Architectural Model of Spatially Enabled

E-Government in Indonesia Adhiawan Soegiharto1, Dana Indra Sensuse2

1,2Magister Teknologi Informasi, Fakultas Ilmu Komputer, Universitas Indonesia

Gedung Pusat Ilmu Komputer Universitas Indonesia

Jl. Salemba Raya No. 4,

Jakarta Pusat, Indonesia

adhiawan@gmail.com

dana@cs.ui.ac.id

Abstract— Data and spatial information has been recognised as

an important element in decision making. The high cost of spatial

data procurement can become an obstacle in accessing spatial

information, so that the spatial data held by certain institutions

cannot be shared by other institutions that may need it.

To overcome this problem, national government has issued

policies on the dissemination of spatial data. This policy appoints

several agencies to act as nodes and node connectors of a

National Spatial Data Network. However, within this policy there

is no reference to the architecture that is required to build an e-

government which can support the use of spatial data for

decision making. To build such an architectural model,

identification and observation is required in every relevant

Indonesian institution designated as nodes and connecting node

in the National Spatial Data Network. To establish current best

practice, a critical observation and comparative review will be

completed of e-government architecture models in other spatially

enabled countries.

The results of this identification and observation become the

basis for the design of an appropriate architectural model.

Architectural models thus formed can provide a reference for

agencies in Indonesia. Such a reference is expected to facilitate

institutions in building the information technology architecture

that can enable them to efficiency and effectively make best use

of spatial data.

Keywords— e-government, e-government architecture, spatial

information, data integration, core diagram

I. BACKGROUND AND MOTIVATION

E-government initiatives in Indonesia began in the early

2000s and confirmed by the issuance of Presidential

Instruction (Inpres) No. 6 year 2001 on Telematics. This

Presidential Directive states that government officials should

use telematics technology to support good governance. The

Initiative is expanding so that the government issued

Presidential Instruction No. 3 year 2003 on e-government

development policy.

E-government development, in addition to improving the

quality of service to the community, also improves the quality

of data and information provided through the service. Data

and information can be used as a basis for decision making,

both by businesses, the public, or other government

institutions.

According to the survey results on Spatial Enablement of

government in Australia by Geoscience Australia in 2007, ‘the

use of spatial information will improve the quality of decision

making, reduce administrative costs, improve the quality of

the governmental activities, and improve opportunities for

industrial development. However, this requires the availability

of spatial data and its access that is well maintained to be

used by most of spatially unaware people’ [46]. Government

that provides extensive spatial information is referred as a

spatially enabled government.

From the initial literature study, the authors conclude that if

spatial elements are embedded in e-government, then the

awareness of the use and dissemination of spatial data and

information is expected to start rising. This is due to the

purpose and nature of e-government itself, which provides

access information to the public in a simple and accessible

form.

This public awareness will lead to increased community

interest in obtaining access to spatial information. The high

public interest is expected to increase the awareness of

government institutions in the larger benefits of wider spatial

data and information dissemination and sharing.

II. PROBLEM STATEMENT

Government regulations on the establishment of the

ministries (UU No. 39 year 2008) states that each function of

governance may not be done by only a particular institution or

ministry. Governance functions which are performed by

several institutions led to the emergence of several programs

which will use similar data and information. About 80 percent

of them contain elements of spatial information [38].

Currently, in Indonesia, the use of spatial data and

information, both by public and government institutions, is

still very small. The expensive provision of spatial data is one

of the main reasons. Currently, each sub-directorate, which

launched a program almost always procure their own spatial

data. Procurement of spatial data is usually a significant cost

of financing the overall program. The high cost provision of

spatial data causes each institution to store spatial data for

their own. This causes the provision of the same spatial data at

different institutions. Multiple procurements cause higher

procurement costs for the same spatial data for the

government as a whole. Other impacts that occur are the lack

of awareness that spatial data use can become widespread, due

to the tendency of not sharing data.

Data sharing has been recognized to be the solution to the

expensive spatial data procurement. To facilitate the

dissemination of spatial data, the Government of Indonesia

has established the NSDI (National Spatial Data

Infrastructure/Infrastruktur Data Spasial Nasional – IDSN).

Spatial data infrastructures are the key to the formation of a

spatially enabled government [37]. This implementation is

followed by the release of Presidential Regulation (Perpres)

No. 85/2007 regarding the NDSN (National Spatial Data

Network/Jaringan Data Spasial Nasional – JDSN).

E-Government architectural models that support spatial

enablement assist institutions in building e-government

architecture which in turn means that institutions become

spatially enabled and, at the same time, take advantage of

spatial data infrastructure available. Because such reference

model architecture supports e-government usage does not

exist yet in Indonesia, the research question to be answered by

this thesis is "what is the architectural model for spatially

enabled e-government in Indonesia."

III. LITERATURE STUDY

Literature review conducted on the documents of

institutions establishment, the journals, articles, and reference

books on spatially enabled government, e-government, spatial

information, spatial data integration, current geospatial

technologies, enterprise architecture, and related studies that

have been done in the field of e-government.

