Contextual Generalisation Implementations in Topographic Mapping

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Contextual Generalisation Implementations in Topographic Mapping
Contextual Generalisation Implementations in Topographic Mapping:
KartoGEN Project
Dursun Er ILGIN, Bulent CETINKAYA, Serdar ASLAN, Y.Selim SENGUN, O.Nuri
COBANKAYA
Harita Genel Komutanligi, 06100 Ankara, Turkey
Email: [email protected]
Abstract
Many generalisation approaches, methods, operators, and algorithms have been developed
aiming to realize the automation of generalisation processes, and still much more is needed. The
integration and implementation of those in a map production line stands as a critical issue with
contextual generalization which makes automation even much more complex and a difficult task.
In this paper; first, the role and the importance of the contextual issues that have to be taken into
account in generalizing the topographic maps are identified and analysed. Next, some practical
solutions are investigated and suggested to realize contextual generalisation and to obtain
acceptable results. Then, some of the implementation of contextual generalisation has been
realized in KartoGEN Project which aims to produce 100K scale topographic maps from 25K
scale content data (TOPO25).
Mentally related data layers have been evaluated through new approaches of real world object
(RWO) classes and problems have been tried to be solved with contextual approaches, such as by
considering the generalisation of contour lines closely together with hydrographic networks and
by transferring building typed utility and facility features into settlement RWO classes rather
than working with flattened thematic layering of FACC (Feature Attribute Coding Catalogue).
Through these kinds of pragmatic solutions, automation ratios in generalisation have been
increased and post generalisation editing needs have been decreased. Finally, the results have
been evaluated, which are quite promising.
KEYWORDS: Contextual Generalisation, Automation, Real World Objects, Topographic
Maps.
1. INTRODUCTION:
The desire to produce small scale maps and databases from master database, makes
generalisation a significant issue to National Mapping Agencies (NMA). Generalization
constitutes an essential and critical part in such a map production system. Generalization can be
defined as a process of deriving smaller scale datasets with the desired specifications from larger
scale spatial data sources or from datasets having much more detailed information (Aslan, et.al.,
2004).
Conventional generalisation production lines, once only done by experienced cartographers
manually, are wanted to be replaced with semi- and fully automated flow lines in digital
environments. Generalization is aimed to be used in digital map production systems with high
standardization and automation (Itzhak, et.al., 2001). The cartographer’s knowledge and
experience in generalizing a map is difficult to define in a computer, even after researchers
throughout the world have laboured on this subject for last 30 years, although some interesting
topics have recently been presented (Kilpelainen, 1999).
Plenty of generalisation approaches, methods, operators, and algorithms have been developed to
realize the automation of generalisation processes, and still much more is needed. The
integration and implementation of those in a map production line stands as a critical issue with
contextual generalization which makes the automation even much more complex and difficult
task.
2. THEORY:
Generalization is a vital issue for Cartography. The automation of generalization processes in the
map production systems is extremely important for data providers due to enabling to speed up
the production and to standardize the derived products. Contextual generalisation has to be taken
into account continuously, almost in every steps of topographic map production line. Preserving
the contexts between the map objects after generalisation is very important and contributes to the
quality and coherence of the output data.
Most of the generalisation operators, algorithms and methods developed are suitable for
generalizing objects independently without considering contextual issues. Taking into account
the contextual issues in generalisation processes requires explicit definitions of objects’
contextual relations which are desired to be preserved in the generalized data.
Elaborate studies and abundant efforts are needed for implementing the generalisation operators,
algorithms and methods harmonically in the generalisation processes of Topographic Mapping to
produce small scale maps from master data set, and for taking care the contextual issues. For the
automation, some problems listed below needs to be overcame and solved;
•
lack of written generalization rules,
•
difficulties in defining generalization rules explicitly so that not requiring any further
interpretation,
•
subjective and complex structure of generalization,
•
automation and standardization level,
•
cartographic satisfaction level,
•
preserving the contexts between objects after generalization, etc.
Data quality, data accuracy, data spatial resolution and data models are closely related with
generalization and play an important role in defining and developing generalization algorithms
and methods in applications.
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3. CASE STUDY:
Data collection is one of the most time-consuming and expensive, yet important of GIS tasks
(Longley, et.al., 2001). Many data providers and map producers want to produce derived datasets
from their detailed master datasets thorough generalization. Since digital datasets are in hands
now, manual generalization methods don’t seem to be feasible anymore and have to be replaced
with modern and automated ones in digital environment. Lack of sufficient generalization tools
and user interfaces in software environment appropriate to specific generalization needs and
existing master datasets make this a severe task for the map producers. Automation in
generalization plays a crucial role in the map production system since it helps in speeding up and
standardizing the generalization processes and the output products.
