An Accuracy Adjustment of GIS Data by Using A Data
Fusion Method

January 2002
Supervisor
Associate Professor Masataka TAKAGI

Department of Infrastructure System Engineering
Kochi University of Technology
Kochi, Japan
1045032

Jong Hyeok JEONG

ABSTRACT
Nowadays, many companies and local governments have produced various
GIS data. But most of them were shared or sold without a specification or a metadata.
When those data overlaid with officially used high accuracy data, some errors appeared.
Therefore, usually an uncertain GIS data, which does not have accuracy information,cannot be used with a high accuracy directly. In this study, the accuracy of the bridge
database was adjusted by the intersections of rivers and roads data in officially used GIS
data. Then distances between bridges and intersections of roads and rivers were
calculated. If bridge length with positional accuracy is shorter than the distance between
the bridge point and the nearest intersection, it was assumed that the bridge point does
not have enough accuracy to be adjusted. By the GIS data fusion method, the accuracyof 60% of bridges over river was adjusted and obtained clear accuracy. The result of
accuracy adjustment of the bridge database classified according to its road attribute,then the result of accuracy adjustment of the bridge database was assessed by theclassification result. The result of accuracy adjustment by data fusion method was
successfully assessed by the attribute data of the bridge database.

ACKNOWLEDGEFirst of all, I would like to appreciate god because I have my family who love me. Without
their great love, I could not finish this master course. And I would like to appreciate associate
professor Takagi MASATAKA who is my supervisor, he gave me much inspiration in my researchand planted positive thinking in my mind. Because of his support and teaching, I could take part in
the JSPRS and ACRS. Thank you very much sir!

When I just came here, I had to study the java programming language for my research. But
there was no one who could teach me the programming language in English. Fortunately, I could
contact a professor in the Depart. His name is P. H. P. Bhatt. He taught me the programming
language like a private tutor. It was great experience for me. His teachings are alive in the appendix
II and III, god bless you! pf. Bhatt. When I just came here, Korean students in Kochi University tookcare of Kim and me, I can’t forget their help, until I die, thanks Dr. Hyang Sook CHOI, Dr. Hee soon
CHOI and doctor candidate Mu Gwun LEE. I hope their great success in Korea. I have friends fromThailand;SupakitSWATEKITITHAM,

Jirapong

PIPATTANADIWONG,

Thammannon

DENPONGPAN, thanks for your kindness and teaching.
Professor Hyung Lae LEE, Eun Kyong SUGIYAMA, they promoted the sisterschool relationship between KUT and National University of Chinju, I would like toappreciate Pf. Myung An Park, Nam Hyung RYU, Ho Chul Kang, Choon Suk LEE andTae Kyoon KIM.
Fortunately, I became a student of the rotary club scholarship program. The
rotary club foundation provided not only scholarship but also assigned a volunteer to me.
The volunteers are Shin UETA and Mitchi UETA. They taught me many things about
Japan; Japanese Culture, Japanese language and so on. I cannot forget their kindness.

LIST OF CONTENTS
1. Introduction
1.1 Background
1.2 Statement of Problem
1.3 Previous Study
1.4 Objectives
1.5 Scope of Study
2.

3.

4.

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Used data sets and computer programs
2.1 General
2.2 National Land Digital Information
2.3 Bridge Database
2.4 Disaster Prevention Information
2.5 Used Programs

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Methodology
3.1 General
3.2 Preprocessing
3.3 Accuracy Adjustment Outline
3.4 Generation of Intersections
3.5 Finding Nearest Intersection
3.6 Overlay
3.7 Distance Calculation
3.8 Specifying Error Information in The Database
3.9 Accuracy Adjustment by Fusion Method
3.10 Accuracy Assessment

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Results
4.1 The Result of Preprocessing
4.2 Generating Intersection
4.3 The Identification of Error Sources on the overlay
4.4 Distance Calculation
4.5 Matching and Registering
4.6 Specifying Error Information in Database
4.7 Accuracy Adjustment by Data Fusion Method

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4.8 Accuracy Assessment28

5. Conclusions and discussion
Conclusion1
Conclusion2
Conclusion3
Conclusion4
Conclusion5
Further Study

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Reference
Appendix I : Literature Review
Appendix II : Program for Overlapped Bridges Detection
Appendix III : The Program for One to One Relationship

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57

LIST OF FIGURES
3-1. Preprocessing procedures
3-2. The outline for accuracy adjustment
3-3. Before generating intersections
3-4. After generating intersections
3-5. The nearest feature calculation
3-6. Bridge points on DPI
3-7. Bridge points on NLDI distance mapping
3-8.The criterion of accuracy adjustment
3-9. Data fusion
3-10. The three categories in the road attribute of the bridge database4-1. Errors from coordinate mistyping
4-2. The histogram of frequency of bridge in distance calculation
4-3. Boxplots of distance calculation result
4-4. Boxplots of the distance calculation result (less than 530m)
4-5. Plotted bridge length and the distance from each bridgeto the nearest intersection
4-6. Accuracy adjustment results in slope
4-7. Classified bridges on slope analysis result of Kochi prefecture area
5-1. Slivers: the classic form of overlay error
5-2. Modified hierarchy of need for modeling errors in GIS
A-1. The life cycle of a soil databaseA-2. A hierarchy of needs for modeling error in GIS operations
A-3. Slivers: the classic form of overlay error
A-4. Another case of overlay error
A-5. A simplified visualization of statistical precision, accuracy, and bias

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47

LIST OF TABLES
2-1. Included information in land digital information
2-2. Summary of metadata
2-3. The number of bridge which does not have its length information
2-4. The number of bridges on river, not on river and others
2-5. The number of overlapped bridges
3-1. Distance calculation result table4-1. The example of arrangement result of making one-to-one relationship
4-2. Specified error and the result of matching (sample)
4-3. Accuracy adjustment result by fusion method (sample)
4-4. The result of accuracy adjustment
A-1.The separation error into time phases and sources
A-2 Relationship Between Scale, Accuracy and Resolution

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1

INTRODUCTION

CHAPTER 1
CHAPTER 1
INTRODUCTION

1.1 BackgroundGIS users suffered from transferring a GIS format to another data format.
Therefore, new movement appeared to make a vendor-neutral GIS data format by using
XML protocol. Open GIS organization in America established GML, and the Ministryof Economy and Industry in Japan established G-XML which was adapted as a standard
GIS data format. It enables GIS users to overlay and integrate various GIS data fromlocal governments, companies and organizations. Although standard GIS data format
environment was established, there appeared a problem of accuracy.
The Department of Construction in Kochi prefecture established a bridge
database and shared the data without accuracy information. When the bridge database
was overlaid with another GIS data based on different scales, positional errors appeared.
Usually bridges on river exist on the intersection of road and river. However when thedata was overlaid with official high accuracy GIS data, bridge points did not match with
the intersection of road and river or lay with the circle of positional error tolerance.
Therefore, the data needed some accuracy adjustment to be used with high accuracy
GIS data.
1.2 Statement of Problem
Two GIS capabilities, which excite enthusiasm among potential user are theability to change map scale and the ability to overlay map at random. Both capabilities
are indeed exceedingly useful; they constitute muｃh of the comparative advantage GIS
holds over spatial analysis based on analogue maps. Both capabilities may also mislead
decision makers who are unaware of the imprecision inherent in all cartography andwho are untutored in the ways errors compound when map scales are changed or whenmaps are merged (Abler, 1987 p.305) 1) .
The main motivation for interest in the accuracy of spatial databases comesfrom an applied perspective – the problem is real, and we need better methods foraddressing it. Besides basic research, we need greater sensitivity to error on the part of
GIS users, greater awareness of the kinds of errors, which can occur, and techniques for2

INTRODUCTION

CHAPTER 1

recognizing and perhaps reducing their impact. Finally, the problem is also technical, inthe sense that we need explicit methods of tracking and reporting error in GIS software,and algorithms and data structures which recognize uncertainty and inaccuracy directly.
In short we need greater sensitivity to accuracy issues among GIS designers anddevelopers (Accuracy of Spatial Databases, edited by Michael Goodchild and Sucharita
Gopal, p1, 1989) 2) .
The widespread availability of Geographic Information System has enabledmany users to integrate geographic information from a wide range of sources. An
inevitable of equal quality that it can contain errors and uncertainty those need to berecognized and properly dealt with (Giulio Maffini, Michael Arno and Wolfgang
Bitterlich , 1989) 3 ) .
In reality a GIS may encourage poor analysis by separating the data collection,compilation and analysis functions, and failing to make the user aware of the possible
danger of indiscriminate use of such functions as scale change, reclassification andoverlay (Michael F. Goodchild, 1989). 4)
It seems likely that all spatial data and all types of spatial analysis contain some
type of error. Therefore it is impossible to perform error- free spatial analysis and it
becomes the task of spatial analysts to reduce error to the point at which it does notinterfere which the conclusions drawn from a particular analysis (A. Stewart
Fotheringham, 1989) 5) .
It is important to recognize the type, severity and implications of errors that are
inherent in the use of a geographic information system (Giulio Maffini, 1989) 6) .
The effects of combining data characterized by differing levels of error anduncertainty need to be identifiable in the final outputs (Stan Openshaw, 1989) 7 ) .