In Indonesia, there are 4 types of institutions; the State

Supreme Agencies, Ministry, Ministerial Level of

Government Institutions and Non-Departmental Government

Institutions (Lembaga Pemerintah Non Departemen – LPND).

Of the four types of them, there are 2 types of institutions

designated to become the nodes and node connector in the

National Spatial Data Network (NDSN/JDSN), the ministry

and LPND.

These institutions were established to carry out the

functions of governance as in the regulation. Each of the

governance functions are described in detail in the Document

of e-Government Blue Print Design issued by the Ministry of

Communications and Informatics (Kemenkominfo).

Rajabifard states that a government or society is said to be

spatially enabled when spatial information has been widely

available for the community and spatial information are

widely used by consumers as the use of non-spatial

information. In developed countries, the use of spatial

information has now become a common part of the e-

government and broader government ICT strategy [46].

Müller estimates that about 80% of data in the process of

governance institutions is a spatial data or data that relate to

spatial data, so the establishment of spatial data infrastructure

becoming a very important part of the processes that exist in

e-government [38].

A. Interoperability

Interoperability is a crucial issue in the formation of this

spatially enabled government. One of the objectives of the

establishment of NSDI is to address this issue. Sukyadi has

constructed a model of e-Government interoperability [57],

but it has not considering the spatial dimension. Other

researches on spatial data interoperability were conducted by

Zhang et.al [69], Huang et.al [19], and Zheng et.al [70] on

web service interoperability model. These researches show a

positive result on implementation of Open Geospatial

Consortium (OGC) standards as interoperability model. From

the perspective of interoperability on spatially enabled e-

government, the research by Sukyadi (e-government) and

other four researches above are complementing each other.

The interoperability models from those five researches

became matters of literature study to form an integration

model in enterprise architecture of e-government.

B. Spatial Data Infrastructure

SDI (Spatial Data Infrastructure) is a collection of

technologies, policies and institutional arrangements that

facilitate access availability to spatial data [46]. Rajabifard

states that the spatial data infrastructure is the key to the

establishment of a spatially enabled government [47].

In Indonesia, the NSDI was formed as a consortium of

concerned institutions for spatial data. This consortium is

coordinated by Bakosurtanal as government institution that

serves as the coordinator of the survey and mapping. The

NSDI consists of 3 components: institutional, legislation, and

primary data. One of NSDI results is the emergence of Perpres

No. 85/2007 regarding the NSDN (National Spatial Data

Network).

C. National Spatial Data Network

To prevent the procurement of the same spatial data

collection, the Government of Indonesia (GoI) has issued

Perpres No. 85/2007 regarding the NSDN. This regulation

states that NDSN administered through an electronic media

(i.e. Internet) and serves as a means of exchange and

dissemination of spatial data.

This regulation specifies which parties are given the

responsibility as a Node and Node Connector of NDSN. Mesh

networks consist of government institutions, departmental and

non-departmental, provincial, and municipal/district. Network

Node Connector played by the Coordination Agency for

Surveys and Mapping (Bakosurtanal). The table below shows

the institutions included in NDSN.

TABLE I

AGENCIES IN NDSN

No. Government Sector Agency

1 Survey and

mapping

Bakosurtanal (Badan Koordinasi Survei

dan Pemetaan Nasional)

2 Land administration BPN (Badan Pertanahan Nasional)

3 Internal affairs Kemendagri (Kementerian Dalam

Negeri)

4 Transportation Kemenhub (Kementerian Perhubungan)

5 Communication and

informatics

Kemenkominfo (Kementerian

Komunikasi dan Informatika)

6 Public works Kementerian PU (Pekerjaan Umum)

No. Government Sector Agency

7 Culture and tourism Kemenbudpar (Kementerian

Kebudayaan dan Pariwisata)

8 Statistics BPS (Badan Pusat Statistik)

9 Energy and mineral

resources

Kementerian ESDM (Energi dan

Sumber Daya Mineral)

10 Forestry Kemenhut (Kementerian Kehutanan)

11 Agriculture Kementan (Kementerian Pertanian)

12 Marine and

fisheries

KKP (Kementerian Kelautan dan

Perikanan)

13 Meteorology and

geophysics

BMKG (Badan Meteorologi,

Klimatologi, dan Geofisika)

14 Space and

aeronautics

Lapan (Lembaga Antariksa dan

Penerbangan Nasional)

There are 44 types of spatial data, which is mentioned in

NDSN regulation, which must be produced by institutions that

have been designated as a nodes and node connector of the

NDSN. Spatial data production process will become the basis

for building spatial information model of each institution in

NDSN.

The NDSN regulation clearly states that NDSN must be

held through an electronic-based information network. This

assumes technologies that utilize the Internet as a medium of

exchange of spatial data. Reviews of this technology include

the standards of publication, representation, processing, and

production of spatial data.

Internet Map Server is one of the most common

technologies in use today which has implemented this

standard. For the processing and production of spatial data,

GIS desktop applications remain a top choice so far, because

this application provides much more complete spatial features

than the web-based mapping applications. Although it’s

desktop-based, the current applications of GIS has been able

to connect spatial database through the Internet with the same

communication standard used by web mapping.

