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Semantic enhanced WebGIS approach to visualize Chinese historical natural hazards
Journal of Cultural Heritage 14 (2013) 181–189
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Original article
Semantic enhanced WebGIS approach to visualize Chinese historical natural hazards Shaochun Dong a,∗ , Xiaoqi Wang b , Hongwei Yin a , Shijin Xu a , Ronghan Xu a a b
School of Earth Science and Engineering, Nanjing University, 210093 Nanjing, PR China Department of History, School of Earth Science and Engineering, Nanjing University, 210093 Nanjing, PR China
a r t i c l e
i n f o
Article history: Received 29 December 2011 Accepted 25 June 2012 Available online 24 July 2012 Keywords: Natural hazard Chinese ancient literature Semantic translation Ontology WebGIS Geodatabase
a b s t r a c t Among China’s vast majority of ancient literature, a wide variety of historical material about natural hazards and natural phenomena are recorded. These records provide significant data and documents for research on historical natural hazards. However, Chinese ancient literature is heterogeneous in syntactic, structural and semantic levels, lacking systematical and scientific information collation, which hinder their use in the research on historical natural hazards. This article presents a solution for promoting comprehensive in-depth understanding of historical natural hazard records by developing a semantic enhanced WebGIS platform. It includes: (1) a geodatabase to systematically store and manage Chinese historical information on natural hazards collated from ancient literature; (2) an ontology to mitigate semantic heterogeneity problems among different datasets; (3) WebGIS tools to visualize and analyze natural hazards in a multidisciplinary way. The platform is compliant to other historical and culture data at spatial and temporal levels. A survey on users’ expectation and satisfactions are conducted. Conclusions and discussions are also raised to suggest further improvements for the semantic enhanced WebGIS platform. © 2012 Elsevier Masson SAS. All rights reserved.
1. Research aims Among China’s vast amount of ancient literature over thousands of years, a wide variety of historical material and data about natural hazards and natural phenomena are recorded, including floods, landslides, earthquakes, eclipses, droughts, sandstorms, volcanoes, tsunamis, wildfires, etc. It is widely recognized that historical natural hazard research can be helpful in hazard investigation and zonation as well as in estimating, managing and mitigating hazards on a regional or global scale [1,2]. Researchers from various disciplines, such as history, geography, geology and global change, show great interest in historical natural hazard research. In addition, the general public is also interested in understanding regional and global natural histories. The aim of this paper is to present a solution that recovers information about China’s historical natural hazards from textual ancient literature to their natural environments through a friendly, comprehensive but easy-to-understand way that is aided by visualization, hence facilitates in-depth understanding of historical natural hazards. We propose to collate information on Chinese
∗ Corresponding author. Tel.: +86 25 83594664; fax: +86 25 83686016. E-mail addresses: [email protected], [email protected] (S. Dong), [email protected] (X. Wang), [email protected] (H. Yin), [email protected] (S. Xu), [email protected] (R. Xu). 1296-2074/$ – see front matter © 2012 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.culher.2012.06.009
historical natural hazards from ancient literature into a spatiotemporal geodatabase and develop an ontology-based WebGIS platform. We attempt to maximize both the platform’s accessibility and adaptability, allowing both professionals who would perform in-depth historical research and the general public who are interested in historical data to access the information easily.
2. Introduction Conducting historical natural hazard research from ancient Chinese writings is not easy. It is not only because ancient Chinese literature was written in classical Chinese, which is different from contemporary Chinese language, but it is also due to the fact that each natural hazard is a complex phenomenon and controlled by interactions of meteorological, geological, environmental and human factors [1]. Comprehensive, in-depth understanding of historical natural hazards requires combining cross-disciplinary factors and analysis of historical data over a range of temporal and spatial scales. Though more and more ancient archives are digitalized and can be read online with full-text or keyword search capabilities, they lack systematic collation for scientific use. This creates great barriers for further understanding and does not address a broader audience that includes both professionals and the general public. Extracting, organizing and interpreting data from ancient archival remain problematic in the following aspects:
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• firstly, historical literature offer a wealth of information about when (time), where (location) and how (magnitude, duration and effects, etc.) a natural hazard happened. These three “W” are important in the analysis of natural hazards’ spatial distribution and occurrence frequency. However, ancient documents were compiled according to various criteria. Due to different purposes, diverse reporting styles and presentations introduced heterogeneity in both syntactic and structural levels, which presents difficulties in comparative statistical analysis to show recurrence pattern as well as flexible visualization by computer systems. Hence, a logical collating process needs to be developed so that the maximum amount of natural hazard information can be extracted from a vast variety of ancient material and transformed into a standard format in order to store, manage, share, integrate and visualize for in-depth understanding; • secondly, names of natural events were diverse in different historical time and places. Ancient authors described natural events and their occurrences in many different ways. For example, sandstorms were recorded using a variety of names in ancient writings, such as haze, yellow fog, rain and dust, rainfall with yellow sand and black fog. This diversity may result from the fact that the ancient Chinese lacked unified scientific standards to name natural phenomena, which resulted in the use of different terminology to describe the same natural phenomena. This led to many synonymous and homonymous terms in ancient literature, which raise semantic heterogeneity problems in information interpretation and integration. If one author’s terminology differs from another’s, keyword-based search or full-text search would have low recalls, which suggests that not all relevant information sources would be discovered [3]. Therefore, it is necessary to establish a translation mechanism on a semantic level, otherwise variable density of records about a specific phenomenon could impede on information discovery, retrieval, interpretation and integration [3], which significantly impacts on statistical analysis results [1]; • thirdly, it is of both theoretical and practical interests to investigate not only what natural hazards occurred in history, but also why they happened. However, the answer to this question cannot be found in any form of textual information, including the digital format. To perform in-depth historical hazard research, data should be combined with cross-disciplinary information in order to produce a comprehensive analysis. Although digitalization of ancient literature makes historical records more accessible, they still need to be recovered from textual forms and translated into their natural environments. Comprehensive in-depth interpretative analysis of historical natural hazards would be more meaningful and effective through the integration of multiple natural and social environmental factors [1]. Fortunately, advanced information technologies have provided new approaches and insights to systematically collate, manage, integrate, visualize and comprehensively analyze heterogeneous historical data in syntactic, structural and semantic levels. For example, the considerable development of ontology and Webbased Geographic Information System (WebGIS), which allow for publish, integrate, analysis and visualization of geospatial data through the Internet [4], seem to shed new lights on solving the aforementioned problems. Ontology, originally from philosophy and later introduced into computer science, is defined as an explicit formal specification of a shared conceptualization [5,6]. It plays a central role in semantic heterogeneities and leads to semantic integration of data [7,8]. It can be used for identification and association of semantically corresponding concepts by providing controlled vocabularies with formal and explicit descriptions of pertinent terminologies and classification schemes [8–10]. A good example of this is Getty’s
Thesaurus of Geographic Names, which provides a structured vocabulary intended to provide terminology about places important to various disciplines, specialized in art, architecture and material culture1 . Many more examples from geology, hydrology, geography and other domains have also proved that the use of ontology can effectively overcome semantic heterogeneities and facilitate semantic query, retrieval, integration and interpretation. For example, Klien et al. (2006) presented a practical use of extent ontology-based service to overcome semantic heterogeneity caused by synonyms and homonyms in disaster management [10]. Fallahi et al. (2008) developed a layer-based ontology to discover geo-services that support semantic interoperability in environmental modeling for describing and research on how the natural environmental changes [11]. Tripathi et al. (2008) discussed a methodology to extend an existing ontology for earth and environment to ensure interdisciplinary knowledge reuse, management and discovery [12]. Moreover, Zhao et al. (2009) defined a set of ontology to represent domain knowledge and achieved effective discovery, automation and integration service of geospatial data [13]. Another technology, WebGIS has powerful capabilities to manage, analyze and visualize diverse historical data sources. As a very popular technological tool used in examining natural hazards and producing hazard maps all over the world, WebGIS offers an effective support for solving problems related to geomorphological processes based on historical data. Many WebGIS applications in fields of historical and cultural geography, archaeology and cultural resources management in the past decades have been reported in literature as a beneficial tool in research. For example, Meyer et al. (2007) developed a simple and accessible system to mange and visualize archaeological sites and monuments based on GIS and XML technology [14]. Lazzari et al. (2009) adopted an integrated GIS-based approach to evaluate the state of conservation-decay of the architectural heritage and its interaction with natural-anthropic components [15]. Kaimaris et al. (2011) achieved systematic management of a large number of historic and contemporary geographic data through a GIS and remote sensing integrated approach in the research of locating buried antiquities [16]. These successful WebGIS applications for incorporating multiple datasets, performing spatial analysis and providing flexible data visualization capabilities all prove that WebGIS is a valuable technology in historical research. Thus, this article presents a solution for promoting comprehensive in-depth understanding of historical natural hazard records by developing a semantic enhanced WebGIS platform. The rest of this paper is organized as follows. Section 3 concerns materials used in this research. Section 4 addresses the methodology that will be used when building the platform and Section 5 describes the results of our platform. Section 6 demonstrates an application scenario based on the platform. Section 7 presents a survey and Section 8 comes to a conclusion and raises a discussion of our current research and in what aspects that future work would concern. 3. Materials 3.1. Historical natural hazard data Every natural hazard can be described in three dimensions: time, location and effect. Among these three aspects, the temporal (time) and spatial (location) attributes are very important in understanding space distribution and time frequency of a particular natural phenomenon, which is essential in analysis of disaster patterns, and