GIS has taken an important role in various fields, and it already has become
familiar to end- users, but they use it without knowing or recognizing its detail accuracy
or errors. Because of this reason, many researchers have warned GIS developers andusers about the errors in GIS and emphasized the necessity of reporting, quantifying or
tracking errors.

3

INTRODUCTION

CHAPTER 1

1.3 Previous Study
The literatures were reviewed that relate to errors in spatial database, accuracy
assessment and adjustment, and also data fusion.
Michael F Goodchild 10) explained how errors should be treated in GIS data
updating. Howard Vergin 11) introduced a hierarchy of needs for modeling error in GIS
operation. Some errors from overlaying digital maps were investigated by Nicholas R.
and Chrisman 13) . What types of errors exist in GIS data and types of errors that canappear have been investigated. Recognizing and specifying errors in GIS that prevent
users from being misled by error prone GIS were considered. Errors in experimental
data need to be investigated in the hierarchy of need for modeling errors for GIS
operation. From the investigation, some accuracy adjustment direction and criteria willbe established (Appendix I).
On going problems for spatial uncertainty researches are following.
1) uncertainty representation for discrete models of issues in spatial
uncertainty for continuous models of spatial variability.
2) the need to make accuracy assessments more accessible to users of spatial
data and model predictions.
3) the effect of scale on uncertainty.
4) how uncertainty estimates from different disciplines can be combined toprovide a composite estimate of uncertainty in final products.
Frequently the need arises to analyze missed spatial databases, and mixed data
sets consist of satellite spectral, topographic and other points from data. All registeredgeometrically mixed data, as might be found in a geographic information system. So far,many remotely sensed data were used to merge with other sources for a special purpose.
For example, Srinivasan and Ricahrd (1993) 8) able to analyze the images jointly andthus develop a cover type map that resolves classes that are confused in either theLandsat or radar data alone. A simple methodology has been developed for
raster-to-vector for use in a GIS. This methodology was employed successfully tointegrate both coarse and fine resolution raster image (e.g. LANDSAT MSS and TM;
and SPOT HRV) into a vector GIS ( N. M. Mattikalli. B. J. Devereux and K. S. Richards,
1995) 9) . But it is very rare that a GIS data merged with another data. There is

4

INTRODUCTION

CHAPTER 1

possibility to merge between the coordinates of high accuracy data and the attribute dataof a low accuracy data. There will be advantages in using a data fusion method, if we
introduce data fusion in GIS. In order to find the potential and possibility of using afusion method in GIS, the data fusion method focused on especially the accuracy
adjustment of a GIS data in this study (Appendix I).
1.4 Objectives
Usually, bridge, dam and so on have low accuracy in spatial data because of
their spatial characteristic because it has long error radii. So the accuracy of those kindsof objects can be adjusted by computer works instead of surveying. In the remotesensing field, many researchers have studied about the data fusion of various satellite
images that were generated by different sensors. However, much works has beenreported on the usage of the data fusion method in GIS. Thus, objectives in this studyare:1) To adjust the accuracy of a GIS data which does not have accuracy
information by using a data fusion method.
2) To improve the reusability of an uncertain GIS data.
3) To find optimized procedure of the accuracy adjustment methods inthe data fusion method.
4) To assess the accuracy adjustment result with attribute data.
1.5 Scope of The StudyIn the whole process of accuracy adjustment of a GIS data, method of adjusting
the accuracy of a GIS data on computer work removing the uncertainty of a GIS data
were investigated.

5

USED DATA SETS AND COMPUTER PROGRAMSCHAPTER 2

CHAPTER 2
USED DATA SETS AND COMPUTER PROGRAMS
2.1 General
Before making accuracy adjustment in the bridge database, the understandingsof data structure, file format, code type and such like are needed. In the Land Digital
Information case, metadata was referred for the understanding of the detail informationof it.
2.2 National Land Digital Information (NLDI)In accuracy adjustment, the National Land Digital Information (NLDI) wasused as a reference data because it had clear accuracy information and metadata. The
NLDI was required to investigate the contents and data structure of it.
The NLDI was established by the Ministry of Land, Infrastructure andTransport in the year of 1930. Currently it is still being updated. The data is being used
as an infrastructure information in various fields (for example, government land use
planning, government land planning, urban planning and so on). It includes various
kinds of information (Table 2-1). Some parts of the data were established by anotherorganization, for example Japan Coast Guard, Geographical Surveying Institute.
At present, the data is being shared by the Ministry of Land, Infrastructure andTransport to support researchers, decision makers, and government organizations, local
governments and universities with free of charge. Kochi Software Center (a governmentorganization) handed us the data that covers Kochi prefecture area. Two themes were
included in the data, one was the road data, and the other is the river, and both based on
1:25,000 scale map. The feature type of roads and rivers in NLDI was line, and its
coordinate system was Cartesian coordinate system. The positional accuracy of this data
was 12.5m because this data was based on a 1 to 25000 scale map (Appendix I, Table
A-2).

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USED DATA SETS AND COMPUTER PROGRAMSCHAPTER 2

Table 2-1 Information included in land digital information
Category

Contents

Selected Area

Forest, Natural Park, Reserved Area, Agricultural Industry
Area

Coastal Area

Harbor, Reserved Area for Water Quality, Coastal Line

NatureLand

Climate Information
Land Use Information

Infrastructure

Road, Rail

Equipment
Statistic of Agriculture

Power Generate Station
Statistic of Agricultural Industry

Hydrology

Dam, River, Lake

The both data cover west boundary 122.93 ° , east boundary 145.82 ° , northboundary 45.52 ° and south boundary 24.04 ° . The attribute data of road are highway,
national road, local government roads and national roads.
The National Land Digital Information is being distributed with a metadata(http://nlftp.mlit.go.jp/ksj/). The metadata of roads and rivers in the NLDI has following
information (Table. 2-2).
Table 2-2 Summary of metadata
Metadata Category

Contents

Catalog information

Metadata Identifier, Title, Volume, Producer Information

122.93 ° (N), 145.82 ° (E), 45.52 ° (S), 24.03 ° (W)Character CodeJIS
Information category Hydrology, Infrastructure
Range of data set

Based map scale

1:25000

2.3 Bridge Database
The Department of Construction in Kochi prefecture established a bridge
database for the purpose of management of bridges, and the bridge database was sharedwithout metadata and specification. Because it was shared without metadata or

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USED DATA SETS AND COMPUTER PROGRAMSCHAPTER 2

specification, the bridge database was considered as an uncertain GIS data. Its
coordinate system is Geodetic coordinate system.
Bridges in the bridge database are divided into the bridges that do not havelength information and others (Table. 2-3).
Table 2-3 The number of bridge which does not have its length information
Numbers (points)
Bridges which do not have its length information

612

Others

735

Total

1347

The contents of the database were examined before making accuracy
adjustment (Table 2-4).
Table 2-4 The number of bridges over river, not over river and others
Contained Information

Numbers (points)

The Bridges over Rivers

685

Bridges Not over Rivers
Bridges with length information

50
735This bridge database cannot be used in the accuracy adjustment withoutarrangement. In this study, only one bridge point should be on a corresponding position,so some overlapped points should be eliminated before accuracy adjustment (Table 2-5).
In addition, bridges located only on rivers were used because their positions were
adjusted by the intersections of road and river in reference data. So the bridges
positioned at rivers were extracted from the bridges which have their length
information.
Table 2-5 The number of overlapped bridges
Numbers (points)
Overlapped Bridge

102

Not Overlapped Bridges over River

583

The Bridges located over River (total)

685

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USED DATA SETS AND COMPUTER PROGRAMSCHAPTER 2

2.4 Disaster Prevention Information (DPI)The Disaster Prevention Information (DPI) was established by the Department
of Disaster Prevention in Kochi prefecture, which was based on a 1 to 2500 scale map.
In this data set, the features of roads and rivers were described by polygons. Theintersections of roads and rivers in this data were used to identify the errors in thebridge database.
2.5 Used Programs
For accuracy adjustment, a commercial software and coded programs wereused. The main platform program is ArcView, the bridge data base was invoked anddisplayed as points in this program. The data format of bridge database is “dbf”
database format, and it was converted to the shape data file format, which is the data
format often used in GIS.
ArcView has a native programming language, “Avenue”. It is commonly usedby GIS application producers and researchers. Environmental Systems Research
Systems Research Institute (ESRI) establish an environment on web for exchanging
Avenue programs, which was produced by GIS programmers and researchers(http://gis.esri.com/scripts/scripts.cfm). And ArcView users can download a program,which was optimized and customized for a particular purpose.
In this study, three Avenue programs were used; one is the “Compile Table
Tools” (coded by Charles Herbold), another is the “Xtools ” (coded by Mike DeLaune),and the third is the “ Nearest Feature” (coded by Jeff Jenness). These three program
were obtained from ESRI (http://gis.esri.com/scripts/scripts.cfm). Compiled Table Tools
enable us to manipulate table of GIS database; we can join, add, import and exporttables with Compiled Table Tools. Xtools has many functions, and mainly the functionof intersect-two-themes was used to make intersections. The Nearest Feature program
detects the nearest object from an object, and it calculated distance between two objects
and records the result in a table. Arc/Info program was used for changing coordinate
systems from geodetic coordinate system to Cartesian coordinate system.