To show a simple/generic form of a spatially enabled e-

government architecture, the authors adopt Ross core diagram

[40]. A core diagram provides main points in the enterprise

architecture to be a reference for the responsible management

in developing and utilizing this architecture [40]. The

management in this case is the Government of Indonesia. This

diagram is expected to facilitate the government in the overall

view of the architecture.

IV. FRAMEWORK AND METHODOLOGY

From the literature study, the author develops a framework

which is adopted from Ross core diagram forming process.

The framework is shown by the following figure.

FIG. 1 ARCHITECTURE DESIGN FRAMEWORK

Model design proceeded in three phases, namely the

identification, creation of core diagrams, and testing. Model

design steps are as follows:

1) Identification of operating model: Performed to get

the most suitable operating model that needed to adopt by

government agencies to be spatially enabled.

2) Identification of key customers: Conducted to see

which parties are to be clients for government agencies.

3) Identification of business model: Performed to obtain

any data/information related to business processes

(governmental function) in this case.

4) Identification of information model: Results of

business model identification is then processed in this activity

to find what data/information that must be shared or

disseminated, and which governmental function that can be

linked.

5) Identification of application and technology model:

This activity is performed to get the right technology to

integrate the spatial information.

6) Core diagram design: Core diagram is formed on the

basis of all the result from above 5 steps.

7) Model testing: Performed by expert panel method.

The reference documents in the above activities are UU No.

39/2008 on Ministry, Keppres No. 103/2001 on Non

Ministerial Government Agencies, Perpres No. 85/2007 on

NDSN, Document of E-Government Blue Print Plan by

Kemenkominfo, and medium term development plan

(RPJMN) 2010-2014 by Bappenas.

To form the core diagram, the references above are

combined with the literature study on the most current

architecture of applications that are already implemented OGC

standards.

V. DISCUSSION

The model will be designed in accordance with the

framework mentioned above.

A. Operating Model

From the study of operating model, the authors found that

in Indonesia case, the agency can simply choose coordination

operating model to prepare the spatial enablement. There are

three points to be considered:

a) Noting the influence of the data produced by

institutions with the interests of other institutions,

while maintaining its own business processes;

b) Following the data standardisation that has been

established by the NSDI; only spatial data related

that need to follow this standardisation;

c) IT architecture and/or infrastructure related decision

making are taken at institutional level.

Implementation and technology selection can be

decided by the units under it, as long as spatial data

related technologies are referring to NSDI.

B. Business Model

The business model in government case is indicated by its

services. Service model is formed by mapping the governance

functions (which exist in the governmental functional system

framework) with the institutions mentioned in NSDN.

The output of this analysis then summarised to a table, then

mapped to a matrix of institution vs. functional block as a

service model.

FIG. 2 SUMMARISED OF SERVICE MODEL MATRIX OF INSTITUTIONS IN NDSN

The shaded part in the matrix above shows:

a) Governance function that is performed by the agency

in its column above.

b) That governance function is coordinate by the agency

in its column above.

c) Other agencies that may perform the same function

have to refer to the procedure or standards set by the

agency in its column above.

The matrix above will be used in the mapping process

between service model and information model, which is

focused on function of national development. The

considerations to choose it are:

a) The function of institutional support and service is an

internal function of each institution. Therefore, no

inter-institutions relationship patterns are identified

in this function.

b) The function of politics and legislation and the

function of defence and security, from the scope of

this thesis (NDSN), are only performed by

Kemendagri.

c) There is no institution mentioned in the NDSN

regulation that is performed function of laws,

regulations, and legislations, and the function of

national monetary and fiscal.

d) Though performed by all institutions, the function of

information publication and government service is

not related to other institutions because the

information is only related to each institution itself.

Although focused on the function of National

Development, but on the core diagram to be constructed, the

seven governance function will be listed. Governance

functions 1, 4, and 5 will be listed on the first level only, while

the functions of governance 2, 3, 6, and 7 will be listed until

the first sub-levels.

C. Information Model

Identification information model is performed by map the

outputs with basic needs of spatial information in each of the

agency in NDSN. The result is then summarised to see the

overall relationship between the functional classifications of

spatial data with the institutions concerned. This summary is

shown in the matrix below.

FIG. 3 AGENCY IN NDSN OUTPUT AND SPATIAL INFORMATION NEEDS

Bakosurt

anal

BP

N

Kem

endagri

Kem

enhub

Kem

enkom

info

Kem

ente

rian P

U

Kem

enbudpar

BP

S

Kem

ente

rian E

SD

M

Kem

enhut

Kem

enta

n

KK

P

BM

G

LA

PA

N

6.1 Governance

6.2 Zoning

6.3 Social

6.4 Facilities & Infrastructures

7. Information Publication & Government Services

1. Institutional Support & Services

2. Politics & Legislation

3. Defense & Security

4. Laws, Regulations, & Legislation

5. Monetary & Fiscal

6.

National

Develo

pm

ent

D. Integration Model

Web-based technology is chosen as integration technology

with the use of Internet Map Service. Integration model

adopted from the model of application architecture, web-

mapping applications that have implemented OGC standards.

Models of spatial data integration for publication can be seen

in Fig. 4.

