Embed Size (px)
Citation preview
FORMULATION & EVALUATION OF TRANSPORT PLANNING
ALTERNATIVES USING
SPATIAL MULTI CRITERIA ASSESSMENT AND NETWORK ANALYSIS.
A case study of the Via Baltica expressway in north-eastern Poland
Sukhad Keshkamat
January, 2007
Course Title: Geo-Information Science and Earth Observation for
Environmental Modeling and Management
Level: Master of Science
Course Duration: September 2005 - March 2007
Consortium partners: University of Southampton (UK)
Lund University (Sweden)
University of Warsaw (Poland)
International Institute for Geo-Information Science and
Earth Observation (ITC) (The Netherlands)
GEM thesis number: 2005-01
FORMULATION & EVALUATION OF TRANSPORT PLANNING ALTERNATIVES
USING
SPATIAL MULTI-CRITERIA ASSESSMENT AND NETWORK ANALYSIS.
A case study of the Via Baltica expressway in north-eastern Poland
by
Sukhad Keshkamat
Thesis submitted to the International Institute for Geo-information Science and Earth
Observation in partial fulfilment of the requirements for the degree of Master of
Science in Geo-information Science and Earth Observation for Environmental
Modelling and Management, (Specialisation: Geo-Information Science and Remote
Sensing for Environmental Assessment and Transportation Planning).
Thesis Assessment Board
Chairperson : Prof. Andrew Skidmore, ITC (The Netherlands)
External Examiner : Prof. Petter Pilesjö, Lund University (Sweden)
Primary Supervisor : Drs. Joan Looijen, NRS-ITC (The Netherlands)
Associate Supervisor : Dr. Mark Zuidgeest, PGM-ITC (The Netherlands)
Project Supervisor : Dr. hab Katarzyna Dabrowska-Zielinska, IGiK (Poland)
International Institute for Geo-Information Science and
Earth Observation, Enschede, The Netherlands
© 2007 by Sukhad Keshkamat. All rights reserved. No part of this work may be
reproduced or distributed in any form, or by any means, without the prior written
permission of the author.
Disclaimer
This document describes work undertaken as part of a programme of study at
the International Institute for Geo-information Science and Earth Observation.
All views and opinions expressed therein remain the sole responsibility of the
author, and do not necessarily represent those of the institute.
I certify that, although I have conferred with others in preparing this research and
drawn upon a range of sources cited in this work, the content and concept of this
research is my original work.
Sukhad Keshkamat
i
Abstract
The accessibility provided by a good highway system plays a key role in the
economic growth of any region. However, when heedlessly planned, it can be
detrimental to the ecology of the region. In transportation route planning generally
one or a few alternative routes are selected, often representing the interest of the
proponent. If required, an environmental impact assessment is carried out on these
alternatives. Such an approach may easily overlook routes which could be much
more suitable. This thesis explores current transportation planning methods to
develop a holistic and coherent GIS based network analysis model for the generation
of optimal routings.
The Via Baltica portion in north-eastern Poland, chosen as the case study for this
thesis, is part of the European Union’s (EU) TEN-T program meant to integrate
transport networks of the newly acceded countries of the EU into contemporary EU
transportation networks, thus creating Europe-wide rapid and effective transport
corridors. This portion from Warsaw to the Lithuanian border, has run into major
conflicts of interest because the, 300km long, 150km wide corridor swath overlaps
with some of the most environmentally sensitive and protected areas of Europe. On
the other hand, are the immense economic benefits of having an international
expressway plying in the region.
The current Government preferred route breaches three Natura 2000 sites and one
Ramsar site, thus stalling EU funding for the project. With such major conflicts of
interest, this region forms the ideal case study for this thesis. Natura 2000 sites,
future intermodality, current traffic counts, protected areas, social utility, safety
concerns, agricultural areas, engineering viability and economic utility are some of
the factors used in a Spatial Multi-Criteria Assessment (SMCA). The results of
which are then used in a vector-based Network Analysis to generate optimal
transport planning alternatives for four different policy visions. All four alternatives
are shorter in distance and have less impedance, indicating that they are more
environmental friendly than the Government preferred route.
This method integrating remote sensing, spatial multi-criteria evaluation and network
analysis techniques, can serve as a strong and versatile decision support system for
transportation route planning. It is hoped that the results of this thesis will be taken
into account in the final outcome of the Via Baltica routing, and that the method
developed in the thesis will find greater utility in the practice of EIA and SEA.
ii
Acknowledgements
So many people played a role in this thesis. All equal. Some just a little more equal
than others. :) So I guess its best to just stick to chronology…
Many thanks to my mother, Sunil, for her many efforts to bring us up well. To my
sister, Shibani - my friend, confidant and supporter. And Hindol and Bhoop. To
Carine, the love of my life. If not for her, I probably would have never applied for
this course. I was far “too secure” in my job and career. To Huib, who
instigated/advised it. To Anna, my friend and weekend refuge, and little Chai-tje and
Coffee-tje. And Monique…and then, not to be forgotten, all my Swedish friends.
To the European Commission for this magnificent scheme. Lovely idea! What a
beautiful study and experience this has been! I hope the benefits that can be reaped
from the method developed in this thesis will pay back the European tax-payer some
of his investment in me. To Stef, at Southampton, for being such an able
administrator at the course-start. To Prof. Petter ‘Everything-is-possible’ Pilesjo,
who spent so much personal effort in trying to get me exactly the thing I wanted to
do for my thesis and to find a suitably prestigious organisation to take me on for
it…LTH, SWECO, WSP…he tried them all, personally. What a lot of effort! Salute!
To Karin Larsson for the love we all felt and for your whole-hearted support.
To Drs. Joan Looijen who did take me on. What an enthusiastic and encouraging
supervisor to have! You guided me firmly but left me free to create this method “my
way” and at my pace. Thank you, it was schitterend working with you! To Dr. Mark
Zuidgeest, for your many insightful questions that made me work just a wee bit
harder…to make this method much more effective and versatile. To Jan de Leeuw,
who first inspired and taught me that the best “scientific research” is not just one that
is a trendy topic, but one that has innovation in solving a social problem.
Andre Kooiman & Kees de Bie, fellow beer-lovers and friends, a grateful thanks for
all your efforts and backing. To the ever cheerful & helpful Ard, Cecille and Harold.
To Kasia (Katarzyna Dabrowska-Zielinska), my Polish supervisor, for a lovely study
tour and then a enormously successful & pleasant field trip. You put in a lot of
personal and official effort to help me to make it a pleasant, comfortable and
successful trip. And to Jedrzej Bojanowski, my friend, mushroom-teacher, guide,
translator and facilitator in Poland. It was good to be there. I hope my thesis shows
atleast some of the love and respect I feel for your country and its people.
iii
To my friends, Matt (Herr-Pioneer), Mila (Meeoowwa), Sarika (Riki-Tiki-Tawi),
Chen (Chengyu), Henok (Knock-knock), Mobushir (Khan), Idham, Abel (Piggie!!),
Moreno and all my other classmate friends. It was great being part of this class. It is
indeed surprising how little personal conflict we had through this course, considering
that we come from so many different cultures and backgrounds! I hope we keep in
touch, for a long time to come.
And, last but never the least, Jorien who managed every demand of this huge
menagerie, spread all over Europe, so very efficiently. Our fairy-godmother (and
punching bag) for all administrative illnesses! You managed so well. Goed zo!
I hope that after all this effort by so many people this thesis doesn’t just stay “Oh,
another MSc thesis.” but will be used by professionals in the field to integrate
environmental laws and concerns with equally important considerations of transport
system efficiency & public safety, economic, financial & engineering viability, and
policy considerations. It is an idea whose time has come…
Next dream…To expand this method with many more criteria for UNESCAP’s
Asian Highway Project…141,000kms of roads across 32 Asian countries. And to
turn this into an intuitive-learning open source software, so that more and more
planners can use the method to plan better roads, without having to be GIS experts!!
It’s time to make a difference…
Cheerio,
Sukhad
iv
Table of Contents
1. General Introduction ........................................................................... 1
1.1. Current status of transport planning.........................................................1
1.2. The Via Baltica transportation corridor project.......................................2
1.2.1. The need for the expressway...............................................................5
1.2.2. EU and Polish policies and decisions relating to the Via Baltica........6
1.2.3. The SEA report (Version 1)................................................................7
1.2.4. Stakeholder reaction .........................................................................11
1.3. Current transportation planning methods and techniques ......................12
1.4. Main aim and objectives........................................................................14
1.5. Requirements of the method to be developed ........................................17
1.6. Thesis Outline........................................................................................17
2. Description of the study area ............................................................ 19
2.1. Topography and soils.............................................................................19
2.2. Socio-economics....................................................................................21
2.3. Ecology..................................................................................................22
2.4. Existing transport network.....................................................................22
3. Data description.................................................................................. 24
3.1. Satellite Imagery....................................................................................24
3.2. Field-collected data................................................................................25
3.3. GIS Data ................................................................................................26
3.4. Accuracy statement of the data ..............................................................28
4. Method Description............................................................................ 29
4.1. Identifying assessment criteria...............................................................29
4.2. Pre-processing .......................................................................................33
4.2.1. Pre-processing of Remote sensing imagery.......................................33
4.2.2. Pre-processing of Vector dataset (roads network) ............................33
4.2.3. Pre-processing of Raster datasets......................................................34
4.3. Weighting of criteria and themes...........................................................36
4.4. Spatial Multi-Criteria Assessment .........................................................37
4.5. Network Analysis ..................................................................................39
4.5.1. Preparation........................................................................................39
4.5.2. Building the Network........................................................................41
4.5.3. Solving the Network .........................................................................42
v
4.5.4. Assessing the Government preferred route ....................................... 43
4.6. Sensitivity and uncertainty analyses ...................................................... 43
4.7. Software used ........................................................................................ 44
4.8. Difficulties faced ................................................................................... 45
4.9. Comprehensive work flow-chart of the methodology............................ 46
5. Results ................................................................................................. 47
5.1. Results of Spatial Multi-Criteria Assessment ........................................ 47
5.2. Results of Network Analysis ................................................................. 48
5.3. Comparison with Government preferred alternative.............................. 50
5.4. Sensitivity Analysis ............................................................................... 52
5.4.1. Effects table ...................................................................................... 52
5.4.2. Uncertainty analysis of weights and scores....................................... 52
5.4.3. Sensitivity analysis of weights using results of ranking .................... 54
5.4.4. Sensitivity analysis of scores using results of ranking ...................... 54
6. Discussion............................................................................................ 56
7. Conclusions and Recommendations ................................................. 59
8. References ........................................................................................... 61
8.1. References of publications..................................................................... 61
8.2. References of datasets ........................................................................... 65
8.3. References of personal communications................................................ 67
9. APPENDICES .......................................................................................I
APPENDIX - 1...................................................................................................... II
APPENDIX - 2...................................................................................................... V
vi
List of figures
Figure Description
1 A schematic trans-European overview of the Via Baltica project.
2 Proposed alignment options of the Rail Baltica.
3 An example of GIS analysis in the SEA Report of the Via Baltica.
4 A simplified overview of alternative routings of the Via Baltica described
by the NGOs.
5 Schematic overview of the study area.
6 A 3-dimensional perspective of the study area.
7 Spatial distribution of Peat strata over the study area.
8 Typical situations on national motorways in study area.
9 Processed and mosaicked ASTER imagery of the study area.
10 Processed DMSP-OLS Radiance-Calibrated night light satellite imagery of
the study area.
11 Screenshot of the completed SMCA table in ILWIS.
12 Tool to inherit suitability values from vision maps to road network layer
and the resultant attribute table.
13 10 year expressway development plan of Poland showing the various
Transport Corridors.
14 Suitability map for equal vision.
15 Suitability map for social vision.
16 Suitability map for ecology vision.
17 Suitability map for economy vision.
18 The Via Baltica expressway: The Warsaw – Budzisko Equal Vision Route.
19 The Via Baltica expressway: The Warsaw – Budzisko Social Vision Route.
20 The Via Baltica expressway: Warsaw – Budzisko Economy Vision Route.
21 The Via Baltica expressway: Warsaw – Budzisko Ecology Vision Route.
22 The Government preferred route.
23 Government preferred route (red) vs. Equal & Social vision routes (blue).
24 Government preferred route (red) vs. the Economy vision route (blue).
25 Government preferred route (red) vs. the Ecology vision route (blue).
26 The Effects table in Definite 2.0.
27 Social Vision: Ranking of routes if 5% weight uncertainty is assumed.
28 Social Vision: Ranking of routes if 5% score uncertainty is assumed.
29 Score position of each route alternative across all the visions.
30 Ranking of each route alternative across all the visions.
vii
List of abbreviations
Abbreviation Description
AADT Annual Average Daily Traffic
AGI American Geological Institute
AMIS Analytic Minimum Impedance Surface
ASTER Advanced Spaceborne Thermal Emission and Reflection
Radiometer
ASTER-GDS ASTER-Global Data Server
BNP Biebrza National Park
BTGPS Blue Tooth Global Positioning System
CAD Computer Aided Drawing (or Drafting)
CLC 2000 CORINE Land Cover 2000
DEM Digital Elevation Model
DMSP-OLS Defense Meteorological Satellite Program – Operational Linescan
System
DVD Digital Video Disc
EIA Environmental Impact Assessment
ESDB European Soil Database
ESRI Economic and Social Research Institute
ETRS European Terrestrial Reference System
EU European Union
GDDKiA General Direckja Drog Krajowych I Autostrad
(General Directorate of National Roads and Highways)
GDP Gross Domestic Product
GIS Geographic Information System
GPS Global Positioning System
HDF Hierarchical Data Format
ILWIS Integrated Land and Water Information System
IMG Erdas Imagine file format
ITC International Institute for Geo-
Information Science and Earth Observation
IUCN International Union for Conservation of Nature
LWM Line Weighted Mean
MSL Mean Sea Level
NGOs Non-Governmental Organisations
ORNL Oak Ridge National Laboratory
viii
OTOP Ogólnopolskie Towarzystwo Ochrony Ptaków
(Polish Society for Birds Protection)
PDF Portable Document Format
PKP Polskie Koleje Państwowe (Polish Railways)
PTRDB Pedo-Transfer Rules Database
SEA Strategic Environmental Assessment
SGDBE Soil Geographical Data Base of Europe
SMCA Spatial Multi-Criteria Assessment
TEN-T Trans European Network-Transport
USDoE United State Department of Energy
USGS United States Geological Survey
UNEP United Nations Environmental Programme
UNEP-GRID United Nations Environmental Programme-Global Resource
Information Database
UTM Universal Transverse Mercator
WCPA World Commission on Protected Areas
WDPA World Database of Protected Areas
WGS 84 World Geodetic System 1984
WWF World Wildlife Fund
ix
Preface
Poland (Polska), officially known as the “Republic of Poland” is a country in Central
Europe. It is bordered by Germany to the west, the Czech Republic and Slovakia to
the South, Ukraine and Belarus to the east, Lithuania, and the Russian region of
Kaliningrad Oblast to the north. To its north is also the Baltic Sea, whereby it shares
a maritime border with Denmark and Sweden.
