which is portable and integrated by virtualgeographic environment (VGE). VGE also integrates geo-data, geo-model operations, pollution result transformations andvisualizations. The visualizations in VGE include 2D style and 3D style for geo-model computation visualization, air pollutiondistribution (overlay) and dispersion (animation) visualization, analysis visualization and collaboration visualization. With

Collaboration rules DEM

Texture

Meteorological

data

Pollution source Collaboration data (Text, file,

video, audio, message) Remote

sensing data Parameter

extraction

Data verification Data conversion and transformation

Air pollution dispersion model

Terrain model Object model Numerical weather prediction model

Analysis model

Visualization arithmetic model

Collaboration model

Data

representation

Model

computation

visualization

Analysis

visualization

Air pollution

distribution/ dispersion

visualization

Collaboration

visualization

Simulation

visualization

others

Decision making

Geo-data

Geo-data

processing

Geo-modeling

Geo-visualizatio

Fig. 4. System architecture CVGE based air pollution simulation for PRD.

CUGrid based MM5 computation

Conflicts detection and negotiation

DEM 3D Texture

Data transform

Pollution result

Pollution

source

GIS data

Environmental

d t

2D/3D visualizationModel operation

Original pollution result

Air pollution

Model

Collaboration

message

User (VGE)

System management node

WAN User (VGE) User (VGE)

Fig. 5. Distributed framework of CVGE based air pollution simulation for PRD.



VGE, users both on campus and abroad can simulate air pollution by several key steps including connecting to the system,generating commands, running geo-models on the CUGrid, collecting computation results and visualizing them. During thisprocess, geo-collaborations happen when multiple users conduct collaborative compilations on air pollution sources, operateon the same models, and carry out collaborative visualizations and analysis.

5.3. Methodology and implementation

5.3.1. Multi-type data integrationGeographic information data, air pollution source data, environmental boundary data, and the output of air pollution

modeling are integrated in the prototype system. The geographic information data are used to create the background andsupport geospatial analysis. The air pollution source data and environmental boundary data are used for MM5 initiation.The outputs of air pollution modeling and computation are pollution concentration distributions or dispersion, and willbe processed by several steps before being visualized in VGE. After pre-processing, the original output of MM5 will be con-verted into VGE readable format, in this case raster (.img and .osg) files. To make it possible to overlay all the data, the sharedprojection is applied. In this system, all the data are projected into WGS84_49N, which covers most of the study area (PRD).

The integrated data are distributed in multiple nodes. Geographic information data are distributed in the user interface(VGE). The air pollution source has many copies and is also distributed into VGE for users’ collaboration on the air pollutionedition. Environmental boundary data are stored on the server of CUGrid. The output data of air pollution modeling withMM5 is of large volume. To save bandwidth of network communications during collaboration, the final readable pollutionimages will also be distributed into the users interface, in this case the VGE.

5.3.2. MM5 integration and computation based on CUGridIn this prototype system, MM5 is used for environmental modeling and air pollution dispersion modeling. MM5 is com-

putation intensive. To decrease model computation time and consequently improve system efficiency, CUGrid is applied.The integration of MM5 into CVGE is based on a designed three-tier framework, which is a mode of Client–Server/Client–

Server and can be presented as in Fig. 6. The mechanism of integrating MM5 modeling and computation on CUGrid is shownin Fig. 6. With VGE, a user prepares some initiation data, such as the study area and pollution source. Then data will be sentto the server of CUGrid (S-CUGrid) and stored there. These data, together with S-Server’s local data, such as the world terrainand environmental boundary conditions, will be used as the input data of MM5. MM5 is a package of program files. We havedeveloped an agent with the Java language to manage the MM5 package. The agent is installed on S-CUGrid and maintainsthe priority of data updating, message receiving and executing, computation resource allocating, MM5 running, and outputreplying.