Production, processing, and management of spatial data

perform by using desktop GIS applications. For spatial data

management, web-mapping applications can be used, but

today, tools for production and processing spatial data in

desktop applications are still have much more complete

features than in web-mapping applications. Features of spatial

analysis on a desktop application are also more sophisticated

than web-mapping.

FIG. 4 INTEGRATION MODEL IN SPATIAL DATA PUBLICATION

In data integration, these desktop GIS applications use the

same communication standard with a web-mapping, which

uses OGC standards. Figure below shows the model of

integration of spatial data through desktop GIS applications.

FIG. 5 INTEGRATION MODEL IN SPATIAL DATA PRODUCTION

Through the database connection layer, GIS application

connects to al required spatial data, vector files, and raster

files. All spatial data that has been connected is processed,

analysed, and/or combined to produce a new spatial data.

Cleaning process at each connected spatial data topology must

be done before performing spatial analysis. RS applications or

extensions are used to perform data mining process in raster

files of satellite imageries or aerial photos. This process

results are vectors that can be processed or analysed further by

GIS application. The final output is then stored into a spatial

data warehouse. Spatial data stored in a data warehouse are

(has to be) already topologically clean. This would be very

useful for other institutions that will use this spatial data,

because it will reduce time of topology cleaning significantly.

Both integration models above are then combined to form

an overall integration model (Fig. 6). Agencies producing

spatial data will access spatial data required via database

connection. It is strongly recommended to use the Internet

channels with specific security levels that can only be

accessed by government agencies.

FIG. 6 INTEGRATION MODEL OF SPATIAL INFORMATION

E. Architectural Model of E-Government

In accordance with literature studies on Ross core diagram,

the analysis then mapped onto the core diagram forming

process. The mapping can be seen in the following table.

TABLE II

MAPPING OF ANALYSIS RESULTS WITH CORE DIAGRAM

Process Analysis Result

Shared customers Public, business, and other government

institutions.

Shared data All spatial data mentioned in NDSN

regulation.

Integrating

technologies

The use of Internet Map Service and GIS

applications which are already

implemented OGC standards.

Linked processes Governance function in governmental

functional framework.

From the analysis of each of the above process, the e-

government architecture formed as shown in the Fig. 7 below.

FIG. 7 E-GOVERNMENT CORE DIAGRAM

As shown in the core diagram, there are 2 main layers, the

interface layer, and system layer. Interface layer is the layer

that connects between the systems with potential data users (in

this case, the general public, businesses, and government

institutions). Interface layer is separated into two types to

differentiate its services, to the general public and business

community, and to government institutions. Layer consists of

database systems, spatial database, data processing

applications, and integration. XML, GML and SVG primarily

are the choice of technology for this integration.

F. Sample Implementation

The authors make an example of architecture

implementation if applied in Ministry of Public Works

(Kementerian PU). Kementerian PU produces six types of

spatial data using nine types of base spatial data in four

different agencies. This example is shown in the Fig. 8 below.

FIG. 8 SAMPLE IMPLEMENTATION IN KEMENTERIAN PU

The six types of spatial data in the spatial data warehouse

(1) are the result of analysis using GIS and/or RS (2). Analysis

is performed on base data/base map from other agencies (3)

which then overlaid with some spatial data (4) or additional

rasters (6). If necessary, the result then stored to a local

database (5) so then it can be analysed further before

generating the final spatial data which then stored to spatial

data warehouse (1). Data that has been stored in a data

warehouse can then be used by other institutions (8) to

produce the required spatial data.

Spatial data in a data warehouse are then published through

the IMS (7) for use by decision-makers (9) either within the

ministry itself, or in other agencies. Referring to the role of

Kementerian PU in the national development function, the

spatial data are used to make decisions related to the function

of 6.1 governance, 6.2 zoning, and 6.4 facilities and

infrastructure.

G. Proposed Solution

Proposed implementation of solutions on the institutions

integration model of spatial data are mapped in Fig. 9 as

follow.

FIG. 9 MAPPING OF PROPOSED SOLUTION

As seen on the proposed implementation above, there are

two important parts on the integration model. The first part (1)

is a production process which includes GIS (overlay, spatial

analysis, network analysis) and RS (georeferencing, image

classification, digitisation). This production process can

retrieve data from a local database (4) and the end result is

stored into a spatial data warehouse (3). The second part (2)

covers the process of publication; publication is only

performed on spatial data stored in spatial data warehouse (3).

FIG. 10 SAMPLE SOLUTION FOR SPATIAL DATA PRODUCTION

1) Sample solution for spatial data production: There

are three activities in the production process spatial data,

namely topology cleaning, image processing, and GIS

processing. An example of the proposed solution is shown in

Fig. 10.

Other solution examples for the production of spatial data

can be seen in Table III below. This table also contains some

proprietary solutions, apart from open source solutions.