The earliest documentary record of that part of Europe, which we now know as
Poland, dates from AD 965-966. Poland became a kingdom in 1025, and in 1569 it
cemented a long association with the Grand Duchy of Lithuania by uniting to form
the Polish-Lithuanian Commonwealth. The Commonwealth collapsed in 1795, and
the Poles were without a state for 123 years. Poland regained its independence in
1918 but lost it again in World War II, when the Russian army took over and turned
it into a satellite state of the Soviet Union. (Wikipedia, 2006)
The Polish people were not content under this supremacy and there followed a series
of internal rebellions, each of which was brutally crushed. Finally in 1989, the
Solidarity movement (Solidarność), succeeded in throwing off the Soviet yoke and
Poland became what it is today. In June 2003, at a referendum, Poles voted
overwhelmingly in favor of joining the European Union, and on 1st May 2004,
Poland became a full member of the European Union (EU).
Poland stands at the crossroads of all historic (and contemporary) trade routes going
from the Balkans to the Baltics and from European countries to Russia. Thus it was
in history, a very prosperous country. This prosperity lasted until the start of World
War II, when in September 1939, Nazi Germany and the Soviet Union invaded
Poland from both sides, and under the Ribbentrop-Molotov pact, split it into two
zones amongst themselves. Of all the countries involved in World War II, Poland
lost the highest percentage of its citizens: over 6 million perished, half of them
Polish Jews. It also made the 4th
largest Allied troop contribution, after the
Americans, the British and the Soviets, which ultimately defeated Nazi Germany.
But the destruction wrought in the country by the Nazis, combined with the
subsequent Soviet take over, impoverished this country greatly and thrust it back by
several decades. (Polish Embassy brochures, 2006)
This dark era rankles, even today, in the psyche of most Poles. The Polish people are
a proud, spirited, intelligent and hard-working people, however. Following Poland’s
accession to the EU, development funds started flowing in, and the latent economy
and infrastructure-quality grew by leaps and bounds. But it is obvious that much
work still needs to be done.
One such vital development project, The Via Baltica, forms the heart of this thesis.
1
1. General Introduction
The accessibility provided by a highway system plays a key role in the economic
growth of any region. However, when heedlessly planned, it can be detrimental to
the ecology of the region. An efficient route planning system that takes into
account environmental considerations facilitates sustainable development. This
chapter of the thesis introduces the reader to current methods in transportation
planning practice (their emphasis & limitations) and lays the foundation for the
development of a holistic and coherent GIS based network analysis model for the
generation of optimal routings.
1.1. Current status of transport planning
From the development of the wheel through the industrial revolution to current times,
economic growth has always needed to go hand in hand with transportation growth.
Thus transportation systems, particularly highways, play a key role in the
development of any country. The country’s economic and social well-being depends
to a large extent on the performance of the highway systems. Not only does the
highway system provide opportunities for the mobility of people and goods, but also
over the long term it influences patterns of growth, land-use and the level of
economic activity through the accessibility it provides to land (Bannister, 2002). On
the other hand, transportation development, when done heedlessly can hopelessly
damage and fragment the natural environment. Human-kind’s quest for development
has led us to a point where any further development threatens the last remaining
natural reserves. Thus in these times, finding the optimal balance between
infrastructure creation and nature conservation is achieving much greater importance
than ever before (World Bank, 1993 & World Bank, 1995).
With such weighty trade-offs to be made, environmental impact assessment (EIA)
came into practice in the late 60s. EIA is described as a systematic process of
determining and managing potential impacts of proposed development actions and
their alternatives on the environment (Lawrence, 2003 and Wood, 2003). The
growing belief that project EIA may actually occur too late in the planning process,
the insufficient consideration of cumulative impacts and alternative options has
realized the need for a similar assessment procedure at a more strategic level of
decision-making. Strategic Environmental Assessment (SEA) evolved in this context.
2
Nowadays most of the larger transport projects and plans are subjected to either EIA
or SEA. The EIA Directive (EU Directive 85/337/EEC, Art. 2(1)) states that projects
likely to have significant effects on the environment by virtue, inter alia, of their
nature, size or location must be made subject to an assessment of the environmental
effects. The EIA Directive outlines also which project categories shall be made
subject to an EIA, which procedure shall be followed and the content of the
assessment (EU Directive 85/337/EEC, Annexes I & II). The purpose of the SEA
directive is to ensure that environmental consequences of certain plans and
programmes are identified and assessed during their preparation and before their
adoption (EU Directive 2001/42/EC).
In general selection of the most suitable route through an EIA follows the process
described in the diagram below.
In the case of transport planning, an identification of route alternatives is done by a
team of experts by order of the proponent. This is followed by an environmental
impact assessment (EIA) on one or a few preferred alternatives. This pre-determining
of alternatives is often purely in the interest of the proponent. Route possibilities that
could otherwise have been more suitable are often completely overlooked and only
an EIA of the preferred routes is carried out. Thus subjective bias dominates the
planning. Political and industrial lobbying is also known to play a key (and
notorious) role in the identification of the route alternatives. This consequently leads
to stakeholder dissatisfaction and disillusionment with the entire planning process.
(e.g. Fitzsimons, 2004)
The Via Baltica, the subject of this thesis, is no exemption to this norm.
1.2. The Via Baltica transportation corridor project
The Via Baltica Transportation Corridor is part of the European Union’s Trans-
European network for transportation (TEN-T) program. This is a program intended
to firmly integrate transport networks of the newly acceded countries of the EU into
3
EU transportation networks, thus creating rapid and effective inter-modal transport
corridors from North to South and East to West of the whole of Europe.
In its current form, the Via Baltica corridor starts in Potsdam (Germany), runs east to
Warsaw (Poland) and then moves northward into Lithuania, Latvia, Estonia, Finland
and Sweden respectively. In Figure 1 below the green-highlighted line shows the
thrust of the corridor routing in Phase-1. Future thrust areas (non-highlighted) are
also seen in the figure.
Figure 1: A schematic trans-European overview of the Via Baltica project
(Source: Interreg EU website: http://www.bsrinterreg.net/programm)
The figure is self-descriptive of the importance and ambitiousness of this program.
Not surprisingly then, this corridor development plan is regarded as the EU’s
highest-profile project in the Baltics. It aims to create a rapid and effective transport
corridor from Scandinavia to Eastern and Central Europe. Until now, the only way
for EU- Finland travelers to avoid half a dozen lengthy border crossings was to take
the sea route to Germany’s ports or drive across Sweden and the Oresund. With the
creation of the Via Baltica, they will be able to drive directly through the Baltics; EU
territory all the way. At the same time the EU frontier countries of Lithuania, Latvia
and Estonia will be better connected with western European countries. Moreover,
4
with current European trade practices, enormous amounts of road freight are
transported to and from Eastern European countries to Western European countries
everyday. Hence the Via Baltica plays a key role in the socio-economic development
of the new EU member countries (Poland, Lithuania, Estonia) and remote
underdeveloped regions in the older EU countries of Finland and Sweden.
While the name “Via Baltica” may conjure up images of a brand-new ribbon of
silken asphalt binding the Baltic nations together, the reality is more down-to-earth.
It is a series of upgrades of contiguous existing roads to expressway standard.
Perhaps the best facet of the Via Baltica, as planned by the EU, is that it embraces
the ideals of inter-modal transport. As per the EU’s TEN-T website, the Via Baltica
transportation corridor consists of the Via Baltica expressway and the Rail Baltica
high speed railway. Thus, it envisages not only the reconstruction and upgrading of
roads in this region, but also a contemporaneous rejuvenation of the existing rail
networks of Poland, Lithuania, Latvia and Estonia.
The Via Baltica enters Poland from Poland’s western border with Germany and
passes the major Polish cities of Poznan and Lodz, via the A2 Autostrada highway,
and arrives in Warsaw. From Warsaw, it proceeds in the north-east direction towards
the Polish-Lithuanian border control point at Budzisko.
This portion of the Via Baltica, Warsaw to Budzisko, is the subject of this thesis.
The “corridor” in this segment is defined as 75 km on each side of the Warsaw-
Budzisko axis. Thus the corridor has a swath width of 150km. The final expressway
will be built on any combination of contiguous roads lying within this swath.
As it happens with most projects of large magnitude, the project has run into major
conflicts of interest because the 150 km swath overlaps with some of the most
environmentally sensitive and protected areas of Europe. With many Natura 2000
sites, national parks, ecological corridors and landscape parks, this area is also
known as the “Green lungs of Poland”. Planning that causes the fragmentation of
these sensitive areas could entail the extinction of several endangered species of
flora, and fauna located in these regions. This may lead to economic losses due to
reduction in nature-tourism, for which this area is famous. On the other hand are the
immense economic benefits that can be reaped by having an international
expressway in the region.
5
1.2.1. The need for the expressway
The need for the Via Baltica can be classified into international, national, regional
and substitute for an efficient rail network. These are elaborated below.
International
The Polish-Lithuanian border is very short. All the other northern and eastern
boundaries are non-EU hence the heavy traffic is focused into this narrow funnel.
Thus, enormous amount of international trade is clearly visible along all roads from
Warsaw to Budzisko. A substantial portion of this trade is used German cars being
transported to the high demand auto markets of Poland, Lithuania and Latvia. The
returning traffic is generally agricultural produce and livestock of these countries
being transported to western European markets. As discussed previously, it is
expected that once the corridors VI and III from Ukraine, Czech Republic and
Slovakia are connected to the Via Baltica, the importance of this road to Eastern
Europe will increase manifold.
National
Many roads in Poland are severely narrow, 1.5 lane roads with poor, frost-heaved
surfaces. The national rail system in the north-eastern region is of poor quality and
low service and speed. In addition there is a general lack of funds available to
upgrade the existing infrastructure. The combination of these two factors hinders
effective transportation, thus severely retarding the growth of tourism, agriculture
and industry.
Regional
This region is notorious for its agriculturally poor soils and long harsh winters. Thus
it is one of the poorest regions of Poland with negligible industrial development,
sustenance agriculture and high unemployment rates. Consequently, typical social
evils such as poverty, youth migration to Warsaw, rampant alcoholism and neglect of
elderly people, are a prominent social feature of this region. Traffic jams, air-
pollution and pedestrian road accidents add to the suffering of the people. This adds
fuel to the increasing public clamour to hasten the construction of the expressway.
Substitute for an efficient rail network
The current railway network of this region is a constant bane of the people of this
region. A popular opinion of the railway system in this region is that it is “less for
transportation but more as a steel-scrap reserve to support the unemployed”. If the
PKP (the Polish railway company) insists on retaining its current monopolistic
policies, the prospects of any improvement in the near future are quite dim.
6
Thus, to the people, the Via Baltica corridor has come to mean “The Via Baltica
Expressway” only. It is not surprising then that the Polish people of this region
believe that the creation of this expressway will fetch not only better and faster
connectivity, but hopefully, economic salvation too.
1.2.2. EU and Polish policies and decisions relating to the Via Baltica
In 2003, Poland was scheduled to join the EU. As EU development funds started
flowing in, the Sejm (The Polish Parliament) invoked a law known as “The Special
Act, 2003”, under which the Ministry of Infrastructure was excluded from an
obligation to carry out a full environmental impact assessment at the stage of the
construction permit. The law permits infrastructure projects of national importance to
be carried out without public participation or the preparation of a comprehensive
environmental impact report. This Act amended the Environmental Protection Act,
diluting the provisions concerning environmental impact assessments and nullifying
many other procedures.
Hence only an abridged assessment of the course of the road and its alternatives was
carried out without examining the “present descriptions of the alternative courses of
the road project in the vicinity or in the areas of nature reserves, landscape parks and
sites covered by nature conservation pursuant to international law as well as in the
areas of intense residential building, along with a rationale for the choice of
alternatives...the need to change the course of the road at its selected sections in the
light of the protection of cultural heritage and nature conservation” as stated in
Annex-1 of the European Union regulations.
As the construction commenced, this contravention of EU regulations was brought to
the notice of the European Commission by various affronted non-governmental
environmental organizations (NGOs) such as Bird Life International, Polish Society
for the Protection of Birds (OTOP), World Wildlife Fund (WWF) (See also section
1.2.4). The result of this intervention of non-governmental organizations, led the
Standing Committee of the Bern Convention (the Convention on the Conservation of
European Wildlife and Natural Habitats) to, formulate unambiguous and obligatory
conditions for continued EU funding of the Via Baltica (in Poland) vide its
Recommendation No. 108 in November 2003. They were in brief:
1. Complete a full Strategic Environmental Assessment followed by a detailed in-
depth Environmental Impact Assessment Report, analysing all possible alternatives
and variants, in order to minimise, as far as possible, any deterioration of important
ecological areas.
7
2. Use the results of the SEA as the basis to decide the routing of the Via Baltica;
3. Organize adequate long-term monitoring of the effects of the modernized express
road and bypasses in view of both ecological and socio-economic consequences
(also secondary effects) and support supplementary mitigation measures to be taken
in the future when and where needed (such as speed limits);
4. Further develop the constructive dialogue between the official administrations, the
provincial, regional and local authorities and population representatives, the NGOs
and the scientific community and communicate openly about the progress of the
decision-making process.