1 Getty’s Thesaurus of Geographic Names, http://www.getty.edu/research/tools/ vocabularies/tgn/.
S. Dong et al. / Journal of Cultural Heritage 14 (2013) 181–189 Table 1 Table structure of China historical natural hazards. Fields
Field type
demo
Hazard ID Hazard name Time Location Original source Original description
Int nvarchar(50) Time nvarchar(50) nvarchar(255) Text
Primary key Format (mm-yyyy) Foreign key Foreign key Original description quoted from ancient literature
would aid disaster mitigation and reduction. The effects of natural hazards which include magnitude, duration, scope of the impact and other related information will improve analysis of hazard consequences and influences. Historical natural hazard data used in this paper are obtained from Chronicle of Great Natural Disasters and Abnormal Phenomena in Ancient China, one of the most comprehensive books funded by National Natural Science Foundation of China. It collates historical natural hazards recorded from a variety of China ancient documents (including local chronicle, Twenty-Four Histories, ancient hydraulics, Miscellanies etc.) dating from around 200 B.C. to 1911 A. D. [17]. In order to maintain as much information as possible at a standard quality so that historical records from different time periods and sources can be integrated and compared, a relational table (Table 1) has been designed to format historical natural hazard data for storage and management. The table contains the following fields: • the “Hazard ID” field is the primary key of the table for the purpose of unique identification; • The “Hazard name” field keeps what ancient authors recorded each hazard event or phenomenon in historical literature as. It might differ from its contemporary name. The original hazard name was kept and would be semantically translated in the next section for retrieval and integration purposes; • many historical literatures contain important descriptions of where and when natural events happened, which could be transferred to WebGIS for spatiotemporal analysis. So two fields have been designed specifically to store spatial and temporal information respectively in Table 1. These two fields would be geo-linked to geological or geographical maps so that historical natural hazard records could be plotted on a map and enable spatiotemporal pattern analysis; • the “Original source” and “Original description” fields are quoted directly from ancient literature that indicates which sources recorded this event and how it was described. These two fields are used to keep connections between records and their original ancient literature. The connections would help users to track back to their original copies if users have further interests to read through. Information is extracted systematically from the book Chronicle of Great Natural Disasters and Abnormal Phenomena in Ancient China and transformed into Table 2. From this table, we can browse and retrieve detailed information about every natural hazard event in a textual format. Meanwhile, once it is geo-linked to a geological or geographical map, the occurrence frequency of every type of hazard during a historical time span can be easily analyzed statistically. Statistical data superimposed on geological maps would display the spatial distribution of each hazard and help further analysis of the trigger factors in addition to improving the process of identifying correlation between different natural hazards and natural environments.
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3.2. Ontology As there are many different terminologies and different classification schemes used to name or describe natural phenomena in ancient Chinese literature, which inevitably introduces a semantic heterogeneity problem in data integration and sharing. This creates a barrier for in-depth analysis and statistics by computer systems. Unfortunately, traditional information searching methods cannot extract implicit meaning and relationships among different terms in datasets. In this paper, an ontological approach is adopted to provide a controlled vocabulary with formal descriptions of the pertinent concepts, which acts like a bridge between ancient literature and source data to reconcile semantic heterogeneities among different ancient literature. A collation of different kinds of terminologies in ancient Chinese literature is made and their corresponding terms in contemporary natural hazard researches are used to construct a natural hazard ontology. The natural hazard ontology can be simply described as a tree (Fig. 1a). O1 , O2 ,. . ., On represent different terms, alias, or synonyms used in ancient Chinese literature to describe a particular natural hazard. O represents the natural hazard ontology. The arrow represents an “is a” relation which means every On is one kind of O. Using sandstorm as an example, we construct a class named sandstorm, a term commonly called today. Rain and dust is a phenomenon that was equivalent to sandstorm in ancient literature. Thus a subclass named “rain and dust” is constructed and an equivalent relationship between the class of sandstorm and the subclass of rain and dust are also established (Fig. 1b). Once the subclasses are mapped to the datasets (Fig. 1c), relationships are established between ancient hazard names in datasets and contemporary vocabularies that are equivalent in meaning. The ontology is coded in OWL2 , which provides an organizational structure for classifying data that can be discovered by both human beings and computer systems. The following is a fragment of natural hazard ontology used in this study, in which a class sandstorm is defined: . . .. . . //define class ” . . .. . .
3.3. Geological/geographical data It is difficult to find a place in China that has no records of occurrences of natural hazards, and it is easy to find some places ranking higher than others in the frequency of a certain natural hazards due to the fact that natural hazards are closely pertinent to natural environments. Therefore, in addition to historical records stored in a relational table, local natural environmental datasets that include lithology, structures, geomorphologies, hydrological conditions, vegetation and climate data, should be considered for comprehensive historical natural hazard research. In this study, a 1: 500,000 geological map and a 1000 mresolution satellite map of China are both used as base maps. They are transformed into vector and raster layers superimposed in a WebGIS-based platform. The former represents the distribution of
2
OWL, Ontology Web Language, http://www.w3.org/TR/owl-features/.
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Table 2 Example records in the table of China historical natural hazards. Hazard ID
Hazard name
Time
Location
Original source
Original description
...
...
...
...
...
...
(Shandong Province)
(Jiajing Reign, Note of Qinzhoufu, Vol. 5)
(In February, strong wind made the sky dark during the daytime. People cannot see the road)
(Shandong Province)
(Kangxi Reign, Note of Dongming County, Vol. 7)
(Strong wind, dark during daytime. . .)
(Shandong Province)
(Qianlong Reign, Note of Dongming County, Vol. 7)
(In December, strong wind, dark in the day time. . .)
...
...
...
(Wind and fog)
2
3
(Wind and fog)
(Wind and fog)
4
...
...
02-1523 (Feb, 1523)
1523 (1523)
12-1534 (Dec, 1534)
...
Fig. 1. Ontology and semantic mapping.
different types of rocks and surficial deposits, as well as the locations of geologic structures such as faults and folds3 , while the latter provides a general survey of the area where historical natural hazards occur. The combination of the geological map and satellite map provide adequate background information that help answer questions such as: where are some certain types of hazard likely to occur; what scientific principles govern the processes responsible for the hazard and how subsurface distribution of porous and impermeable rocks affect the flow of heavy rainstorms. 4. Methodology
The platform is implemented in a four-layer architecture that follows service oriented architecture (SOA) principles as illustrated in Fig. 2. Each layer has distinctive functions and provides interface to the layer above, which makes the whole system able to support a wide range of functions and has the extensible ability to implement new ones. This architecture allows remote users interact with the system, exploiting historical data of their interests and issuing queries to retrieve information they need without extensive trainings in using the system. The platform is also compliant to other historical GIS developments and applications, which allows easy dissemination and visualization, flexible retrieval and analysis of historical culture data.
