Analysis of Cartographic Generalization based on PYTHON

Programming Language on Digital Topographic Maps

Marko Stojanović

, Siniša Drobnjak

, Jasmina M. Jovanović

, Nenad Galjak

and Ana Vučićević

Military Geographical Institute, Mije Kovačevića 5, Belgrade, Serbia

Faculty of Geography, University of Belgrade, Studentski trg 3/3, Belgrade, Serbia

Military Academy, University of Defense, Pavla Jurišića Šturma 33, Belgrade, Serbia

PE “Roads of Serbia”, Brodarska bb, Belgrade, Serbia

vucicevic.ana@outlook.com

Keywords: GIS, Automatic Cartographic Generalization, Digital Topographic Maps, Geospatial Database, PYTHON.

Abstract: Cartographic generalization is a creative process of abstraction, which is used in the design and content

preparation of topographic maps. It includes the study of the geographic environment, processing of

geographic data, and an evaluation with regard to type, purpose, and scale of the map, or selecting and

merging their graphical presentation, with a big or small degree of abstraction. In the era of digital

cartography more attention is paid to developing tools for automatic generalization of cartographic content.

In this paper, automatic cartographic generalization is analyzed based on PYTHON programming language

for production of digital topographic map scale 1:50 000 (DTM50) from digital topographic map scale 1:25

000 (DTM25).

https://orcid.org/0000-0001-6566-5538

1 INTRODUCTION

Cartographic generalization is generally performed

based on previously developed criteria. These

criteria are formed upon maps development on the

basis of tests before making the map and in the

course of preparation, they do not change. It is a

requirement that the map has uniformed values and

standard quality throughout the territory being

mapped. The need for a broader range of all of the

map, not only in its size and content but also the

form and manner of presentation, cartographer is

bound to seek and find real special cartographic

generalizing criteria for each map. Success in this is

one of the key factors to create good and meaningful

maps.

The research of automatic map generalization

can be connected to different platforms for

development. Automatic cartographic generalization

is the process on which many studies are focused.

The main task of mapping and the generalization

process is to solve the problem of expressing the

core, typical and characteristic features of the

mapping territory and the occurrence of it in

accordance with the purpose and scale of the map.

From a large number of geographic data that exist on

the mapping territory a logical amount of data

should be drawn, which are of general interest and

can be clearly shown on maps. Data selection is the

result of a need for analysis with regard to the

purpose of the map, the opportunities provided by a

map scale and a geographic result of a study of the

situation on the ground. (Burghardt et al., 2008;

Kazemi et al, 2007; Lamy et al., 1999; Lee & Hardy,

2005; Regnauld, 2005).

Following the development of standards in the

field of collection, organization, processing, and

presentation of spatial data in the Military

Geographical Institute (MGI) - Belgrade, spatial data

of digital topographic maps at the scale 1:25 000

(DTM25) are organized in the Central Geospatial

Database at the scale 1:25 000 (GSD25). It is used to

generate other scale-based series maps produced in

MGI, digital topographic maps in the scale of 1:50

000 (DTM50), 1:100 000 (DTM100) and 1:250 000

(DTM250) (Drobnjak et al, 2016; Tatomirović,

2017).

Stojanovi

c, M., Drobnjak, S., Jovanovi

c, J., Galjak, N. and Vu

cevi

c, A.

Analysis of Cartographic Generalization based on PYTHON Programming Language on Digital Topographic Maps.

DOI: 10.5220/0009396501910198

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 191-198

ISBN: 978-989-758-425-1

191

2 GEOSPATIAL DATABASE AT

SCALE 1:25 000 (GSD25)

The technological process of production spatial data

for GSD25 is introduced into the technology of

digital photogrammetric restitution, as well as global

positioning technology (GPS) to support the GSD25

update on the ground. Applicability of GSD25 made

this particularly important aspect of the whole

production of different scale series topographic

maps with cartographic generalization tools,

cartographic reviewer and support in printing

topographic map sheets.