The full text of the resolution is placed in Appendix 1. At its meeting in Strasbourg,
in December 2004, the Standing Committee of the Bern Convention once again
reiterated its earlier resolution that a full SEA should be done. Finally in mid-2005,
the consultancy firm M/s Scott Wilson Kirkpatrick was contracted for preparing a
comprehensive SEA. The first version of the SEA was released to the public in
September 2006 and is in Polish language only. (The Author has translated this
report into English. Courtesy: Prof. Dr. hab. Katarzyna Dabrowska-Zielinska, Head
of Remote Sensing Department, OPOLIS-IGiK Poland).
1.2.3. The SEA report (Version 1)
This version of the SEA report states that it is based on four main principles:
a) The expressway must be from Warsaw to Budzisko
b) Must connect with TEN-T Corridor 2
c) Will preferably use existing national and woiwodship roads (though not an
orthodox must)
d) Minimize conflicts with Natura 2000 sites.
The methodology pursued in the SEA report is to first identify all possible route
variants. Thereafter it recommends the elimination of variants that cause (social or
natural) conflicts for more than 2.5% of the total length of the road. Once this
elimination is done, it recommends that the most suitable variant be identified by
assessing the remaining variants using ecological, social, technical and economic
factors identified below.
Formulation of route variants, criteria & assessment
The SEA report goes on to introduce a total of 42 variants in 7 main groups for the
expressway from Warsaw to Budzisko section. These are, basically, all (contiguous)
8
permutations and combinations of national and provincial roads lying in the corridor
swath.
The methodology of elimination is to award “punishment points” for each natural or
social area that the variant encounters. Accordingly various criteria are listed with
their corresponding punishment points. The report recommends that the group of
people who will compare the variants award the punishment points based on the
recommendation table and the length of the route in the conflicting regions. Because
of this there will be an elimination of variants which conflict with protected nature
areas or social areas. Though it is stated that the number of punishment points should
be awarded as per the length of the conflict, no specific guidelines are given as to
how this should be done and it is left to the subjectivity of the decision makers. It is
not clear how these punishment points were arrived at.
Ecological and Social factors
Migratory corridors of large mammals, such as wolves, elk, bison etc, are an
important issue for maintaining biodiversity and genetic variation within the gene-
pool. The disruption of these migratory corridors disruption due to fenced
motorways and expressways in Poland is a well-known issue (Nowak and Myslajek,
2005), but this criterion has not been allotted any punishment points. For analyzing
impact on fauna, distance of the population from the road is to be considered,
whereas for Natura 2000 the standard punishment points will be -10. This renders the
decision-making process highly ambiguous. To further worsen the situation, no
punishment points for conflicts with settlement areas are specifically attributed. Thus
it seems that the SEA report is created only to quell the furore caused by the NGOs.
The turmoil that will be caused to the settlements, agriculture and industry that will
have to be compensated and/or re-settled is not considered. The only social aspect
considered is the number of citizens that will be served by the road. For this
criterion, the census population residing within 20km on either side of the road
centreline is considered.
Technical factors
Technical (traffic) factors considered are vehicle-kilometres, vehicle-hours, traffic
intensity. The SEA also proposes to separate the contribution of heavy trucks from
amongst the traffic; however strangely, it states that after separating this contribution,
the same formulas used to calculate vehicle-kilometres and vehicle hours will
continue to apply. Other technical (physical) criteria which are considered are length
of road analyzed against current category of the road, number of ancillary structures
required (such as intersections, bridges, barriers, acoustic screens and migratory
9
passages). However, only acoustic screens are analyzed for length, despite the known
fact that bridges and intersections are the most expensive ancillary structures in any
highway construction (the longer they are the more expensive is the highway).
Intermodality
The SEA report makes only a brief mention to the Rail Baltica but does not consider
it (and future intermodality) a significant criterion. It states that “since there is a lack
of information regarding other means of transport in this elaboration, it is proposed
to keep road transport only as the contemporary mode of transport and assume that
there will be no drastic change in the means of transport in the future”. This is a
highly short-sighted decision considering that the feasibility report for the Rail
Baltica (prepared by the Rail Baltica project consultants M/s Cowi A/S.) was already
released to the public in October 2006 and that Lithuania, Latvia and Estonia give
this project greater importance than the Via Baltica expressway. Since the
expressway project is an international project with these countries as key
stakeholders, this is a significant issue that has been ignored. Ironically, the Rail
Baltica feasibility report affords substantial attention to the Via Baltica expressway,
owing to the fact that under all the Polish National Development Plans, the two are
accorded nearly-equal priority. The national rail network (PKP), though currently
handicapped in this region, can also not be ignored as a viable future competitor. The
three alignments proposed by the Rail Baltica, overlaid on the PKP rail network are
seen in Figure 2 below.
Figure 2: Proposed alignment options of the Rail Baltica.
(Source: Feasibility study on Rail Baltica railways-Draft interim report: July 2006.)
10
Economic factors
Within the economic criteria considered in the SEA report, arithmetic “costs”
accrued by the project are considered and summarized into a variable known as
Index cost. Under this variable are included the cost of buying land, cost of various
ancillary structures, cost of management, cost of limited usage and the environmental
cost. This is a biased approach, as the benefits accrued by industry, agriculture or
population, due to the expressway are not considered.
Spatial information
In the SEA report GIS is not used. The hand-drawn figure below is the only example
of a GIS-like analysis in the entire report (Figure 3). Another example is the manner
of attributing assessment points to various technical criteria without doing a
spatial assessment.
Figure 3: An example of the poor GIS analysis in the SEA Report of the Via Baltica
(Source: The Strategic Environmental Assessment report for Pan-European
transport corridor I (version 1). Accessed from www.viabaltica.scottwilson.com.pl).
Thus, despite the benefit being finally offered by an SEA being conducted on the
project, the following drawbacks still need to be addressed:
♦ Only a limited number of criteria are considered.
♦ Method of assessing these criteria is not wholly transparent
♦ Rail Baltica and future intermodality not considered
♦ Only economic costs are considered, ignoring economic benefits.
♦ No spatial assessment.
11
1.2.4. Stakeholder reaction
WWF-Poland, Birdlife International, OTOP and Bankwatch were the main NGOs
that spearheaded the protest against what they called “the short-sighted
environmental decisions of the Polish government”. When the government
commenced construction on the Via Baltica, these NGOs formed a coalition and
created an intense international signature campaign hosted on about 25 websites. The
zealous interest in this cause is indicated by the fact that it has been signed by over
250,000 people, including 150,000 Polish citizens. The NGOs argued that routing
the highway through Białystok would cause the expressway to cross the Augustów
Forest, the Rospuda River Valley, the Biebrza National Park and probably the
Knyszyn Forest Landscape Park. It would also run through the Biała Forest (the Bug
River Valley Landscape Park), along the edge of the Wigry National Park and the
Narew National Park, and would cross migration routes of large mammals. Routing
the expressway through Łomża, would be much shorter and avoid most of the above
mentioned negative effects. They demanded that the government use scientifically
supported information to define proper ecological and social criteria. A simplified
sketch of the alternatives discussed by them is placed in Figure 4.
Figure 4: A simplified view of the alternative routes of the Via Baltica described by
the NGOs. (Source: Petition to the European Parliament by Birdlife, OTOP, WWF
Poland etc, June 2003. Accessed from http://www.darzbor.v24.pl/via-baltica).
12
In their petition, the coalition of environmental organizations stated that the results
of traffic measurements performed in 2000 and 2005, which are used as an argument
for the Białystok option, are not credible, since traffic on route 61 (via Łomża) was
limited by an administrative decision when the measurements were conducted (Via
Baltica - A protest, 2003). They claimed quite correctly that many drivers would still
drive the road 61 through Łomża rather than use the Via Baltica and travel 30-40
kilometres more through Białystok. This would render a major portion of the
prestigious Via Baltica redundant on top of the enormous environmental damage it
had caused.
1.3. Current transportation planning methods and techniques
An oft heard observation of infrastructure financiers (e.g. Kennedy & Haumer, 1999)
and professionals in SEA/EIA formulation (see Section 9.3) alike is that whilst it
may make sense to take a strategic view of environmental impacts, the question of
how to integrate the environmental concerns and regulations with the equally
important considerations of transport system efficiency and safety, techno-
commercial viability, and policy considerations, still remains unsatisfied.
The author feels that, in order that this demand is fulfilled, GIS and spatial multi-
criteria assessment (SMCA) need to be incorporated more tenaciously in
transportation route planning. Although much research has been carried out in the
use of GIS methods in environmental impact assessments (Li et al, 1999, Blaser et
al, 2004, Affum & Brown, 1997 etc.), the use of GIS in the very preliminary stage of
route planning itself has almost never been explored. In 2001, Grossardt, Bailey and
Brumm introduced the first coherent methodology to route formulation based on
environmental criteria. In their study for a local State Highway Agency (SHA) in
south-eastern United States, Grossardt, Bailey and Brumm (2001) presented a GIS
based corridor planning method which they called Analytic Minimum Impedance
Surface (AMIS). In this method they combined stakeholder priorities such as
economic development, connectivity, ecological factors (wetlands and endangered
species), recreational areas etc to generate a continuous geographic surface which
they defined as an impedance map. This map is a raster map in which every pixel
corresponds to a weighted sum of the scores of individual impedance elements. This
preliminary step of their process is an SMCA approach.
SMCA, or SMCE (Spatial Multi-criteria Evaluation) as it is also called, has
permeated, and proven its effectiveness, in every sphere where spatial decisions is
required, especially in the face of multiple alternatives and/or large stakeholder
groups. It gained universal acceptability from the work of Jankowski, 1995 and
13
Malczewski, 1996, when traditional multi-criteria evaluation methods were combined
with GIS and support for numerous alternatives in a group decision making
environment. Since then, the method has been used for applications ranging from
residential quality of life (Malczewski &Rinner, 2005), locating suitable sites for
garbage disposal (Higgs 2006) or wind farms (Sparks & Kidner, 1996), consensus
identification (Malczewski, 1996), pipeline routing (Rescia et al, 2006; Yusof &
Baban, 2004) to telecommunication network design (Paulus et al, 2006). Using
SMCA as a key process, inherently supports a cumulative assessment of impact.
Grossardt et al, then proceed to use the Cost-Weighted Distance function found
within the ArcGIS Spatial Analyst extension to find the least cost path across this
SMCA surface (that they designated as AMIS). Avenue scripts within ArcView were
used to generate graphical elements demarcating this least cost path.
The advantage of the AMIS method is that it was built from ground-up to enable
interactive and continuous stakeholder participation. It was a web-based, graphical
application, thus enabling any stakeholder to view the preference orderings generated
by him/her from any remote site at any time. The AMIS method has some drawbacks
however:
1. The cost-distance function used by Grossardt et al is a (raster-based) analysis in
which the friction surface (or impedance map or travel-cost surface) “…is used to
determine the least cost path between a designated origin and any other point/s.
The end result is a route, one cell wide, which delineates the least cost path
between the points”. This method tries to find the path of least impedance
regardless of the length. Hence the “total route length” is never under the control
of the method. This is observed by the many unnecessary hairpin bends and loops
in the graphical results of Grossardt et al. Such a route would not only be
uneconomical to construct and maintain but also very inefficient in terms of
vehicle-kilometres and vehicle-hours.
2. Since it is a raster based approach it is ideal for creation of new roads where none
exist but is not applicable to upgrade existing roads.
3. Further, the selection of pixel size in this method is done more from a point of
view of data-processing convenience than from a “spatial effects perspective”.
Grossardt et al acknowledged that this was a “necessary compromise”.
However, as a path-breaking analytical approach, the AMIS system was indeed
commendable in its possibilities. It is thus surprising that after this paper, little work
was done on this technique for transportation planning either by Grossardt et al or
anyone else.
14
1.4. Main aim and objectives
In order that the assumptions of arbitrariness be quelled and transparency
entrenched, the author opines that the alternatives should not be predetermined but
be formulated as a result of the environmental assessment. Using stakeholder
participation meetings, key effects and criteria should first be identified and ranked
(or weighted). If used within the context of national policies and processed within a
spatial decision support system, optimised routes that account for all stakeholder
concerns can be generated.
If every step of the process has transparency and simplicity built into it, any
stakeholder can review and retrace the steps that led to the generated results. This
will lead to greater stakeholder satisfaction and trust in the decision-making process,
and eventually, to a more positive and strengthened stakeholder involvement in the
process, in a transparent manner. The method to be developed in this thesis, aims at
achieving this merit. Such a method, when carried out in the context of an SEA will
provide a spatial decision support system (SDSS) tool that enables better
transportation.
The main aim of this thesis is to develop a coherent methodology for the formulation
of transport planning alternatives. In order to reach this aim the following table of
questions and objectives is formulated.
15
Su
mm
ary
of th
e research
pro
blem
s, qu
estion
s an
d o
bjectiv
es
Pro
blem
s
Ob
jectives
R
esearch
Qu
estion
s
Exp
ected O
utp
ut
Imp
act assessmen
t of o
nly
som
e
po
rtions
of
the
Go
vern
men
t
preferred
route w
as carried o
ut.
T
o
determ
ine
what
criteria w
ere
used
to
fo
rmulate
the
preferred
alternativ
e.
To
iden
tify an
d u
se these an
d o
ther
imp
ortan
t criteria that sh
ould
hav
e
been
consid
ered.
a)
What facto
rs and
constrain
ts
were u
sed to
com
e up
with
the p
rop
osed
alternativ
e?
b)
What
oth
er im
po
rtant
criteria sh
ould
hav
e b
een
consid
ered?
a)
Iden
tification
of
criteria co
nsid
ered
by
the P
olish
Go
vern
men
t.
b)
List
of
po
ssible
transp
ort,
ecolo
gy,
econo
my,
social
and
technical
factors
to
be
consid
ered.
GIS
is no
t wid
ely u
sed in
EIA
s
and
S
EA
s. L
ack o
f data, co
sts
and
lack o
f exp
erience m
ight b
e
a reason.
T
o u
se remo
te sensin
g im
agery
(or
data
based
o
n
it) to
allev
iate th
e
pro
blem
o
f lack
o
f sp
atial
info
rmatio
n.
Is it p
ossib
le to co
mp
ensate fo
r
gap
s in
in
form
ation
by
usin
g
remo
te sensin
g (R
S) im
agery
?
Id
entificatio
n o
f criteria
that
can
be
inferred
from
R
S
imag
ery
(or
data b
ased o
n it).
