Embed Size (px)
Citation preview
This article was downloaded by: [Tufts University]On: 26 September 2014, At: 08:40Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Geocarto InternationalPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tgei20
Geospatial database organization andspatial decision analysis for biodiversitydatabases in Web GIS environmentHarish Karnatak a , Sameer Saran b , Karamjit Bhatia c & P. S. Roy aa National Remote Sensing Centre, Indian Space ResearchOrganization , Hyderabad, Indiab Indian Institute of Remote Sensing, Indian Space ResearchOrganization , Dehradun, Indiac Department of Computer Science , Gurukul Kangri University ,Haridwar, IndiaPublished online: 01 Apr 2009.
To cite this article: Harish Karnatak , Sameer Saran , Karamjit Bhatia & P. S. Roy (2010) Geospatialdatabase organization and spatial decision analysis for biodiversity databases in Web GISenvironment, Geocarto International, 25:1, 3-23, DOI: 10.1080/10106040802677045
To link to this article: http://dx.doi.org/10.1080/10106040802677045
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
Geospatial database organization and spatial decision analysis for
biodiversity databases in Web GIS environment
Harish Karnataka*, Sameer Saranb, Karamjit Bhatiac and P.S. Roya
aNational Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India;bIndian Institute of Remote Sensing, Indian Space Research Organization, Dehradun, India;
cDepartment of Computer Science, Gurukul Kangri University, Haridwar, India
(Received 8 December 2008; final version received 9 December 2008)
The spatial decision-making process inmulti-user environment is a quite challengingand complex task in group decision-making environment. The effective databasedesign andGIS analysis techniques are very important at the host organization level.The biodiversity conservation prioritization is one of the complex issues for theconservation authorities. Various ecological and socio-economic drivers govern thespatial distribution of biologically rich communities. These drivers are importantinputs to themodelling processwith different rank criteria and probabilistic weight inorder to arrive at a decision-making process. In the present article, a new databasedesign and decision analysis technique is proposed for the natural resourcemanagement and planning in Web geographic information systems environment.The study is demonstrated for the geospatial decision analysis with an outputvalidation utility where the analytic hierarchy process is used to derive the eigenvectors with given multiple constraints and conflicting criteria and aims at selectingan optimal site for the biodiversity conservations.
Keywords: database design; SDSS; Web GIS; AHP; biodiversity conservation
1. Introduction
Database and database systems have become essential components of everyday lifein modern society. In the past few years, advances in technology have been leading toexciting new applications of database systems. Geospatial data of geographicinformation systems (GIS) can store and analyse maps, weather data and satelliteimages. Data warehouses and online analytical processing (OLAP) systems are usedto extract and analyse useful information from very large database for planning anddecision making. Real-time and active database technologies are used in controllingthe industrial and manufacturing processes. Simultaneously, the database searchtechniques are applied to the World Wide Web to improve the search forinformation that is needed by the users browsing through the Internet.
Geospatial technology is an emerging multi-disciplinary approach involvingdisciplines namely computer science, geography, photogrammetry, cartography,remote sensing, surveying, global positioning system (GPS) technology, statistics andother disciplines concerned with handling and analysing spatially referenced data.Traditional GIS can only serve dedicated users with sophisticated software and
*Corresponding author. Email: [email protected]
Geocarto International
Vol. 25, No. 1, February 2010, 3–23
ISSN 1010-6049 print/ISSN 1752-0762 online
� 2010 Taylor & Francis
DOI: 10.1080/10106040802677045
http://www.informaworld.com
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
hardware resulting in limited impact to the public. The network infrastructure andhardware specification for Internet GIS (based on client/server model) provide high-speed communication channels for publishing and accessing geographic informationusing computer network (Peng and Tsou 2003). The Web GIS model offers anefficient way to provide information and services to concurrent user(s) at the sametime. Utilization of client/server model architecture for natural resource manage-ment for better planning and decision making is the need of the modern society.
The present study is based on the Department of Space (DOS) and Departmentof Biotechnology (DBT) collaborative project entitled ‘Biodiversity characterizationat landscape level using satellite remote sensing and geographic information system’.The primary objective of the project is to generate the vegetation type map,fragmentation, disturbance index (DI) and biological richness (BR) maps of thestudy area using satellite remote sensing data (IRS-LISS III data are used) andspatial landscape modelling (SPLAM). In the first and second phases of this project,intensive ground sampling was conducted in Northeast India, Western Ghats,Western Himalayas, Andaman and Nicobar Islands, Central India, Eastern Ghatsand East Coast of India. The objective is to obtain training sets for the classificationof the digital data and also to obtain layers like species richness (SR), ecosystemuniqueness (EU) and biodiversity value (BV) based on the ground observations.
During this national level programme, the ground truth data were collected byvarious research groups across the country. In the initial phase of this study, the datawere collected in various data formats and organized in traditional file base system.For example, the raster data are stored as TIFF, GRID or IMG format and thevector data are stored as shape file or ESRI coverage format.