TABLE III

SAMPLE SOLUTION FOR DATA SPATIAL PRODUCTION

Sample Solution Topology

Cleaning

Image

Processing

GIS

Processing

Open Source

GRASS GIS

grass.itc.it ■

OSSIM

www.ossim.org ■

Quantum GIS

www.qgis.org ■

Ilwis

www.ilwis.org ■ ■

Proprietary

ArcGIS

www.esri.com ■ ■ ■

Erdas Imagine

www.erdas.com ■

Idrisi

www.clarklabs.org ■ ■

Manifold

www.manifold.net ■ ■

GeoMedia

www.intergraph.com ■ ■ ■

2) Sample solution for spatial data publication: There

are three layers in the publication of spatial data, the server

layer, application layer, and web servers, as seen in Fig. 11.

FIG. 11 SAMPLE SOLUTION FOR SPATIAL DATA PUBLICATION

Other solution examples for the production of spatial data

can be seen in the following Table IV. This table also contains

some proprietary solutions, apart from open source solutions.

TABLE IV

SAMPLE SOLUTION FOR SPATIAL DATA PUBLICATION

Sample Solution Map

Server

Application/

Framework

Web

Server

Map

Viewer

Map

Catalog

Open Source

MapGuide Open Source

mapguide.osgeo.org ■ ■ ■

MapServer

mapserver.org ■ ■ ■

GeoServer

geoserver.org ■ ■ ■

OpenLayers

openlayers.org ■

CartoWeb

www.cartoweb.org ■ ■

Apache

httpd.apache.org ■

GeoNetwork

sourceforge.net/projec

ts/geonetwork/

■

Proprietary

ArcGIS Server

www.esri.com ■ ■ ■ ■

Manifold IMS

www.manifold.net/inf

o/ims.shtml

■ ■ ■

GeoMedia WebMap

www.intergraph.com ■ ■ ■ ■

IIS (Windows Server)

www.microsoft.com/se

rvers

■

3) Sample solution for database and data warehouse:

Currently, open source databases have not been able to use as

a data warehouse because, currently, the maximum data size

that is accommodated by open source solutions is 4

Gigabyte. Data warehouse, especially for storing spatial data,

should be able to accommodate larger data sizes, with no size

limit.

TABLE V

SAMPLE SOLUTION FOR DATABASE AND DATA WAREHOUSE

Sample Solution Spatial

Database

Spatial Data

Warehouse

Open Source

Postgres+PostGIS

www.postgresql.org

postgis.refractions.net

■

MySQL Spatial

www.mysql.com ■

Proprietary

Postgres Plus

www.enterprisedb.com ■ ■

Oracle Spatial

www.oracle.com ■ ■

Cubewerx CubeSTOR

www.cubewerx.com ■ ■

H. Implication of Architecture Implementation

There are several implications of the adoption of this

spatially enabled e-government architecture.

1) Management: To ensure the integration of spatial

data in the architectural model in this thesis, the government

should establish a policy to choose a georeference standard for

all agencies in Indonesia. On the other hand, the application of

spatial data warehouse means necessary enforcement of SOPs

from every production process in the use of spatial data and

data cleaning.

2) System: Open source applications for GIS and web

mapping is now approaching features of proprietary

applications, so the cost of purchasing and updating

applications can be suppressed. Because the integration of the

proposed architecture is built upon the network (Internet), then

the government needs to establish a special channel of data

communication between agencies. This data communication

lines can be a WAN or MAN. Implementation of spatial data

warehouse will minimise data inconsistency.

3) Academic perspective: From the academic side, the

implications of the adoption of e-government architecture

which spatially enabled in this thesis are:

Study on the implementation of this spatially enabled

architecture at agencies that have not been included in

NDSN.

Research on ICT governance for the implementation of

architecture that is spatially enabled.

Research on the communication security between

agencies in the implementation of this architecture.

Study on spatial data acquisition speed improvement for

decision making on the implementation of the

architecture.

Study on the improvement in term of speed and accuracy

of interventions for the decision based on the use of

spatial data.

VI. CONCLUSION AND SUGGESTION

A. Conclusion

1) Model of data spatial integration: Can be done by

implementing the OGC standards, whether for publication or

for the production and processing.

2) Open source applications: Already have a fairly

complete feature in the publication of spatial data, while for

the production, processing, and spatial data management, use

of open source applications still need to be combined with the

use of proprietary applications.

3) Result of expert panel: The proposed architectural

model has been considered to be a national benchmark or

reference for the development of spatially enabled e-

government.

B. Suggestion

a) The government should issue a policy to choose a

georeference standard for all agencies in Indonesia

that produce spatial data.

b) Each agency shall issue, enforce, and supervise the

implementation of the SOP on spatial data production

processes.

c) Government and the agencies producing spatial data

need to assess the utilization of open source

applications, both on the production and publication

processes.

d) Government should consider the establishment of the

government service provider to provide electronic

channels (Internet) for communication between

government agencies, to ensure the security and

speed of delivery and exchange of information,

especially spatial information.

e) Each agency needs to ensure the implementation of

spatial data warehouse concept, to maintain the

validity of data.

C. Further Research

Conduct further research or study on:

a) The implementation of this spatially enabled

architecture on other agencies that are not yet

designated as NSDN nodes;

b) The proper ICT governance for the implementation

of a spatially enabled architecture;

c) Security factor in communication channel (Internet)

between agencies to accommodate the integration in

this spatially enabled architecture;

d) The increase of speed in spatial data acquisition for

decision making on the implementation of the

architecture;

e) The speed and accuracy of intervention upon the

decision based on the use of spatial data.