No
altern
ative
route
op
tions
iden
tified.
T
o
form
ulate
differen
t p
olicy
visio
ns.
To
use
constrain
ts, co
sts an
d
ben
efits traditio
nally
gen
erated fo
r
an E
IA, in
a SM
CE
to in
fer routin
g
suitab
ility v
alues.
W
hat p
olicy
visio
ns are relev
ant
to co
nsid
er?
Ho
w
can
SM
CE
b
e used
to
prep
are a contin
uo
us g
eograp
hic
surface
that
sho
ws
routin
g
suitab
ility v
alues?
R
outin
g
Suitab
ility
Map
s fo
r th
e vario
us
po
licy
visio
ns
consid
ered.
16
The
use
of
GIS
bas
ed N
etw
ork
Anal
ysi
s as
a k
ey c
om
po
nen
t o
f
a S
pat
ial
Dec
isio
n
Sup
po
rt
Syst
em
is
no
t ad
equat
ely
exp
lore
d.
T
o
use
a
GIS
-bas
ed
Net
wo
rk
Anal
ysi
s in
co
mb
inat
ion
wit
h
Ro
uti
ng
Suit
abil
ity
Map
s to
form
ula
te o
pti
mal
ro
uti
ngs.
To
co
mp
are
op
tim
al r
outi
ngs
wit
h
the
Go
ver
nm
ent
pre
ferr
ed r
oute
.
a)
W
ith
refe
rence
to
th
e
consi
der
ed
po
licy
vis
ions,
are
ther
e an
y
alte
rnat
e
“unse
en”
route
s w
hic
h m
ay
be
bet
ter
op
tio
ns?
b)
If s
o,
ho
w d
o t
hes
e o
pti
mal
op
tio
ns
and
the
Go
ver
nm
ent
pre
ferr
ed
route
co
mp
are,
consi
der
ing
bio
tic,
ab
ioti
c
and
so
cio
-eco
no
mic
imp
acts
?
Op
tim
al ro
uti
ngs
und
er
the
consi
der
ed
po
licy
vis
ions
Quan
tita
tive
and
qual
itat
ive
com
par
iso
n
of
the
routi
ngs.
17
1.5. Requirements of the method to be developed
In order that the method gains acceptability amongst stakeholders, investors and
current practitioners of EIA and SEA formulation, this author feels that the following
requirements are important to the method.
1) Needs to be holistic and cross-disciplinary in its approach and should be
capable of addressing the whole range of criteria relevant to the above
mentioned targeted groups. It should also be amenable to addition of other
criteria not included in this case study.
2) Should be developed in such a way that the basic “backbone” of the method
can serve as the basis for use of the method in other locales and/or other
transport projects.
3) It should be uncomplicated, transparent, back-traceable and capable of
stakeholder involvement.
4) It should be user-friendly, cost-effective and time-efficient.
1.6. Thesis Outline
In Chapter 1, the practice of EIA and SEA in transportation planning has been
briefly introduced along with a background of the current scientific developments in
this field. Thereafter, the research intent of this thesis and the requirements of the
method have been presented.
Chapter 2 will go on to briefly describe the relevant physical, socio-economic and
ecological characteristics of the study area.
As mentioned in the research problems above, one of the principal reasons for non-
use of GIS in EIA and SEA is the cost associated with data procurement. A
deliberate effort has been made, in this study, to use datasets freely available in the
public domain to the greatest extent possible. Chapter 3 describes these and other
datasets used in this study.
Chapter 4 describes the method, enumerates the criteria used in this study, the
techniques used to obtain and process them from the datasets. In this chapter, this
author wishes to draw the special attention of the reader to the keystone process of
connecting the SMCA process to the network analysis in order to generate the
optimal routes.
18
Chapter 5 consists of the results of the SMCA, optimal routes for each vision (and
their quantitative parameters), quantitative comparison with the government
preferred route and results of the sensitivity analysis.
The results are discussed in Chapter 6 and conclusions drawn in Chapter 7.
19
2. Description of the study area
The study area for this thesis is almost the shape of an obtuse-angled isosceles
triangle, lying between (51.76N, 19.35
E) and (54.35
N, 23.81
E). It is
geographically located in, and almost covers, the whole north-eastern quadrant of
Poland. Thus it starts just south of Warsaw and ends at the Polish Lithuanian border
near Budzisko, covering the three provinces (woiwodships) of Podlasie, Mazowiecki
and Warminsko-Mazurskie. The average north-south length is 295 km and average
east-west width is 315 km. Figure 5, gives a schematic overview of the study area.
Figure 5: Schematic overview of the study area.
(Source data: Global GIS-Global Coverage DVD-2003)
2.1. Topography and soils
The topography of the study area is best described as “gentle rolling terrain”. There
are no steep slopes or sudden breaks in the terrain. The average elevation range of
the entire study area is 80-120m above MSL. The highest elevation is 300m (at the
protected Nadbuzanski landscape park near Warsaw) and the lowest elevation is 59
m approximately 50 km away. Hence from the perspective of highway planning,
slope regimes do not form a serious consideration in any part of this region. The
20
Figure 6 below, derived from a DEM of the area overlaid with land-cover, shows the
virtual flatness of the area.
Figure 6: A 3-dimensional perspective of the study area (with a vertical
exaggeration of 10 for clarity). The city of Warsaw is seen in the foreground and
Budzisko at the far end. (Source data: CORINE Land Cover 2000, IRS-DEM)
The soils range vastly from glacial soils and peat to fluvial soils. Peat strata are more
common in the northern part of the study area. As per Helena Bartoszuk, scientific
officer of the Biebrza National Park (BNP), borehole data reveals that the thickness
of the peat overburden ranges from 1.5 m to 6 m thickness. Peat fires are not
uncommon in this area and also it is forms a serious geotechnical concern during the
construction of the highway. Thus it forms one of the important analysis layers of
this thesis. Figure 7 (facing page) gives an idea of the extent of Peat soils in the area.
Other than peat, the usual concerns about clayey soils (prone to swelling and
shrinkage), poorly graded soils (prone to frost heave conditions) etc are expected to
be encountered. However soil maps representing engineering properties of soils are
not available. This author’s preliminary investigation shows that, though it has not
yet been attempted, such soil maps can be approximated from the European Soil
Database (ESDB) parameters. However, due to the limited time available for this
thesis, this aspect was excluded from the current study.
21
Figure 7: Spatial distribution of Peat strata over the study area.
(Source data: ESDB Raster Library)
2.2. Socio-economics
Amongst the three comprising the study area, the Mazowiecki province, which
contains the capital Warsaw is the most populated and economically potent region.
The region of Podlasie is the most impoverished. The socio-economics of this region
are governed by nature-tourism and the trade flowing through this region. Most of
the land in this region is agricultural pastures and in the form of small individual
holdings. Bialystok is the biggest (and most populated) city in the region followed by
Elk. A large sprawling and modern city, with a population of almost 300 thousand,
Bialystok is trying hard to attract foreign investment to its infrastructure and
industry. The Bialystok City Council’s development strategy is aimed at increasing
the importance of the city as an international economic centre. It is geographically
well placed for this ambition. But it is these growing ambitions of the Bialystok City
Council, as it lobbies to bring the Via Baltica close to it, that are seen by many
stakeholders as the root cause of the conflict.
22
2.3. Ecology
The study area hosts 4 National Parks, 12 Landscape Parks, 10 National Reserve
areas and numerous other unprotected and/or transitional woodlands of significant
ecological importance. In its close proximity are areas such as the Bialoweiza
National Park, which is reputed to be Europe’s oldest natural forest.
Many endangered plant species such as musk orchid etc are protected here. Amongst
the animal species, various endangered fauna such as lynx, wolf, elk, brown bear,
European bison, fire-bellied toad, natterjack toad, tree frog and great crested newt
inhabit the national parks and the co-formed ecological corridors. Endangered avi-
fauna, protected under the Ramsar convention, such as the greater spotted eagle,
lesser spotted eagle, corncrake, great snipe, aquatic warbler, black cormorant and
capercaillie also form protected species found in this area. A full list of the
endangered species sorted by species can be found at
http://natura2000.mos.gov.pl/natura2000/en/gatunki.php.
Due to such a rich heritage, the area is rich in its ecological bio-diversity and has
earned the sobriquet of “Green Lungs of Poland”.
2.4. Existing transport network
The UNECE transport assessment report for 2006 remarks that Poland lacks a
coherent network of motorways and expressways, which could link major cities and
industrial areas. The quality of existing roads cannot handle growing number of cars
and traffic volume. This is undeniably observed even on the main motorways. A
typical situation on extensive sections of the Warsaw Budzisko road is seen in the
photos below. Moreover the pavement of large part of Polish roads is not suited for
heavy loads in freight transport – only 5% is suited for 115 kN axle load.
Figure 8: Typical situations on national motorways in the study area.
23
Though better developed rail networks exist in the southern and western part of
Poland, in the north-eastern part (in the study area) much of the railway network is
so underutilised that there are even bushes overgrowing some sections of the railway
tracks. Due to poor service, timings and many changes required, this is the least
preferred mode of transport in this region. The UNECE report and the Rail Baltica
report authenticate this account, “…the quality of railway network is insufficient,
resulting in lower competitiveness of rail transport. Only 2,300 km allows the speed
of 120 km/h or higher. Inadequate infrastructure hinders also development of
seaports and airports…Rail transport still characterises by low competitiveness and
services quality. At the same time it absorbs enormous public funds. Modernisation
of Polish State Railways is a huge strategic challenge for the Polish government.”
24
3. Data description
The data used in this thesis can be broadly divided into three categories: Remotely
sensed (satellite) imagery, field collected data and GIS datasets (which consists of
data derived from remote sensing and/or field measurements and surveys). The
following sub-sections describe this in detail.
3.1. Satellite Imagery
ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)
imagery: Five ASTER images dated 4th
July 2006 and five more dated 11th
July 2006
covering the area of (51.92° Lat, 21.73° Lon) to (54.16° Lat, 23.26° Lon) were
obtained in HDF format from ASTER-GDS website. Ortho-rectification and
mosaicking of the images was done using Erdas Imagine software. As the study area
is very vast, and impossible to tour in its entirety given the limited time span, these
images formed the “ground-truth” data against which accuracies of data and analysis
were checked.
Figure 9: Processed and mosaicked ASTER imagery of the study area.
(Date of acquisition: July 2006)
DMSP-OLS Radiance-Calibrated night light satellite imagery: As is usual in
developing countries, spatially explicit socio-economic data of the study area was
not available. However it has been proven that extent and radiance intensity of
nocturnal lighting have a high correlation with socio-economic indicators such as
25
economic activity, GDP, and energy & electricity use (Welch & Zupko, 1980;
Elvidge et al., 1997; Gallo et al., 2004). Hence the radiance-calibrated night light
satellite imagery was used as a proxy indicator of the magnitude of socio-economic
activity in the region.
Figure 10: Processed DMSP-OLS Radiance-Calibrated night light satellite imagery
of the study area. (Date of acquisition: 2003)
3.2. Field-collected data
GPS data was recorded on field using an HP iPAQ hx4700 Pocket PC and a
Bluetooth enabled Emtac BTGPS on the ArcPad 6.03 platform. The entire length of
the study area was traversed by car and continuous GPS tracking done for all major
national roads to and from Warsaw to Augustow, via Lomza, Bialystok, Suwalki and
other major towns. Points where two major roads over-fly each other without
intersecting were taken note of, as being relevant to the network analysis. Further,
some boundary points of two national parks were also recorded using GPS. In
addition to the ASTER images, the GPS collected data was used for verification of
the data obtained from various sources. They were found to tally with each other,
with differences ranging from 0.5m to 3.5 m maximum.
The field trip was also used to acquire the opinions, preferences and concerns of
various stakeholders such as environmental organizations, environmental experts,
geographers and other citizens. Also their expectations from the Via Baltica were
enquired into. Current levels of development of industry and agriculture in the area
were also noted with the help of local experts (See Section 8.3).
26
3.3. GIS Data
Due to the cross disciplinary nature of the methodology, a diverse variety of data
sources were accessed. A brief description of the datasets is given herein.
CORINE Land Cover Map 2000: The CORINE Land Cover Map of Poland project
is part of a Europe wide initiative of the European Environmental Agency. Along
with Polish scientific organizations, land cover maps of the whole of Poland were
generated using ground validated satellite images. The CORINE Land Cover
inventory is based on Landsat ETM+ satellite images. Land cover is mapped in 44
classes with a resolution of 25 ha. Land cover changes are detected at a resolution of
5 ha. The CORINE map of Poland used in this thesis was completed in July 2004. It
uses the Polish National Coordinate Reference System "PL_EUREF89/1992". The
Level 3 vector version of this land cover map which is at a spatial resolution of
higher than 100 m was used in the analysis.
(www.igik.edu.pl/clc_final_report_pl.pdf)
European Soil Database (ESDB) v 2.0 raster and vector libraries:
The ESDB project is implemented by the Land Management and Natural Hazards
Unit, a part of the European Commission’s Joint Research Center. The ESDB v2.0
comprises of the SGDBE and PTRDB databases which enlist a total of 73 soil
attributes. These attributes can only be used as the building-blocks from which the
parameters required by the user have to be derived by raster calculations or vector
overlays. All ESDB data are in the ETRS89 Lambert Azimuthal Equal Area
(ETRS_LAEA) co-ordinate projection system. This data was used for inferring best
agriculture areas, peat areas and areas with problem soils for construction.
(http://eusoils.jrc.it/ESDB_Archive/ESDB_data_1k_raster_intro/ESDBv2_1K_raste
rs.htm)
LandScan data: This is a global population database at <1km resolution developed
by the United States Department of Energy’s (USDoE) Oak Ridge National
Laboratory (ORNL). It was primarily developed as part of the US initiative on
homeland security against natural disasters and terrorism threats. It was subsequently
expanded to a global scale. It uses census counts and refines them by using a
population distribution model based on 6 primary geospatial datasets viz. roads,
slope, night-time light imagery, exclusion areas, urban density factors and land-
cover. Most national censuses are concerned with population based primarily on
where people reside rather than where they work or travel. LandScan distribution
represents an ambient population which integrates diurnal movements and collective
travel habits into a single more effective measure. Thus, since it is better indicator of
27
population distribution than purely an administrative division census count, it was
the preferred data choice for this thesis. Version 2004, which is the latest version, at
the time of commencing this analysis, was used for this thesis.