To develop an effective geospatial analysis tool for better planning and decisionmaking we need a well organized, well managed and tuned database system for aquick and accurate analysis in multi-user environment. In the data standardizationprocess of non-spatial data, the developer needs to design a normalized oroptimization structure of the database. The main objective of normalization is toreduce the redundancy, remove atomicity and make data accessing faster. In thespatial data organization, the ESRI geodatabase system is introduced andimplemented in this study.
Geospatial analysis in Web GIS environment is an emerging and challenging areawhere many constrains of Web technology such as performance of the application,data security, etc. are of major concerns. In the present study, we demonstrate thegeospatial analysis in Web GIS environment by taking multi-criteria decisionanalysis for biodiversity conservation as a case study. The operational databasedesign is developed and proposed for one of the most important real time databasesat the national level on Indian plant biodiversity. A linking mechanism of spatial andnon-spatial data in real time environment is also established and demonstrated. Theproposed database design in this study is adopted in national level programme of theGovernment of India on ‘Biodiversity characterization at landscape level’ foroperational purpose.
2. Approach
2.1 Problem definition
Biodiversity conservation prioritization is one of the complex and challenging issuesfor conservation authorities. Various ecological and socio-economic drivers governthe spatial distribution of biologically rich communities. These drivers are important
4 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
inputs to the modelling process with different rank criteria and probabilistic weightin order to arrive at a decision-making process for the conservationist and planners.Any decision-making process starts with an identification of the decision problem. Inthe present study, our objective is to design an effective database architecture forgeospatial analysis in multi-user environment and multi-criteria decision analysis forbiodiversity conservation and prioritization. The inputs for this study are drivenfrom a national level programme of the Government of India entitled ‘biodiversitycharacterizations at landscape level using remote sensing and GIS’. The officialwebsite of this project is www.bisindia.org.
In the available literature, it is shown that the different landscape models aredeveloped to characterize biodiversity, which addresses only the structured problemson biodiversity (Karnatak et al. 2007). Karnatak et al. 2007 have addressed thesolution for semi-structured problem having various degrees of uncertainty onbiodiversity where computer-based models can directly interact with the biodiversityexperts to generate a knowledgebase for the biodiversity conservation prioritizationby implementing analytical hierarchy processing (AHP) technique to identifybiologically rich sites using spatially defined multiple criteria. On the technologyside, the development of the Web-based spatial decision support system (Web-SDSS)using multi-criteria decision analysis techniques needs a well-organized and tuneddatabase at the server end with an appropriate software system architecture for aneffective and quick decision making. The geospatial analysis in Web environmentusing traditional GIS system is not very effective and useful due to the concurrentuser access of the system. In the present study, an effort has been made to design aneffective database design and software system configuration for Web-basedgeospatial decision analysis.
2.2 Multi-criteria spatial decision analysis
The spatial decision analysis is a set of systematic procedures for analysing complexspatial decision problem. The strategy of decision analysis is to divide the originaldecision problem into small parts; analyse each part and integrate the parts in a logicalmanner to produce a meaningful solution (Malczewski 1999). The decision-makingprocess is by itself a broadly defined term and has importance in many fields like social,economical and natural resource management, and disaster management includingGIS. The spatial decision analysis is a specific sub-class of decision analysis processwhere the decisionmaker has to choose the best alternatives from a set of geographicallydefined alternatives (events) on the basis of multiple, conflicting and incommensurateevaluation criteria. The evaluation and ranking of alternatives by multi-criteriadecision-making (MCDM) techniques is based on the associated criteria values,objectives and preferences of the different decision makers. Spatial multi-criteriaanalysis is immensely different from the conventional MCDM techniques due to itsadditional explicit geographic component. In the spatial multi-criteria decision analysistwo concerns are of vital importance: (1) the GIS component (e.g. data acquisition,storage, retrieval, manipulation and analysis capability); and (2) the MCDM analysiscomponent (e.g. aggregation of spatial data and decision maker’s preferences intodiscrete decision alternatives) (Carver 1991, Jankowski 1995).
The multi-criteria spatial decision support system (MCSDSS) is a part of thewider field of SDSS where the GIS-based decision problems can be solved usingspatial multi-criteria decision analysis technique. The need of such a system isderived from the situation where the decision makers have to solve a complex
Geocarto International 5
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
semi-structured spatial problem or they are not able to exactly define the problem.The MCSDSS provides a flexible problem-solving environment and offers uniquecapabilities for automating, managing and analysing single-user and collaborativespatial decision problems with large sets of possible alternatives and multiple andconflicting evaluation criteria.