A browser/server architecture based on ArcGIS server + Microsoft Visual Studio .NET framework is adopted to build the entire platform with the aim to provide a user-friendly interface for data viewing and retrieval to users.
4.1. Data layer
3
Geological map, http://geoinfo.nmt.edu/publications/maps/geologic/whatis. html.
In the data layer, ArcGIS geodatabase is used for storage, management and integration of multi-source datasets. It combines “geo” (spatial data) with “database” (data repository) to create a
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Fig. 2. Architecture of Web-based GIS platform.
central data repository for spatial data storage and management4 . Datasets, including historical datasets, geological maps and satellite maps, etc., are stored in attribute, vector (points, lines, polygons) and raster types. They are represented as multiple layers superimposed together or joined in GIS-based tools for browsing, retrieval and spatial analysis in the application layer.
4.4. Application layer The top layer is the application layer, which serves users over Internet or via LAN. Users can simply use Internet explorer to browse, retrieve, locate data and represent spatial analysis results. Detailed functions and applications for different user scenarios would be addressed in the next section.
4.2. Semantic layer
5. Results
The second layer is the semantic layer. It consists of ontologies and mapping mechanisms for semantic translation and data mapping, which will help to mitigate semantic heterogeneity problems and make query, integration and statistics possible and accurate. More specifically, ancient terminologies could be translated into uniformed expressions when analyzed, whereas contemporary vocabularies could be translated into ancient terminologies when retrieved. This makes it possible for different users to access to the historical natural hazard records in a uniform fashion no matter what different terminologies were used in the past.
Based on the above platform architectural design and implementation, a system has been developed that can not only integrate various historical natural hazards recorded in diverse ancient literature, but also provide a well-designed graphical user interface to access the database and represent data in an enhanced interpretative and visualizing way. Users can connect to the platform simply using a Web browser and accomplish the following functions in an easy-to-use way.
4.3. Service layer The third layer is the service layer which implements GIS functionalities and Web services. ArcGIS server is adopted to develop Web-based GIS system in this research. It is responsible for introducing advanced GIS functions to the Internet and to geovisualization resources managed in the geodatabase. It is used for entering and handling historical data in response to the need for rapid access to databases and to represent data that references the position of natural phenomena. The Web Server is used to deploy Web services and host Web applications running in the GIS server.
4
Geodatabas, http://www.esri.com/software/arcgis/geodatabase/index.html.
5.1. Information browsing: viewing China’s historical natural hazards both in a conventional HTML mode and a map navigation mode This function gives users the ability to switch between two browsing environments to view historical natural hazard information: the textual and map environment. In the textual environment, all historical natural hazard data is represented in a table, sorted by type, time and place in a hierarchical tree structure on the left side of the page (Fig. 3). Users can select one natural hazard event to navigate through detailed information that is extracted from the geodatabase dynamically. Clicking on the map navigation button would lead users to switch to a map visualizing navigation environment from the textual one, which would enable users to browse natural hazards according to their geographical locations on a map (Fig. 4). In this mode, users can manipulate maps with universal GIS tools such as
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Fig. 3. An example of textual information browsing mode.
Fig. 4. An example of map navigation mode.
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preview, pan, zoom in/out throughout the map server, identify features on the map and control different map layers display or hide. Natural hazard events would be plotted on a map so that it is easy to navigate the spatial distribution. 5.2. Data retrieval: search for relevant data by semantic translation It is a semantic enhanced search function that facilitates users to discover detailed historical natural hazard events on a map with textual contexts through time and space. Simple queries by keywords: time (in solar calendar or Chinese ancient dynasties), place and type of events or combination of keywords for several characteristics are possible. The search requests would be semantically translated into vocabularies that were used in history and mapped to targeted records in the geodatabase so that users can seamlessly search across different terminologies without exclusion of relevant information. In the map visualizing navigation mode, users are able to search for detailed hazard information by drawing a rectangle on the map to explore which natural hazards in history occurred within the specified spatial scope indicated by the rectangle on the map. Users are also able to search for spatial distribution of natural hazards by specifying a hazard type as an input parameter. 5.3. Data exploration: discover damage frequency and distribution of phenomena by using map navigation Users can utilize this function to produce interpretable and informative thematic maps by symbolizing aggregated data. The common forms of aggregated symbols for representing numerical data on maps are graduated circles, density dots, stacked bars, proportional columns in which the area of the circle, the mass of the dot or the height of the column are scaled in proportion according to data values. This function enables users to get a full understanding of the spatiotemporal distribution of any type of natural hazards for the specified time span. These thematic representations would facilitate the following spatial analysis, but not limited to: • examining occurrence frequency of a certain natural hazard in interested areas or interested time span; • producing historical hazard zonation maps based on spatial distributions; • comparing correlations of multiple phenomena occurrences; • identifying areas where events occur repeatedly or with great intensity. Using plague and drought as examples, Fig. 4 shows the spatial distribution correlation of these two hazards. In this thematic map, the area of the dot indicates the frequency of plagues (from 1423 A.D. to 1904 A.D.). The larger the dot, the more frequently plagues occurred. The gradient color indicates the frequency of droughts (from 394 A.D. to 1901 A.D.). The darker the color, the more often droughts occurred. These two hazards show a positive correlation in spatial scales. 6. Experimental applications In this section, a user scenario will be illustrated to demonstrate applications of this platform. A middle school student, Kevin, is interested in if China has had severe sandstorms in the past. He does not know what sandstorm was named in ancient China. He types a keyword “sandstorm” into the query textbox and constrains the time span from 1200 to
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1900 A.D., but there are not any results returned by the conventional searching method, because there is no explicit use of the word “sandstorm” in the natural hazard geodatabase, and the search is unable to infer the inner relationship between sandstorm and equivalent ancient terminology. However, in our platform, when the Web server receives a request for “sandstorm”, the system generates a query against the ontology, and sends the query to the mapping illustrated in Fig. 1c. The mapping will deconstruct the query into several sub-queries, such as haze, yellow fog, rain and dust, rainfall with yellow sand, black fog, and sends each of them to their targeted terms in the geodatabase. Kevin is then provided with the illusion of using the same vocabulary as ancient Chinese documentation, bridging disparities between modern Chinese and ancient Chinese terminology. All different ancient terminologies that are mapped to “sandstorm” are clustered and counted as the same natural phenomenon. After the semantic translation and search process, Kevin would receive hundreds of records matching his search request. In addition, Kevin wants to perform an analysis to identify where sandstorm occur repeatedly or with great intensity in history. This analysis could not be accomplished without the accurate search results from the previous step. With China’s provincial map as a base map, he would clearly see that ancient sandstorms occurred with a high frequency in the north part of China, a low-temperature zone that is arid, less vegetated and has low level of precipitation, all of which are necessary conditions to form sandstorms. Conversely, sandstorms could cause substantial drops in temperature that lead to heavy snow, frozen soils and nutrition loss in the region that reduce the agricultural productivity and influence local inhabitants’ normal lifestyles [18,19]. If this information is combined with historical facts, Kevin can comprehend historical events even further. For instance, Kevin would be able to learn that northern China was the ideal homeland for nomadic people in ancient China, but this abominable weather and environment once increased the fatality of both people and livestock, therefore it caused nomadic people to invade the Han nationality that lived in less northern parts on several occasions for a better living environment [20]. This crossdisciplinary explanation deepens Kevin’s understanding of history and may also further his interests in natural history. In the next application scenario, Kevin drags his mouse on the map and places it over the northeast part of China. He finds a place indexed in the UNESCO World Heritage list called Wudalianchi where the search results indicate that many volcanoes erupted in history. Examining the historical volcano distribution map, Kevin can see very clearly that historical volcanic eruptions are densely distributed in Heilongjiang and Jilin provinces, Northeastern China. These areas are economic development regions in agriculture, forestry and tourism. This then brings up the question of “Why are volcanoes more conductive to take place in these areas than other places in China?”. Using geological maps as base maps, Kevin can understand that these areas are situated along converging plate boundaries where subduction occurs, which is one of the principal geological settings for volcanism. The process of volcanisms molds these areas into spectacular landforms that are now known as World Geological Parks, attracting a large amount of worldwide tourists every year. After volcanoes erupted, large amounts of volcanic ashes were discharged and reactions with water and atmospheric gases occurred, forming very fertile soil that supported lush vegetation and crops. This made Northeastern China one of the major grain producer in China. However, these eruptions also represented significant disasters, including destruction of crops, buildings, transportation and communication systems in addition to pollution of water supplies. Moreover, forests and all living creatures within a distance of 10 km from the volcanoes would have been destroyed and killed. This implies that even nowadays, they are still high-risk volcanoes because over 100,000 people
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live near them. With the addition of the above mentioned background information, Kevin can obtain a better understanding of the natural history of Northeastern China, particularly in the field of volcanic-related hazards, which compliments his knowledge of the sightseeing of the area. The user journey in this platform presents to Kevin an innovative way to read history and comprehensively understand historical events.