(http://www.ornl.gov/sci/landscan/landscanCommon/landscan_doc.html) The
original data was in geographic coordinate system WGS 84.
World Database of Protected Areas (WDPA): The WDPA provides the most
comprehensive dataset on protected areas worldwide and is managed by UNEP-
WCMC in partnership with the IUCN World Commission on Protected Areas
(WCPA) and the World Database on Protected Areas Consortium. The WDPA is a
fully relational database containing information on the status, environment and
management of individual protected areas. The 2006 version of the WDPA data was
used. This version included all the shape and attribute data for designated nationally
protected areas of IUCN categories I to VI, designated nationally protected areas
without an IUCN Category, and areas defined under international conventions and
agreements. This was thus the most authoritative source on officially recognized
boundaries of protected areas, which were required in the analysis in this thesis.
(http://www.unep-wcmc.org/wdpa) The original data was in geographic coordinate
system WGS 84.
Global GIS: Global Coverage DVD (2003): The Global GIS database DVD is a joint
initiative of the United States Geological Survey (USGS), American Geological
Institute (AGI) and ESRI Inc. It contains global coverages of administrative
divisions, places, elevation, land-cover, seismicity, and resources of minerals and
energy at a nominal scale of 1:1 million. This data is too coarse for use in this thesis
hence only the country map, province map and place names used in this thesis were
derived from this data source. The original data was in geographic coordinate system
WGS 84.
Annual Average Daily Traffic (AADT) counts and Strategic Environmental
Assessment report: These data were obtained from the General Direckja Drog
Krajowych I Autostrad (GDDKiA) which, as discussed earlier, are the implementing
government agency for the Via Baltica project in Poland. The AADT 2005 data was
provided in the form of a segmented multi-page photocopied paper map with traffic
counts printed above its corresponding road section. These counts were manually
input into the road network shape file by graphically selecting the portions of the
road referred in the paper map and adding the printed traffic count to the attribute
table of the selected portions. It must be stressed here that great care has been taken
to ensure accuracy of input.
28
SEA Report: An electronic PDF copy of the SEA report (version 1), prepared by
M/s Scott Wilson Ltd Sp. z o.o., was provided to the author by the GDDKiA. The
document is in Polish only and was translated to the author by Dr. Katarzyna
Dabrowska-Zielinska, Head of Remote Sensing Department-OPOLiS, Institute of
Geodesy and Cartography (Poland).
Ministry of Environment-Poland: The Ministry was contacted to gain insight into
Polish policies, Natura 2000 data and submissions by the Ministry of Infrastructure
till date. The results of these discussions were used at various places in this thesis.
Data from Geosystems Polska: This organization, a subsidiary of Leica Geosystems
Inc., must be noted as being the most significant Polish source of accurate and
comprehensive GIS data on roads, DEM, rail and park boundaries used within this
thesis. All digital data which was obtained from this firm, when verified for accuracy
against the ortho-rectified ASTER images and field verification data, proved to be
accurate to within 2-3 metres.
3.4. Accuracy statement of the data
All data for this thesis was acquired from established, authoritative and reputed
sources. Despite this, most data were cross-verified for quality and accuracy before
inclusion in the analysis. The hierarchy used for quality standardization was:
1) Satellite imagery (ASTER and DMSP-OLS)
2) Field collected data
3) CORINE land cover map 2000
For data such as boundaries of protected areas,
1) WDPA
2) Ministry of Environment
As per the ESDB metadata the ESDB dataset has a positional accuracy of 0.5 - 5 mm
at scale 1:1,000,000. For thematic accuracy all polygons representing areas above 25
km2 have been corrected against the original soil map.
Other data such as LandScan data and AADT counts were not verifiable and hence
were accepted as provided, with the knowledge that they are being provided by
reputed, standardised sources.
29
4. Method Description
A brief overview of the principles of the method and its requirements were discussed
in Chapter 1. In pursuance of these principles a conceptual diagram of the method is
shown below.
This chapter will lead the reader through a description of each of the components of
the diagram and summarise the entire method in the form of a detailed work
flowchart at the end.
4.1. Identifying assessment criteria
As seen in Section 1.2.3, the criteria considered by the GDDKiA (and its
consultants) are extremely limited, in number as well as in quality. To add to these
criteria, stakeholder perceptions and concerns were enquired into through personal
communication (Section 8.3) and listed. Thereafter, personal experience as a
highways & bridges engineer was drawn upon in listing relevant technical criteria.
Though every effort was made to incorporate all decisive criteria, certain specialised
aspects such as engineering properties of soil (e.g. frost-heave susceptibility,
expansive soils etc) could not be considered due to lack of data availability.
30
These criteria are divided into groups based on four main themes. They are the input
for the SMCE analysis further in the process. Each criterion will be represented by
its own raster map. The raster dataset thus consists of maps of the following criteria:
THEME CRITERIA PURPOSE
Proximity to PKP rail
network
The closer the expressway is
built to an existing rail
network, the better the future
intermodality.
Proximity to the Rail
Baltica
The Rail Baltica is expected
to be built shortly. The closer
the expressway is built to the
proposed route, the better the
future intermodality.
TRANSPORT
Current traffic density
(AADT) Spatial benefitαααα . The higher
the current traffic density, the
more is the reason to upgrade
the road.
Internationally protected
natural areas:
Natura 2000 sites
Spatial constraintββββ . Natura
2000 sites are strictly
protected under EU
regulations 79/409/EEC and
92/43/EEC (1).
ECOLOGY
Nationally protected
areas:
National Parks,
Landscape Parks and
National Reserves
Spatial costγγγγ . May be passed
through at a high cost.
α Spatial benefit is defined as a criterion that contributes positively to the output; the
more you have (the higher the values), the better it is.
β Spatial constraint is defined as a criterion that determines, in the calculation of the
main goal, areas which are considered as absolutely not suitable. These areas will
always obtain value 0 for that pixel in the final output. γ Spatial cost is defined as a criterion that contributes negatively to the output; the
less you have (the lower the values), the better it is.
31
Forests & semi natural
areas
Spatial cost.
Wetlands & peat bogs Spatial cost.
Water courses & lakes Spatial cost.
Urban Areas Spatial benefit. However, the
closer the route is to an urban
area, the greater the social
convenience.
Urban Areas Spatial cost. The closer the
route is to an urban area, the
greater are the incidences
where resettlement of homes
and establishments will be
required.
Population served Spatial benefit. The higher
the population served, the
more is the reason to upgrade
the road.
SOCIAL & SAFETY
Hazardous areas Spatial cost. The closer it is
to a hazard prone area, the
more will be the cost
associated with providing
safety features.
Current agriculture land-
use
Spatial cost. Current
livelihood.
Economic zones Spatial benefit. The more the
economic activity in the area,
the more is the reason to
upgrade the road.
Best agricultural soils Spatial cost. Potentially
productive areas. ECONOMY
Current status of the road Category of the road (see
section 4.2.2). The higher the
current category of the road,
the lower will be the
engineering cost of upgrading
it. Hence prefer higher
category roads.
32
Intersections with
perennial or seasonal
water bodies
Spatial cost. Intersections
with water courses and water
bodies, involves the
construction of expensive and
time consuming structures
such as bridges, culverts,
causeways etc. Moreover, the
longer the bridge, the higher
the cost.
Intersections with
secondary roads
Spatial cost. For roads built
to expressway standards, all
intersections with secondary
roads need to be upgraded.
This involves the
construction of expensive and
time consuming structures
such as flyovers, turnpikes,
clovers etc. Moreover, the
longer the intersection, the
higher the cost.
Problem soils for
construction
Spatial cost. As mentioned
briefly in section 1.3.1, soils
like peat are prone to
differential settlement and
pose a potentially high
construction cost and/or a
high maintenance cost. If
possible, such areas should be
avoided else, costs of
mitigation should be
accounted for.
ECONOMY (contd.)
Urban Areas The closer the route is to an
urban area, the higher will be
the engineering costs
associated with building
acoustic barriers and ancillary
structures for pedestrian
safety
33
4.2. Pre-processing
In order to acquire all the data needed for the SMCA analysis, remote sensing
imagery, vector data and raster data had to be processed.
4.2.1. Pre-processing of Remote sensing imagery
Ortho-rectification and mosaicking of the ASTER images was done using established
procedures in Erdas Imagine 8.7 and Leica Photogrammetry Suite 9.0.
DMSP-OLS night-time light images are available radiance calibrated and only had to
be re-projected to UTM-34N, the co-ordinate system used in this thesis. Thereafter
the portion relevant to the subject area was extracted by using a raster mask.
4.2.2. Pre-processing of Vector dataset (roads network)
The data to be preserved in vector format consisted of only one map layer: the road
network layer. This layer was based on a vector file provided by M/s Geosystems
Polska, which consisted of 7 categories of roads ranging from national roads to
unpaved village roads. Survey of the study area by the author, revealed that the lower
category roads are usually so narrow and of poor structure that upgrading them to
international expressway standards would be highly uneconomical. Further, the
spokesperson of GDDKiA, which is the government department in charge of
construction of the Via Baltica in Poland, confirmed that they will consider only the
four most important road categories for route selection. This is reflected in the SEA
report prepared by M/s Scott Wilson Kirkpatrick. Hence from the Geosystems
dataset, only the following four categories of roads were considered for analysis. The
official terminology for the various categories of roads in this dataset are as below
(most important category first).
Polish name Indicative type Category
1. Droga Jednojezdniowa Glowna Main road, single
carriageway
1
2. Droga Jednojezdniowa Drugorzedna Secondary roads, single
carriageway
2
3. Droga Dwujezdniowa Glowna Main road, dual
carriageway
3
4. Droga Dwujezdniowa Drugorzedna Secondary road, dual
carriageway
4
M/s Geosystems uses this data in the preparation of their GPS-based car navigation
systems, brand named “AutoMapa”. In pursuance of their objectives, every road
consisted of small segments of varying lengths. Though positionally accurate and
34
holding many relevant attribute fields, this data was unusable as-is in this network
analysis due to its incomplete/incompatible topology. Processing the data to render
usable topology was initially started in ArcGIS but was found to be very tedious,
time consuming and error prone. Hence, the data was imported to AutoCAD Map 3D
2007, which being a CAD-based GIS software has very efficient tools for this
particular process.
Within AutoCAD, all small individual segments comprising of a road from one
junction (node) to the next junction (node) were combined into a single polyline by
use of the PEDIT tool in AutoCAD. Thereafter the drawing clean up tool was used
with appropriate tolerances to polyline any segments that were missed, delete
duplicates, fillet lines that do not meet, trim at edges, delete extremely short objects,
adjust roundabouts etc. After this topology rectification process, the data was
exported to a shape file and re-imported back into ArcGIS.
4.2.3. Pre-processing of Raster datasets
Except for the raster maps of Natura 2000 sites and nationally protected areas, none
of the raster layers were ready for use right away. Most of the raster layers were
derived by applying an appropriate GIS processing method to one or more vector or
raster data sets. Since some of the processes are an atypical use of such data, a brief
description of the processes is provided below.
Thematic
raster layer
Process (in brief)
Distance to
PKP rail
Imported rail network layer into ILWIS. Used “Distance
calculation” command.
Distance to
Rail Baltica
Selected segments from PKP rail network, based on Rail Baltica
feasibility report. Imported into ILWIS and used “Distance
Calculation” command.
Current
Traffic
density
Input numerical data from paper document provided by GDDKiA
into road network shape file in a field count_2k5. Imported into
ILWIS and rasterised based on count_2k5 value.
Natura 2000
sites
Ready vector data from WDPA database, only needed to be
reprojected, clipped and rasterised.
Nationally
protected
areas
Ready vector data from Geosystems, only needed to be reprojected,
clipped and rasterised.
35
Forests &
Semi-natural
Areas
Derived from reprojected, clipped CORINE 2000. Selected
categories 3.1.1 to 3.3.4 and exported them to a separate shape file.
Rasterised in ILWIS based on subclasses.
Wetlands &
Bogs
Same as above, except selected categories 4.1.1 and 4.1.2.
Water courses
& Lakes
Same as above, except selected categories 5.1.1 and 5.1.2.
Resettlement
in urban areas
Ready vector data of urban areas, only needed to be reprojected,
clipped and rasterised.
Safety in
urban areas
Used above urban area map with Distance calculation command of
ILWIS
Peat areas
(Fire hazard)
Ready data from ESDB only needed to be reprojected, clipped and
rasterised based on field Peat.
Population
served
Used the rectified and complete road network layer overlaid on the
LandScan data image. Then:
Buffered the polyline network (flat ended, no dissolve, 10km)
Added field in the buffer layer of type single. Called it Code.
Calculated field Code=FID+1. Ran Spatial analyst > Zonal
statistics (summation) for buffer layer vs Population raster layer.
Used Hawth's tools> Analysis> line raster intersection statistics
(Line Weighted Mean). The layers used here are the original
polyline network layer vs. Zonal statistics layer. Created a new
field and calculated it using formula POP=LWM/LENGTH.
Rasterised the line segments using POP as the field.
Current
Economic
activity
Same as above except, used the re-projected DMSP-OLS Night-
time Light image and road network layer.
Potentially
best
agriculture
areas
Created by selecting areas where the following fields of ESDB data
have the following values:
AGLIM=1; (No limitation to agricultural use)
ROO=1; (No obstacle to roots upto 80cm depth)
3<WMI<7; (Existing water management systems)
2<TXSRFDOM<5; (Dominant surface texture of soil)
USEDOM= 1,3,6,7,12,13,14,15,16,17,19; (Dominant land use)
USESEC=1,3,6,7,12,13,14,15,16,17,19; (Secondary land use)
Existing agri-
zones
Same as above, except selected categories 2.1.1 and 2.4.3.
36
Construction
in urban areas
Used above urban area map with Distance calculation command of
ILWIS
Intersections
needed
Nodes of rectified network layer
Road category Rasterised in ILWIS based on field “road status”.
Bridges
needed
Intersection of road layer with water bodies and water courses. This
gives length as well as number. Buffered these line segments to
100m and resampled to 1000m.
Construction
on Peat
Ready data from ESDB only needed to be reprojected, clipped and
rasterised based on field Peat.