The multi-criteria decision making is a broad and productive field of research andapplications aimed for more efficient and effective decision making. The weightedsum model is the simplest and the most commonly used method in multi-criteriadecision making. The basic principle behind this technique is the additive utility.AHP proposed by Saaty (1980) is one of the most popular MCDA technique whichis widely used by the researchers for various applications. Many papers have beenpublished describing the case studies in which AHP is applied to support a particulardecision making (Finnie et al. 1993, Lee 1993, Yau and Davis 1993, Karnatak et al.2007). Another important technique for the multi-criteria spatial decision-making isspatial compromise programming (SCP). Tkach and Simonovic (1997) combined theconventional compromise programming (CP) technique with GIS technology anddeveloped a spatial MCDM technique called SCP. The use of SCP is in its ability toaddress the irregular spatial distribution of criteria values in the evaluation andranking of alternatives (Tkach and Simonovic 1997).
The multi-criteria decision analysis using geospatial data becomes morechallenging when the decision problem needs to be solved in a group decision-making environment. In the recent years, the Internet environment is emerging asone of the best medium to access, share and disseminate the geospatial data andinformation in multi-user environment. Multi-criteria spatial decision analysis inInternet GIS environment is a specific type of geospatial analysis system where theinteraction between decision maker (at client end) and GIS data and application (atserver end) is required (Karnatak et al. 2007). SDSS using multi-criteria spatialdecision analysis is commonly considered as application-specific software solutions.Wellar (1990) and Crossland et al. (1995) showed that the use of GIS as a type ofSDSS reduced the decision time and increased the accuracy of individual decisionmakers, while Peng and Tsou (2003) emphasize that the Internet provides an idealplatform for non-experts to realize the power and benefits of GIS. Integrating thesetechnologies in a Web GIS-based SDSS has the potential to increase the use andaccessibility of spatial data, as well as the accuracy and efficiency of decision making.
Rinner (2003) categorized Web-SDSS into three major categories viz. server-sideWeb-SDSS, mixed client- and server-sideWeb-SDSS, and client-sideWeb-SDSS. In theserver-side Web-SDSS, the decision analysis-related operations execute at the serverend, and the client can only visualize the outputs into simple Web browser. Web-SDSSfor group decision-making is typically supported by a mix of client- and server-sideWeb-SDSS. The third category is strictly client-side Web-SDSS that represents morerecent development in Web-SDSS. The client-side Web-SDSS suggests advancedvisualization and multi-criteria evaluation methods, and integrates Web-SDSS withstate-of-the-art in other geographic information techniques (Rinner 2003).
2.3 Database organization process for Web-based spatial decision analysis: a casestudy
The database organization in geospatial domain is quite different from thetraditional attribute-based data organization. The entire database organization
6 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
process established in the present study can be understood as presented in Figure 1,where the database organization process of this study can be broadly categorizedinto two major activities, i.e. non-spatial database organization and spatial databaseorganization.
2.3.1 Non-spatial data
The non-spatial data are also called as attribute data or tabular data in IT and GIScommunities. In the present study, data available under this category includesground truth data containing location information, plant species data as perliterature, outcome of phyto-sociological analysis, data about trees, herbs, shrubs,climbers, etc. After analysing the nature and importance of the data, a common datacollection format is designed and circulated among the data collection groups. Afterstudying the structure of data and requirement of GIS standards, the non-spatialdata are categorized into five major categories:
(1) Sample plot location information;(2) Species information;(3) Total importance of the species (total importance value (TIV) table);(4) Tree, herb, shrub and climber information;(5) Photograph of plant species or any other supporting information.
The database structure is designed based on these five major categories. One ofthe major issues in database organization is to establish a relation between spatial
Figure 1. Database organization process.
Geocarto International 7
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
and non-spatial data in GIS domain. During the identifications of relational keys,the linking mechanism with spatial data is also taken care of.
2.3.1.1 Database structure – non-spatial data. The database structure plays a vitalrole in any computer based database management system (DBMS). To understandthe data and its relations, the database structure is designed in entity relationshipmodel, based on which the database system has been designed in Oracle DBMS. Theinput data sets in this study are very complex and sensitive due to their spatialdistributions and scientific importance. A relational schema is designed for betterdatabase organization and management. Technically, a relational scheme is a planthat indicates attributes involved in one or more relations. The ER diagram ofdatabase used in this study is shown in Figure 2.
2.3.1.2 Database design. The database generated under DOS-DBT project isavailable in three levels, i.e. national level, regional level and state level. The groundtruth data are collected as state wise at the regional level. In the process of fieldsample collection, the project team laid down the sample plot in the study sites. Thesample plots have been selected by stratified random sampling using the probabilityproportionate to size technique (Anon 2002). Stratified random sampling withprobability proportion to the size was adopted for analysing vegetation compositionof all the types recorded. The ideal aim was to sample nearly 0.01% of the total area.The achieved sampling ranges from 0.001 to 0.0005% of the total area. Optimumnumbers of sample points have been taken up covering all vegetation types, indifferent disturbance regimes and micro-climatic conditions, and a minimum of 20samples are taken for each forest type (Anon 2002).