7. Evaluation A concise survey had been conducted to assess users’ satisfaction and expectation on the platform, and allow users to offer suggestions for modifications and further improvements. Fifty-six anonymous users participated in this survey and gave positive feedback on the system (Table 3). Ninety-three percent of participants indicated that the system is interesting, 86% indicated that the system is meaningful, and 84% believed that the system can promote and deepen historical natural hazard research. Moreover, the survey tells us that the cross-disciplinary perspectives on analyzing historical natural hazards through visualized presentation is the most popular and attractive functions for users, whereas semantic search and information browsing are ranked second and third. In addition to these statistics, survey participants also give valuable suggestions, which are concluded as follows: • some users suggest to develop new functions to support updated information not only by system developers from the server side, but also to provide access to insert or modify data or add comments at the client side by general users, like Wikipedia, Facebook and other websites. This allows users to become more engaged and positively interactive with the system rather than limit users to reading the contents from a screen at a distance;
• besides the descriptive information relating to each historical natural hazard, users also want to learn about the latest research conducted in specific districts or areas. So it could be more meaningful to make links to the latest literature, reports or media news related to each historical natural hazard founded in the database; • in order to make this system more accessible and convenient, users suggest developing a mobile version so that they could visit it through mobile phone or iPad any time any place. Once the system performs a data update, the system would post update announcements through social networking sites such as Twitter, Weibo (a Chinese Web service that functions in a similar way to Twitter), or Facebook so that targeted users would be notified automatically; • linking or integrating historical natural hazard information outside of China could also strengthen the system. This may help raise potential international research cooperation at a global scale and provide a wider perspective in global change.
8. Conclusions and discussion The goal of this article is to present an example of building a platform to store, manage, represent, inquire and analyze historical natural hazard records in a flexible, convenient and interpretative way. To do this, a semantic enhanced WebGIS platform has been developed which combines: • a geodatabase to provide spatiotemporal data storage and management; • a domain ontology to mitigate semantic heterogeneity problems; • WebGIS tools to offer cross-disciplinary perspectives to arouse interests and inform people of not only what happened in history but also why it happened.
Table 3 Feedbacks from survey users. Questions Do you think this system is interesting? Very interesting
Not interesting at all
Percentage (%) 5 4 3 2 1
39 54 5 0 2
Do you think this system makes historical natural hazard research meaningful? 5 Very meaningful 4 3 2 1 Not meaningful at all
52 34 10 0 2
Do you think this system can help you understand historical natural hazard in a new way? 5 Yes 4 3 2 1 No at all
54 30 12 2 2
Do you think this system can promote historical natural hazard research deeper and more comprehensively? Yes 5 4 3 2 1 No at all
64 23 11 0 2
What function(s)/information do you expect most from the system? Information browsing: viewing China historical natural hazards both in a conventional HTML mode and a map navigation mode.
68
Data retrieval: search for interested data by semantic translation
71
Data exploring: discover phenomena damage frequency and distribution by using map navigation
57
Integrate geological, geographical and environmental factors to analysis and provide flexible data visualization
82
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The experiments that we have done in this paper show that: • the geodatabase is flexible to store and manage diverse heterogeneous datasets, including attribute data, vector data and raster data. It provides a central data repository for easy data entry and user access in the field of historical natural hazard research. It would be improved by extracting more individual traits of different natural hazards for richer representations and further practices in the geodatabase world; • ontology-based semantic integration is promising for scientific data integration and navigation. Construction of natural hazard ontology and development of semantic mapping methodology enable us to identify and retrieve information among ancient literature. It mitigates semantic gaps between ancient literature and common language used today, makes semantic search possible and helps to improve searching accuracy. The development of ontology is an iterative process, it needs to be updated and improved when more ancient terminologies are systematically collated; • the WebGIS-based platform presented in the paper provides conventional and advanced functionalities for visualizing and analyzing historical natural hazards distributed in spatiotemporal scales. It allows different users to utilize historical data for multiple purposes. The proposed platform is compliant and supports not only historical data but also modern data. It also supports natural hazard data as well as data from other disciplines. The novel design of the platform makes dynamically inserting new data very easy. Current application cases and feedback affirm that this platform is effective and meaningful. They also suggest further directions for iterative modifications and improvements. Integration of diversity of environmental factors, mapping mechanisms and highly visualized data representation modes should be consistently updated in order to adopt more complex requirements. Evaluation and questionnaires on users’ expectation and feedback would be conducted continuously to increase familiarity with targeted audiences. As a historical research approach, we believe that it is necessary to develop innovative functions to facilitate in-depth studies of extraordinary cultural and historic resources recorded in ancient literature such as the one we have hereby presented as constant renewal is the best way of preservation [21]. Acknowledgements This research has been supported by National Natural Science Foundation of China (Project No. 40802080), Jiangsu Planned Projects for Postdoctoral Research Funds (Project No. 0206003405) and Humanities Fund of Nanjing University. Authors would like to appreciate American Association of Museum and Professor
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Christina Hsu from Taiwan for their continuous supports in creative thinking, constructive suggestions and encouragements. Careful reviews from two anonymous reviewers are also greatly appreciated. Thanks also go to Miss Anna Mo Zhou from Tufts University for proofreading the whole paper. References [1] C. Audisio, G. Nigrelli, G. Lollino, A GIS tool for historical instability processes data entry: an approach to hazard management in two Italian Alpine river basins, Comput. Geosci. 35 (8) (2009) 1735–1747. [2] K.T. Chau, Y.L. Sze, M.K. Fung, W.Y. Wong, E.L. Fong, L.C.P. Chan, Landslide hazard analysis for Hong Kong using landslide inventory and GIS, Comput. Geosci. 30 (4) (2003) 429–443. [3] M. Lutz, J. Sprado, E. Klien, C. Schubert, I. Christ, Overcoming semantic heterogeneity in spatial data infrastructures, Comput. Geosci. 35 (4) (2009) 739–752. [4] M. Kulawiak, A. Prospathopoulos, L. Perivoliotis, M. Luba, S. Kioroglou, A. Stepnowski, Interactive visualization of marine pollution monitoring and forecasting data via a Web-based GIS, Comput. Geosci. 36 (8) (2010) 1069–1080. [5] T.R. Gruber, A translation approach to portable ontology specifications, Knowl. Acquis. 5 (2) (1993) 199–220. [6] E. Simperl, Reusing ontologies on the Semantic Web: a feasibility study, Data Knowl. Eng. 68 (10) (2009) 905–925. [7] K. Lin, B. Ludascher, A system for semantic integration of geologic maps via ontologies, (2003), available at: http://users.sdsc.edu/∼ludaesch/ Paper/scisw03-geon.pdf. [8] H. Wache, T. Vogele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, S. Hubner, Ontology-based integration of information a survey of existing approaches, in: IJCAI-01 workshop: ontologies and information sharing, Seattle, WA, 2001, pp. 108–117. [9] K. Lin, B. Ludascher, GEON: Ontology-enabled map integration, (2004), available at: http://www.geongrid.org/pubs/local/kai lin ESRI-2004.pdf. [10] E. Klien, M. Lutz, W. Kuhn, Ontology-based discovery of geographic information services – an application in disaster management, Comput. Environ. Urban 30 (1) (2006) 102–123. [11] G.R. Fallahi, A.U. Frank, M.S. Mesgari, A. Rajabifard, An ontological structure for semantic interoperability of GIS and environmental modeling, Int. J. Appl. Earth Obs. Geoinf. 10 (3) (2008) 342–357. [12] A. Tripathi, H.A. Babaie, Developing a modular hydrogeology ontology by extending the SWEET upper-level ontologies, Comput. Geosci. 34 (9) (2008) 1022–1033. [13] P. Zhao, L. Di, G. Yu, P. Yue, Y. Wei, W. Yang, Semantic Web-based geospatial knowledge transformation, Comput. Geosci. 35 (4) (2009) 798–808. [14] E. Mayer, P. Grussenmeyer, J.P. Perrin, A. Durand, P. Drap, A web information system for the management and the dissemination of culture heritage data, J. Cult. Herit. 8 (4) (2007) 396–411. [15] M. Lazzari, M. Danese, N. Masini, A new GIS-based integrated approach to analyse the anthropic-geomorphological risk and recover the vernacular architecture, J. Cult. Herit. 10 (S1) (2009) e104–e111. [16] D. Kaimaris, S. Sylaiou, O. Georgoula, P. Patias, GIS of landmarks management, J. Cult. Herit. 12 (1) (2011) 65–73. [17] Z.H. Song, Chronicle of Great Natural Disasters and Abnormal Phenomena, Guangdong Eudcation Publishing House, Guangdong, China, 1992. [18] Y.Q. Song, L.X. Zhang, Reason of sand-dust storm eastward expansion during historical period, J. Desert Res. 22 (6) (2002) 632–635. [19] C.S. Wang, Research of sandstorm in chinese historical period: an overview, J. Desert Res. 30 (5) (2010) 1182–1185. [20] Z.Y. Xu, Guan Shi Bian, First Edition, Press of Guangxi Normal University, Guangxi, China, 2008. [21] M. Carrozzino, A. Scucces, R. Leonardi, C. Evangelista, M. Bergamasco, Virtually preserving the intangible heritage of artistic handicraft, J. Cult. Herit. 12 (1) (2011) 82–87.