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METHODOLOGYCHAPTER 3
CHAPTER 3
Methodology

3.1 General
The hierarchy of needs for modeling error in the GIS operation concept (Figure
A-2 in Appendix I) was used to make a direction for error recognition and accuracy
adjustment. On which the point of a bridge was surveyed that was not specified in thebridge database, so the center of each bridge was assumed as the actual surveyed pointof each bridge. A data fusion method was used for making accuracy adjustment of thebridge database. Also their accuracy was assessed by their road attribute.
3.2 Preprocessing
As mentioned in the Chapter2, the bridge database could not be used directly
for accuracy adjustment. The file format of the bridge database cannot be used withanother format of a GIS data. Therefore, preprocessing before the accuracy adjustment
was necessary.
Total 685 bridges are located on rivers. Some overlapped points of them weredetected by a written program (Appendix II) and eliminated because only one pointshould be on a coordinate (position) in this case.
Some parts of a bridge were described as points, and they were overlapped on a
position for its structural management, so it means many points were overlapped on thesame position (coordinate). In this study, only bridges over rivers were needed, because
the accuracy of bridge points were adjusted by the intersection of a road and a river in areference data.
The bridge points not over rivers were not considered in this study. The
coordinate system of the bridge database causes a problem in overlay. Because thecoordinate system is the Geodetic coordinate system, it changed into the Cartesian
coordinate system in order to overlay with other data sets. Finally, the database isconverted to “Shape” data file format that is a common data format in GIS (Figure 3-1).

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METHODOLOGYCHAPTER 3

Figure 3-1 Preprocessing procedures.

3.3 Accuracy Adjustment Outline
To identify the source of error, the intersection points of roads and rivers in the
National Land Digital Information (NLDI) and the Disaster Prevention Information(DPI) were generated. The bridge points were overlaid with the intersections in the
NLDI and DPI, then positional errors were observed. The nearest intersection to eachbridge point was matched, and their relationship was registered the relationship of themin the bridge database. The distance from the nearest intersection in reference data toeach bridge point was calculated, and the error was measured. The measurement of
positional error was specified in the bridge database for error management. In the lastprocess, the strategy for error reduction was established.

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METHODOLOGYCHAPTER 3

Figure 3-2 The outline for accuracy adjustment
3.4 Generation of Intersections
To identify the source of error, the bridge points in the bridge database wererequired to overlay with the intersections of roads and rivers in the National Land
Digital Information and in the Disaster Prevention Information. ODF XTools programand Spatial Analysis (ESRI) were used to make intersection points. Figure 3-3 shows
the roads and rivers in the NLDI. Figure 3-4 shows the result of intersecting roads andriver in the NLDI.

12METHODOLOGYCHAPTER 3

Figure 3-3 Before generating intersections

Figure 3-4 After generating intersections
3.5 Finding the Nearest Intersection
The distance from each bridge point to the nearest intersection point wascalculated. They were linked and registered in the bridge database and assumed theactual position of each bridge point. The Nearest Feature program (coded by Jeff
Jenness) was used. It enables us to calculate the nearest feature from each object and to
specify the most nearest, the next nearest and for the third nearest features.

13

METHODOLOGY

Figure 3-5 The nearest feature calculation

CHAPTER 3

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METHODOLOGYCHAPTER 3

Figure 3-5shows the result of distance calculation. The nearest feature program
detected the closest intersection, the next closest intersection, the less closest
intersection from each bridge.
Table 3-1 Distance calculation result table
UID is bridge’s unique ID number, 1_ID is the most nearest intersection’s ID
number and the distance between the bridge and the nearest intersection, 2_ID
is the secondly closest intersections’ ID number from a bridge point.

Table 3-1 shows an example of the result of distance calculation, Uid is theunique identification number of each bridge, 1_ID means the nearest intersection pointof identification number, the 1_C_Dist is the distance between the most nearest
intersection and a bridge point, and 2_ID is the secondly closest intersections’ id. In the
distance calculation result, some bridge points matched more than one intersection
points, so their relationship were rearranged to make one-to-one relationship, and a
program was used for the arrangement of one-to-one relationship (appendix III).
3.6 Overlay
For thematic error source identification, bridge points were overlaid withintersection of road and river in two reference data, one is the DPI and the other is the
NLDI. The feature type of the DPI is the polygon, and the generated intersection shapeis almost same as bridge, so when bridge are ove rlaid with the polygon, the positional

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METHODOLOGYCHAPTER 3

errors were clearly shown (Figure 3-6). On the other hand, generated intersection pointsof roads and rivers in NLDI were overlaid with distance circle to show the existing
phase of the intersect points from each bridge point (Figure 3-7). From the two overlays,
the thematic errors of the bridge were recognized at a glance.

Figure 3-6 Bridge points on the DPI16

METHODOLOGY

Figure 3-7 Bridge points on the NLDI distance mapping

CHAPTER 3

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METHODOLOGYCHAPTER 3

3.7 Distance Calculation
Positional Accuracy Handbook (www.mnplan.state.mn.us/press/) described
how to measure the positional error in GIS data. In the specification, positional error in
GIS data was calculated by differencing x and y coordinates of reference data and x andy coordinates of points in a test data, then the error radii were calculated. In this study,
the error radii were calculated directly by using the Nearest Feature Avenue Script
program. In the process of finding the nearest intersection of each bridge point, theNearest Feature program was used to calculate the distances from the nearest
intersection to each bridge point. The calculated distances were used as error radii in theaccuracy adjustment. Errors in this data quantified by the calculated distances.
Some bridges achieved one-to-one relationship, but they had still uncertainty inaccuracy domain because some of them have the result of distance calculation, which islonger than the longest bridge in the bridge database. To select bridges, which haveshorter distance than the length of the longest bridge, a threshold value was introduced
in making the accuracy adjustment. The longest bridge length could be used as a
threshold value because it had the longest error radius. The longest bridge length was
1007m, but this value was not used as the threshold value because it was too far fromthe median (central tendency of the bridge lengths in the bridge database), and it was the
only bridge which was longer than 530m, therefore the second longest bridge length(530m) was used as the threshold value. Bridges having the distance from the nearest
intersection longer than 542m (threshold length 530m + the positional accuracy of the
NLDI 12.5m) were not considered in accuracy adjustment because it was assumed thatthey could not be matched with the nearest intersection.
3.8 Specifying Error Information in the Database
Errors were quantified from the distance measurement in the error
measurement process of the accuracy adjustment, and then the result of error
measurement of each bridge points was specified in the bridge database. Specifying
error information in database is the most important process in the accuracy adjustment
because it opened the next processes, which are the accuracy adjustment andassessment.

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METHODOLOGYCHAPTER 3

3.9 Accuracy Adjustment by Fusion Method
There are some methods to adjust accuracy of GIS data but the cost should beconsidered. The accuracy adjustment level depends on the accuracy level needed for aparticular purpose. In this study, the error tolerance radii of bridges in the bridge
database are long because the actual surveyed point of each bridge was considered to beat the center pointer of each bridge.
In the former step of the accuracy adjustment, the distances from the nearest
distance to each bridge point was cons idered as an error radius. The one-to-one
relationship between a bridge point and the nearest intersection was established. Eachbridge point matched with the nearest intersection in one-to-one relationship was
evaluated if the information of them could be fused with each other or not.
The radius of error tolerance consisted of bridge length and positional error in
the NLDI. Since the scale of NLDI was based on a 1 to 25000 scale map, the positional
error, which is 12.5m (Appendix I, Table A-2), was added to the bridge length tocalculate the radius of error tolerance circle.
The condition for the evaluation for the fusion is simple. If the distance
between each bridge point and the nearest intersection with positional accuracy of NLDI
(12.5m) is shorter than the bridge length with positional accuracy, it was considered thatthe bridge has enough accuracy to be adjusted by the fusion method. If a bridge point
did not satisfy the above condition, the statement that the bridge could not be adjusted
was specified in the bridge database (Figure 3-8). In short, if the error radius of a bridge
is shorter than the error tolerance radius, it was considered that the bridge could be
adjusted and fused with each other.
The data fusion in this study is combining the coordinate of a reference datathat has high accuracy with the attribute of GIS data, which had low positional accuracy.
If a bridge satisfied the condition of evaluation, the attribute of bridge point in thebridge database combined with the coordinate of intersection of road and river in the
NLDI (Figure 3-9).