VII. BIBLIOGRAPHY

[1] John W. Barnes and Richard J. Lisle, Basic Geological Mapping, 4th

ed. England: John Wiley & Sons Ltd, 2004.

[2] Karin Berkhoff and Sylvia Herrmann, "Modeling Land Use Change: A

GIS Based Modeling Framework to Support Integrated Land Use

Planning (NabanFrame)," in Advances in GIScience, Monika Sester,

Lars Bernard, and Volker Paelke, Eds.: Springer-Verlag Berlin

Heidelberg, 2009.

[3] Rick Bradford and Daniel Vincent, "Development of Temporal

Mapping Techniques to Support Urban Retrospectives Database," US

Geological Survey, 1998.

[4] Günay Çakir, Fatih Sivrikaya, Salih Terzioğlu, and Emin Zeki Başkent,

"Mapping Secondary Forest Succession with Geographic Information

Systems: A Case Study from Bulanıkdere, Kırklareli, Turkey," Turkish

Journal of Agriculture and Forestry, vol. 31, pp. 71-81, February 2007.

[5] Daniel M. Campbell, Local Government Handbook for GIS

Implementation Within the Commonwealth of Pennsylvania.:

Pennsylvania Mapping and Geographic Information Consortium

(PaMAGIC), 2002.

[6] Commonwealth Cultural Resources Group, Inc., "Minnesota Deep Test

Protocol Project," Minnesota Department of Transportation, Jackson,

2006.

[7] Leslie M. Cone, "Explaining the National Integrated Land System

(NILS)," U.S. Bureau of Land Management, 2008.

[8] Michele Crosetto and Paolo Pasquali, "DSM Generation and

Deformation Measurement from SAR Data," in Advances in

Photogrammetry, Remote Sensing and Spatial Information Sciences:

2008 ISPRS Congress Book, Zhilin Li, Jun Chen, and Emmanuel

Baltsavias, Eds. London: Taylor & Francis Group, 2008.

[9] Christopher Daly, E. H. Helmer, and Maya Quińones, "Mapping the

Climate of Puerto Rico, Vieques and Culebra," International Journal of

Climatology, vol. 23, pp. 1359-1381, August 2003.

[10] Rolf A. de By et al., Principles of Geographic Information Systems,

3rd ed., Rolf A. de By, Ed. Enschede, The Netherlands: The

International Institute for Geo-Information Science and Earth

Observation (ITC), 2004.

[11] Direktorat E-Government, Direktorat Jenderal Aplikasi Telematika,

Rancangan Blue Print E-Government. Jakarta, Indonesia: Kementerian

Komunikasi dan Informatika, 2006.

[12] R.J.J. Dost and C.M.M. Mannaerts, "Generation of Lake Bathymetry

Using Sonar, Satellite Imagery, and GIS," in ESRI Users Group

Conference, 2009.

[13] Ralph Dubayah, Robert Knox, Michelle Hofton, J. Bryan Blair, and

Jason Drake, "Land Surface Characterization Using Lidar Remote

Sensing," in Spatial Information for Land Use Management, Michael J.

Hill and Richard J. Aspinall, Eds., 2000.

[14] W. E. Featherstone, "On the Use of the Geoid in Geophysics: A Case

Study Over the North-West Shelf of Australia," Exploration

Geophysics, vol. 28, no. 1, pp. 52-57, 1997.

[15] FGDC, Geographic Information Framework Data Content Standard

Part 4: Geodetic Control.: Federal Geographic Data Committee

(FGDC), 2008.

[16] Witold Fraczek, "Mean Seal Level, GPS, and the Geoid," ArcUser, pp.

36-41, September 2003.

[17] Maria Grazia Fugini, "A Security Model and Architecture for

Multichannel E-Government Systems," in Second International

Conference on Availability, Reliability and Security, ARES, Vienna,

2007.

[18] Guangping He and Greg Orvets, "Capturing Road Network Data Using

Mobile Mapping Technology," International Archives of

Photogrammetry and Remote Sensing, vol. XXXIII, 2000.

[19] Fang Huang, Dingsheng Liu, Guoqing Li, and Jian Wang, "Preliminary

Study on the Application of OGC Interoperability Specifications in

Spatial Information Grid," in International Conference on New Trends

in Information and Service Science, NISS. , Beijing, 2009, pp. 515-

520.

[20] International Association of Assessing Officers, Standard on Manual

Cadastral Maps and Parcel Identifiers. Chicago: International

Association of Assessing Officers, 2004.

[21] Noritoshi Kamagata et al., "A New Method of Vegetation Mapping by

Object-based Classification Using High Resolution Satellite Data," in

International Conference on Object-based Image Analysis, Salzburg,

2006.

[22] A. J. Kenny, M. Desprez I. Cato, G. Fader, R. T. E. Schüttenhelm, and

J. Side, "An Overview of Seabed-mapping Technologies in the Context

of Marine Habitat Classification," ICES Journal of Marine Sciences,

vol. 60, pp. 411-418, 2003.