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Original article
Semantic enhanced WebGIS approach to visualize Chinese historical natural hazards Shaochun Dong a,∗ , Xiaoqi Wang b , Hongwei Yin a , Shijin Xu a , Ronghan Xu a a b
School of Earth Science and Engineering, Nanjing University, 210093 Nanjing, PR China Department of History, School of Earth Science and Engineering, Nanjing University, 210093 Nanjing, PR China
a r t i c l e
i n f o
Article history: Received 29 December 2011 Accepted 25 June 2012 Available online 24 July 2012 Keywords: Natural hazard Chinese ancient literature Semantic translation Ontology WebGIS Geodatabase
a b s t r a c t Among China’s vast majority of ancient literature, a wide variety of historical material about natural hazards and natural phenomena are recorded. These records provide significant data and documents for research on historical natural hazards. However, Chinese ancient literature is heterogeneous in syntactic, structural and semantic levels, lacking systematical and scientific information collation, which hinder their use in the research on historical natural hazards. This article presents a solution for promoting comprehensive in-depth understanding of historical natural hazard records by developing a semantic enhanced WebGIS platform. It includes: (1) a geodatabase to systematically store and manage Chinese historical information on natural hazards collated from ancient literature; (2) an ontology to mitigate semantic heterogeneity problems among different datasets; (3) WebGIS tools to visualize and analyze natural hazards in a multidisciplinary way. The platform is compliant to other historical and culture data at spatial and temporal levels. A survey on users’ expectation and satisfactions are conducted. Conclusions and discussions are also raised to suggest further improvements for the semantic enhanced WebGIS platform. © 2012 Elsevier Masson SAS. All rights reserved.
1. Research aims Among China’s vast amount of ancient literature over thousands of years, a wide variety of historical material and data about natural hazards and natural phenomena are recorded, including floods, landslides, earthquakes, eclipses, droughts, sandstorms, volcanoes, tsunamis, wildfires, etc. It is widely recognized that historical natural hazard research can be helpful in hazard investigation and zonation as well as in estimating, managing and mitigating hazards on a regional or global scale [1,2]. Researchers from various disciplines, such as history, geography, geology and global change, show great interest in historical natural hazard research. In addition, the general public is also interested in understanding regional and global natural histories. The aim of this paper is to present a solution that recovers information about China’s historical natural hazards from textual ancient literature to their natural environments through a friendly, comprehensive but easy-to-understand way that is aided by visualization, hence facilitates in-depth understanding of historical natural hazards. We propose to collate information on Chinese
∗ Corresponding author. Tel.: +86 25 83594664; fax: +86 25 83686016. E-mail addresses: [email protected], [email protected] (S. Dong), [email protected] (X. Wang), [email protected] (H. Yin), [email protected] (S. Xu), [email protected] (R. Xu). 1296-2074/$ – see front matter © 2012 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.culher.2012.06.009
historical natural hazards from ancient literature into a spatiotemporal geodatabase and develop an ontology-based WebGIS platform. We attempt to maximize both the platform’s accessibility and adaptability, allowing both professionals who would perform in-depth historical research and the general public who are interested in historical data to access the information easily.
2. Introduction Conducting historical natural hazard research from ancient Chinese writings is not easy. It is not only because ancient Chinese literature was written in classical Chinese, which is different from contemporary Chinese language, but it is also due to the fact that each natural hazard is a complex phenomenon and controlled by interactions of meteorological, geological, environmental and human factors [1]. Comprehensive, in-depth understanding of historical natural hazards requires combining cross-disciplinary factors and analysis of historical data over a range of temporal and spatial scales. Though more and more ancient archives are digitalized and can be read online with full-text or keyword search capabilities, they lack systematic collation for scientific use. This creates great barriers for further understanding and does not address a broader audience that includes both professionals and the general public. Extracting, organizing and interpreting data from ancient archival remain problematic in the following aspects:
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• firstly, historical literature offer a wealth of information about when (time), where (location) and how (magnitude, duration and effects, etc.) a natural hazard happened. These three “W” are important in the analysis of natural hazards’ spatial distribution and occurrence frequency. However, ancient documents were compiled according to various criteria. Due to different purposes, diverse reporting styles and presentations introduced heterogeneity in both syntactic and structural levels, which presents difficulties in comparative statistical analysis to show recurrence pattern as well as flexible visualization by computer systems. Hence, a logical collating process needs to be developed so that the maximum amount of natural hazard information can be extracted from a vast variety of ancient material and transformed into a standard format in order to store, manage, share, integrate and visualize for in-depth understanding; • secondly, names of natural events were diverse in different historical time and places. Ancient authors described natural events and their occurrences in many different ways. For example, sandstorms were recorded using a variety of names in ancient writings, such as haze, yellow fog, rain and dust, rainfall with yellow sand and black fog. This diversity may result from the fact that the ancient Chinese lacked unified scientific standards to name natural phenomena, which resulted in the use of different terminology to describe the same natural phenomena. This led to many synonymous and homonymous terms in ancient literature, which raise semantic heterogeneity problems in information interpretation and integration. If one author’s terminology differs from another’s, keyword-based search or full-text search would have low recalls, which suggests that not all relevant information sources would be discovered [3]. Therefore, it is necessary to establish a translation mechanism on a semantic level, otherwise variable density of records about a specific phenomenon could impede on information discovery, retrieval, interpretation and integration [3], which significantly impacts on statistical analysis results [1]; • thirdly, it is of both theoretical and practical interests to investigate not only what natural hazards occurred in history, but also why they happened. However, the answer to this question cannot be found in any form of textual information, including the digital format. To perform in-depth historical hazard research, data should be combined with cross-disciplinary information in order to produce a comprehensive analysis. Although digitalization of ancient literature makes historical records more accessible, they still need to be recovered from textual forms and translated into their natural environments. Comprehensive in-depth interpretative analysis of historical natural hazards would be more meaningful and effective through the integration of multiple natural and social environmental factors [1]. Fortunately, advanced information technologies have provided new approaches and insights to systematically collate, manage, integrate, visualize and comprehensively analyze heterogeneous historical data in syntactic, structural and semantic levels. For example, the considerable development of ontology and Webbased Geographic Information System (WebGIS), which allow for publish, integrate, analysis and visualization of geospatial data through the Internet [4], seem to shed new lights on solving the aforementioned problems. Ontology, originally from philosophy and later introduced into computer science, is defined as an explicit formal specification of a shared conceptualization [5,6]. It plays a central role in semantic heterogeneities and leads to semantic integration of data [7,8]. It can be used for identification and association of semantically corresponding concepts by providing controlled vocabularies with formal and explicit descriptions of pertinent terminologies and classification schemes [8–10]. A good example of this is Getty’s