Figure 2. ER diagram of non-spatial database.
8 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
. Plot information table: The data under this category contain the locationinformation of the plot of each study site. This entity contains all the detailsabout the plots being laid in different states.
To reduce the data redundancy and to develop a national data standards weproposed to store all the plot information at the national level in a single table whichcan be further linked spatially with different administrative boundaries like regional,state, district, and taluk boundaryies. In general, the plot numbering system of theraw data for each state is the same, which can not be represent each plot uniquely atthe national level. Therefore, we need to identify one primary key to make separateidentity of each plot for unique representation. A new derived attribute namedFALSEID is added into the plot information table. This key is of character type withthe first four characters referring to the state to which the plot belongs (for e.g.‘MEGH’ for Meghalaya state), the next three characters specify the number of theplot in that state or study site (for e.g. ‘001’). Thus, the FALSEID for a plot lookssomething like ‘MEGH001’. Thus, the key FALSEID can be taken as the primarykey in PLOTINFO table.
. Species information table: This entity has all the necessary details of the speciescollected and reordered during the field sampling of DOS-DBT project. Thespecies collected during field sampling are collected in state and region levels ina study area. The plant species database of entire country is organized in asingle table to reduce the redundancy and inconsistency in database. The
Figure 3. Development of SDSS shell in Web GIS environment.
Geocarto International 9
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
unique identifications of each species at state and regional levels areestablished. The identifications of the unique ID at two different levels arevery important and crucial for phyto-sociological analysis at that defined level.The species identity is responsible for querying the species information up todifferent levels like, distribution of particular plants in the local, regional orcountry levels. For example, if any plant species is recorded in Assam state ofNortheast India and also recorded in Meghalaya and Arunachal Pradesh ofthe Northeast region or any other parts of the country, then we need oneunique identity of that particular plant in Assam, Meghalaya and ArunachalPradesh and also in the entire region. We add two derived attributes, i.e.SPFALSE and SPFALSE2 in species table for the unique identification of theplant species in local and regional levels. The attribute SPFALSE will act as aprimary key at the state level and attribute SPFALSE2 at the regional level.The primary key SPFALSE is of character data type with the first fourcharacters specifying the state to which the species belongs and the next fivecharacters specify the number of the species in that state. Thus, the SPFALSEof the species looks something like ‘MEGH00001’.
. PLOTSPEC table: This is the master table for making a relation between theplot and the species tables. In this table, FALSEID and SPFALSE are treatedas foreign keys. FALSEID refers to the PLOTINFO table from where we canget all the necessary information about the location of the species, andSPFALSE refers to the SPECIES table from where we can get detailedinformation about the plant species. SPFALSE2 acts as a reference key to thespecies table.
Figure 4. Input for decision factor and criteria.
10 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
. IMPORTANCE table: The importance of plant species for various uses isstored and managed under this entity. The table also stores the informationabout the ecological parameters like whether the species is rare, vulnerable,endangered for a particular state in a selected region.
. OVERSHANON table: This table stores the results of the phyto-sociologicalanalysis of the plant species at the regional level. It stores the result for theanalysis parameter of Shannon Index.
. OVERIVI table: This table also stores the results of the phyto-sociologicalanalysis at the species level. Primary key in this table is SPFALSE2. It storesthe result for the analysis parameter of Importance Value Index (IVI) at theregional level.
. CODE table: This table stores the detailed information of the vegetation typesmentioned in the PLOTINFO table. Primary key in this table is CODE.
2.3.2 Spatial data
The spatial data on vegetation and land use is generated using satellite remote sensingdata through digital and visual classification techniques. This yields the vegetationtype map, which is used to derive all further outputs. The spatial and non-spatial data
Figure 5. Pair wise comparison of decision factors based on Saaty’s scale.
Geocarto International 11
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
from other ancillary data sources are combined to generate habitat maps. Landscapeanalysis for determining the parameters like fragmentation, porosity, proximity andother patch characteristics have been used to derive DI using proximity fromsettlements and roads.
The landscape parameters have been derived using customized software packagecalled ‘SPLAM’, which has been developed at the Indian Institute of RemoteSensing (NRSC). The knowledge base with respect to EU, SR and BV is used tocreate attribute information of the composite strata of vegetation type anddisturbance regimes. The terrain complexity (TC) and DI were spatially combinedwith the above knowledge base to model the BR. The study uses digital imageprocessing software and GIS for the data generation, storage, analyses, results andoutput in the form of maps on 1:250,000 scale on the regional level and 1:50,000 scaleon the state level.