The technological process of GSD25 is based on

mapping methods, map content and digital

photogrammetric restitution cartographic, processing

it into a GIS environment, using reference

alphanumeric data. The vectorization process is

implemented in strict compliance with the logical

data model respecting the possibility, or mode of the

selected software environment. Anticipated

technological solutions making GSD25 imply that

the content and update methods also perform digital

photogrammetric restitution. The whole

technological process of developing the capabilities

of GSD25 data distribution is shown in Figure 1.

Figure 1: The technological process of GSD25.

To create GSD25 software, U.S. Company ESRI,

ArcGIS platform was chosen, which contains a

completely new approach to the formulation of

geospatial databases. The software platform

selection has caused the brand new technology in all

phases of the work, but they retained the existing

mapping solution. Given these requirements, the

development process includes the following phases

GSD25 work (Sekulović & Drobnjak, 2011):

 Making a logical data model;

 Creating a model for generalization;

 Making a physical data model;

 Creating symbology;

 Making a logical model of the process;

 Making a physical model of the process;

 Creation of procedures for generalizing and

 Training.

In the process of developing the logical data

model DTM25, the geographical map elements are

differentiated by the thematic groups. Individual

cases each of system elements are defined by layer

and codes as a unique indicator of belonging to the

appropriate thematic group (Marković, 2009).

The physical data model is defined by the

appearance of a database or “space” to store

elements defined by tasks' logical model. Data types,

method of data storage as well as all columns that

are used for input attributes of object classes and

individual objects are also defined in the design of

the physical data model. Defining the visual

appearance GSD25 physical data modelling in

which will be used in the interface software

determines the order of topics and, further defines

the visual display of GSD25, a level that cannot be

achieved through symbolism. This phase is the last

step in the development of GSD25 in terms of

practical preparation. Figure 2 shows the

visualization of complete produced part of GSD25.

Figure 2: Visualization of complete produced part of

GSD25.

The entire process of making GSD25 was

conducted by relying on the ArcGIS environment,

and the available hardware resources in the MGI-

networked. This resulted in the division of

responsibilities in working with spatial data,

establishing a system of accountability in the process

of sharing and flow of information in the network,

and data archiving. During the whole process of

making GSD25, special attention was paid to the

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backup data and monitoring the implementation of

the entire task in map sheets and operators

(Sekulović & Drobnjak, 2011).

3 AUTOMATIC

CARTOGRAPHIC

GENERALIZATION

The automation of cartographic generalization

processes in the digital environment is increasingly

evolving, with the aim of obtaining accurate and up-

to-date spatial information through high-quality

cartographic representations. Automatic generaliza-

tion procedures are sets of algorithms that can be

modified by parameters, depending on purpose,

theme, and scale of the map. Automatic

generalization should make possibility to obtain

accurate choice as much possible as of the original

data from the same database to different map details.

The complexity of automatic generalization is

determined by the selection of criteria that influence

the choice of object type according to its importance

for display. The generalization system involves a

generalization of the original spatial database, taking

into account the different attributes of spatial data, as

well as adequate graphical solutions when

presenting a generalizable dataset (Jovanović, 2017).

The goal of automation in cartographic

generalization is to perform the steps by software in

map-making process. In addition to speeding up

work, automation avoids repetitive actions that do

not require human decision-making or can be

formulated. It eliminates the possibility of errors that

may occur and optimizes map-making process. The

problem is defining rules by which generalization

would be done and organizing them into a single

system. To translate all the knowledge and

experience, rules and techniques from classical

cartography, in order for a computer to simulate

human decisions is a difficult task and a special

challenge. The operations performed should be as

close as possible to what a person would perform in

each case.

One of the main problems in automation are

steps in execution order, which are interdependent.

All cartographic generalization procedures should be

viewed as a unique process, not as a series of

isolated independent procedures. The displacement

of one content element often results in the

displacement or elimination of another, and it may

be that other elements must neither be eliminated

nor displaced. To reduce complexity, the entire

process is often subdivided into individual

subprocesses (João & Elsa, 1998).