Thesaurus of Geographic Names, which provides a structured vocabulary intended to provide terminology about places important to various disciplines, specialized in art, architecture and material culture1 . Many more examples from geology, hydrology, geography and other domains have also proved that the use of ontology can effectively overcome semantic heterogeneities and facilitate semantic query, retrieval, integration and interpretation. For example, Klien et al. (2006) presented a practical use of extent ontology-based service to overcome semantic heterogeneity caused by synonyms and homonyms in disaster management [10]. Fallahi et al. (2008) developed a layer-based ontology to discover geo-services that support semantic interoperability in environmental modeling for describing and research on how the natural environmental changes [11]. Tripathi et al. (2008) discussed a methodology to extend an existing ontology for earth and environment to ensure interdisciplinary knowledge reuse, management and discovery [12]. Moreover, Zhao et al. (2009) defined a set of ontology to represent domain knowledge and achieved effective discovery, automation and integration service of geospatial data [13]. Another technology, WebGIS has powerful capabilities to manage, analyze and visualize diverse historical data sources. As a very popular technological tool used in examining natural hazards and producing hazard maps all over the world, WebGIS offers an effective support for solving problems related to geomorphological processes based on historical data. Many WebGIS applications in fields of historical and cultural geography, archaeology and cultural resources management in the past decades have been reported in literature as a beneficial tool in research. For example, Meyer et al. (2007) developed a simple and accessible system to mange and visualize archaeological sites and monuments based on GIS and XML technology [14]. Lazzari et al. (2009) adopted an integrated GIS-based approach to evaluate the state of conservation-decay of the architectural heritage and its interaction with natural-anthropic components [15]. Kaimaris et al. (2011) achieved systematic management of a large number of historic and contemporary geographic data through a GIS and remote sensing integrated approach in the research of locating buried antiquities [16]. These successful WebGIS applications for incorporating multiple datasets, performing spatial analysis and providing flexible data visualization capabilities all prove that WebGIS is a valuable technology in historical research. Thus, this article presents a solution for promoting comprehensive in-depth understanding of historical natural hazard records by developing a semantic enhanced WebGIS platform. The rest of this paper is organized as follows. Section 3 concerns materials used in this research. Section 4 addresses the methodology that will be used when building the platform and Section 5 describes the results of our platform. Section 6 demonstrates an application scenario based on the platform. Section 7 presents a survey and Section 8 comes to a conclusion and raises a discussion of our current research and in what aspects that future work would concern. 3. Materials 3.1. Historical natural hazard data Every natural hazard can be described in three dimensions: time, location and effect. Among these three aspects, the temporal (time) and spatial (location) attributes are very important in understanding space distribution and time frequency of a particular natural phenomenon, which is essential in analysis of disaster patterns, and
1 Getty’s Thesaurus of Geographic Names, http://www.getty.edu/research/tools/ vocabularies/tgn/.
S. Dong et al. / Journal of Cultural Heritage 14 (2013) 181–189 Table 1 Table structure of China historical natural hazards. Fields
Field type
demo
Hazard ID Hazard name Time Location Original source Original description
Int nvarchar(50) Time nvarchar(50) nvarchar(255) Text
Primary key Format (mm-yyyy) Foreign key Foreign key Original description quoted from ancient literature
would aid disaster mitigation and reduction. The effects of natural hazards which include magnitude, duration, scope of the impact and other related information will improve analysis of hazard consequences and influences. Historical natural hazard data used in this paper are obtained from Chronicle of Great Natural Disasters and Abnormal Phenomena in Ancient China, one of the most comprehensive books funded by National Natural Science Foundation of China. It collates historical natural hazards recorded from a variety of China ancient documents (including local chronicle, Twenty-Four Histories, ancient hydraulics, Miscellanies etc.) dating from around 200 B.C. to 1911 A. D. [17]. In order to maintain as much information as possible at a standard quality so that historical records from different time periods and sources can be integrated and compared, a relational table (Table 1) has been designed to format historical natural hazard data for storage and management. The table contains the following fields: • the “Hazard ID” field is the primary key of the table for the purpose of unique identification; • The “Hazard name” field keeps what ancient authors recorded each hazard event or phenomenon in historical literature as. It might differ from its contemporary name. The original hazard name was kept and would be semantically translated in the next section for retrieval and integration purposes; • many historical literatures contain important descriptions of where and when natural events happened, which could be transferred to WebGIS for spatiotemporal analysis. So two fields have been designed specifically to store spatial and temporal information respectively in Table 1. These two fields would be geo-linked to geological or geographical maps so that historical natural hazard records could be plotted on a map and enable spatiotemporal pattern analysis; • the “Original source” and “Original description” fields are quoted directly from ancient literature that indicates which sources recorded this event and how it was described. These two fields are used to keep connections between records and their original ancient literature. The connections would help users to track back to their original copies if users have further interests to read through. Information is extracted systematically from the book Chronicle of Great Natural Disasters and Abnormal Phenomena in Ancient China and transformed into Table 2. From this table, we can browse and retrieve detailed information about every natural hazard event in a textual format. Meanwhile, once it is geo-linked to a geological or geographical map, the occurrence frequency of every type of hazard during a historical time span can be easily analyzed statistically. Statistical data superimposed on geological maps would display the spatial distribution of each hazard and help further analysis of the trigger factors in addition to improving the process of identifying correlation between different natural hazards and natural environments.
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3.2. Ontology As there are many different terminologies and different classification schemes used to name or describe natural phenomena in ancient Chinese literature, which inevitably introduces a semantic heterogeneity problem in data integration and sharing. This creates a barrier for in-depth analysis and statistics by computer systems. Unfortunately, traditional information searching methods cannot extract implicit meaning and relationships among different terms in datasets. In this paper, an ontological approach is adopted to provide a controlled vocabulary with formal descriptions of the pertinent concepts, which acts like a bridge between ancient literature and source data to reconcile semantic heterogeneities among different ancient literature. A collation of different kinds of terminologies in ancient Chinese literature is made and their corresponding terms in contemporary natural hazard researches are used to construct a natural hazard ontology. The natural hazard ontology can be simply described as a tree (Fig. 1a). O1 , O2 ,. . ., On represent different terms, alias, or synonyms used in ancient Chinese literature to describe a particular natural hazard. O represents the natural hazard ontology. The arrow represents an “is a” relation which means every On is one kind of O. Using sandstorm as an example, we construct a class named sandstorm, a term commonly called today. Rain and dust is a phenomenon that was equivalent to sandstorm in ancient literature. Thus a subclass named “rain and dust” is constructed and an equivalent relationship between the class of sandstorm and the subclass of rain and dust are also established (Fig. 1b). Once the subclasses are mapped to the datasets (Fig. 1c), relationships are established between ancient hazard names in datasets and contemporary vocabularies that are equivalent in meaning. The ontology is coded in OWL2 , which provides an organizational structure for classifying data that can be discovered by both human beings and computer systems. The following is a fragment of natural hazard ontology used in this study, in which a class sandstorm is defined:
3.3. Geological/geographical data It is difficult to find a place in China that has no records of occurrences of natural hazards, and it is easy to find some places ranking higher than others in the frequency of a certain natural hazards due to the fact that natural hazards are closely pertinent to natural environments. Therefore, in addition to historical records stored in a relational table, local natural environmental datasets that include lithology, structures, geomorphologies, hydrological conditions, vegetation and climate data, should be considered for comprehensive historical natural hazard research. In this study, a 1: 500,000 geological map and a 1000 mresolution satellite map of China are both used as base maps. They are transformed into vector and raster layers superimposed in a WebGIS-based platform. The former represents the distribution of
2
OWL, Ontology Web Language, http://www.w3.org/TR/owl-features/.