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METHODOLOGYCHAPTER 3

Figure 3-8 The criterion of accuracy adjustment
3.10 Accuracy Assessment
In this study, a quite disruptive method was used to assess the accuracy of theadjustment result by the fusion method. The result of accuracy adjustment wasclassified by the road attribute in the bridge database. In the result of classification, atendency of adjustment result was identified. There are three attributes existed in roadfield of the bridges database; one was a national road, another was a prefecture road,and the third was a local government road (Figure 3-10). Especially, the bridges thatbelonged to the national road were obtained the lower result of accuracy adjustment,
and most of them existed on the steep slope area, therefore they were overlaid with theresult of slope analysis of Kochi prefecture area and NLDI. Each bridge point taken
slope information from the result of slope analysis result of Kochi prefecture, then thenumber of adjusted bridges and not adjusted bridges were compared in the result ofslope analysis, and the relationship between accuracy adjustment result of each bridgeand slope under each bridge was investigate.

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METHODOLOGYCHAPTER 3

Figure 3-9 The criterion of the data fusion

Positional Accuray
Adjusted data

Bridges of National
Road

Bridges of Local
Government Road

Bridges of Prefecture
Government Road

Figure 3-10 Three categories in the road attribute of the bridge database

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RESULTSCHAPTER4
CHAPTER 4
RESULTS

4.1 The Result of Preprocessing
Bridges that required in the accuracy adjustment were extracted from thebridge database. Total 1347 bridge points are founded in the bridge database, 612
bridges without length information were not considered. Then, 685 bridges located overrivers were selected and used in accuracy adjustment (Table 2-3, Table 2-4).
4.2 Generating IntersectionsODF Xtools was used to generate intersections between rivers and roads in the
NLDI, and then 2778 intersections were generated. The number of generated
intersections (2778 points) was substantially lager than the number of bridge points(685points). It implied that one-to-many relationship could be generated in the distancecalculation (Chapter3, 3.7) between each bridge point and the nearest intersection.
4.3 The Identification of Error Sources on the Overlay
The sources of errors were identified on the overlay of the intersections and thebridge database. When the bridge database was established, it appeared that the
producer of this data entered some wrong coordinates in the bridge database. Figure4-1
shows the error from mistyping in the bridge database, some bridges that were quite farform the Shikoku Island.

22

RESULTSCHAPTER4

Figure 4-1 Errors from coordinate mistyping
When bridge points in the bridge database overlaid with intersections of roads
and rivers in NLDI, they were not matched with the intersections of reference data. The
data on which the bridge database was based has low accuracy, it also causes the
positional error (Figure 3-7, Figure 3-8). Two error sources of the bridge database wereidentified, one is the low accuracy of the bridge database, and another is the mistyping
coordinate of bridge points.
4.4 Distance Calculation (Error Measurement)
The distance from the nearest intersection to each bridge point was calculated.
Figure 4-1 shows the result of distance calculation. Although the threshold of bridge
length is 530m, the result of the distance calculation contained distances that are longer
than the threshold bridge length (Figure 4-2).

23

RESULTSCHAPTER4

57
0
ov 630er
1007Distance Interval(m)

51
0

39
0
45
0

27
0
33
0

15
0
21
0

90

200
180
160
140
120
100
80
60
40
20
0
30

Freqency

Frequency Histogram of Bridge in Distance Calculation

Figure 4-2 The histogram of frequency of bridge existence indistance intervals
Figure 4-3 and Figure 4-4 show the Boxplots for the result of distancecalculation. The range of distance calculation was 848821.2933m (the range of error
radius) and the range of bridge length was 997m (the range of error tolerance radius).
The range of error radii was too long compare with the range of the error tolerance radii
because there were some bridge points had wrong coordinate. A threshold value wasnecessary to select those bridge points, and not to consider them in the accuracy
adjustment.
The median of error radii was considered as the central tendency, the median
was approximately 35.317m. The positional error of the bridge database was quantified
with the central tendency (the median of error radii) of the error radii.

24

RESULTSCHAPTER4

Figure 4-3 Boxplots of distance calculation result

Figure 4-4 Boxplots of the distance calculation result (less than 530m)

25

RESULTSCHAPTER4

From Figure 4-5, bridge lengths and the distances from bridge to the nearest
intersection are plotted, the distance between 0 and 200, most of two themes (bridge
length and the distance from a bridge to the nearest intersection) are close to each other.

Figure 4-5 Plotted bridge length and the distance from each bridge to thenearest intersection.

Most of Bridges having the distance within 200m from the nearest intersection
were adjusted in the accuracy adjustment.
4.5 Matching and Registering
The main object of the Distance calculation is to make one to one relationshipbetween a bridge point and the nearest intersection of roads and rivers in NLDI, but the
number of intersection is lager than the number of bridge points, and therefore some
one-to- many relationships were generated in the distance calculation. To make one-toone relationship, the program that was described in Appendix II was used.

26

RESULTSCHAPTER4

Table 4-1 The example of arrangement result of making one-to-one
relationshipUid is the bridge ID number, 1_id and 1_c_dist is the nearest intersection ID
and distance from bridge point to the intersection, 2_id is secondly closest
bridge ID.

Table 4-1 shows an example of the result of the arrangement for one to one
relationship. UID 7, 15 and 16 were not matched either because there is no nearest
intersection around them or the result of distance calculation is longer than the thresholdvalue (530m). 583 bridge points over river are in the bridge database (Table 2-5), andsome of them were abandoned in accuracy adjustment if bridge’s length is longer than
the threshold value of bridge lengths, 441 bridge points possessed one-to-one
relationship.
4.6 Specifying Error Information in Database
The bridges that did not match with the intersection points in the distancecalculation were detected (Table 4-1), and then the result of matching each bridge with
the nearest intersection was specified in each record. The distance between a bridge andthe nearest intersection was considered as a positio nal error (error radius), and thebridges that did not match with an intersection, “not matched” was specified in the

27

RESULTSCHAPTER4

result_mat field, and “0” was specified in the 1_id field of not matched intersection(Table 4-2).
Specifying error (1_c_dist in Table 4-2) in the bridge database was a very
important process in accuracy adjustment, because the errors were evaluated in order todecide whether a bridge can be adjusted or not, and it also reduces the uncertainty of thebridge database.
Table 4-2 Specified error and the result ofmatching (example)
Uid is the bridge id, 1_id is the id of the nearest
intersection, 1_c_dist means the distance from eachbridge to the nearest intersection.

Specifying error process reduced the uncertainty of the bridge database, and thereusability of the bridge database was improved also. The result of error measurement
can be basic information of metadata of accuracy
4.7 Accuracy Adjustment by Data Fusion Method
Table 4-3 shows the result of combination between the coordinates of thenearest intersections and the attribute data of the bridge database. 441 bridge points

28

RESULTSCHAPTER4

achieved one-to-one relationship, they matched with the nearest intersections. The
accuracy of 266 of them was adjusted by the fusion method. Approximately, 60%of bridges on river were adjusted and required accuracy information was obtained(Table 4-4).
Table 4-3 Accuracy adjustment result by the fusion method (sample)

Table 4-4 The result of accuracy adjustment
NumberBridges, which achieved one to one relationship 441
and had the result of distance calculation with in
the threshold value of bridge lengths.

(/685)

Adjusted bridges by fusion method266(/441)

Bridges over Rivers

685

4.8 Accuracy Assessment
The bridge database has three attrib utes in the road field, one was national road,
another was prefecture road, and the third was local government road. The accuracy
adjustment result was classified by the three themes. After the classification, they wereoverlaid with the slope analysis result of Kochi prefecture (Figure 4-2), the result ofclassification showed the following characteristic of the accuracy adjustment.
In the accuracy adjustment result, 48 percents of bridges which belong to29

RESULTSCHAPTER4

national road were adjusted, 69 percents of bridges which belong to prefecture roadbridge were adjusted, and 60 percents of bridges which belong to local government roadwere adjusted.
The reason why the bridges belong to national road have obtained lower
accuracy adjustment result was investigated. Figure 4-6 shows accuracy adjustment
result in terms of slope. From the figure, it can be seen that with increasing slope, thenumber of adjusted bridge decreased. The classification result was overlaid on theslope analysis result of the Kochi prefecture to investigate the relationship between theaccuracy adjustment result and slope (Figure 4-6). In Figure 4-7, dark red area is steep
slope area and bright pink area is gentle slope area. Most of bridges, which belong tonational road were on steep slope area. The reason why national road bridges obtained
low accuracy adjustment was they could not be matched with intersection, usually
narrow branches of river existed on high slope area, but the NLDI did not have enoughresolution to describe small branch, so the intersections on high slope area were notgenerated, therefore the bridge points that belong to national road could not be adjusted.