3.1 Mapping Generalization

Subprocesses

The mapping generalization subprocesses perform

some action on map elements. Each subprocess

defines a transformation that can be applied to one

or a group of spatial objects. Some are for one type

of data only (point, line or polygon) and some for

two or more. Despite the frequent use of

subprocesses worldwide, there is no general

agreement on either the number or the terminology

used to describe these subprocesses. Although there

are several classifications with different numbers of

mapping generalization subprocesses, the ones that

stand out to Lee are the following.

Elimination - This subprocess rejects different

geographic objects because of their small size or less

importance in relation to the map purpose (eg.

elimination of small islands, elimination of short

streets).

Figure 3: Subprocess Elimination (Lee, 1996).

Simplification - This subprocess is the selective

exclusion, rejection, elimination, omission of single

point or groups of points that make up an object.

Parts of the building are discarded in order to

simplify its appearance, but with the preservation of

key parts, in order to maintain recognition.

Figure 4: Subprocess Simplification (Lee, 1996).

Aggregation - By grouping groups of identical

or similar and territorially close objects that do not

touch each other, one object is displayed that

represents them all. It is possible to present a group

of points with polygons related to the surface on

which they are located (eg. merging of small nearby

lakes into one larger lake).

Analysis of Cartographic Generalization based on PYTHON Programming Language on Digital Topographic Maps

193

Figure 5: Subprocess Aggregation (Lee, 1996).

Size reduction - This subprocess reduces the

size of a particular geographic object or stack of a

group of parallel or near-parallel lines to a smaller

number of lines.

Figure 6: Subprocess Size reduction (Lee, 1996).

Typification - This subprocess reduces the

density of spatial objects as well as the level of

detail while maintaining a representative distribution

pattern of these objects.

Figure 7: Subprocess Typification (Lee, 1996).

Exaggeration - This subprocess increases the

spatial extension of the geometric representation of a

given object, to focus on its significance and

improve readability.

Figure 8: Subprocess Exaggeration (Lee, 1996).

Classification and Symbolization - This

subprocess combines elements that share similar

geographical attributes into a new object, which in

turn has a higher degree of abstraction, in addition to

the new symbol.

Figure 9: Subprocess Classification and Symbolization

(Lee, 1996).

Displacement - This subprocess is used to

resolve conflicts, that is, used to move an object on

the map if it overlaps with another occurrence or is

too close to it. When moving, the object retains its

shape. This is the most complicated generalization

operator since it requires complex measurements.

Figure 10: Subprocess Displacement (Lee, 1996).

Refinement - This subprocess changes and

adjusts the geometry or appearance of an object to

enhance the aesthetic (visual) aspect while ensuring

its similarity to reality (eg. "smoothing" a line,

modifying the orientation of some symbols).

Figure 11: Subprocess Refinement (Lee, 1996).

3.2 Defining the Automatic Map

Generalization Model

The model of cartographic generalization includes

generalization of the entire content of digital

topographic maps, which essentially can be graphic

and conceptual. Processes related to the graphic

generalization mainly deal with the geometric

component of spatial data so that they can be

automated. Opposed to them, the processes of

conceptual generalization mainly affect component

characteristics that occur, and those are harder to

automate. The problem of automated generalization

of geographical names is even greater due to fact

that processes contain both types of generalization.

(Stoter et al, 2009). This problem is solved by

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additional processing in preparation for printing.

Some subprocesses are present in both

conceptual and graphic generalization, but there are

differences in the causes of their application.