2.3.2.1 Database organization – spatial data. The spatial outputs of DOS-DBTnational project includes a primary map on the vegetation type and the derived spatialoutputs on DI, fragmentation and BR across the landscape in vector and rasterformats. The derived outputs are the results of landscape model for BR mapping. Theintermediate map layers like SR, BV, TC and EU are also generated and used asdecision criteria map during this study for multi-criteria decision analysis. In additionto that, the Survey of India (SOI) based map layers like state, district and talukboundaries in vector data format are also available.
The first objective of the spatial database organization is to bring the entire datasets into a common data standard for which the ESRI geodatabase model is taken
Figure 6. Decision matrix after pair wise comparison of factors.
12 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
that provides excellent utility using feature data sets and feature class. The satelliteimages and the derived outputs of different regions were processed at various nodalcentres. For creation of a national repository these were made uniform by physicallyregistering all the data layers with reference to the registered boundary of India,supplied by SOI and subsequently taking them under a standard projection system.
One more important input to the infrastructure was the point layer of the GPSpoints of sample plots acquired, which had the relevant information about the plot’slocation, topography and species identified within the plot. The GPS locationsrecorded at each sample plot as numeric values in a table were then converted into apoint feature class using GIS. Each feature in this feature class contains informationabout the sample plot (altitude, place name, etc.) in addition to its geographic co-ordinates. The sample plot feature class contains the primary key named asFALSEID which acts as a foreign key for PLOTINFO table of non-spatial data. Thepoint feature class has the same coordinate system of other spatial layer of thatparticular state. Hence, it will provide an appropriate database linking for spatialdata.
2.3.3 Database designing in Oracle 10 g
DBMS gives the user access to their data and helps them transform the data intoinformation. Such DBMSs include dBase, Paradox, IMS, Oracle, etc. These systemsallow users to create, update and extract information from their databases.Typically, a database is a structured collection of data.
Figure 7. Overall priorities of decision factors and criteria.
Geocarto International 13
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
After designing of ER-diagram of the database, our next objective is to design adatabase structure in a DBMS. The efforts have been made for an organized andmanaged spatial data in RDBMS environment by introducing geodatabase conceptfor biodiversity database. For that, the Oracle 10 g is selected for storing andmanaging the geospatial data of the present study. We propose to create two similardatabase schemas in Oracle 10 g, one for data cleaning and normalization and theother for managing the final organized data. To upload the attribute data intoOracle environment, the SQL loader utility of Oracle is used for which a control filefor each data object is required.
The geodatabase model of ESRI also creates a new database schema in Oraclefor spatial database (the default is SDE). A geodatabase or a geographic database isa relational database containing geographic information like vector data, raster datatables and GIS objects. In geodatabase model, the entities are represented as objectsand these objects include simple objects, geographic features, networks andtopology, annotation features and other more specialized feature types.
The spatial database engine of ESRI, i.e. ArcSDE, provides the gateway betweenGIS and the DBMS to store and manage spatial data as tables in a heterogeneousdatabase environment. It also provides a common model for geographic informa-tion. ArcSDE organizes features in feature classes. A feature is a geometricrepresentation of a spatial object; define as a sequence of one or more x, ycoordinates and the attributes for that geometry. Features are stored so that one rowin a table equals one feature. ArcSDE does not change the existing DBMS or affect
Figure 8. Spatial decision outcome in thin client mode.
14 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
current applications, but it simply adds spatial column to tables and provides toolsfor a client application to manage and access the geometry data referenced by thatcolumn.
The GIS vector data sets are versatile, and frequently used geographic datarepresentation are suited for representing features with discrete boundaries such aswells, streets, rivers, states and parcels.
Raster data represent a significant portion of the total data used in GIS. ArcSDEhandles raster data very much like vector data. For each raster type column in abusiness table, ArcSDE automatically creates four additional tables like Metadatatable for raster (SDE_RAS_ 5id#4 ), Metadata table for raster band(SDE_BND_ 5id#4 ), Auxiliary table for raster band (SDE_AUX_ 5id#4 ),and Block table (SDE_BLK_ 5id#4 ). The first two Meta tables are used to storeinformation about a raster and a raster band such as the image dimension and thepixel depth. The auxiliary table is used to save additional information about a rasterband.
2.4 Geospatial data presentation and analysis in Web GIS environment
Traditional GIS can only serve dedicated users with sophisticated software andhardware resulting in a limited impact to the public. The Internet technology as adigital communication medium enhances the capability of GIS data and softwareapplication by making them more accessible and reachable to wider range of users,
Figure 9. Query on sample plot location with phyto-sociological analysis.
Geocarto International 15
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
planners and decision makers. The Web-enabled GIS facilitates decision making atthe strategic, tactical, and operational levels; support for the performance ofadministrative operations; and serve as a gateway for decision makers and generalusers to access the system conveniently and effectively. The database representationalong with query facility in Web GIS environment is now a well-establishedtechnology based on which various GIS Web portals are available on the Internet.The map server and related technologies provide a new dimension to GIS and itsapplications in group decision-making environment.