100%

adjusted
not̲adjusted

80%
60%
40%
20%

11
-1
5
16
-2
0
21
-2
5
26
-3
0
31
-3
5
m
36
or
-4
e
0
th
an
40

610

05

0%

slope (degree)

Figure 4-6 Accuracy adjustment results in slope

30

RESULTSCHAPTER4

Figure 4-7 Classified bridges on slope analysis result of Kochi prefecture area

31

CONCLUSIONS AND SUGGESTIONCHAPTER 5

CHAPTER 5
CONCLUSIONS AND SUGGESTION
By the data fusion method, the accuracy of the bridge database was improved.
Modified hierarchy of needs modeling for error modeling in data fusion was introduced
for continuous error modeling and adjustment. 60 percents of bridges on rivers took
accuracy information from the NLDI.
Consusion1.
The Accuracy of 60% of Bridges on River were Adjusted by Data Fusion Method
There are some sensitive conditions in the accuracy adjustment by using thedata fusion method. Two aspects were mainly considered in the sensitive condition of
the accuracy adjustment. Ones are some problems in data fusion method, and the others
are the limitations of a test data and reference data in accuracy domain.
Following problems were encountered in the accuracy adjustment by using
data fusion method; when the bridge database was converted to a “dbf” file, and when
the format (range) of fractional numbers is not defined, some parts of fractional
numbers of coordinate and length disappear. And when a field of database imported
directly to ArcView program, some part of fractional numbers of coordinate weredisappeared. But the disappeared parts were too small number, and they are in the rangeof error radius, so that they could be ignored. The sens itivity of fractional number in
GIS data might be changed according to the accuracy level of a test data. We shouldtake care of the fractional number in GIS. In the distance calculation from each bridge
point to the nearest intersection, a lot of one-to- many relationship s were generated
between bridge points and the intersections in the NLDI, and they prevent accuracy
adjustment from making one-to-one relationship. Although the bridges achieved
one-to-one relationship, they needed to be evaluated because some of them had
mistyping coordinate, therefore the threshold value (530m) was introduced, and they
could be arranged by the coded program that was described in Appendix III.

Slivers (Figure 5-1) existed in NLDI, so some intersections need not to begenerated. This problem was handled by comparing an error radius with an error
tolerance radius.

32

CONCLUSIONS AND SUGGESTIONCHAPTER 5

The accuracy adjustment result by the data fusion method was dependent on
not only the accuracy level of uncertain GIS data but also the accuracy level ofreference data. The error radii were generated according to the accuracy of NLDI
(reference data). The error of the bridge database quantified with the central tendency ofthe bridge lengths (35.317).

Figure 5-1. Slivers: the classic form of overlay error(Crisman, 1994) 13)

Conclusion2.
The Reusability of The Bridge GIS Data Improved
Before accuracy adjustment, the bridge database could not be used with another
GIS because of its uncertainty of accuracy. But after the accuracy adjustment, theinformation about the accuracy of the bridge database were quantified and recorded.
The distance from each bridge point to the nearest intersection (error radius)
was calculated, indicating the error quantity of each bridge point. Then the result was
specified in the bridge database. The fact that if each bridge adjusted in the accuracy
adjustment or not was specified in the bridge database. Also scale of reference data was
specified.
By specifying the generated accuracy information about each bridge point, thereusability of the data was improved, since the basic accuracy information for metadata
was established, and it was possible to separate the bridges, which achieved accuracy

33

CONCLUSIONS AND SUGGESTIONCHAPTER 5

adjustment and not achieved.
Conclusion3.
Modified The Hierarchy of Need for Modeling Errors in GIS Was Suggested
When new GIS data, which have road and river information established, theaccuracy of the bridge on river can be updated and adjusted continuously. But Veregin(1994)’s the hierarchy of needs for modeling errors was not enough to explain thecontinuous accuracy adjustment by another reference data. So modified and customized
hierarchy of need for modeling errors in GIS was required.

Figure 5-2 Modified hierarchy of need for modelingerrors in GIS
Information updating was not considered in the the Vergin(1994)’s hierarchy ofneeds for modeling error. After the accuracy adjustment and assessment, the results need
to be specified in the database (new step), the step of strategy for error management wasneeded again in the accuracy adjustment by using the data fusion method (Figure 5-2)
If we obtain another data which can be a reference data in accuracy adjustment

34

CONCLUSIONS AND SUGGESTIONCHAPTER 5of the bridge database by using data fusion, the accuracy of it can be adjusted andupdated continuously, therefore it requires the feedback process between the step ofstrategy for error management and error measurement (Figure 5-2).
Conclusion4.
Successfully the Accuracy Adjustment Result was Assessed by Attribute Data
To assess or to validate the result of accuracy adjustment, the overlay method,
surveying data, and such on have been used. In this study, positional accuracy wasevaluated by the attribute data of the test data because the accuracy adjustment result
strongly depends on the accuracy of the reference data. In the assessment of theaccuracy adjustment result, the bridges belong to the national road obtained lowest
accuracy adjustment not only because of the accuracy of the bridge database but also
because of the limitation of the NLDI. Not only the accuracy of a test data (accuracy
unknown GIS data) but also the accuracy of the reference data in an accuracy
adjustment by the data fusion method need to be considered. The classified result of
accuracy adjustment according to road attribute of the bridge database showed thelimitation of the reference data. The reference data used in this study (NLDI) did nothave enough resolution for the bridges, which belong to the national road, small
branches of rivers or narrow roads were not described in steep slope area in NLDI, someintersections were not generated in steep slop area. Therefore the national road bridge
category that has many bridges obtained lower accuracy adjustment result.
Conclusion5.
Standard data structure and input method required for the data fusion method.
In accuracy adjustment and assessment, there was a link data between thebridge database and the intersection of river and road of NLDI, both data have the roadattribute information. However the information could not be used as a link data because
although the contents of information were same, the character was different. If thecharacters of the road attributes of the two data were same, the matching, accuracy
adjustment and assessment could be done automatically. It is better for Japan Industrial
Standards to establish a standard data input method and GIS data structure for GIS data
linking and fusion.

35

CONCLUSIONS AND SUGGESTIONCHAPTER 5

Further Study
In accuracy adjustment, the result of the data fusion method strongly depends
on the accuracy of reference data. Various data for accuracy adjustment and validationof accuracy adjustment result were needed in using the data fusion method.
Unfortunately, the frequency of GIS data updating is quite low, and the speed of
establishing GIS data is quite slow. But if it is possible to extract the positions ofbridges from high-resolution remote sensing data, those problems will be solved. As
further study on the accuracy adjustment of the bridge data by using high- resolution
remote sensing data will be investigated.

36

REFERENCESREFERENNCES
1) Abler, R. F., The National Science Foundation National Center for Geographic
Information and Analysis. International Journal of Geographical Information Systems
1, pp. 305, 1987
2) Accuracy of Spatial Databases, edited by Michael Goodchild and Sucharita Gopal,
p1, 1989
3) Giulio Maffini, Michael Arno and Wolfgang Bitterlich, Observation and commentson the generation and treatment of error in digital GIS data. Accuracy of Spatial
Databases, M. Goodchild and S. Gopal, Eds. ,Taylor and Francis, Philadelphia, pp. 55,
1989.
4) Goodchild, M. F., Modelling error in objects and fields. Accuracy of Spatial
Databases, M. Goodchild and S. Gopal, Eds. ,Taylor and Francis, Philadelphia, pp.
107, 1989.
5) A. Stewart Fotheringham, Scale-independent spatia l analysis. Accuracy of Spatial
Databases, M. Goodchild and S. Gopal, Eds., Taylor and Francis, Philadelphia,1989.
6) Giulio Maffini, Michael Arno and Wolfgang Bitterlich, Observation and commentson the generation and treatment of error in digital GIS data. Accuracy of Spatial
Databases, M. Goodchild and S. Goopal, Eds. , Taylor and Francis, Philadelphia, pp.
55.
7) Stan Openshaw, “Learning to live with errors of spatial data transformations ”,
Michael F. Goodchild & Sucharita Gopal (Eds), Accuracy of Spatial Database, 1989.
8) John A. Richards, Xiuping Jia, Remote Sensing Digital Image Analysis, Springer, pp.
305.
9) N. M. Mattikalli. B. J. Devereux and K. S. Richards.” Integration of RemotelySensed Satellite Images with a Geographical Information System”, Computers &Geosciences Vol.21, No. 8, pp. 947-956,1995.
10) Michael F. Goodchild, “Communicating the Results of Accuracy Assessment:
Metadata, Digital Libraries, and Assessing Fitness for use”, Ann Arbor, H. Todd
Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in Natural
Resources, pp3. 1999.
11) Howard Veregin, Error modeling for the map overlay operation, Michael F.
Goodchild & Sucharita Gopal (Eds), Accuracy of Spatial Database, pp.4, 1994.
12) Fred C. Collins and James L. Smith, ”Taxonomy for Error in GIS”, International
Symposium on the Spatial Accuracy of Natural Resource Data Base, ASPRS, pp.3,
1994.