Subprocesses performing graphic generalization are

interdependent and cannot be viewed separately

from other content on the map. Some activities

require concurrent compliance with the resulting

changes resulting from their implementation. For

example, polygons representing a class and being

displaced should merge with polygons of the same

class as soon as they come into contact. Such

simultaneous execution of activities can be upgraded

by dividing the whole process into stages. This

would lead to the activation of a predetermined

subprocess after each phase, which would be

executed if the given condition is fulfilled. For

example, each time after moving polygons for a

certain distance, if they are in contact with polygons

of the same class, the displaced polygons merge with

them. After that comes the second step in which the

others move further and it is checked again that now

these displaced polygons are in contact with

polygons of the same class. Another factor that can

influence the outcome of cartographic generalization

is the order in which subprocesses will be used.

Proper selection of the execution order can influence

the final appearance of the map. Also, there are a

number of different algorithms for each subprocess.

Not all subprocesses are equally represented in the

generalization process. Sublimation of two or more

operators placed in a proper arrangement is a model

of cartographic generalization (Stojanović, 2018).

It is possible to automate those forms of

generalization that can be numerically interpreted

and expressed in mathematical form, as well as those

that necessarily generalize the classifications of

mapped objects by creating models of cartographic

generalization. It is easy to automatically reduce

objects smaller than the established census or to

select objects determined by normative indicators. In

doing so, a set of choice indicators can be used on

the computer at the same time, taking into account

the correlation of an occurrence with other objects, if

it can be expressed in mathematical form (eg. by

setting a minimum distance between adjacent

objects at the expense of reducing less significant

ones) and may change the value indicators in

different regions. The census approach can also be

applied to geometric side of generalization in terms

of automatic contours generalization or other lines,

e.g. automatically reduce curves and fractures on

lines smaller than a given size (Drobnjak, 2016).

Figure 12 shows an example of automatic curvature

reduction in detail and on an entire object using the

Simplification subprocess.

Figure 12: Automatic curve reduction using simplification

subprocesses, detail view (left) and entire object (right)

(Lee & Hardy, 2005).

3.3 Requirements and Limitations for

Cartographic Generalization

Cartographic generalization is a complex process

because of subjectivity and lack of well-defined

rules in decision making processes necessary to

compensate visual problems. During this demanding

process, it is important to understand why, when,

and how to generalize, in order to select and apply

relevant subprocess to spatial objects (McMaster &

Shea, 1992).

The relevance of the generalization subprocess

depends on the particular design specifications to

which the solution applies. These specifications are

limitations that cartographers have to deal with. The

restrictions apply to the accuracy, scale, and purpose

of the map required, as well as to your visualization

medium (Stoter et al., 2008). For example, when a

tourist map is generated, priority is given to

semantic content elements that represent objects of

tourist interest in a picturesque way. This type of

object does not require the use of complex

subprocesses that offer high geometric accuracy. On

the other hand, such subprocesses may be required

when a map is generated for cadastral or military

use. Moreover, constraints also apply to handle,

readability of spatial objects (visibility threshold),

forms, spatial relationships (positioning of objects

relative to each other), and semantics. Considering

the fact that it is difficult, even impossible, to

overcome all limitations during cartographic

generalization, it is important to identify those that

are prioritized in relation to the purpose and scale of

the map (Plazanet et al, 1998).

For successful cartographic generalization, the

choice of the relevant subprocesses, as well as their

interlocation, are important. The same subprocess

will depend on where it is executed, generalize

different content in different ways. Also, a particular

subprocess may resolve a conflict that may re-occur

Analysis of Cartographic Generalization based on PYTHON Programming Language on Digital Topographic Maps

195

after the execution of other subprocesses. Figure 13

shows the impact of selecting the relevant subprocess,

set in the right place, on cartographic generalization.

The upper part of the picture shows how inadequate

selection of a subprocess can create new conflicts, and

thus necessitates new subprocesses, while the lower

part shows how the correct choice of subprocess

simply ie. with fewer steps, a quality can resolve a

particular conflict (Stojanović, 2018).

Figure 13: Impact of subprocess selection on

generalization results (http://downloads.esri.com).