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Table 2 Example records in the table of China historical natural hazards. Hazard ID
Hazard name
Time
Location
Original source
Original description
...
...
...
...
...
...
(Shandong Province)
(Jiajing Reign, Note of Qinzhoufu, Vol. 5)
(In February, strong wind made the sky dark during the daytime. People cannot see the road)
(Shandong Province)
(Kangxi Reign, Note of Dongming County, Vol. 7)
(Strong wind, dark during daytime. . .)
(Shandong Province)
(Qianlong Reign, Note of Dongming County, Vol. 7)
(In December, strong wind, dark in the day time. . .)
...
...
...
(Wind and fog)
2
3
(Wind and fog)
(Wind and fog)
4
...
...
02-1523 (Feb, 1523)
1523 (1523)
12-1534 (Dec, 1534)
...
Fig. 1. Ontology and semantic mapping.
different types of rocks and surficial deposits, as well as the locations of geologic structures such as faults and folds3 , while the latter provides a general survey of the area where historical natural hazards occur. The combination of the geological map and satellite map provide adequate background information that help answer questions such as: where are some certain types of hazard likely to occur; what scientific principles govern the processes responsible for the hazard and how subsurface distribution of porous and impermeable rocks affect the flow of heavy rainstorms. 4. Methodology
The platform is implemented in a four-layer architecture that follows service oriented architecture (SOA) principles as illustrated in Fig. 2. Each layer has distinctive functions and provides interface to the layer above, which makes the whole system able to support a wide range of functions and has the extensible ability to implement new ones. This architecture allows remote users interact with the system, exploiting historical data of their interests and issuing queries to retrieve information they need without extensive trainings in using the system. The platform is also compliant to other historical GIS developments and applications, which allows easy dissemination and visualization, flexible retrieval and analysis of historical culture data.
A browser/server architecture based on ArcGIS server + Microsoft Visual Studio .NET framework is adopted to build the entire platform with the aim to provide a user-friendly interface for data viewing and retrieval to users.
4.1. Data layer
3
Geological map, http://geoinfo.nmt.edu/publications/maps/geologic/whatis. html.
In the data layer, ArcGIS geodatabase is used for storage, management and integration of multi-source datasets. It combines “geo” (spatial data) with “database” (data repository) to create a
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Fig. 2. Architecture of Web-based GIS platform.
central data repository for spatial data storage and management4 . Datasets, including historical datasets, geological maps and satellite maps, etc., are stored in attribute, vector (points, lines, polygons) and raster types. They are represented as multiple layers superimposed together or joined in GIS-based tools for browsing, retrieval and spatial analysis in the application layer.
4.4. Application layer The top layer is the application layer, which serves users over Internet or via LAN. Users can simply use Internet explorer to browse, retrieve, locate data and represent spatial analysis results. Detailed functions and applications for different user scenarios would be addressed in the next section.
4.2. Semantic layer
5. Results
The second layer is the semantic layer. It consists of ontologies and mapping mechanisms for semantic translation and data mapping, which will help to mitigate semantic heterogeneity problems and make query, integration and statistics possible and accurate. More specifically, ancient terminologies could be translated into uniformed expressions when analyzed, whereas contemporary vocabularies could be translated into ancient terminologies when retrieved. This makes it possible for different users to access to the historical natural hazard records in a uniform fashion no matter what different terminologies were used in the past.
Based on the above platform architectural design and implementation, a system has been developed that can not only integrate various historical natural hazards recorded in diverse ancient literature, but also provide a well-designed graphical user interface to access the database and represent data in an enhanced interpretative and visualizing way. Users can connect to the platform simply using a Web browser and accomplish the following functions in an easy-to-use way.
4.3. Service layer The third layer is the service layer which implements GIS functionalities and Web services. ArcGIS server is adopted to develop Web-based GIS system in this research. It is responsible for introducing advanced GIS functions to the Internet and to geovisualization resources managed in the geodatabase. It is used for entering and handling historical data in response to the need for rapid access to databases and to represent data that references the position of natural phenomena. The Web Server is used to deploy Web services and host Web applications running in the GIS server.
4
Geodatabas, http://www.esri.com/software/arcgis/geodatabase/index.html.
5.1. Information browsing: viewing China’s historical natural hazards both in a conventional HTML mode and a map navigation mode This function gives users the ability to switch between two browsing environments to view historical natural hazard information: the textual and map environment. In the textual environment, all historical natural hazard data is represented in a table, sorted by type, time and place in a hierarchical tree structure on the left side of the page (Fig. 3). Users can select one natural hazard event to navigate through detailed information that is extracted from the geodatabase dynamically. Clicking on the map navigation button would lead users to switch to a map visualizing navigation environment from the textual one, which would enable users to browse natural hazards according to their geographical locations on a map (Fig. 4). In this mode, users can manipulate maps with universal GIS tools such as
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Fig. 3. An example of textual information browsing mode.
Fig. 4. An example of map navigation mode.
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preview, pan, zoom in/out throughout the map server, identify features on the map and control different map layers display or hide. Natural hazard events would be plotted on a map so that it is easy to navigate the spatial distribution. 5.2. Data retrieval: search for relevant data by semantic translation It is a semantic enhanced search function that facilitates users to discover detailed historical natural hazard events on a map with textual contexts through time and space. Simple queries by keywords: time (in solar calendar or Chinese ancient dynasties), place and type of events or combination of keywords for several characteristics are possible. The search requests would be semantically translated into vocabularies that were used in history and mapped to targeted records in the geodatabase so that users can seamlessly search across different terminologies without exclusion of relevant information. In the map visualizing navigation mode, users are able to search for detailed hazard information by drawing a rectangle on the map to explore which natural hazards in history occurred within the specified spatial scope indicated by the rectangle on the map. Users are also able to search for spatial distribution of natural hazards by specifying a hazard type as an input parameter. 5.3. Data exploration: discover damage frequency and distribution of phenomena by using map navigation Users can utilize this function to produce interpretable and informative thematic maps by symbolizing aggregated data. The common forms of aggregated symbols for representing numerical data on maps are graduated circles, density dots, stacked bars, proportional columns in which the area of the circle, the mass of the dot or the height of the column are scaled in proportion according to data values. This function enables users to get a full understanding of the spatiotemporal distribution of any type of natural hazards for the specified time span. These thematic representations would facilitate the following spatial analysis, but not limited to: • examining occurrence frequency of a certain natural hazard in interested areas or interested time span; • producing historical hazard zonation maps based on spatial distributions; • comparing correlations of multiple phenomena occurrences; • identifying areas where events occur repeatedly or with great intensity. Using plague and drought as examples, Fig. 4 shows the spatial distribution correlation of these two hazards. In this thematic map, the area of the dot indicates the frequency of plagues (from 1423 A.D. to 1904 A.D.). The larger the dot, the more frequently plagues occurred. The gradient color indicates the frequency of droughts (from 394 A.D. to 1901 A.D.). The darker the color, the more often droughts occurred. These two hazards show a positive correlation in spatial scales. 6. Experimental applications In this section, a user scenario will be illustrated to demonstrate applications of this platform. A middle school student, Kevin, is interested in if China has had severe sandstorms in the past. He does not know what sandstorm was named in ancient China. He types a keyword “sandstorm” into the query textbox and constrains the time span from 1200 to