5.3.3. Geographic visualization of air pollution distribution/dispersionThe output of MM5 has multiple layers, which are regular grids with some attributes such as pollution concentration at-

tached. This feature is very similar to that of GIS raster. So, it is easy and reasonable to apply GIS raster as the air pollutionformat for one layer visualization. To generate the raster of pollution, several key steps should be taken. First of all, the pro-jection is set as WGS84_49N, which is the default of the prototype system. Secondly, according to the location of the left andbottom corner of the simulated area, and the numbers of column and row and grid distance (or grid resolution), an emptyraster is created. Thirdly, the number of raster bands is specified as 3, which means a pseudo color raster will be created.Finally, each grid will be filled with pollution color interpolated according to the output of the MM5 computation.

Agent on server of CUGrid

Receive command and run the executable CSH

files (Written with command lines)

Collect the output

message and send back to client.

Collect and send results (information and files)

to client.

Job execution on CUGrid

member computers Job allocation

Results (message and

files)

Computation resource of CUGrid

Client (VGE)

Send

command to server

Receive message,

information and file

Syst

em m

anag

emen

t

Fig. 6. Mechanism of MM5 integration and computing on CUGrid.


The layer visualization is in two dimensions as in Fig. 8(C). To achieve three dimensional geographic visualization, wehave developed a method of creating pollution boxes to represent the volume of air pollution space. The vertices for eachgrid have their air pollution concentration calculated by MM5. Using these layers on the grid, virtual cubes are formed, asshown in Fig. 7(A) by linking neighboring vertices. In the original MM5 output data, the distances between neighboring lay-ers are not equal. The original cubes thus have different volumes. In order to simplify the process of box rendering, pollutionlayers are relocated with equal distances using interpolation. The pollution concentrations on the new layers are interpo-lated from the two neighboring layers from above and below (Fig. 7(B)). With the relocated layers, the cubes are equal involume. These cubes are then used to create the pollution boxes. Each pollution box has six surfaces. There are two waysto render these surfaces. The first way is to color all the surfaces with white in different transparences. In this way, each sur-face is assigned a pollution concentration interpolated by its four corner values. The surface pollution concentration is usedto determine surface transparency between 0 for overall maximum pollution concentration and 1 for no pollution concen-tration. The effect of this method is presented as Fig. 8(A). The second way is to render pollution boxes with pseudo color,which is composed of Red, Green and Blue (RGB). The pollution concentration of the rendered surface is interpolatedbetween 0 and 255 � 255 � 255, and then allocated to three color components. The effect of pseudo color rendering is shown

z

x

(A) Pollution result by

MM5/SYSUM

with non-equal distance layers

(C) Pollution Box (B) Pollution result by

MM5/SYSUM

with equal distance layer

y

(D) Coordinates

P11

P21

P12

P13P14

P22

P23P24

Fig. 7. Method of creating pollution box. (A) The original output of MM5 with unequal distances between two neighboring layers. (B) Neighbor layerinterpolation to equal distances. (C) Pollution box. (D) Coordinates for pollution box.

Fig. 8. Air pollution representations in 3D and 2D environments.


in Fig. 8(B). The pollution boxes are presented with geographically referenced locations according to their vertices’ coordi-nates. Before visualization, the coordination is re-projected into WGS84_49N, which is the default in VGE. The VGE can pres-ent geographic information, in this case as a terrain (DEM) covered by texture (TM image), road, country boundary, lake,grass land, railway, and some buildings.

The dynamics of air pollution dispersion is achieved by switching the air pollution distribution of plan images in the 2Denvironment or pollution boxes in the 3D environment. Two types of 2D dynamic visualization are implemented. One is tokeep the layer unchanged to see the air pollution dispersion according to time sequences. The other is to keep the time un-changed to see the pollution distribution on different layers in the up-down direction. There are also two kinds of dynamicpresentation of air pollution dispersion in the 3D environment. The first one is to switch air pollution boxes based on timesequences. The second is to keep time unchanged but to continuously change vertical transactions of air pollution distribu-tion in the south–north and east–west directions.