4 ANALYSIS OF AUTOMATIC

CARTOGRAPHY

GENERALIZATION USING

PYTHON PROGRAMMING

LANGUAGE

Automatic methods of cartographic generalization of

spatial data that are the content of the GSD25 are

additionally programmed using the Python

programming language. Using ArcGIS software

applications based on the development of automated

tools using Model Builder, and their conversion to

Python scripts are very useful tools. These tools are

used for automatic conversion of content GSD25 to

spatial data of digital topographic maps of smaller

scales, primarily DTM50 (Figure 14).

Figure 14: Example of tools for cartographic

generalization of contour lines.

Different aspects of cartographic generalization

can be automated by creating a generalization

model, both numeric interpreted and expressed in

mathematical form as well as those which generally

classify mapped objects. Thus, for example simply is

automatically reduced by objects whose size is less

than the established threshold or select specific

objects normative indicators. In doing so, the

computer can simultaneously exploit a number of

indicators of choice and to take into account the

connection of phenomena with other phenomena, if

it can be expressed in mathematical form (eg.

providing the minimum distances between adjacent

buildings at the expense of reduction of less

important), and finally, it can change the value of the

indicator in different regions. A census approach can

also be applied to geometric side of generalization.

For example, automatically reduce lines curve and

fractures less than some specified size. In modern

software, there are options that support reduction of

curvature on the lines. For example, within the

ArcGIS software, there is an option Smooth Lines

performing this procedure.

Figure 15: Example of urban area generalization in

switching DTM25 (above) to DTM50 (below).

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The main source for the cartographic

generalization was DTM25 with predefined model

data in a central database. The use of different, pre-

defined tools and generating new and using Model

Builder gave a very powerful tool for automated

map generalization, which enabled the creation of a

new, generalized DTM50.

The highest degree of generalization was defined

by using thematic areas of populated places, where

individual objects are grouped into two different

sections, the city property, and settlement blocks.

Objects of public interest are exempt from this kind

of generalization so that they are a simple

generalization of selection and reduction of

geographic content (Figure 15).

Table 1 shows a portion of cartographic

generalization model for thematic layer “Stagnant

water 3” which is the content of the GSD25 and

represents a conceptual generalization which define

certain limitations and conditions for transfer from

topographic map, scale 1:25 000 topographic maps

at the scale of 1:50 000 1: 100 000 and 1: 250 000.

Table 1: A portion of cartographic generalization model

for thematic layer “Stagnant water 3”.

Number 246 247

Name of the symbol

A lake

smaller bigger

DTM25

LAYER

SIFRA

461 462

Generalization

condition

DТM25>DТM50

An area

greater than

5.000 m

An area

greater than

10.000 m

DTM50

LAYER

SIFRA

461 462

Generalization

condition

DТM50>DТM100

An area greater than

20.000 m

DTM100

LAYER

SIFRA

461

Generalization

condition

DТM100>DТM250

An area greater than

125.000 m

DTM250

LAYER

SIFRA

461

Other aspects that have a great influence on

position accuracy assessment are cartographic

generalization and symbolization processes. In this

way, it should be left clear, as in some product

specifications, the hierarchy applied in the

generalization processes, since this affects possible

displacements of the GSD25 elements.

5 CONCLUSIONS

Automatic cartographic generalization is the

integration of many theories, methods, and

techniques. Generalization quality and estimation is

the main problem in contemporary cartography. In

this article, during the technological process GSD25,

we translate the content of the raster topographic

map sheet 1:25 000 publication of the Military

Geographical Institute in vector form with

referential alphanumeric data and the direct

photogrammetric mapping with modern substrates

such as orthophotos. That can be achieved by using

a mixture of generalization techniques (such as

selection, merging, simplifying, symbology and

displacement). The results of the analysis of tested

areas on the topographic map show that the

efficiency of automatic generalization can be

improved, and the loss of information or distortion

reduced.

Despite the current limitations, tested software

can be applied in the production with automatic

generalization. Finding complete solutions in

commercial software requires a huge investment,

given the small number of potential customers and a

lot of effort in adapting commercial solutions in

partial fulfilment of a specific request.

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