Selection of generalization methods, algorithms, operators, parameters and workflow are highly
depended on the source and target datasets and their specifications. Therefore the quality,
accuracy and contents of the source dataset used as an input in generalization become a crucial
key role in generalization. Most often, a data re-engineering should be needed before
generalization processes to standardize the input data, remove data errors, and enhance the data
contents. Another factor that has to be taken into account is that the input data should not be in
sheet base files. Seamless input datasets stored in a database are preferable due to get rid of
merging and union files and features that is needed before generalization processes.
3.1. KartoGEN Project:
A generalization project, named KartoGEN, has been established in General Command of
Mapping (Harita Genel Komutanligi-HGK) (GCM), the NMA of Turkey, in order to produce
1:50 000 and 1:100 000 scale Standard Topographic Maps (STM) using master geographic
dataset TOPO25. For the time being, ArcGIS software is being used to produce derived datasets
thorough generalization.
3.2. Input Data:
In the KartoGEN project, TOPO25 is used as an input data which is a master geographic dataset
used in the production of 1:25 000 scale STMs. TOPO25 data model has a close similarities with
US Vector Map (VMAP) level 2 and FACC rulings. TOPO25 data model consists of 9 thematic
layers and one annotation coverage, listed as below;
Boundary (BND),
Elevation (ELE),
Hydrography (HYD),
Industry (IND),
Physiography (PHY),
Population (POP),
Transportation (TRA),
Utility (UTI),
Vegetation (VEG),
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Annotation (TXT),
And each of the thematic layers consists of 3 different geometry type coverages such as
polygon, line, point.
3.3. Methodology:
In order to implement generalization processes in the map production and to preserve contextual
issues of map objects efficiently, the input data have been analyzed thoroughly by giving
importance to mental and contextual relationships among objects. Being aware of data model not
being perfect for generalisaiton, it is aimed to launch a map production line with the existing data
to produce 1:100 000 scale STMs in a short time period. Practical solutions are found and
implemented in the project, and some of them will be presented in this paper.
Actually, the data modelling mentioned above was built on the approaches at 1980’s, and the real
world was modelled as flattened. But for the generalization issues, the real world needs to be
modelled with real world objects and object classes. In TOPO25 data model, building type
objects comes from various different thematic layers and coverages, such as POP, IND, and UTI.
In this study, the input real world objects have been put in one mental object class in order to
handle it and realize generalization with its high contextual relationships.
In the TOPO25 data model, the thematic layer entities with different geometry types are also
separated into different binary data files (in ArcInfo coverages). For example, polygon geometry
type objects such as forest, vineyards, culture vegetations, etc. and point geometry type objects
such as trees are stored in different data layers. Thus, in generalisation processes, topology and
consistency of those objects should be taken into account and the existing contexts should be
preserved.
3.4. Implementing Contextual Generalisation in Topographic Mapping:
Some of the practical solutions found and implemented in the KartoGEN project will be
described below;
In generalisation processes, population (POP) and transportation (TRA) layers are considered
and handled together due to having close contextual relations among their objects. As mentioned
above, building type objects has been separated among different thematic layers in the current
data model. So, at first, those building type objects have been selected and then transferred into a
settlement object class due to having strong context between them. As a result, the newly created
settlement object class contains;
Settlements, buildings, cemeteries, public buildings, etc. from POP layer,
energy building, facilities, etc. from UTI layer,
industrial buildings, facilities, etc. from IND layer.
As investigating deeply the contexts between settlement object class objects and road objects,
many cases of context can be listed. Building objects within certain distance from road objects
should be parallel to the nearest road objects. This context should be preserved. In KartoGEN
project, this is automatically done after generalisation processes of roads and settlements as
shown in Figure 1. Figure 1a and 1b show pre- and post- generalisation situations, respectively.
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The coordinates and directions of the map features can be changed after the processes of some
generalization operators such as simplification, smoothing, and refinement. For esthetic purposes
some feature rotation operations are needed. Figure 1c shows automatic rotation of buildings so
as to become parallel to nearby roads. This is just needed for cartographic purposes and
cartographic satisfaction.
(a)
(b)
(c)
Figure 1: Rotating automatically the buildings to preserve buildings being parallel to road
linear objects after generalisation.
Some contextual relations that exist between the objects can be damaged after simplifying and
smoothing processes of linear objects. For example, a building at the right of the road can be on
the other side of the road after the road simplification process. This has to be taken into account
in generalisation processes. In KartoGEN project, this contextual generalisation part is
implemented automatically as depicted in Figure 2. Figure 2a shows pre-generalisation data.
Figure 2b shows the data after the simplification and smoothing processes of road type objects.