5.3.4. Geo-collaborations of air pollution simulationThe geo-collaborations for air pollution simulation in this research include air pollution source collaboration edition (data

collaboration), collaborative modeling of MM5 (modeling collaboration), collaborative geographical visualization (visualiza-tion collaboration), and collaborative geo-spatial analysis (analysis collaboration). The air pollution source is stored as shownin Fig. 2(C). Pollution source collaboration is based on methods of copy-paste and lock-unlock. There is only one instance ofMM5 running on CUGrid. MM5 accepts only one set of input parameters including land use land cover (LULC), environmentalboundary, pollution source, study area, grid resolution and so on. This set of input parameters is packaged by the systemmanagement node that coordinates multiple participants’ input. The participants can conduct operations on differentparameters or shared parameters. The collaborative geo-visualization of air pollution distribution/dispersion is obtainedby sharing the same virtual camera. Meanwhile, the objects in each participant’s virtual environment are consistent. Thatmeans when a participant updates their virtual object, such as terrain or industry, the change can be automatically detectedand re-rendered by other participants. Analysis collaboration combines and shares the analysis of all participants. Each par-ticipant conducts their analysis alone, and these are then published for sharing automatically. Participants can also subscribeto others’ analyzes. Therefore, the final content of analysis collaboration is the sum of each participant’s analysis.

During geo-collaboration, conflicts are unavoidable. The object oriented method is used for conflict detection while onlinenegotiation is applied for conflict resolution. The object oriented conflict detection (OOCD) treats an operation as an object,composed of attributes and functions. When multiple participants conduct their operations on a shared object, for example aparameter of MM5, all the operations will be recorded as objects. Then, the comparisons among these objects are launched.Only if all the attributes and functions are the same will the operation be performed. Otherwise, conflict is detected. In thisresearch, the implemented conflicts for air pollution simulation based on geo-collaboration happen during collaborative pol-lution source compiling and collaborative MM5 parameter setting.

Although a variety of electronic technologies can be employed as a means of conflict resolution in virtual settings, such ase-mail, intranet and on-line chat, the last is ideal for virtual workers because it allows multiple parties (including the medi-ator) to communicate synchronously. In this research, communication based negotiation is selected because it has beenwidely used and is a more open and efficient way of resolving conflict. The forms of communication implemented in thisprototype include online text, audio and video.

5.4. The results of prototype system test

The prototype system is implemented with VC++, Java, OSG and ArcEngine. With this prototype system, a case of air pol-lution in PRD is used to test its rationality. The period of the test case is from 12:00, September 27, 2004 to 18:10, September27, 2004, during which typhoon HaiTang passed through Taiwan Strait and caused serious air pollution in PRD. To conduct acollaborative simulation with CVGE, multiple participants from Guangdong province and Hong Kong joined the shared CVGEand carried out their authorized works. When a participant joins the CVGE, they must first pass system authentication and begiven a role. In this research, four roles are identified: computer scientists, geographers, atmospheric scientists, and govern-mental officials. Governmental officials supply information on updates of pollution sources and requirements for visualiza-tion and analysis. Atmospheric and environmental scientists operate on model setting and computation, visualization andanalysis. Geographers work on air pollution result data transformation, geographical visualization and spatial and temporalanalysis. Computer scientists perform operations related to high performance model computation. Fig. 9 shows the steps forcollaborative air pollution simulation. In Fig. 9, the central workflow from the top down shows the steps involved in air pol-lution simulation. Around the workflow are the multiple participants who conduct geo-collaboration on air pollution sourcecompiling, MM5 modeling, data transformation, geo-visualization and analysis. Each role may have one or more participants.Thus, conflicts may happen among a single role or multiple roles. When there is conflict, online communication will belaunched to allow mediation between participants.

In this case, the geo-data collaboration is collaborative compiling of air pollution sources including updating, editing,deletion, insertion in CVGE. Fig. 10 is the effect of two participants collaboratively inserting pollution sources for differentregions from two geographically dispersed locations, in this case Hong Kong and Guangdong. The geo-modeling collabora-tion in this case is on collaborative model configuration for MM5 as shown in Fig. 11. The geographer, environmental scien-tist and computer technician are involved in this collaboration. The geographer has the right to set the TERRAIN module. The

Step 3: Pollution sources compiling

Step 2: Study area selection

Step 4: Set MM5 parameters

Step 5: MM5 computation

TERRAIN, INTERPF, Pollution, REGRID, MM5

CUGrid based MM5 computation

Step 1: Role identification

Geographer

Environmental scientist

Governmental official

Computer technician

Step 6: Data transformation and geo-visualization

Step 7: Visualized Analysis

Step 8: Model evaluation

Participants

Fig. 9. CVGE based workflow of air pollution simulation for PRD.