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1900 A.D., but there are not any results returned by the conventional searching method, because there is no explicit use of the word “sandstorm” in the natural hazard geodatabase, and the search is unable to infer the inner relationship between sandstorm and equivalent ancient terminology. However, in our platform, when the Web server receives a request for “sandstorm”, the system generates a query against the ontology, and sends the query to the mapping illustrated in Fig. 1c. The mapping will deconstruct the query into several sub-queries, such as haze, yellow fog, rain and dust, rainfall with yellow sand, black fog, and sends each of them to their targeted terms in the geodatabase. Kevin is then provided with the illusion of using the same vocabulary as ancient Chinese documentation, bridging disparities between modern Chinese and ancient Chinese terminology. All different ancient terminologies that are mapped to “sandstorm” are clustered and counted as the same natural phenomenon. After the semantic translation and search process, Kevin would receive hundreds of records matching his search request. In addition, Kevin wants to perform an analysis to identify where sandstorm occur repeatedly or with great intensity in history. This analysis could not be accomplished without the accurate search results from the previous step. With China’s provincial map as a base map, he would clearly see that ancient sandstorms occurred with a high frequency in the north part of China, a low-temperature zone that is arid, less vegetated and has low level of precipitation, all of which are necessary conditions to form sandstorms. Conversely, sandstorms could cause substantial drops in temperature that lead to heavy snow, frozen soils and nutrition loss in the region that reduce the agricultural productivity and influence local inhabitants’ normal lifestyles [18,19]. If this information is combined with historical facts, Kevin can comprehend historical events even further. For instance, Kevin would be able to learn that northern China was the ideal homeland for nomadic people in ancient China, but this abominable weather and environment once increased the fatality of both people and livestock, therefore it caused nomadic people to invade the Han nationality that lived in less northern parts on several occasions for a better living environment [20]. This crossdisciplinary explanation deepens Kevin’s understanding of history and may also further his interests in natural history. In the next application scenario, Kevin drags his mouse on the map and places it over the northeast part of China. He finds a place indexed in the UNESCO World Heritage list called Wudalianchi where the search results indicate that many volcanoes erupted in history. Examining the historical volcano distribution map, Kevin can see very clearly that historical volcanic eruptions are densely distributed in Heilongjiang and Jilin provinces, Northeastern China. These areas are economic development regions in agriculture, forestry and tourism. This then brings up the question of “Why are volcanoes more conductive to take place in these areas than other places in China?”. Using geological maps as base maps, Kevin can understand that these areas are situated along converging plate boundaries where subduction occurs, which is one of the principal geological settings for volcanism. The process of volcanisms molds these areas into spectacular landforms that are now known as World Geological Parks, attracting a large amount of worldwide tourists every year. After volcanoes erupted, large amounts of volcanic ashes were discharged and reactions with water and atmospheric gases occurred, forming very fertile soil that supported lush vegetation and crops. This made Northeastern China one of the major grain producer in China. However, these eruptions also represented significant disasters, including destruction of crops, buildings, transportation and communication systems in addition to pollution of water supplies. Moreover, forests and all living creatures within a distance of 10 km from the volcanoes would have been destroyed and killed. This implies that even nowadays, they are still high-risk volcanoes because over 100,000 people
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live near them. With the addition of the above mentioned background information, Kevin can obtain a better understanding of the natural history of Northeastern China, particularly in the field of volcanic-related hazards, which compliments his knowledge of the sightseeing of the area. The user journey in this platform presents to Kevin an innovative way to read history and comprehensively understand historical events.
7. Evaluation A concise survey had been conducted to assess users’ satisfaction and expectation on the platform, and allow users to offer suggestions for modifications and further improvements. Fifty-six anonymous users participated in this survey and gave positive feedback on the system (Table 3). Ninety-three percent of participants indicated that the system is interesting, 86% indicated that the system is meaningful, and 84% believed that the system can promote and deepen historical natural hazard research. Moreover, the survey tells us that the cross-disciplinary perspectives on analyzing historical natural hazards through visualized presentation is the most popular and attractive functions for users, whereas semantic search and information browsing are ranked second and third. In addition to these statistics, survey participants also give valuable suggestions, which are concluded as follows: • some users suggest to develop new functions to support updated information not only by system developers from the server side, but also to provide access to insert or modify data or add comments at the client side by general users, like Wikipedia, Facebook and other websites. This allows users to become more engaged and positively interactive with the system rather than limit users to reading the contents from a screen at a distance;
• besides the descriptive information relating to each historical natural hazard, users also want to learn about the latest research conducted in specific districts or areas. So it could be more meaningful to make links to the latest literature, reports or media news related to each historical natural hazard founded in the database; • in order to make this system more accessible and convenient, users suggest developing a mobile version so that they could visit it through mobile phone or iPad any time any place. Once the system performs a data update, the system would post update announcements through social networking sites such as Twitter, Weibo (a Chinese Web service that functions in a similar way to Twitter), or Facebook so that targeted users would be notified automatically; • linking or integrating historical natural hazard information outside of China could also strengthen the system. This may help raise potential international research cooperation at a global scale and provide a wider perspective in global change.
8. Conclusions and discussion The goal of this article is to present an example of building a platform to store, manage, represent, inquire and analyze historical natural hazard records in a flexible, convenient and interpretative way. To do this, a semantic enhanced WebGIS platform has been developed which combines: • a geodatabase to provide spatiotemporal data storage and management; • a domain ontology to mitigate semantic heterogeneity problems; • WebGIS tools to offer cross-disciplinary perspectives to arouse interests and inform people of not only what happened in history but also why it happened.
Table 3 Feedbacks from survey users. Questions Do you think this system is interesting? Very interesting
Not interesting at all
Percentage (%) 5 4 3 2 1
39 54 5 0 2
Do you think this system makes historical natural hazard research meaningful? 5 Very meaningful 4 3 2 1 Not meaningful at all
52 34 10 0 2
Do you think this system can help you understand historical natural hazard in a new way? 5 Yes 4 3 2 1 No at all
54 30 12 2 2
Do you think this system can promote historical natural hazard research deeper and more comprehensively? Yes 5 4 3 2 1 No at all
64 23 11 0 2
What function(s)/information do you expect most from the system? Information browsing: viewing China historical natural hazards both in a conventional HTML mode and a map navigation mode.
68
Data retrieval: search for interested data by semantic translation
71
Data exploring: discover phenomena damage frequency and distribution by using map navigation
57
Integrate geological, geographical and environmental factors to analysis and provide flexible data visualization
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The experiments that we have done in this paper show that: • the geodatabase is flexible to store and manage diverse heterogeneous datasets, including attribute data, vector data and raster data. It provides a central data repository for easy data entry and user access in the field of historical natural hazard research. It would be improved by extracting more individual traits of different natural hazards for richer representations and further practices in the geodatabase world; • ontology-based semantic integration is promising for scientific data integration and navigation. Construction of natural hazard ontology and development of semantic mapping methodology enable us to identify and retrieve information among ancient literature. It mitigates semantic gaps between ancient literature and common language used today, makes semantic search possible and helps to improve searching accuracy. The development of ontology is an iterative process, it needs to be updated and improved when more ancient terminologies are systematically collated; • the WebGIS-based platform presented in the paper provides conventional and advanced functionalities for visualizing and analyzing historical natural hazards distributed in spatiotemporal scales. It allows different users to utilize historical data for multiple purposes. The proposed platform is compliant and supports not only historical data but also modern data. It also supports natural hazard data as well as data from other disciplines. The novel design of the platform makes dynamically inserting new data very easy. Current application cases and feedback affirm that this platform is effective and meaningful. They also suggest further directions for iterative modifications and improvements. Integration of diversity of environmental factors, mapping mechanisms and highly visualized data representation modes should be consistently updated in order to adopt more complex requirements. Evaluation and questionnaires on users’ expectation and feedback would be conducted continuously to increase familiarity with targeted audiences. As a historical research approach, we believe that it is necessary to develop innovative functions to facilitate in-depth studies of extraordinary cultural and historic resources recorded in ancient literature such as the one we have hereby presented as constant renewal is the best way of preservation [21]. Acknowledgements This research has been supported by National Natural Science Foundation of China (Project No. 40802080), Jiangsu Planned Projects for Postdoctoral Research Funds (Project No. 0206003405) and Humanities Fund of Nanjing University. Authors would like to appreciate American Association of Museum and Professor
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