37

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13) Nicholas R. Crisman, “Modeling error in overlaid categorical maps”, Michael F.
Goodchild & Sucharita Gopal (Eds), Accuracy of Spatial Database, pp.27,1994.
14) Giulio Maffini, Michael Arno and Wolfgang Bitterlich, Observations and commentson the generation and treatment of error in digital GIS data. Michael F. Goodchild &Sucharita Gopal (Eds), Accuracy of Spatial Database, pp.55, 1994.
15) Weldon A. Lodwick, Developing confidence limits on errors of suitability analyses
in geographical information systems. Michael F. Goodchild & Sucharita Gopal (Eds),
Accuracy of Spatial Database, pp.69,1994
16) Daniel and Griffith, Distance calculation and errors in geographic database. Michael
F. Goodchild & Sucharita Gopal (Eds), Accuracy of Spatial Database, 199417) Shunji Murai, GIS Work Book, Japan Association of Surveyors, p.35, 199718) Michael F. Goodchild(1999), “Communicating the Results of Accuracy Assessment:
Metadata, Digital Libraries, and Assessing Fitness for use”, H. Todd Mowrer &Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in Natural
Resources.p.14.
19) H. Todd Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in
Natural Resources.p.16 (in introduction),2000.
20) H. Todd Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in
Natural Resources.p.17 (in introduction),2000.
21) H. Todd Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in
Natural Resources.p.18 (in introduction),2000.
22) H. Todd Mowrer & Russell G. Congalton (Eds), Quantifying Spatial Uncertainty in
Natural Resources.p.19 (in introduction),2000.
23) E. Waltz and J. Llinas., Multisensor Data Fusio n. Artech House, 1990.
24) Wald L., “Some terms of reference in data fusion”, IEEE Transactions on
Geosciences and Remote Sensing, 37, 3, 1190-1193,1999.
25) Wald L., “Definitions and Terms of Reference in Data Fusion. International
Archives of Photogrammetry and Remote Sensing”, Vol. 32,Part 7-4-3 W6,
Valladolid, Spain,3-4 June,1999.
26) Wald L., “A European proposal for terms of reference in data fusion. International
Archives of Photogrammetry and Remote Sensing”, Vol. XXXII, Part 7, 651-654,
1998, or Wald L., “Some terms of reference in data fusion”, IEEE Transactions on
Geosciences and Remote Sensing, 37, 3, 1190-1193, 1999.
27) L, F. Pau. Sensor data Fusion. Journal of Intelligent and Robotics Systems, vol.1,
pp.103-116, 1988.
28) John A. Richards, Xiuping Jia, Remote Sensing Digital Image Analysis, Springer,

39

APPENDIXAPPENDIX I
LITERATURE REVIEW
2.1 General
The literatures were reviewed that relate to errors in spatial database, accuracy
assessment and adjustment, and also data fusion.
2.2 Accuracy of Spatial Database
2.2.1 Accuracy in GIS Life Cycle
Michael F. Goodchild (1999) 10) explained accuracy in GIS data life cycle(Figure A-1). Accuracy is a dynamic property of the data life cycle. With reference toFigure A-1, changes of data model in the left-hand column, and changes of custodian in
the right-hand column, produce the potential for significant changes of accuracy as thecurrent representation is compared to the current reference source.
Life cycle stage of which effective updating after changes in data model, or
repeated reassessment of accuracy, can ensure that adequate information on accuracy
available to the data’s users.
2.2.2 Hierarchy of Needs for Modeling Error in GIS Operations
Howrd Veregin (1994) 11) established a hierarchy of needs for modeling error in
GIS operations (Figure A-2). From the lowest level to the highest level, it has five steps,
these are error source identification, error detection and measurement, errors
propagation modeling, strategies for error management, and strategies for error
reduction. The needs at a specific level must be met before, and individual can progress
to higher levels, and if these needs are not met, the attainment of higher levels is
retarded or thwarted.

40

APPENDIX

Figure A-1 The life cycle of a soil database (M. F. Goodchild, 1999) 10)

Figure A-2 A hierarchy of needs for modeling error in GIS
operations

41

APPENDIX
2.2.3 Positional Verses Attribute Errors
Positional accuracy is the absolute positional accuracy or the departure in thegeographic location of an object in relation to its location on the ground. Attributeaccuracy is concerned with whether attribute that are assigned to feature (points, lines,
and polygons) in the GIS are correct and free of bias. By correct we mean that attributesor labels placed on the spatial objects actually correspond to their counterpart on the
ground (Fred C. Collins and James L. Smith, 1992) 12) .
2.2.4 Errors in Overlaid Maps
The most common form of error in overlaid maps is called a “sliver”. As
demonstrated in Figure A-3, a simple silver occurs when a boundary between two
categories is represented slightly differently in two source maps for the overlay. A small,unintended zone is created. Goodchild (1978) reports that some systems become
clogged with the spurious entities that provide evidence of autocorrelations at different
levels. These reports are a part of the unwritten lore of GIS, because most agencies are
smallest of these, up to the level a user is willing to tolerate (Dougenik, 1980). The
availability of the filter makes it important to understand its relation to theory(Crisman 1994) 13) .

Figure. A-3 Slivers: the classic form of overlay error (Crisman, 1994) 13)
It would be possible to have a feature on one map source which is completely
missing on the other, as shown in Figure A-4. While the silver error seems to arise frompositional error, such an error is caused by classification and depends on taxonomic

42

APPENDIX
similarities of the two categories. This taxo nomic similarity could be modeled in somecontinuous phase space, or otherwise.

Figure A-4 Another case of overlay error (Crisman
1994) 13)
Considering the public and private investment in GIS, additional research on
the error of overlaid maps is required. Categorical coverages must be approached with adual error model, separating the distinct forms of error commonly termed positional andattribute accuracy. The focus on categorical coverages will complement other strengths
in spatial analysis of surfaces and other spatial distributions.
2.2.5 Errors in Digital GIS Data
Sources of error in GIS were investigated by Giulio Maffini, Michael Arno andWolfgang Bitterlich 14) . A schematic of the sources contribute to likely error. The likely
error can be thought of as occurring due to three major causes. The first is due to theinherent properties of nature. Unlike geographic information data structures, the realworld is not always distinct and clear, but is gradual and fuzzy. For example, an
photograph or satellite image taken from high altitude may appear to show a road as a
distinct line feature on a plane, but as one approaches more closely the perception thatthe road is a single line becomes inappropriate. Similarly, for coverages, an area ofgrassland is not always distinct, it can shift into woods or desert in a very gradualmanner.
A second major source of error is from the nature of measurement in geography.
Any measurements that are acquired with instruments inevitably introduce error. The
capability of the person using the measuring device can also clearly affect the amount of43

APPENDIX
error introduced. Finally, the scale at which the measurements are made and thefrequency of sampling will also introduce potential errors in geographic data.
A third source of error is due to the data models that we use to communicate
our measurements. The very structure of the geographic model can be a source oferror. In the case of vector, the representation of a line or an edge implies a level ofcertainty or precision that may not be discernable in the geographic model can be asource of error. In the case of vector, the representation of a line or an edge implies alevel of uncertainty or precision that may not be discernable in the real world.
Maffini, Arno & Biotterlich 14) illustrated the size and range of some type oferrors generated by GIS users in the digitized process. Simple trials were conducted inorder to develop an appreciation of the rage of digitizing errors that can be generated bytypical GIS users. The trials were concerned with exploring positional errors. They
excluded other types of errors, such as topological problems associated with data basecreation.
In the trials an attempt was made to isolate the effects of two factors that one
would expect to influence the propagation of error. The first was the scale of the source
conducted the digitizing, and the second factor was the speed with which the operator
conducted the digitizing. Although efforts were made to be systematic the trials were
not scientifically controlled experiments. It is also important to recognize that trial
results were based on the digitizing performance of one person.
In the results of digitizing trials, the number of points generated to represent anentity, and the frequency distribution of error classes for each entity. In the results, it isapparent that the discrete entities, which changes in the scale of the source of the source
document used for digitizing, have a more significant impact on positional error than
the time taken to digitize.
From the digitizing trials, following conclusions were obtained by Maffini,
Arno & Biotterlich 14)1) Cartographic and digital products should distinguish between judgments
about the interpretation and integration of geographic data.
2) Geographic Information Systems should, and can be used to assess the44

APPENDIX
consequences of error in geographic data, with GIS tools users will be able
to make more informed judgments about the interpretation and integrationof geographic data.
3) The design and execution of controlled scientific experiments to determine
the error ranges propagated in commonly available geographic data
products, a systematic investigation and documentation of theseexperiments and investigations users could make more informed andappropriate use of the data.
2.2.6 Developing Confidence Limits on Errors of Suitability Analyses in GIS
Confidence limits based on measures of sensitivity are developed for the mostwidely used types of suitability analyses, weighted intersection overlay and weighted
multidimensional scaling. Some underlying types of sensitivity analyses associated withcomputer map suitability analyses are delineated as a way to construct measures ofthese delineated sensitivities that in turn become the means by which confidence limits
are obtained. Weldon 15) conducted analyses to obtain confidence limits on errors,
which are geographic sensitivity and error propagation analysis and the computational
process and mathematical representation of suitability analysis. Two approaches(geographic sensitivity and error propagation analysis, the computational process andmathematical representation of suitability analysis) to confidence limits in the attributevalues generated by geographic suitability analyses were developed using suitability
measures, attribute sensitivity measures , position sensitivity measures, map removal
sensitivity measures, polygon sensitivity measures, and area sensitivity measures(Weldon A. Lodwick) 15) .
2.2.7 Distance Calculation and Errors in Geographic Database
Daniel and Griffith 16) insisted that we should be compelled to understand theimpact of these different sources of error on analytical model before incorporating themas standard options in GIS software.
2.2.8 Separation of Error Into Time Phases
Arnoff(1989) 16) , separates error into time phases and sources (Table A-1).