[23] Gottfried Konecny, Geoinformation: Remote Sensing, Photogrammetry

and Geographic Information Systems. New York: Taylor & Francis

Inc, 2003.

[24] Marwan Koudmani, "Applications of Remote Sensing to Hydrology

and Hydrogeology," in International Conference on Water Resources &

Arid Environment, Riyadh, 2004.

[25] Alphonce Kyessi and Victoria Mwakalinga, "GIS Application in

Coordinating Solid Waste Collection: The Case of Sinza

Neighbourhood in Kinondoni Municipality, Dar es Salaam City,

Tanzania," in International Federation of Surveyors (FIG) Working

Week, Eilat, 2009.

[26] Kamal Labbassi, Amina Tajdi, and Ahmed Er-raji, "Remote Sensing

and Geological Mapping for a Groundwater Recharge Model in the

Arid Area of Sebt Rbrykine: Doukkala, Western Morocco," in

Geoscience and Remote Sensing Symposium, Cape Town, 2009, pp.

112-115.

[27] Nag Yeon Lee, E-Government Applications.: United Nations Asian

and Pacific Training Centre for Information and Communication

Technology for Development, 2009.

[28] Hyejin Lee and Lee Hyunah, "Design and Implementation of

Integrated MapServer in Web Mapping Environments," in International

Geoscience and Remote Sensing Symposium, 2005, Seoul, 2005.

[29] Bambang Edhi Leksono, Yuliana Susilowati, Hendriatiningsih, and

Denisanto, "The Influence of Urban Accessibility in Determining

Average Indicated Land Values for the Region.," in FIG Congress

2010: Facing the Challenges – Building the Capacity, Sydney, 2010.

[30] Arika Ligman-Zielinska, Richard L. Church, and Piotr Jankowski,

"Spatial Optimization as a Generative Technique for Sustainable

Multiobjective Land Use Allocation," International Journal of

Geographical Information Science, vol. 22, no. 6, pp. 601-622, June

2008.

[31] Feng Ling, Qiu-wen Zhang, and Cheng Wang, "Comparison of SRTM

Data with other DEM sources in Hydrological Researches," in

International Symposium on Remote Sensing of Environment, St.

Petersburg, 2005.

[32] Lifan Li, Qing Yu, and Jiechen Wang, "Assistant Decision System of

E-Government Based on WebGIS," in International Symposium on

Information Engineering and Electronic Commerce, Ternopil, 2009.

[33] Xiaolin Lu, "A Unified E-Government Information Management

Platform Based on Web GIS Technology," in International Conference

on Computational Intelligence and Software Engineering, Wuhan,

2009, pp. 1-4.

[34] M.L. Manchanda, M. Kudrat, and A.K. Tiwari, "Soil Survey and

Mapping Using Remote Sensing," Tropical Ecology, vol. 43, pp. 61-

74, 2002.

[35] Yannis Manolopoulos, Apostolos N. Papadopoulos, and Michael Gr.

Vassilakopoulos, Spatial Databases: Technologies, Techniques, and

Trends.: Idea Group Publishing, 2005.

[36] MapServer Team University of Minnesota, MapServer

Documentation.: University of Minnesota, 2010.

[37] I. Masser, A. Rajabifard, and I.P. Williamson, "Spatially Enabling

Governments through SDI implementation," International Journal of

GIS, vol. 21, pp. 1-16, July 2007.

[38] Hartmut Müller, "Information Technology and Spatial Data

Infrastructure for E-Government," in FIG Workshop on eGovernance,

Knowledge Management and eLearning, Budapest, Hungary, 2006.

[39] Oil and Gas Management Branch, "Seismic Exploration," Yukon

Government, Whitehorse, Yukon Government Oil and Gas Best

Management Practices 2006.

[40] Open Geospatial Consortium. (2010, May) OGC Website. [Online].

http://www.opengeospatial.org

[41] Open GIS Consortium, OpenGIS Simple Features Specification for

SQL.: Open GIS Consortium, Inc., 1999.

[42] Gordon Petrie and Kenneth Smillie, "Airborne Digital Imaging Sensors

and Systems," in Advances in Photogrammetry, Remote Sensing and

Spatial Information Sciences: 2008 ISPRS Congress Book, Zhilin Li,

Jun Chen, and Emmanuel Baltsavias, Eds. London: Taylor & Francis

Group, 2008.

[43] Peter Poole, "Cultural Mapping and Indigenous Peoples," UNESCO,

2003.

[44] Presiden Republik Indonesia, Peraturan Presiden Republik Indonesia

No. 85/2007., 2007.

[45] Yu-kun Qiao, Shi-cheng Wang, Jin-sheng Zhang, Li-hua Chen, and Qi

Zhang, "Surface Spline Based Constructing Method for Geomagnetic

Reference Map," in IEEE International Conference on Information and

Automation, Zhuhai/Macau, 2009.

[46] Abbas Rajabifard, "SDI Design to Facilitate Spatially Enabled

Society," in ISPRS workshop on Geoinformation and Decision Support

Systems and 4th GIS Conference, Teheran, Iran, 2008.

[47] Abbas Rajabifard, "Spatially Enabled Government and Society: A

Scenario for the Future," International Seminar on Land Administration

Trends and Issues in Asia and The Pacific Region, August 2008.