Although in the area of biodiversity conservations and prioritizations there arevarious Web portals available on the Internet, very few are serving original GIS datain public domain. In India, very few Web portals are available on the Indianbiodiversity and bio-resources of India. Two major Web portals namely ‘biodiversityinformation system’ (www.binsindia.org) and ‘Indian bio-resource network’(www.ibin.co.in) are based on database server developed during this study.
The GIS analysis in Web GIS environment is still in its evolving stage. Thegeospatial multi-criteria decision analysis in Web environment is a complex andchallenging task due to its complex data structure and availability of less bandwidthon the Internet. In the present study, we have developed a MCSDSS for biodiversityconservation and prioritization using AHP technique and database design approachproposed and developed in this study. The system implementation process (the s/wdevelopment flow) of MCDA in Web GIS environment is shown in Figure 3.
Figure 10. More closer view of the map and buffer analysis for sample plot.
16 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
The geospatial analysis process can be divided into two major components, i.e.the process in non-GIS environment and process in GIS environment. The developedSDSS shell read the primary layer, i.e. vegetation type map as factors and derivedmaps, i.e. EU, SR, BV, DI and TC map as criteria map (Figure 13) from thecentralized database server. The landscape model calculates the BR as below:
BR ¼ZfEcosystem uniqueness, species richness or diversity, biodiversity value,
terrain complexity and disturbance indexg
BR ¼Xni¼1
DIi �Wti1 þ TCi �Wti2 þ SR1 �Wti3 þ BVi �Wti4 þ EUi �Wti5
where DI ¼ Disturbance index, BR ¼ Biological richness, TC ¼ Terrain complexitySR ¼ Species richness, BV ¼ Biological values, EU ¼ Ecosystem uniqueness,Wt ¼ Weight.
In this model, the BR map is generated by a simple summation of the criteria.The weightage (Wt) is fully dependent on conflicting choice and field knowledge ofthe decision maker (Roy et al. 2004). In the present study, we have introducedSaaty’s AHP technique to normalize the decision maker’s choice and weightages.The SDSS shells developed as a part of this study calculate overall priorities ofalternatives using AHP, and send it to a spatial shell as input for deriving BR map.All the processing is done on the real-time basis so that concurrent users can do thesame analysis with the same data sets.
Figure 11. Satellite image overlay of the study area.
Geocarto International 17
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
The outcome of the AHP method is overall priorities of the factors, which is aninput to the GIS environment of SDSS shell for further processing. At the server end,the SDSS shell performs overlay operation with these input layers and prepared oneSDSS layer with a large number of polygons. For instance, the polygon representinga particular vegetation type will again break into smaller polygons with each criteriavalue. The SDSS layer represents a unique code for each factor and criteria by anattribute in a feature class. On the basis of the attribute value and calculatedpriorities of factors and criteria a priority vector is calculated and based on thepriority vector a class value is derived after normalization of the class values as a newattribute. The final spatial output map has been generated based on the priorityvector which represents the priority of the area for biodiversity conservation. Thevegetation type map along with the other spatial layers available in data server canbe overlaid and queried in the standard Web browser without having any specificGIS software installed at the client end. Similarly, unlimited number of spatialoutputs can be generated based on the decision maker’s preferences and choices.
The SDSS shell is basically a software application developed by using InternetMap server (ArcIMS 9.1) and active server pages (ASP) and JAVA programminglanguage. The SDSS shell developed by using ASP provides a lighter version anddoes not require any plug-in software at the client end. The developed system isbased on multi-tiered system architecture which can be divided into two broadcategories, i.e. client-end process and server-end process. The server-end processescan be again categorized into two parts – (1) Application server and; (2) Data server.
Figure 12. Map output display in thick client mode with enhanced GIS capability.
18 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
The developed application is a Web GIS-based application where most of the GISfunctionalities are available in the Web browser. In ASP bases application, most ofthe programming is done using the server side programming language. The multi-criteria decision model AHP is implemented by using VB script and JAVA for vectorand raster data sets, respectively.
Figure 13. Decision criteria maps.
Geocarto International 19
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
3. Results and discussions
The present study is an extension and advancement to the earlier study of Karnataket al. (2007) on ‘multi-criteria decision analysis in Web GIS environment’. In thepresent study, the database designing and management approach for Indianbiodiversity database is demonstrated and implemented for one of the mostimportant national level programme on ‘biodiversity characterizations at landscapelevel using RS and GIS’. The various database organization activities like datacleaning, normalization, uploading, etc. are demonstrated for operational database.The database linking mechanism for spatial and non-spatial database has beenestablished and implemented for operational activities. The database serversdeveloped in this study are ready for the future R&D and operational activities.At the database server level the geodatabase architecture is adopted with OLAPusing Oracle 10 g. The designed database architecture and standards are not specificfor this application. The database applications like biodiversity information system(www.bisindia.org) and Indian bio-resource information network (www.ibin.co.in)are already in place based on the present study on database organizations forbiodiversity database. These Web applications are basically serving GIS data withbasic query facility in Web GIS environment.