45

APPENDIX
Table A-1 The separation error into time phases and sources
Stage

Sources of Error

Data Collection

l
l
l
l

Inaccuracies in field measurements
Inaccurate equipment
Incorrect recoding procedures
Errors in the analysis of remotely sensed data

Data Input

l
l
l
l

Digitizing error
Nature of fuzzy natural boundaries
Numerical precision
Spatial precision ( in raster systems)

Data ManipulationlllWrong class intervals
Boundary errors
Spurious polygons and error propagation with overlay
operation

Data Output

l
l
l
l

Scaling
Inaccurate output device
Incorrect understanding of information
Incorrect use of data

Data Storage

Use of Results

2.2.9 Separation of Error Into Time Phases
In digital GIS database, there is no scale but resolution, expressed as pixel size
(interval or dot per inch), grid cell size or grid interval, ground for satellite images andso on. There is rough relationship between scale and resolution, as follows (Table A-2).
Grid interval = M (scale denominator)/1,000 (meter)
Table A-2. Relationship Between Scale, Accuracy and Resolution (S. Murai, GIS Work
Book) 15)
Scale

Contour Interval (m)Accuracy
Height Accuracy (m)

1:500

0.5

0.17

0.25

1:10001:2500

1
2

0.33
0.7

0.5
1.25

1:5000

5

1.7

2.5

Position Accuracy (m)46

APPENDIX
1:10000

10

3

5.0

1:25000

10, 203, 7

12.5

2.2.10 Summary
Michael F Goodchild 10) explained how errors should be handled in updating
or changing GIS data. Howard vergin 11) introduced a hierarchy of needs for modelingerror in GIS operation. Some errors from overlaying digital maps were investigated by
Nicholas R. and Chrisman 13) , including the kinds of errors that types of errors which
can arise. It was considered about how to recognize or how to specify the errors in GIS
to prevent users from being misled by error prone GIS. In this study, errors inexperimental data were investigated in each hierarchy of need for modeling errors for
GIS operation. From the investigation, some accuracy adjustment direction and criteria
were established.
2.3 Accuracy (Uncertainty) Assessment and Adjustment
2.3.1 Spatial Uncertainty
Spatial uncertainty in attribute values and in position includes accuracy,
statistical precision and bias in initial values, and in estimated predictive coefficients instatistically calibrated equation of errors (Michael F. Goodchild,1999) 18) .
Uncertainty also means different things in different disciplines. For example,
the concept of numerical precision arises from computer science and databasemanagement, and refers to the exactness or degree of detail with which an individual
observation is measured. Statistical precision refers to the dispersion of repeatedobservations about their own mean (Quantifying Spatial Uncertainty in Natural
Resources,2000) 19) .
Error may be random or systematic. Random error refers to the difference
between and observed value and a predicted or true value (Vogt, 1993). These random
deviations are completely the result of chance effects. A common statistic used todescribe accuracy is the root mean square error (RMSE), the square root of the quantity,
the sum of squares of the errors divided by the number of errors (Slama, 1980).
Systematic error, or bias, is the consistent difference between the central tendency

47

APPENDIX(mean) of repeated observations, and the known “true ” value (Figure. A-5),(Quantifying Spatial Uncertainty in Natural Resources,2000) 20) .

Figure A-5 A simplified visualization of statistical precision, accuracy, and bias

20)

Scale has important uncertainty implications. When information collected at alarger scale is compared to information represented at a much smaller scale, uncertainty
is introduced. Quattrochi and Goodchild (1997) state that, “the effect of generalization
is to introduce uncertainty into the representation of a real phenomenon that could only
be mapped perfectly at a much larger scale.” And they point out that, rather than stating
the metric scale of a digital representation, it is better to define positional accuracy andcontent: the exactness of the location of the smallest observable object (Quantifying
Spatial Uncertainty in Natural Resources) 21) .
The scale of observation also affects the relative degree of accuracy (orstatistical precision) and numerical precision. The scale of observation also affects therelative degree of accuracy or statistical precision in the scale of observation also
affects the relative degree of accuracy or statistical precision in the observed
phenomenon, in that the variance of the means is always smaller than the variance of
the individual observation (Quantifying Spatial Uncertainty in Natural Resources) 22) .
2.3.2 On Going Problems for Spatial Uncertainty Research
Currently following subjects are focused in the research of spatial uncertainty.
1) Uncertainty representation for discrete models for issues in spatial
uncertainty for continuous models of spatial variability
2) The need to make accuracy assessments more accessible to users of spatial

48

APPENDIX
data and model predictions
3) The effect of scale on uncertainty4) How uncertainty estimates from different disciplines can be combined toprovide a composite estimate of uncertainty in final products.
2.3.3 The Quantification of Positional and Attribute Errors
Perhaps the single most compelling reason for the separation of positional andattribute errors is the difference between the quantification of the two error types.
Qualitative attribute errors have traditionally been quantified using an error orconfusion matrix (Congalton, 1983, 1991,1993) and (Rosenfield, 1986). Positional
errors, however, can be quantified as some true value with an RMS error value(Merchant, 1987). Models for positional accuracy have been developed for points,
lines and polygons. Due to the qualitative nature of attribute, no single model has beendeveloped which encompasses both attribute and positional error, but some authors
suggest using the Cohen’s Kappa as a method of accuracy comparison(Chrisman,1989).
2.4 Data Fusion
Data fusion research and de velopment were conducted under a wide
variety of systems, methods and names. Using recent words such as "data fusion",or "information fusion" translates the recent understanding that whatever theapplication domain, these synergistic approaches share common problems andcommon properties (E. Waltz and J. Llinas.) 23)

2.4.1 Definitions and Terms of Data Fusion
Terms of reference in information fusion are presented: Merging, Combination
Integration, Concatenation Data assimilation, Optimal Control? Measurements, Signal,
Image, Radiances, Reflectance Sample, Pixel, Voxel, Commensurate, Observation,
Electronic Imagery, Gray levels, Image Multi-Modality, Multi-Channel, Multi-Band,
Multispectral, and Multi-Frequencies (Wald L.) 24) .
It then has been suggested to use the terms like merging and combination in a

49

APPENDIX
much broader sense than fusion, with combination being even broader than merging.
These two terms define any process that implies a mathematical operation performed on
at least two sets of information. These two terms define any process that implies amathematical operation performed on at least two sets of information. These definitions
are intentionally loose and offer space for various interpretations. Merging or
combinations are not defined with opposition to fusion. They are simply more general,also because we often need such terms to describe processes and methods in a generalway, without entering details. Integration may play a similar role though it implicitly
refers more to concatenation (i.e. increasing the state vector) than to the extraction of
relevant information (Wald L) 25) .
Another domain pertains to data fusion: data assimilation or optimal control.
Data assimilation deals with the inclusion of measured data into numerical models forforecasting or analysis of the behavior of a system. A well-known example of amathematical technique used in data assimilation is the Kalman filtering. Dataassimilation is used daily for weather forecasting.
Data fusion may be sub-divided into many domains. For example, the militarycommunity uses the term positional fusion to denote aspects relevant to the assessmentof the state vector or identity fusion when establishing the identity of the entities is atstake. If observations are provided by sensors and only by sensors, one will use the term
sensor fusion. Image fusion is a sub-class of sensor fusion; here the observations areimages. If the support of the information is always a pixel, one may speak of pixel
fusion. Other terms easily understandable are measurement fusion, signal fusion,features fusion, and decision fusion. Evidential fusion means that the algorithms behindcall upon the evidence theory, etc.
Data fusion is a formal framework in which are expressed the means andtools for the alliance of data originating from different sources. It aims at obtaining
information of greater quality; the exact definition of 'greater quality' will depend upon
the application. The rationales for proposing this definition are expressed in thisdocument .（Wald L.） 26) .
2.4.2 Properties of Data Fusion
The properties of information fusion are listed: fusion of attribute, fusion of

50

APPENDIX
analysis and fusion of representations (L, F. Pau.) 27) .
2.4.2.1 Fusion of Attributes
Fusion of attributes consists in merging the attributes of a same object, derived
from two representations ( X s(1) ) t and ( X s (2 ) ) t at instant t obtained by means of thesources of information S(1) and S(2), in order to obtain new attributes in the space ofsources S=S(1) U S(2).
2.4.2.2 Fusion of Analysis
Assume the sources of information are aligned and associated. Fusion ofanalysis consists in aggregating representations ( X s(1) ) t and ( X s (2 ) ) t , into a new
representation ( X s(1) ) t and ( X s (2 ) ) t , then in generating an analysis or interpretation of
the object for further use at instant (t+1), or at step i in an iterative process.
2.4.2.3 Fusion of Representations
Fusion of representations is defining and performing meta-operations
applicable to representation ( X s(1) ) t and ( X s (2 ) ) t , to obtain a new representation( X s ) t . Fusion of representations includes fusion of decisions. This fusion ofrepresentations may be performed at any moment.
2.4.2.4 Data Fusion in Remote Sensing
In remote sensing, data fusion processes, such as classification techniques formapping, are performed since long, without naming it, and even less looking at it as aconcept. According to discussions, remote sensing specialists often say that data fusion
is fully illustrated by the merging (the word “combination” can also be used) of images
having a higher spatial resolution. Examples are SPOT-XS and P, and Landsat 7-ETM
and P. The HIS method is one of the most used method to perform such an operation