[48] H.M. Rajesh, "Application of Remote Sensing and GIS in Mineral

Resource Mapping - An overview," Journal of Mineralogical and

Petrological Sciences, vol. 99, pp. 83-103, 2004.

[49] I. Prasada Rao, B. Kangadurai, P. K. Jain, and Neelam Jain,

"Information System for Rural Road Network Planning - a case study,"

in Map India Conference, 2003.

[50] J. K. Rawat, Alok Saxena, and Sudhanshu Gupta, "Remote Sensing

Satellite Based Forest Cover Mapping: Some Recent Developments,"

Indian Cartographer, 2003.

[51] T. J. Reerink, M. A. Kliphuis, and R. S. W. van de Wal, "Mapping

Technique of Climate Fields between GCM’s and Ice Models,"

Geoscientific Model Development, vol. 3, pp. 13-41, January 2010.

[52] Ravi Prakash Rivastava, Nimisha Vedanti, and V.P. Dimri, "Optimal

Design of a Gravity Survey Network and its Application to Delineate

the Jabera-Damoh Structure in the Vindhyan Basin, Central India,"

Pure and Applied Geophysics, vol. 164, pp. 2009-2022, October 2007.

[53] Jeanne W. Ross, Peter Weill, and David C. Robertson, Enterprise

Architecture as Strategy: Creating a Foundation for Business

Execution.: Harvard Business School Press, 2006.

[54] M. Sarpoulaki, A. Eslami Rad, and A. Saleknia, "E-Government

Concept and Spatial Information: A Case Study in Islamic Republic of

Iran," The International Archives of the Photogrammetry, Remote

Sensing and Spatial Information Sciences, vol. XXXVII, 2008.

[55] James Sinnott, Michael Lowry, Bill Cooter, Jennifer Lloyd, and Roger

Anzzolin, "EPA Drinking Water Mapping Application: Client Focus

and Architecture Design," in 23rd Annual ESRI International User

Conference, 2003.

[56] State of Montana. (2010) Montana Base Map Service Center. [Online].

http://giscoordination.mt.gov

[57] Didi Sukyadi, Model Interoperabilitas Sistem Informasi Layanan

Publik Studi Kasus: E-Government. Jakarta, Indonesia: Program Studi

Magister Teknologi Informasi Fakultas Ilmu Komputer Universitas

Indonesia, 2009.

[58] The Open Source Geospatial Foundation. (2010) MapServer History.

[Online]. http://trac.osgeo.org/mapserver/wiki/MapServerHistory

[59] Waldo Tobler, "Movement Mapping," Center for Spatially Integrated

Social Science, Santa Barbara, 2003.

[60] Yaw A. Twumasi, Andrew Manu, Tommy L. Coleman, Brian Schmid,

and A. Moustapha, "Use of Geo-Spatial Data for Sustainable

Management of Solid Waste in Niamey, Niger," Journal of Sustainable

Development in Africa, vol. VIII, no. 1, 2006.

[61] University of Melbourne, Department of Geomatics, Country Report

2003: Indonesia.: PCGIAP-Working Group 3 "Cadastre"; FIG-

Commission 7 "Cadastre and Land Management", 2003.

[62] J. Wallace, I. P. Williamson, A. Rajabifard, and R. Bennet, "Spatial

Information Opportunities for Government," Spatial Science, vol. 51,

2006.

[63] Shi-cheng Wang, Yu-kun Qiao, Jin-sheng Zhang, Li-hua Chen, and Qi

Zhang, "SVM Based Constructing Method for Geomagnetic Reference

Map," in Pacific-Asia Conference on Circuits,Communications and

System, Chengdu, 2009.

[64] Qihao Weng, Remote Sensing and GIS Integration: Theories, Methods,

and Applications.: The McGraw-Hill Companies, Inc., 2010.

[65] Clay G. Wescott, "E-Government in the Asia-Pacific Region," Asian

Development Bank, Working Papers January 2005.

[66] World Bank. (2002) The World Bank Group Web Site. [Online].

http://go.worldbank.org/M1JHE0Z280

[67] Albert K. W. Yeung and G. Brent Hall, Spatial Database Systems:

Design, Implementation, and Project Management.: Springer, 2007.

[68] Yun Zhang, "Pan-sharpening for Improved Information Extraction," in

Advances in Photogrammetry, Remote Sensing and Spatial Information

Sciences: 2008 ISPRS Congress Book, Zhilin Li, Jun Chen, and

Emmanuel Baltsavias, Eds. London: Taylor & Francis Group, 2008.

[69] Shuliang Zhang, Jiayan Gan, LiZhi Miao, Guonian Lv, and Jiazhu

Huang, "Study on GML Spatial Interoperability based on Web

Service," in 31st Annual International Computer Software and

Applications Conference, COMPSAC., Beijing, 2007, pp. 649-656.

[70] Wenfeng Zheng, Zhengtong Yin, and Xiaolu Li, "The Integration

Evaluation and Explanation System of Geo-information in OpenGIS

Environment," in International Conference on New Trends in

Information and Service Science, NISS., Beijing, 2009, pp. 515-520