The developed Web-SDSS in this study is based on the server side and also a mixof client- and server-side technologies. The spatial data are available at thecentralized server as a geodatabase format. The developed system addresses thesemi-structured problem having various degrees of uncertainty on biodiversity, i.e.where computer-based models can directly interact with the biodiversity experts togenerate knowledge base for the biodiversity conservation prioritization. The SDSShas two major parts in application server level, i.e. first it takes input from thedecision maker and calculates the overall priority of the alternatives using AHP, thenit takes the output from AHP used as input for geoprocessing and spatial decisionanalysis.
The related development by Zhu et al. (2001) describes a Web-based informationand decision support system, VegMan, for regional vegetation management. Thesystem was designed to sustain biodiversity and native vegetation in Queensland,Australia. The Internet is used to disseminate information and provide access toanalytical tools. VegMan is a client/server system using HTML pages and JAVAapplets as its user interface. Another important development by Karnatak et al.(2007) demonstrated Web-based SDSS for biodiversity conservation and prioritiza-tions (BioconsSDSS) using AHP techniques. The ASP pages were developed forimplementation of AHP and the map server technology was used for map renderingin Web browser.
For an operational Web-based SDSS in group decision-making environment awell-organized and properly tuned database server is very much required at the hostorganization level. In the present study, the developed Web-SDSS is an integrationof landscape model developed by Roy and Tomar (2000) and multi-criteria decisionanalysis technique AHP (Saaty 1980) to address the semi-structured problem havingvarious degrees of uncertainty on biodiversity. The main advantage of this system isthat it integrates the landscape model with AHP for deriving biologically rich sites ingroup decision-making environment. The vegetation type map, SR map, fragmenta-tion map, DI and BV map are derived by using landscape model and stored asgeodatabase in the central repository along with the sample plot data. The decision
20 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
maker can interact with these spatial layers and after value addition the landscapemodel generates new BR map based on the decision maker’s choice and preferences.The decision-making process of developed system is as follows.
When the decision maker runs this system it asks for the selection of decisionfactors and primary weight to decision criteria (Figure 4). In the next steps, theAHP technique is implemented where the decision maker has to do the pair wisecomparison of decision factors and criteria (Figures 5 and 6) based on which thepriorities of the alternatives and criteria is being calculated (Figure 7). Thesepriorities entered as an input into the GIS shell where the various GIS operationswill take place and the system will generate an SDSS layer with priorities of theareas. The output can be visualized in thin as well as thick client mode in Webbrowser (Figure 8). Various GIS tools (like zooming, panning, identify, query,distance measurement, buffer analysis, map output generation, etc) are availablefor further analysis and decision making. The system also provides a facility tovalidate the results in the same map viewer window. The ground truth sampleplot can be overlaid with output SDSS layer and can further queried for phyto-sociological analysis and status of the plant species at that particular point oflocation (Figure 9). The actual ground situation is enhanced with availableliterature for each plant species recorded at that particular point during fieldsampling. For each sample plot the SDSS shell calculates the ‘total number ofspecies recorded’, ‘total number of medicinal, economical and endemic plants andtheir importance’. It also calculates the TIV index for each plant which is basedon the available literature and supporting study. The other important GIScapabilities like closer view of the map output with buffer analysis (Figure 10),satellite image overlay (Figure 11), etc. can also be performed in simple Webbrowser at the user-end without having any specific GIS software installed atclient machine. The outputs are also available in thick client mode (Figure 12)where various complex GIS operations like editing of the map, add layer at theuser-end, submission of the comments, etc, are available. These analyses are veryimportant for the quick decision making for any GIS-based decision analysissystem. If the decision maker is not satisfied with the results he/she can re-enterthe values for the new output. Otherwise, the each output window can be savedat the user-end as standard Web compatible data formats.
In a Web environment, performance is usually the most important factor, thus adeveloper should keep in mind the network performance when designing thedatabase. The database normalization and indexing provides the best performancefor an Internet GIS application which is operationally demonstrated in this study.
In the last few years, the Internet GIS has become quite a mature technologyfor geospatial data accesses and disseminations. The fully interoperable InternetGIS becomes more promising as Internet standards and technologies rapidlygrow. The advent of JAVA – a portable, object-oriented Internet language –promises to remove many of the constraints inherent in early World Wide Webprotocols and further extends the capabilities of Web-based data browsing sys-tems. By moving much of the requisite display, processing, and analysis func-tionality to the client-end of the Internet connection, performance delays due toserver load and Internet bandwidth limitations may be greatly reduced. The recentdevelopment in Web-based geovisualization using AJAX and Web 2.0 technol-ogies are very important for enhancing the performance and capabilities of theWeb-based GIS systems.