Due to area being smaller than certain criteria, the polygon geometry type cemetery object
collapsed into point geometry type cemetery object as shown in Figure 2c. For the contextual
issues, point geometry type cemetery is shifted at certain distance towards the right of the road so
as to preserve being staying at the right side of the road as it is used to be in pre-generalisation
situation. This is shown in Figure 2d.
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(a)
(b)
(c)
(b)
Figure 2: Preserving the context between cemetery and road objects after various generalisation
processes.
Special attentions have to be taken in various cases in order to realize contextual generalisation.
Generalisation of polygon shaped cemetery objects through which roads passes should be
handled differently as shown in Figure 3. Polygon shaped cemetery objects having area below
certain criteria are not wanted to be shown as a polygon shaped object in the output data. In
collapse operation, they should be collapsed to point shaped objects. In the example shown in
Figure 3, the polygon shaped cemetery objectis divided in two parts using the road objects, and
then the collapse operation is applied. As a result, the polygon part on the left side of the road
collapsed into point due to its area being smaller than the specified criteria while the other
polygon on the right side of the road preserves its geometry type.
(a)
(b)
Figure 3: Preserving the context between cemetery and road objects after collapse operation.
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Due to having strong contextual relations and for aiming to have better and cartographically
acceptable results, generalisation of hydrographical networks and elevation data objects
(contours from ELE and geographic control points from UTI) have been carried out closely
together. As a result, the characteristic lines of the terrain, such as V shaped parts of contours on
rivers have been preserved automatically. In the generalized data, the logical and topological
consistency between rivers and contours has been preserved. This implementation has decreased
post-generalisation conflicts and thus post-editing needs. Figure 4a and 4b show the pre- and
post generalisation situations.
(a)
(b)
Figure 4: Preserving the context between river and contour objects after generalisation.
4. CONCLUSION:
Automation of generalisation needs explicitly defined generalization rules and their applicable
definitions, generalisation processes and their defined orders. Theoretical ideas should be
transferred to actual production environments to realize automation. In this case, geographic data
model comes across as a crucial point that has to be taken into account. It directly affects the
generalization methods applied and the way of transforming the generalization rules into codes
in actual digital production systems. The better modelling the world as RWOs, the easier
becoming the transfer of the generalisation rules from cartographers mind to algorithms. Object
oriented data model seems to be better in modelling the RWOs.
The content and quality of source master data is also extremely important and it directly affects
the automation, generalization methods applied, processes and the production line.
Reengineering the input data before generalization could affect the automation, generalization
and the quality of the output product. There is still need for lots of more sophisticated and
applicable algorithms for generalization and its automation.
In this paper, some practical solutions for realizing contextual generalisation are suggested that
can be applied in a map production. Contextual generalisation is a key element in generalisation
and has to be taken into account in each steps of topographic mapping through generalisation.
Contextual generalisation contributes directly the quality and coherence of the output generalized
data.
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5. REFERENCES:
Aslan S., Cetinkaya B., Ilgin D.E., Yildirim A., 2004. “Some intermediate results of KartoGEN
generalization project in HGK”, ICA Commision on Generalisation and Multiple Representation
– Research Workshop, Leicester, UK.
Aslan S., 2003. “Generalisation of Buildings and Settlement area in Topographic Maps”, M.Sc.
Thesis, Yildiz Technical University, Turkey.
Bank Emin, 1998. “The Production of 1:100 000 Scale Maps from 1:25 000 Scale Maps
thorough Computer Aided Generalization”, Ph.D. Thesis, Yildiz Technical University, Turkey.
Generalisation Instructions (1:100.000), 1964. Harita Genel Komutanligi, Ankara.
Instructions for the Generalisation of 1:50.000 Scale Maps, 1999. Harita Genel Komutanligi,
Ankara.
Gokgoz Turkay, 1999. “A New Approach in Simplification the Contours”, Ph.D. Thesis, Yildiz
Technical University, Turkey.
Itzhak E., Yoeli P., Doysther Y., 2001. “Analytic Generalization of Topographic and Hydrologic
Data and its Cartographic Display – Intermediate Results”, Geodetic Engineering, Technion
Institute, Haifa.
Kilpelainen Tiina, 1999, “Map Generalisation In The Nordic Countries”, Reports of The Finnish
Geodetic Institute, Finland.
Lee Dan, 2000. “Map Generalization in GIS: Practical Solutions with Workstation ArcInfo
Software”, An ESRI White Paper.
Lee Dan, 2002, “Moving Towards New Technology for Generalization”, 4th Workshop on
Progress in Map Generalization, ICA.
Longley P.A., Goodchild M.F., Maguire D.J., Rhind D.W., 2001. “Geographic Information
Systems and Science”, ESRI Press.
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