Since ILWIS requires that all raster overlays used in the SMCA have the same pixel
size, a pixel size of 1000m was chosen for rasterizing all the layers. This was done
for 3 reasons:
1) A road layer in a vector represents a shape having no lateral dimension,
whereas in real life a road does have width. Moreover environmental
effects are felt more in the width direction, than along the length. Hence
the width dimension is very important to the analysis. Referring to Polish
road impact studies, i.e Cyglicki (2005), and personal communication
with Polish EIA experts, it was found that the minimum direct impact
distance under the EU’s Special Protection Act Birds directive, for
existing roads upgraded to expressway status is 500m from the centreline
of the road.
2) Only 2 percent of all the road segments used in this analysis are less than
1km in segment length, hence this will not cause a significant error in the
analysis.
3) Of the three raster sources used in this thesis viz. Landscan, and Night-
light imagery data come with a pixel resolution of 994m and ESDB with
a resolution of 1km, hence the no major accuracy loss occurs during re-
sampling, thus avoiding issues of data smoothing.
4.3. Weighting of criteria and themes
As seen in the conceptual diagram, the weighting process consists of two sub-
processes. The first step involves stakeholder priorities & expert knowledge,
wherein weights were applied to each class within a criterion and then to each
criterion within a theme.
Thereafter, with reference to stated National Policy documents, EU regulations
and with the assistance of Polish & Dutch environmental & EIA experts (See
section 8.3), four main policy visions were formulated for use in this thesis.
37
These were generated using the “Expected Value Ranking method” in ILWIS
and are as below:
1) Equal vision
Transport efficiency 0.25
Ecology criteria 0.25
Social and safety criteria 0.25
Optimise economic criteria 0.25
2) Economy vision
Transport efficiency 0.27
Ecology criteria 0.06
Social and safety criteria 0.15
Optimise economic criteria 0.52
3) Ecology vision
Transport efficiency 0.27
Ecology criteria 0.52
Social and safety criteria 0.15
Optimise economic criteria 0.06
4) Social and safety vision
Transport efficiency 0.27
Ecology criteria 0.06
Social and safety criteria 0.52
Optimise economic criteria 0.15
4.4. Spatial Multi-Criteria Assessment
In this section the use of SMCA to prepare a continuous geographic surface wherein
each pixel shows the overall suitability for routing the expressway, is elaborated for
the four policy visions described in the previous section. This proposed surface is
similar by nature, but antonymous in meaning, to a friction map (Yusof & Baban,
2004) or an impedance map (Grossardt et al, 2001).
As ILWIS 3.3 has a strong SMCE module, this part of the analysis was carried out in
ILWIS. The following methods were used to prepare the raster datasets for use in
ILWIS. Depending on the data type, one of the following methods was chosen.
38
a) The original (derived) vector datasets from which the raster layers were
created were imported into ILWIS. Then a new georef file (and a co-
ordinate system file) was prepared in ILWIS which would act as the
common geo-ref file for all raster datasets. Then, the derived vector layer
was rasterised using the common geo-ref file within ILWIS.
b) The other method (used for data maps which were not prepared from a
vector layer but from another raster layer) was to first project the .IMG
raster map in ArcGIS to some other coordinate system which used the same
datum. Thereafter, the .IMG was imported into ILWIS. This raster was then
re-sampled to the common georef file, using the Nearest Neighbour method.
Based on the thematic groups, factors, constraints and weights identified in tables in
the previous sections, a criteria tree was built in ILWIS for each policy vision. Each
criterion is represented by a map. Once all criteria and maps were inserted in
position in the criteria tree, standardization of all the criteria was done using either
“Attribute”, “Goal” or “Maximum” method, depending on the type of data
represented in each criterion. In standardisation all the input maps are normalised to
utility values between 0 (not suitable) and 1 (highly suitable).
An example of a completed criteria tree for economy vision is seen in Figure 11.
Figure 11: A screenshot of the completed SMCA table in ILWIS
39
This process resulted in four maps, one for each policy vision, showing the suitable
locations. These four maps were then exported to .IMG format from ILWIS for
further analysis. They were then opened in ArcGIS where they had to be re-projected
to UTM-34N (the projection used throughout this study), to be used in the next
phase - Network Analysis to generate optimal routings.
4.5. Network Analysis
In this section the values of the suitability maps prepared in the previous process
were attributed to a network. Based on these values, impedances were calculated
and appropriately assigned to the network. Then a network analysis was
performed to find the path of least impedance. The route that was obtained
assumed the best advantages of a spatial multi-criteria decision making system
and network analysis.
4.5.1. Preparation
To commence this stage of the analysis, the suitability maps of the four visions, and
the pre-processed road vector layer were added to a new ArcGIS project. The
Analysis Tools-> Line Raster Statistics tool of the “Hawth’s tools” extension of
ArcGIS was used to extract the line weighted means (LWM) from each resultant
map to the road vector layer. This attributed the mean (suitability) value of each
resultant vision to each segment of the line layer based on its location.
Beyer (2004), creator of the Hawth’s tools extension, defines Line Weighted Mean
by the following equation:
( )
Line Weighted Mean ( ) =
n
i i
i=1
l v
LWML
∑………………………(1)
Where,
li is the length of a segment i,
vi is the suitability value of the raster cell underlying that segment, and
L is the total length of the polyline of which this segment forms part.
40
Figure 12: Tool used to inherit suitability values from the vision maps to the road
network (line) layer and the resultant attribute table.
However the object of this network analysis is to find the path of least cost (least
resistance). Hence, all these obtained values were inverted by subtracting them from
1. Since the pixel size in this case is 1000m (1 kilometre), this then gives the
“impedance per kilometre” of road. In order that the total impedance of each
segment (from node to node) is obtained, the “impedance per kilometre” value was
multiplied by the corresponding length of the line in kilometres. It was these value
fields that were then used as vision-impedances to build the network in the ArcGIS
41
Network Analyst. Using the LWM values, Impedance (Ω) of each polyline within the
road network layer is then formulated as:
(1 )LWM LΩ = − × …………………………………………………….(2)
Thereafter, the network was solved in ArcGIS for all 4 visions viz, equal vision,
economy vision, ecology vision and social vision consecutively using equal-vision-
impedance, economy-vision-impedance, ecology-vision-impedance and social-
vision-impedance respectively. Thus four routes, having the same origin and
destination, were generated. The total impedance accumulated by each route can be
seen in the ArcGIS network window, defined within this method as Total Route
Impedance (ΩR), and was noted. Mathematically the total route impedance can be
expressed as:
RΩ = Ω∑m
j
j=1
…………………………………………………………….(3)
Where,
Ωj= Impedance of polyline j, and
m= number of polylines comprising the optimal route.
The higher the ΩR value, the greater are the costs associated with the route and/or the
lower are the benefits reaped by it. Each of these four optimal routes, their lengths
and Total Route Impedances are described further in the Section 5.1.
4.5.2. Building the Network
In the ArcGIS Build Network dialog box, the connectivity, elevation and turn
settings were maintained at default. Thereafter four new attributes were added,
namely, social vision impedance, equal vision impedance, economy vision
impedance and ecology vision impedance. These attributes are of the type “cost” and
“double”. Then using the “Evaluators” button, in the same dialog box, the source
data of each of these attributes, were allocated as “Constants” corresponding to each
Ω field as obtained in the previous section.
With these settings, the “Build network” command was executed and the final built
network obtained.
42
4.5.3. Solving the Network
It is noted here that Warsaw is chosen as the “Origin” and Budzisko as the
“Destination” for this study. For this purpose, a map of the expected (and proposed)
development of the road infrastructure of Poland in the next 10 years was obtained
from the GDDKiA. The map, reproduced in Figure 13, shows that all major
development is planned considering that the intersection of 6 future corridors will
occur at Warsaw.
For that reason, the centre of Warsaw is chosen as the origin, in this study. However
this is done solely for the reason that it lies at the centroid of the city of Warsaw. It is
anticipated that as the corridors are developed, Warsaw city authorities will
eventually have to build a ring road (of similar expressway standards) on the
periphery of the city, connecting all the corridors. But since, at the moment, these
corridors do not exist, the centroid as a point origin is a valid and convenient
assumption. For the selection of destination, it was found that there are four border
control points on the Polish-Lithuanian border, viz. Budzisko, Ogrodniki, Berżniki
and Kuznica. However, it is the unanimous choice of most stakeholders, planners
and NGOs that Budzisko should be preferred. In fact, all documents accessed by the
author reflect this preference only. Hence, Budzisko was taken as the destination.
Figure 13: 10 year expressway development plan of Poland showing the various
Transport Corridors (Source: M/s Scott Wilson Kirkpatrick’s Via Baltica website.
http://www.viabaltica.scottwilson.com.pl/)
43
It may be noted however, that mathematically speaking, this is a global model and
the suitability rasters are a continuous geographical surface, hence the model will
hold good for any node, anywhere on the network, to be chosen as either origin or
destination.
Using the origin and destination above and the appropriate impedances, one after the
other, the “Solve network” command was executed for each of the 4 visions, thus
yielding 4 optimal routes. The properties of each optimal route show the numerical
values of total route length and the total (accumulated) route impedance (ΩR) of each
generated route.
4.5.4. Assessing the Government preferred route
Though this author is against the practice of predetermining of routes and then
assessing their impacts subsequently, this methodology also supports (as seen in this
section) the assessment of predetermined routes. This procedure is elaborated here
purely to demonstrate the effectiveness of this method in opposition to the existing
method of predetermining routes, by comparing the outcomes of the two methods.
To commence this assessment procedure, the same built network (Section 4.5.2) was
used, but with minor modifications to the procedure mentioned in Section 4.5.3.
An additional “Stop” was created at Bialystok, and the “Solve network” command
executed again. This procedure generated routes through Bialystok. Finer
manipulation of this route, so as to exactly duplicate the Government preferred route,
was done by the use of “Barriers” in the network. Once the Government preferred
route was exactly duplicated in each vision, the properties of the route were
inspected (in each vision) and the numerical values of total route length and the total
(accumulated) route impedance (ΩR) of the route in each vision were recorded.
Thus the results of Section 4.5.3 and Section 4.5.4 could be quantitatively compared
as will be shown in the results (Section 5.3).
4.6. Sensitivity and uncertainty analyses
Sensitivity analysis was performed to examine the accuracy of the ranking results
calculated with a multi-criteria method for uncertainties in the assigned weights and
effect scores. In order to do a sensitivity analysis, the following steps had to be
carried out:
1) Create effects table.
2) Standardisation
3) Weight assignment
4) Ranking using MCA weighted summation
44
5) Sensitivity analysis
Uncertainty
Sensitivity
It will also examine how the assignment of scores and weights affects the variability
of the output ranking results. Thus it provided information on which criteria are the
most influential in the output routes. Due to time, language and logistics constraints,
a formal stakeholder solicitation was not performed as in a real-life scenario. Hence,
subjective bias of the experts consulted may have percolated the weighting process
and therefore eventually, the results. Hence the uncertainty analysis provides not
only an indication of the probability that the results will remain the same when such
a process is carried out in a real-life scenario, but also, verifies the effect of possible
data inaccuracy.
After the resultant routes for each version were generated, they were exported to a
different shape file (and ArcGIS project). They were then buffered, and the buffer
layer overlaid on original layers (component data layers of the SMCA) viz. current
agriculture zones, protected nature areas, forests, wetlands, settlement zones etc.
Thus the areal extents of affected regions for each criterion were quantified (scores).
These scores were then transferred individually into the effect table prepared in
Definite 2.0 (a non-spatial decision support software), and the MCE carried out to
determine the sensitivity and uncertainty of the scores and weights (Janssen & van
Herwijnen, 1994). The problem definition matrix showing the criteria scores used in
Definite is presented in Section 5.4.1.
The entire process of this methodology is summarised pictorially in the flow chart in
Section 4.9.
4.7. Software used
As mentioned in the sections above, a variety of CAD, Remote Sensing and GIS
software was used in combination for the processing and analysis of the data. These
are:
b) ArcGIS 9.1
c) Erdas 8.7 and Leica Photogrammetry Suite 9.0
d) AutoCAD Map 3D 2007 (trial version)
e) ILWIS 3.3 academic
f) PCI Geomatica 10.0
g) Definite 2.0
h) Microsoft Excel
45
4.8. Difficulties faced
Few people in Poland speak or understand a second European language. As a result,
typical problems in communication were faced. Notable, however, was the problem
due to official websites being in Polish only. This when combined with the fact that
there are only three Polish-English online translation websites (and these are often
mal-functional), meant that a substantial resource was erratically unavailable for the
writing of this thesis. Other major problems were:
• An obvious and wide-spread official reluctance to disclose even “open”
data. This, very surprisingly, even extended to an international scientific
organization like the UNEP-GRID (Warsaw).
• Digital data being very hard to obtain access to.
• Data using many different projection systems (often with obscure
transformation parameters known only to a few).
• Quirks, limitations and cross-compatibility issues of the various GIS
software used in this thesis process.
46
4.9
. C
om
pre
hen
siv
e w
ork
flo
w-c
ha
rt o
f th
e m
eth
od
olo
gy
47
5. Results
5.1. Results of Spatial Multi-Criteria Assessment
Based on the criteria identified in section 4.1 and the spatial multi-criteria
assessment carried out in section 4.4, routing-suitability maps for each of the four
considered visions were generated. These are shown below in Figures 14 to 17.
Figure 14: Suitability map for equal vision
Figure 15: Suitability map for social vision
Figure 16: Suitability map for ecology
vision
Figure 17: Suitability map for economy
vision
48
A common feature visible in all the 4 suitability maps above is that the path along
the proposed Rail Baltica generally acquires very high suitability. In the social vision
map, it can be seen that urban areas are seen to have very low suitability. In the
ecology vision map, it can be seen that natural areas have lower suitability compared
to other areas, but other than Natura 2000 sites there is no area having zero
suitability. In the economy vision though, it can be seen that there are several areas,
other than Natura 2000 sites, which indeed do carry zero suitability values.
5.2. Results of Network Analysis
The route alternatives generated for the four visions are seen in Figures 18 to 21.
Figure 18: The Via Baltica
expressway: The Equal Vision Route.
Figure 19: The Via Baltica
expressway: The Social Vision Route.