Geocarto International 21
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
The success of SDSS in Internet GIS domain is fully dependent on the perfor-mance of the application. Development of SDSS in Web GIS environment with allthe spatial analysis capabilities is also in its evolving stage. The implementation ofOLAP and related techniques at the spatial data server level certainly improves theperformance and the analytical capability of Web-based SDSS.
4. Conclusion
The present article demonstrates the Web-based geospatial analysis with effectivedatabase organization and management approach. The developed approach can beused for any other related studies. The database design for the biodiversity databasebeing generated under the national level DOD-DBT programme has been developedand implemented. The database of phase I and phase II of DOS-DBT project hasbeen integrated into the database server for the effective data dissemination andanalysis. Web-SDSS is developed by integrating standard multi-criteria decisionanalysis technique AHP with landscape model for biodiversity characterization. Anattempt has been made to generate the alternative decisions based on priorityvectors. The non-spatial data undergo different phases of data cleaning andnormalization and finally get uploaded into Oracle 10 g data server by using SQLloader utility of oracle. The concept of geodatabase has been used and implementedfor Oracle 10 g where the spatial data are stored and organized in RDBMSenvironment. The database linking mechanism for spatial and non-spatial databasehas been established and implemented. The main advantage of storing GIS data inRDBMS environment is that the DBA/application developer can use all theRDBMS functionalities for GIS data which is very important when the proposedapplication or system is planned in the client/server environment. In the developmentof SDSS for Internet GIS domain the data versioning also plays an important role.The use of customized GIS package ArcSDE for managing spatial data into DBMSis useful for handling multi-session environment for Web application. Theorganization and management of spatial data in RDBMS environment is greatlyhelpful to implement client/server technology for GIS environment. The databaseserver is ready for its R&D and operational use.
Acknowledgements
The authors express sincere gratitude to the Director, NRSC for the support andencouragement while carrying out the study. The authors duly acknowledge the DOS-DBTproject team for providing base map layers and necessary inputs required for this study.
References
Anonymous, 2002. Biodiversity Characterization at Landscape Level in North-East Indiausing Satellite Remote Sensing and Geographic Information System – A joint project ofthe Department of Biotechnology, Government of India and Department of Space,Government of India. (ISBN – 81-901418-0-5).
Carver, S.J., 1991. Integrating multi-criteria evaluation with geographical informationsystems. International Journal of Geographical Information Systems, 5, 321–339.
Crossland, M.D., Perkins, W.C., and Wynne, B.E., 1995. Spatial decision support systems: anoverview of technology and a test efficiency. Decision Support Systems, 14 (3), 219–235.
Finnie, G.R., Wittig, G.E., and Petkov, D.I., 1993. Prioritizing software developmentproductivity factors using the analytic hierarchy process. Journal of Systems and Software,22, 129–139.
22 H. Karnatak et al.
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
Jankowski, P., 1995. Integrating geographical information systems and multiple criteriadecision-making methods. International Journal of Geographical Information Science andSystems, 9 (3), 251–273.
Karnatak, H.C., et al., 2007. Multicriteria decision analysis in Web GIS environment.Geoinformatica, 11, 407–429.
Lee, H., 1993. A structured methodology for software development effort predication usinganalytic hierarchy process. Journal of Systems and Software, 21 (2), 179–186.
Malczewski, J., 1999. GIS and multicriteria decision analysis. New York, NY: John Wiley andSons, 392.
Peng, Z.-R. and Tsou, M.-H., 2003. Internet GIS: distributed geographic information servicesfor the internet and wireless networks. Hoboken, NJ: Wiley.
Rinner, C., 2003. Web-based spatial decision support: status and research directions. Journalof Geographic Information and Decision Analysis, 7 (1), 14–31.
Roy, P.S. and Tomar, S., 2000. Biodiversity characterization at landscape level usinggeospatial-modeling technique. Biological Conservation, 95 (1), 95–109.
Roy, P.S., Sameer, S., and Harish, C.K., 2004. Biodiversity characterization at landscape levelusing geospatial modeling. International workshop on Biodiversity Informatics, Organizedby National chemical laboratory, December 2004 Pune.
Saaty, T.L., 1980. The analytic hierarchy process. New York: McGraw-Hill publications.Wellar, B., 1990. Science, applications, coherence and GIS: seizing the moment. In: GIS/LIS
090 Proceedings, Vol. 2, 7th–10th November, California.Yau, C. and Davis, T., 1993. Using analytic hierarchy process (AHP) to prioritize auditing
tasks for large-scale software systems. Journal of Systems Management, 44 (11), 26.Zhu, X., et al., 2001. Web-based decision support for regional vegetation management.
Computers, Environment and Urban Systems, 25 (6), 605–627.
Geocarto International 23
Dow
nloa
ded
by [
Tuf
ts U
nive
rsity
] at
08:
40 2
6 Se
ptem
ber
2014