51

APPENDIX(Pohl, Van Genderen 1998).
The knowledge-based analysis system developed by Srinivansan and Richards(1993) is able to analyze the images (Landsat MSS band 5 and 7 and an L band SIR-B
synthetic aperture radar image) jointly and thus develop a cover type map that resolves
classes that are confused in either the Landsat or radar data alone(John A. Richards,
Xiuping Jia) 28) .
2.4.3 Integration Between Remotely Sensed Data and GIS Data
A simple methodology has been developed for raster-to-vector for use in a GIS.
This methodology was employed successfully to integrate both coarse and fineresolution raster image (e.g. LANDSAT MSS and TM; and SPOT HRV) into a vector
GIS. (N. M. Mattikalli. B. J. Devereux and K. S. Richards) 29) .
2.4.4 Summary
Frequently the need arises to analyze missed spatial data bases, mixed data setsconsist of satellite spectral, topographic and other points from data. All registeredgeometrically, as might be found in a GIS. So far, many remotely sensed data were used
to merge with other sources for any special purpose. But it is very rare that a GIS data
merged with another data. There are advantages of data fusion in GIS, so in this study,
data fusion focused on especially GIS data accuracy adjustment.

52

APPENDIXAPPENDIX IIlProgram for Overlapped Bridges Detection/*** @version 1.0* @author Jung .
*/import java.io.*;import java.util.*;public class Dist2{
static Bridge[] readData(BufferedReader in)throws IOException{ int n = Integer.parseInt(in.readLine());
Bridge[] e = new Bridge[n];int i;for (i = 0; i < n; i++){ e[i] = new Bridge();e[i].inIt();e[i].readData(in);}return e;}
public static void main(String[] args){
int z;
int k;
int h;
int o;
int j;

53

APPENDIX
try{
String fileName="";if(args.length >0)
fileName = args[0];else{System.out.println("");
System.out.println("");
System.out.println("

Usage; java Dist yourfilename.csv");

System.out.println("");
System.exit(0);}BufferedReader in = new BufferedReader(new FileReader(fileName));
Bridge[] e = readData(in);//******************************************************************************
int[] l = new int[e.length];for(j=0;j<e.length;j++){ l[j]=0;}
for( j=0; j<e.length; j++){double minimum=100000000.0;
for(h=0; h<e.length; h++){if(e[j].getPid1() == e[h].getPid1()){
if (minimum>e[h].getdist1()){ minimum=e[h].getdist1();
if (minimum < e[j].getdist1()){e[h].doChange(); l[j]=l[j]+1;}}

54

APPENDIX}}}
for(k=0; k<e.length; k++){for(o=0; o<l[k];o++){for( j=0; j<e.length; j++){double minimum=100000000.0;
for(h=0; h<e.length; h++){if(e[j].getPid1() == e[h].getPid1()){
if (minimum>e[h].getdist1()){ minimum=e[h].getdist1();
if (minimum < e[j].getdist1()){ e [ h ] . doChange();
}
}
}
}
}
}
}
for(z=0 ; z< e.length; z++){e[z].print();}//******************************************************************************
in.close();}catch(IOException e){
System.out.print("Error:" + e);
System.exit(1);55

APPENDIX}}}
class Bridge{
public Bridge(int bid, int pid1, double dist1){
bridge1 = bid;
polyid1 = pid1;
distance1 = dist1;}
public Bridge(int bid, int pid1, double dist1, int pid2,double dist2, int pid3, double
dist3){
bridge1 = bid;
polyid1 = pid1;
distance1 = dist1;
polyid2 = pid2;
distance2 = dist2;
polyid3 = pid3;
distance3 = dist3;}
public void doChange(){ polyid1 =polyid2;
polyid2= polyid3;
polyid3 = 0;d i s t a n c e 1 = d i stance2;
distance2 =distance3;
distance3 = 0.0;}
public void inIt(){polyid1 = 0;

56

APPENDIX
polyid2 = 0;
polyid3 = 0;
distance1 =0;
distance2 =0;
d i s t a n ce3 =0;}
public int getPid1(){ return polyid1;}
public int getPid2(){ return polyid2;}
public int getPid3(){ return polyid3;}
public double getdist1(){ return distance1;}
public double getdist2(){ return distance2;}
public double getdist3(){ return distance3;}
public void print(){
System.out.println(bridge1+","+polyid1+","+distance1+","+polyid2+","+distance2+","+polyid3+","+distance3);}
public Bridge(){}public void readData(BufferedReader in) throws IOException{
String s = in.readLine();StringTokenizer t = new StringTokenizer(s,",");
bridge1 = Integer.parseInt(t.nextToken());
polyid1 = Integer.parseInt(t.nextToken());
distance1 = Double.parseDouble(t.nextToken());
polyid2 = Integer.parseInt(t.nextToken());
distance2 = Double.parseDouble(t.nextToken());
polyid3 = Integer.parseInt(t.nextToken());
distance3 = Double.parseDouble(t.nextToken());}
private int bridge1,polyid1,polyid2,polyid3;
private double distance1,distance2,distance3;}

57

APPENDIXAPPENDIX IIIl

The program for one to one relationship/*** @version 1.0* @author jung .
*/import java.io.*;import java.util.*;public class Dist2{
static Bridge[] readData(BufferedReader in)throws IOException{ int n = Integer.parseInt(in.readLine());
Bridge[] e = new Bridge[n];int i;for (i = 0; i < n; i++){ e[i] = new Bridge();e[i].inIt();e[i].readData(in);}return e;}
public static void main(String[] args){
int z;
int k;
int h;
int o;
int j;
try{
String fileName="";if(args.length >0)58

APPENDIX
fileName = args[0];else{System.out.println("");
System.out.println("");
System.out.println("

Usage; java Dist yourfilename.csv");

System.out.println("");
System.exit(0);}BufferedReader in = new BufferedReader(new FileReader(fileName));
Bridge[] e = readData(in);//******************************************************************************
int[] l = new int[e.length];for(j=0;j<e.length;j++){ l[j]=0;}
for( j=0; j<e.length; j++){double minimum=100000000.0;
for(h=0; h<e.length; h++){if(e[j].getPid1() == e[h].getPid1()){
i f ( m i nimum>e[h].getdist1()){ minimum=e[h].getdist1();
if (minimum < e[j].getdist1()){e[h].doChange();l[j]=l[j]+1;}}}}}
for(k=0; k<e.length; k++){for(o=0; o<l[k];o++)59

APPENDIX{
for( j=0; j<e.length; j++){double minimum=100000000.0;
for(h=0; h<e.length; h++){if(e[j].getPid1() == e[h].getPid1()){
if (minimum>e[h].getdist1()){ m i nimum=e[h].getdist1();
if (minimum < e[j].getdist1()){e[h].doChange();
}
}
}
}
}
}
}
for(z=0 ; z< e.length; z++){e[z].print();}//******************************************************************************
in.close();}catch(IOException e){
System.out.print("Error:" + e);
System.exit(1);}}}class Bridge{
public Bridge(int bid, int pid1, double dist1){
b r i d ge1 = bid;

60

APPENDIX
polyid1 = pid1;
distance1 = dist1;}
public Bridge(int bid, int pid1, double dist1, int pid2,double dist2, int pid3, double
dist3){
bridge1 = bid;
polyid1 = pid1;
distance1 = dist1;
polyid2 = pid2;
distance2 = dist2;
polyid3 = pid3;
distance3 = dist3;}
public void doChange(){ polyid1 =polyid2;
polyid2= polyid3;
polyid3 = 0;
distance1 =distance2;
distance2 =distance3;
distance3 = 0.0;}
public void inIt(){p o l y i d 1 = 0;
polyid2 = 0;
polyid3 = 0;
distance1 =0;
distance2 =0;
distance3 =0;}
public int getPid1(){ return polyid1;}
public int getPid2(){ return polyid2;}
public int getPid3(){ return polyid3;}
public double getdist1(){ return distance1;}
public double getdist2(){ return distance2;}
public double getdist3(){ return distance3;}
public void print(){

61

APPENDIX
System.out.println(bridge1+","+polyid1+","+distance1+","+polyid2+","+
distance2+","+polyid3+","+distance3);}
public Bridge(){}public void readData(BufferedReader in) throws IOException{
String s = in.readLine();StringTokenizer t = new StringTokenizer(s,",");
bridge1 = Integer.parseInt(t.nextToken());
polyid1 = Integer.parseInt(t.nextToken());
distance1 = Double.parseDouble(t.nextToken());
polyid2 = Integer.parseInt(t.nextToken());
distance2 = Double.parseDouble(t.nextToken());
polyid3 = Integer.parseInt(t.nextToken());
distance3 = Double.parseDouble(t.nextToken());}
private int bridge1,polyid1,polyid2,polyid3;
private double distance1,distance2,distance3;}