Figure 20: The Via Baltica
expressway: The Economy Vision
Route.
Figure 21: The Via Baltica
expressway: The Ecology Vision
Route.
49
These four routes have the following resultant parameters:
Route Total
Route-Impedance
Total Length
(km)
Equal Vision Route 123649.75 304.97
Social Vision Route 132354.18 304.97
Economy Vision Route 93606.55 323.87
Ecology Vision Route 155823.24 313.10
From these results it can be summarised that:
• The Equal vision route and the Social vision have the same routing and thus
have the same length. However, the Equal vision route has lower total route
impedance than the Social vision route. These two visions have the lowest
total length out of all the four optimal routes.
• The Economy vision route and the Ecology vision route are similar to each
other for almost 75% of the path from Warsaw. However, at Grajewo they
separate. The Ecology vision route prefers to avoid the Augustow forest
region and proceeds via Elk, whereas the Economy vision route prefers to
go via Augustow. They again realign together at Suwalki and follow the
common path to Budzisko.
• The Ecology vision route has the highest total route impedance out of all
the optimal routes and the Economy vision route has the lowest. However
the Economy vision route has the greatest total route length of all the four
optimal routes.
The Government preferred alternative is shown in Figure 22 below.
Figure 22: The Government preferred alternative.
50
Analysing this route within this methodology revealed the following resultant
parameters:
Vision Total
Route-Impedance
Total Length
(km)
Equal Vision 140437.06
Social Vision 149698.39
Economy Vision 109497.92
Ecology Vision 173740.98
343.34
It can be seen from the above that the Government preferred alternative has the
highest total route impedance in the Ecology vision and lowest in the Economy
vision.
5.3. Comparison with Government preferred alternative
The four optimal routes generated under the 4 visions are compared with the
Government’s currently preferred alternative in Figures 23 to 25.
Figure 23: Government preferred route
(red) vs. Equal and Social vision routes
(blue).
Figure 24: Government preferred route
(red) vs. Economy vision route (blue).
51
Figure 25: Government preferred route (red) vs. Ecology vision route (blue).
Comparison of the impedances of the optimal routes vs. the Government preferred
route:
Equal
Vision
Social
Vision
Economy
Vision
Ecology
Vision
Optimal route 123649.75 132354.18 93606.55 155823.24
Government
preferred route 140437.06 149698.00 110032.71 174378.13
Comparison of the lengths (in km) of optimal routes vs. Government preferred route:
Equal
Vision
Social
Vision
Economy
Vision
Ecology
Vision
Optimal route 304.97 304.97 323.87 313.10
Government
preferred route 343.34 343.34 343.34 343.34
From these resultant parameters it can be seen that the government preferred route
has a difference of 20-40km compared to the four optimal routes, which will increase
the construction and operation costs. When the total route impedances are compared
across visions, it is seen that in every vision, the Government preferred route has
much higher impedance than its corresponding vision’s optimal route.
Graphs of these comparison tables are presented in Appendix 2.
52
5.4. Sensitivity Analysis
5.4.1. Effects table
The effects table in Definite 2.0, showing criteria, their cost benefit status and their
scores under each generated optimal route alternative is shown below.
Figure 26: The Effects table in Definite 2.0
5.4.2. Uncertainty analysis of weights and scores
If a 5% uncertainty is assumed for the weights of each criterion, it is found that
except for the social vision (Figure 27), the position of the routes in the ranking is
stable in all the other 3 visions. Further uncertainty increase might make the Ecology
route change position with the Economy route.
53
Figure 27: Social Vision: Ranking of routes if 5% weight uncertainty is assumed.
In the above graph, the diameter of circle indicates the probability that the route will
have the rank shown on X-axis. Thus, for example, the equal & social route has a
very high probability that, even with 5% weight uncertainty, it will continue to stay
at rank 3 and no probability that it will achieve rank 1 or 2.
Similarly, if a 5% uncertainty is assumed for the scores of each criterion, it is seen
that (Figure 28), the position of the routes in the ranking remains stable in all 4
visions. There is little chance of the social and equal route gaining top rank on
account of score uncertainty in any vision.
Figure 28: Social Vision: Ranking of routes if 5% score uncertainty is assumed.
54
It is also observed in the analysis that in 3 of the 4 visions, viz. the equal, economic
and ecology visions, no rank reversal occurs due to traffic count data (AADT).
Further that, in these 3 visions, upto 20% uncertainty in the traffic count data can be
sustained without rank reversal occurring.
5.4.3. Sensitivity analysis of weights using results of ranking
For ecology vision and economy vision, no rank reversal occurs within 10% of the
original weight.
For social vision:
a) For "Weight Ecology Affected" the Economy Route will lose rank to
Ecology Route at value 0.0255. i.e within 10% of original weight.
b) For "Weight Economy" the Ecology Route will lose rank to Economy
Route at value 0.0822. i.e within 10% of original weight.
For equal vision:
a) For "Weight Ecology Affected" the Economy Route will lose rank to
Ecology Route at value 0.0682. i.e within 10% of original weight.
b) For "Weight Economy", the Ecology Route will lose rank to Economy
Route at value 0.2069. i.e within 10% of original weight.
5.4.4. Sensitivity analysis of scores using results of ranking
For social vision, rank reversal of the economy and ecology routes occurs within
10% of original score, only for the scores of traffic count, economy served and
displacement of settlements. For all the other visions, rank reversal due to scores of
any of the parameters does not occur.
Figure 29: Score position of each route alternative across all the visions
55
Figure 30: Ranking of each route alternative across all the visions
To summarise, it can be said that:
a) The Ecology and Economy variants retain top ranking for all visions.
Ecology variant is the best in all visions except the Economy vision. In the
Economy vision, the Economy variant takes over the top rank.
b) Social route always stays low.
c) In social vision, the rankings of ecology and economy variants are so close
that there can be a probability that the economy route becomes the best and
ecology becomes second best.
56
6. Discussion
It can be inferred from the above results that the government preferred alternative
always has a higher amount of total impedance compared to any of the routes
generated in this study. Also, this route is left with no chance but to cross atleast 3
Natura 2000 sites and numerous other (non-protected) natural areas (as seen in
Figure 23 to 25). A detailed inspection, done by overlaying the route with sub-
component suitability maps, reveals that this high impedance is not just because of
the high cost it incurs by breaching Natura 2000 sites, but also because it has too
little gain on the social and economic fronts. This means that it incurs a high cost
without commensurate benefits. Also in terms of total route length, the government
preferred route is always longer than any of the four optimal routes by atleast 20km.
This lends credence to the NGOs claims that on top of the enormous environmental
degradation caused, a major portion of the expressway will be made redundant
because road-users will tend to take some shorter route. Thus, besides the ecological
damage it will cause, this route neither serves the people nor the economy. From an
immediate and long term perspective, the route is financially unviable and is
therefore not recommended for further consideration.
Amongst the other four alternatives, it is seen that the Warsaw-Budzisko Economy
route has the lowest total impedance value i.e it has the best financial and economic
viability. This route is however longer than the shortest alternative (Equal/Social
vision’s optimal route) by 20km.
The Ecological route variant has the highest impedance value out of all the optimal
routes. This is because it aims to avoid even the least natural area. In doing so, it
incurs impedances from the urban areas it intersects. This is due to the engineering
and social costs associated with passing through urban areas. It is however just 8km
longer than the shortest variant. A major advantage of this variant is that it passes the
city of Elk. Elk is the second biggest city of this region, and a hub of the PKP rail
network. It is also the gateway to the north-western quadrant of Poland (which is
adjacent to this quadrant). An equally poorly developed region of the country, this
area has large natural areas and many vast and highly scenic interconnected lakes
(Mazurian Lakes) and thus enormous tourism potential. Currently, this area is poorly
connected to the rest of the country and hence this tourism potential can not be
sufficiently exploited. In terms of the potential future benefits that can be accrued to
57
the country, as a whole, and not just to the region under the consideration of this
thesis, this is by-far the most attractive variant. If the social costs (resettlement of
affected population, safety etc) can be mitigated, it would best fulfil the letter and
spirit of the Via Baltica project as it was intended by the EU.
The Social vision route follows the same path as the Equal vision route. This route is
the shortest variant. Standing at just 305km length, it has impedances lying midway
between the other two variants. As a “compromise alternative”, this would be a good
variant as it impacts the people and the environment equally and fairly. However,
this author opines that, in terms of the development that it could bring to the region
and the country, this is but a mediocre alternative.
The network analysis method of this thesis is a vector based approach, using existing
roads. Since there is a greater weightage given to higher category roads and since,
only certain category of roads are selected for use in the network analysis, the total
length continues to stay under control. Furthermore, the impedance for each road
segment is calculated by using the length of the segment as a multiplier, thus the
“total route length” continues to play an important role, although not the dominant
one. This way, the vehicle-kilometres continue to be accounted for.
For this case-study in particular, where it is mandated that, “no new roads should be
created, only upgrading of existing roads is allowed”, the final route can only follow
existing roads. Therefore only a vector-based network analysis can serve the
purpose. Another advantage of this method is that the final routes generated continue
to be polyline shapes; hence they can be used for further analysis (as required)
without requiring any additional processing.
This method improves upon previous scientific research in the field, by building a
comprehensive methodology that integrates the use of SMCE and network analysis
in these kinds of studies. Despite its limited scope and time availability, this thesis
improved upon existing procedures by encompassing more cross-disciplinary
criteria, examining alternative policy visions, using a more graphical and spatial
method. It also improves on the research of Grossardt et al (2001) by selecting a
pixel size designed as per the effect-range of the highway (rather than processing
convenience) and most importantly, using a vector based network analysis.
A visual test done by overlaying the optimal routes on the original criteria layers
shows that the spatial logic of each vision is firmly (and unambiguously) asserted
throughout the entire route for that vision, despite it not always being obvious at first
58
glance. This firmly proves the author’s assumption that the existing (and popular)
methodology of predetermining various route alternatives and conducting impact
assessments on them can often overlook other alternatives that may be more
environmental friendly.
It is also proved that GIS, remote-sensing and spatial multi-criteria evaluation
techniques can go a long way in improving the quality of SEA and EIA reports. In
this case-study, traffic count data was provided to this author by GDDKiA. However,
if such data are not available, it may be possible to infer traffic counts by the use of
NTL imagery. If ready road network data is not available, it can be extracted from
remote sensing imagery using established feature extraction packages such as
Definiens or Feature Analyst. Thus it is proved that it is possible to alleviate many of
the problems due to non-availability of ground data by the use of remotely sensed
imagery.
In this case study, it was mandated that “no new roads be created and the expressway
be built only by upgrading existing roads”. However, if building of some new
segments was allowed, the methodology would need the following additional steps:
1) Decide regions in which new roads are needed, or can be created.
2) Use the resultant suitability maps obtained for each vision within the
raster based network analysis, and generate new segments for these
regions only, as specified by Grossardt et al (2001).
3) Add these segments to the vector layer of existing roads.
4) Continue to use the network analysis proposed in this thesis.
By doing so, advantages of both the methods can be reaped. This small widget also
adds additional capabilities to the method propounded in this thesis, thus making it
even more versatile.
A press release by the European Commission (12th
December 2006) revealed that
“…the European Commission has officially opened legal infringement procedures
against the Polish government for consenting to a series of eight road developments
along the Via Baltica route which encroach upon designated or potential Natura
2000 areas”. This is a serious turn of events in the Via Baltica issue. At such a grim
juncture, the resultant optimal routes presented in this thesis offer the GDDKiA (and
the Polish government) strong, scientifically supported alternatives that could help
them in respecting EU environmental regulations and, at the same time, holistically
fulfill the conditions prescribed to them in Recommendation no. 108 (2003), of the
23rd
meeting of the Standing Committee of the Bern Convention (The Convention on
the Conservation of European Wildlife and Natural Habitats).
59
7. Conclusions and Recommendations
The main findings of this thesis are:
The criteria considered by the government in formulating the preferred route in
the original stage were not available. However prima facie it appears that the
main (and perhaps, the only) criterion was advancement of Bialystok city. In the
SEA report, several other criteria such as protected ecological areas, population
served, vehicle-kilometres, vehicle-hours, traffic intensity, number of ancillary
structures required and current category of the road are taken into consideration.
As an input for the SMCE & network analysis of this method, many of these
criteria were used in this analysis with the necessary modifications to make them
more effective. In addition, several new criteria considered equally relevant
were used in this analysis. They are enumerated in section 4.1.
LandScan data, DMSP-OLS Night-time Light imagery, ASTER imagery and the
Corine Land Cover map (i.e, data using remote sensing techniques) can fill in
gaps in information and form the basis of this speedy and cost-effective method.
Four policy visions, equal, social, economy and ecology, were considered in this
analysis. However, the method is readily amenable to the use of other policy
visions which can be derived from stated national policy documents and
stakeholder perceptions.
Using SMCE four different routing suitability maps were derived, one for each
policy vision.
By integrating SMCE and network analysis four optimal routings could be
created.
The optimal routes generated for each policy vision prove that there are indeed
more environmental friendly alternatives possible than the Government
preferred alternative. These routes are optimised taking into account a wide
range of environmental criteria, thus enhancing the positive impacts of the
project, while at the same time minimising the adverse biotic, abiotic and socio-
economic impacts.
60
Any form of infrastructure construction, especially transportation routing has
traditionally been associated with “destruction of nature” and has had a bad public
image. By enhancing the use of economic and social criteria, this method
acknowledges the need for improving transportation infrastructure (in Poland and
elsewhere) but emphasises that if ecological criteria and stakeholder concerns are
coupled to the planning system at the route determination stage itself, a lot of
unnecessary environmental damage can be avoided. This can reduce the potential
ecological-damage, increase the utility of the project and thereby also increase
stakeholder confidence in the planning process.
It was the concept of this thesis to present a systemic spatial methodology that can
formulate and / or assess effect based transport route alternatives that integrate
environmental regulations and concerns without ignoring the equally important
considerations of transport system efficiency, safety, socio-economic demands,
financial & engineering viability, and policy considerations. Not only has this been
proved but also that as a decision support tool, this method could help to improve the
practice of SEA and EIA for transportation planning.