Fig. 10. Air pollution collaborative compiling. (A) Two participants collaboratively inserting pollution sources for different regions from two geographicallydispersed locations. (B) The inserted pollution source can be automatically presented in both 2D and 3D environments.


environmental scientist has the right to set the modules of TERRAIN, REGRID, INTERPF, POLLUTION SOURCE, and MM5. Thecomputer technician has rights to model computation on CUGrid. The visualization collaboration is the virtual camera andthe contents shared in the virtual environment, which means each participant can present their visualization content in theshared visual field. The visualization contents are the volume or layer of air pollution distribution/dispersion in VGE. Fig. 12presents collaborative visualization of the volume of air pollution distribution between two participants. The geospatialanalysis collaboration is dependent on visualization collaboration, and can be achieved by replacing the contents of virtualenvironments with visualized analysis. The geospatial analysis functions include point profile analysis, transection analysis,overlay analysis, isosurface analysis, and animation analysis.

Fig. 11. Collaborative modeling and computation of MM5.

Fig. 12. Visualization collaboration and analysis collaboration. The analysis collaboration can be achieved by replacing visual content with analysis results.



6. Conclusions and remarks

The grand challenge of GIScience has shifted from the presentation of the form of the earth surface to dynamic geo-processes. To simulate and predict geo-processes, the technique platform should integrate geo-data, geo-visualization,spatial analysis, and geo-models. In addition, to satisfy the demands of multiple participants accessing geographic informa-tion, geo-collaboration should also be integrated into the platform. These requirements on the platform demand a newframework that surpasses that of GIS based systems on which GIScience was promoted. This research is based on thisnew framework; we term it a concept of collaborative virtual geographic environments (CVGE). We use CVGE based air pol-lution simulation for the PRD to prove the concept of CVGE. The case tackled all the five key component of CVGE, which aregeo-data integration, geo-model integration, geo-visualization, geospatial analysis, and geo-collaboration.

In the prototype system, geo-data are geographic information data, air pollution source data, and the output data from airpollution modeling. The integration of these data is based on the method of layer management and overlay borrowed fromGIS. The models in this case are MM5 for atmospheric circulation and air pollution dispersion modeling. The integration ofMM5 is based on the mode of server-client/server-client. The computation of the models is based on CUGrid, which mini-mized computation time to improve system response time. Air pollution distribution/dispersion is presented in the 3D envi-ronment with pollution boxes. The geospatial analysis implements functions of point profile, transection, overlay, isosurface,and animation. The geo-collaborations are tackled with geo-data collaboration, geo-modeling collaboration, geo-visualiza-tion collaboration, and geospatial analysis collaboration.

There are two main contributions of this research. In the community of GIScience, we developed the concept of CVGE andits frameworks, which promotes the integration of geo-data, geo-models, geo-visualization, and geo-collaboration. This ef-fort matches the grand challenge of GIScience and consequently is valuable for the development of GIScience. In terms ofenvironmental science, integration of air pollution sources, an air pollution dispersion model, geo-visualization and analysisinto a CVGE supplies a new research methodology and platform for air pollution simulation. Meanwhile, coupling air pollu-tion dispersion models with geo-information opens the opportunity for geographically referenced location based cross-studybetween air pollution and other research areas, such as the economy, public health and urban planning.

Acknowledgements

This research is supported by CUHK RGC Project No. 447807, the National High Technology Plan (863) of the People’sRepublic of China, Project No. 2006AA12Z204, Open Research Fund of State Key Lab of Resource and Environment Informa-tion System of China, and the National Basic Research Program of China, (‘‘973’’ Program, 2007CB714402).

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collaborative virtual geographic environments: a case study of air pollution simulation

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