Because of the limited time and scope of this thesis, only the most important criteria,
that were available, were considered in the analysis. In fact, though this thesis
encompasses more criteria than the SEA report, this author opines that by
incorporating other important environmental criteria, the method could be enhanced
much further. Adding other relevant criteria such as migratory corridors, slope
regimes, environmentally susceptible soils, problem soils for construction, traffic
noise, air pollution etc would increase the versatility of the method and make it
amenable for use elsewhere in the world.
Though this case study was restricted to one-fourth of Poland, the method and model
can be expanded to any scale and size. This thesis also proves that the use of remote
sensing, GIS and spatial techniques can readily yield the oft-repeated (but as yet,
unsatisfied) demands of professionals associated with transportation planning and
development funding for a more effective Decision Support System.
It is hoped that the results of this thesis will be taken into account in the final
outcome of the Via Baltica routing, and that the method developed in the thesis will
find greater utility in the practice of EIA and SEA worldwide.
61
8. References
8.1. References of publications
Affum, J.K., & Brown, A.L., 2002, A GIS-based environmental modelling system
for transport planners. Computers, Environment & Urban Systems, 26(6), 577-590.
Banister, D., 2002, "Untitled - Editorial introduction." Transport Reviews 22(2): p
133.
Beyer, H. L., 2004, Hawth's Analysis Tools for ArcGIS. Accessed from
http://www.spatialecology.com/htools in October 2006.
Bird Life International, Polish Society for the Protection of Birds (OTOP),
World Wildlife Fund (WWF- Poland), 2003, Via Baltica- Petition to the
European Parliament: June 2003. Accessed from http://www.darzbor.v24.pl/via-
baltica 10th
August 2006.
Blaser, B., Liu, H., McDermott, D., Nuszdorfer F., Phan, N., Vanchindorj, U.,
Johnson, L., Wyckoff, J., 2004, GIS-based cumulative effects assessment, Colorado
Department of Transportation – Research, Report No. CDOT-DTD-R-2004-6.
Accessed from http://www.dot.state.co.us/Publications/PDFFiles/ on 10th October
2006.
(M/s) COWI A/S, 2006, (Lead consultant of the consultancy consortium appointed
for Rail Baltica techno-commercial feasibility studies), European Commission
Directorate-General Regional Policy: Feasibility study on Rail Baltica railways-
Draft interim report (July 2006).
Cyglicki R., 2005, Polish road development plan in clash with Natura 2000. CEE
Bankwatch Network. Accessed from www.coalition-on-
eufunds.org/presentations_Krakow/RobertCyglicki.pdf on 26th July 2006.
Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W., Davis, E. R. and Davis,
C.W., 1997, Relation between satellite observed visible-near infrared emissions,
62
population, economic activity and electric power consumption, International Journal
of Remote Sensing, Volume 18, Issue 6, Apr 1997, Pages 1373 – 1379.
EUROPA-Lex, The access to European Union Law & Directives. All EU directives
accessed from Europa-Lex (http://eur-lex.europa.eu) on 30th
December 2006.
EUROPA-Rapid-Press Releases, 2006, Poland: Commission pursues legal action
for violation of nature directives, Press release (IP/06/1757) of the European
Commission, dated 12th
December 2006. Accessed from http://europa.eu/rapid/ on
9th
January 2007.
Fitzsimons, J., 2004, Analysis of Transit New Zealand's assessment of the Inner
City Bypass project, Press release of the co-leader of the Green Party of Aotearoa-
New Zealand dated 29th
April 2004. Accessed on 10th
October 2006 from
http://www.greens.org.nz/searchdocs/other7406.html.
Gallo, K.P., Elvidge C.D., Yang L., Reed B.C., 2004, Trends in night-time city
lights and vegetation indices associated with urbanization within the conterminous
USA, International Journal of Remote Sensing, Volume 25, Issue 10, May 2004,
Pages 2003 – 2007.
Grossardt T., Bailey, K. and Brumm, J., 2001, Analytic minimum impedance
surface: Geographic information system-based corridor planning methodology,
Transportation Research Record. No 1768 (13 ref.), pp. 224-232.
Higgs G., 2006, Integrating multi-criteria techniques with geographical information
systems in waste facility location to enhance public participation, Waste
Management & Research. 2006: Volume 24: 105–117.
Interreg EU website: http://www.bsrinterreg.net/programm.
Jankowski, P., 1995, Integrating geographical information systems and multiple
criteria decision making methods. International Journal of Geographical Information
Systems Volume 9: p.p 251–273
Janssen, R., van Herwijnen M., 1994, Multi-objective decision support for
environmental management + DEFINITE decisions on a finite set of alternatives:
demonstration disks and instruction, Environment & Management; 2
Publisher: Kluwer, Dordrecht. 232 p. + 16 p. ISBN 0-7923-1908-7
63
Kennedy, W., and Haumer, A., 1999, SEA and the European Bank for
Reconstruction and Development. Proceedings of the OECD/ECMT Conference on
Strategic Environmental Assessment for Transport – Warsaw: 14-15 October 1999.
Lawrence, D.P., 2003, Environmental Impact Assessment: Practical Solutions to
Recurrent Problems, Published: John Wiley & Sons, UK, 576 pages.
Li, X., Wang, W., Li, F., Deng, X., 1999, GIS Based map overlay method for
comprehensive assessment of environmental impact. Transportation Research Part
D, 4 (1999) 147-158.
Malczewski, J., 1996, A GIS approach to multiple criteria group decision making,
International Journal of Geographic Information Systems, Volume 10, no.8: p.p.
955-971.
Malczewski, J., 1999, GIS and Multicriteria Decision Analysis (New York: John
Wiley & Sons) ISBN: 0-471-32944-4. (392 pages)
Malczewski, J. and Rinner, C., 2005, Exploring multicriteria decision strategies
in GIS with linguistic quantifiers: A case study of residential quality evaluation.
Journal of Geographical Systems, Volume 7, Number 2 / June, 2005: p.p. 249-268.
Nowak, S. and Myslajek, R.W., 2005, Problems affecting migration corridors for
large terrestrial mammals caused by the network of fenced motorways and express
roads within the TEN-T program: the situation in Poland. Accessed from
http://ec.europa.eu/ten/transport/external_dimension/hlg/2005_03_31_tent_consultat
ion/doc/stakeholders_contributions/poland/wolf_association_for_nature.pdf on 10th
October 2006.
Paulus, G., Krch, M. and Scholz, J., 2006, Scenario-based Spatial Decision
Support for Network Infrastructure Design, VisASDS Workshop 2006 – GIScience
2006 – Working Paper. (9 pages)
Polish Ministry of Environment, 2005, Press release of 20th
June 2005 by the
Minister of Environment. Accessed from
http://www.mos.gov.pl/mos/news/press_releases/2005.06.21.html on 12th August
2006.
64
Rescia, A.J., Astrada, E.N., Bono, J., Blasco, C.A., Meli, P., Adamoli, J.M.,
2006, Environmental analysis in the selection of alternative corridors in a long-
distance linear project: A methodological proposal. Journal of Environmental
Management 80 (2006): p.p. 266–278.
(M/s) Scott Wilson Kirkpatrick Ltd., Strategic Environmental Assessment Report
for Pan European Network Corridor 10- Version 1. Obtained from Dr. Tomasz
Zapasnik, Head of Environment Division-Project Preparation office, General
Direckja Drog Krajowych I Autostrad (GDDKiA-Poland) on 10th
October 2006, for
use in this thesis.
Sparkes, A., Kidner, D., 1996, A GIS for the Environmental Impact Assessment of
Wind Farms. Proceedings of the 11th ESRI European User Conference. Accessed
from http://gis.esri.com/library/userconf/europroc96/PAPERS/PN26/PN26F.HTM on 15th January 2006.
Standing committee of the Bern convention, 2003, Resolution No. 108 (2003).
Accessed from http://www.coe.int/T/e/Cultural_Co-
operation/Environment/Nature_and_biological_diversity/Nature_protection/
Welch, R; Zupko, S., 1980, Urbanized area energy utilization patterns from DMSP
data (Defense Meteorological Satellite Program), Photogrammetric Engineering and
Remote Sensing. Feb. 1980 Vol. 46, pp. 201-207.
Wood, B., 2003, Building Care, Published: Blackwell Publishing, 194 pages.
World Bank, 1993, GIS for Environmental Assessment and Review: Source book
update, Environmental Department, The World Bank, January 1995, number 9.
World Bank, 1995, Implementing GIS for Environmental Assessment: Source book
update, Environmental Department, The World Bank, April 1993, number 3.
Yusof, K. W. and Baban, S., 2004, Least-cost pipeline path to the Langkawi
Island, Malaysia using a geographical information system (GIS). Proceedings of
Map India Conference 2004. (7 pages)
65
8.2. References of datasets
ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)
images dated 4th
and 11th
July 2006 for (51.92° Lat, 21.73° Lon) to
(54.16° Lat, 23.26° Lon) obtained in HDF format from ASTER-GDS website by Dr.
Kees de Bie (ITC) on 18th
August 2006, for use in this thesis.
AADT counts for 2005. M/s General Dyrekcja Drog Krajowych I Autostrad
(GDDKiA), Poland. Obtained from: Dr. Tomasz Zapasnik, Head of Environment
Division-Project Preparation office, GDDKiA and Ms. Katarzyna Mlynik, Biuro
Przygotowania Inwestycji - Wydzial Srodowiska, GDDKiA on 10th
October 2006,
for use in this thesis.
CORINE land cover: CLC2000 vector data for Poland, GIOS, 2004. Accessed
from the European Environment Agency Data service,
http://dataservice.eea.europa.eu vide authorization dated 27th
July 2006.
DMSP-OLS Nighttime Lights Time Series Version 2. Composite image label:
F152003. Image and data processing by NOAA's National Geophysical Data Center.
DMSP data collected by US Air Force Weather Agency. Accessed on 10th
September 2006 from http://www.ngdc.noaa.gov/dmsp/global_composites_v2.html
European Soil Database (v 2.0), European Soil Bureau Network and the European
Commission, EUR 19945 EN, March 2004. Accessed from
http://eusoils.jrc.it/ESDB_Archive/ESDB/ESDB_Data/ESDB_v2_data_smu_1k.htm
vide authorization dated 24th July 2006.
Global GIS: Global Coverage DVD (2003). Developed by United States
Geological Survey, American Geological Institute and ESRI Inc.
(http://webgis.wr.usgs.gov/globalgis) Obtained from ITC library.
LandScanTM Global Population Database. Oak Ridge, TN: Oak Ridge National
Laboratory. Accessed at http://www.ornl.gov/landscan/ vide authorization dated 13th
September 2006.
Natura 2000 species data from Ministry of Environment, Poland. Accessed from
http://natura2000.mos.gov.pl/natura2000/?lang=en on 9th October 2006.
66
Road and Rail network of study area, IRS Digital Elevation Model (DEM) at 20m
resolution, National protected areas obtained from M/s Geosystems Polska (a
subsidiary of Leica Geosystems LLC), Poland. Contact person: Dr. Witold
Fedorowicz-Jakowski, CEO, M/s Geosystems Polska, Warsaw on 17th
October 2006,
for use in this thesis.
World Database on Protected Areas – 2006. WDPA Consortium. Copyright
World Conservation Union (IUCN) and UNEP-World Conservation Monitoring
Centre (UNEP-WCMC), 2004 accessed from the Global Land Cover Facility on 10th
July 2006.
67
8.3. References of personal communications
(Dr.) Bożenna Wójcik, Environmental assessments specialist, Institute for
Sustainable Development, Nabielaka, Warsaw. (Poland).
(Dr.) Helena Bartoszuk, Scientific Officer, Biebrza National Park Authority,
Biebrza. (Poland).
(Drs.) Joan Looijen, Lecturer - EIA/SEA practices, Department of Natural
Resources, International Institute for Geo-Information Science and Earth
Observation (ITC), The Netherlands.
(Mr.) Jedrzej Bojanowski, Researcher, OPOLiS, Institute of Geodesy and
Cartography, Warsaw. (Poland).
(Dr.hab.) Katarzyna Dabrowska-Zielinska, Professor of Earth Sciences, Head of
Remote Sensing Department-OPOLiS, Institute of Geodesy and Cartography,
Warsaw. (Poland).
(Ms.) Katarzyna Twardowska, Department of Environment Issues and Climate
Change, Ministry of Environment, Government of Poland, Warsaw. (Poland).
(Ms.) Katarzyna Mlynik, Project Preparation Office, Environment Division,
GDDKiA, Warsaw. (Poland).
(Ms.) Marta Babicz, Project Officer (EU Funds for Sustainable Development -
Species Conservation Programme), WWF-Poland Headquarters, Warsaw. (Poland).
(Ms.) Malgorzata Znaniecka, Important Bird Area (IBA) casework officer, OTOP
– Polish Society for the Protection of Birds, Warsaw/Biebrza/Augustow. (Poland)
(Dr.) Witold Fedorowicz-Jackowski, CEO, M/s Leica Geosystems Polska,
Warsaw. (Poland).
(Dr.) Witold Lenart, Professor of Environmental Conservation, Warsaw University
- School of Geography and Regional Studies, Warsaw; Specialist in estimation of
interactions with environment, Warsaw University Centre for Research on Natural
Environment; and Member of the consultation team for preparation of the SEA
report. (Poland).
I
9. APPENDICES
1. Reproduction of Recommendation no. 108 (2003), of the 23rd
meeting of the
Standing Committee of the Bern Convention (The Convention on the
Conservation of European Wildlife and Natural Habitats).
2. Graphical representations of the comparison of the Government preferred route
vs. generated optimal routes
II
APPENDIX - 1
REPRODUCTION OF RECOMMENDATION NO. 108 (2003), OF THE 23RD
MEETING OF THE STANDING COMMITTEE OF THE BERN
CONVENTION (THE CONVENTION ON THE CONSERVATION OF
EUROPEAN WILDLIFE AND NATURAL HABITATS).
III
IV
This resolution is obtained and reproduced from:
http://www.coe.int/T/e/Cultural_Cooperation/Environment/Nature_and_biological_d
iversity/Nature_protection/sc23_tpvs23erev.pdf?L=E
V
APPENDIX - 2
GRAPHICAL REPRESENTATIONS OF THE COMPARISON OF THE
GOVERNMENT PREFERRED ROUTE vs. OPTIMAL ROUTES
