Vector Calculus in Regional Development Analysis: Comparative Regional Analysis Using the Example of Poland

175 Pages·2009·9.198 MB·English

by  Kesra Nermend (auth.)

Contributions to Economics
Kesra Nermend
Vector Calculus in Regional
Development Analysis
Comparative Regional Analysis Using
the Example of Poland
Physica‐Verlag
ASpringerCompany
Dr.inzˇ.KesraNermend
InstituteofInformaticsinManagement
ul.Mickiewicza64
71‐101Szczecin
Poland
[email protected]
Book first published in Polish under the title Wydawnictwo Naukowe Uniwersytetu
Szczecin´skiego,Szczecin2008‘‘Rachunekwektorowywanalizierozwojuregionalnego’’
ISBN978-3-7908-2178-9 e-ISBN978-3-7908-2179-6
DOI:10.1007/978-3-7908-2179-6
ContributionstoEconomicsISSN1431-1933
LibraryofCongressControlNumber:2009921251
#Physica-VerlagBerlinHeidelberg2009
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Contents
ListofVariablesandNotation ................................................ vii
Introduction ...................................................................... 1
1 RegionalDevelopmentEconomicPerspective ............................. 5
1.1.NotionandFactorsDeterminingRegionalDevelopment ............... 5
1.2.DataMonitoringforRegionalDevelopmentAssesment ............... 17
1.3.RegionalDevelopmentIndicators ..................................... 24
2 MethodicalDilemmaOverRegionalDevelopment
Analysis ..................................................................... 31
2.1.OrganizationofAnalyticalProcessesinRegionalDevelopment
Investigation ........................................................... 31
2.2.ReviewofMethodsUsedforRegionalDevelopmentAnalysis ....... 36
2.3.ReasonsBehindUsingVectorCalculusforRegionalDevelopment
Analysis ................................................................ 61
3 MethodologyofVectorCalculusinRegional
DevelopmentAnalysis ...................................................... 63
3.1.ProcedureforApplyingVectorCalculusinRegionalDevelopment
Analysis ................................................................ 63
3.2.TaxonomicVectorMeasureofRegionalDevelopment ................ 74
3.2.1.InterpretationofDatainSpace ................................ 74
3.2.2.VectorComponentAlongAnotherVector ..................... 78
3.2.3.ComparisonofVectorsinUnitarySpace ....................... 86
3.3.VisualizationofLocalDevelopmentMeasuresin3DSpace .......... 90
4 TaxonomicSyntheticVectorMeasureinthe
AssessmentofRegionalDevelopment:Results
ofEmpiricalResearch ..................................................... 95
4.1.SelectionofDiagnosticVariables ..................................... 95
4.2.ConstructionoftheStandardObject ................................... 99
v
vi Contents
4.3.InvestigationofSpatialRelationshipsBetweenGroups
ofVariables ............................................................ 119
5 Computer-AidedRegionalDevelopment
Analysis .................................................................... 125
5.1.ComputerSystemforRegionalDevelopmentAnalysis
asaDecisionSupportSystem ........................................ 125
5.2.ConceptofDecisionSupportSysteminRegionalDevelopment
Analysis ............................................................... 134
5.3.Data-BaseManagementSystem(DBMS) ............................ 136
5.3.1.SystemFunctions .............................................. 136
5.3.2.Model-BaseManagementSystem(MBMS) ................... 139
5.3.3.DialogGenerationandManagementSystem(DGMS) ........ 141
5.4.FunctioningofComputerSystemforRegional
DevelopmentAnalysis ................................................ 144
Conclusions .................................................................... 157
Bibliography ................................................................... 159
ListofFiguresandTables .................................................... 169
List of Variables and Notation
X Matrixofobjectsdescribingvariables
x Valueofkthvariableofithobject(firstindex–objectnumber,second
ik
index–variablenumber)
w Numberofobjects
n Numberofvariables
z Standardizedvalueofkthvariableofithobject
ik
s Standarddeviationofkthvariable
k
x(cid:1) Meanvalueofkthvariable
k
v Variabilitylevelmeasureofkthvariable
k
S Standarddeviationofithobject’svariables
i
I Stimulantsset
K Destimulantset
L Nominantsset
P Pointrepresentingithobject
i
P Pointrepresentingthestandard
o
d Similaritymeasurebetweenithandjthobjects
ij
dðP;PÞ Similaritymeasurebetweenithandjthpoints
i j
m Developmentmeasureofithobject
i
m(cid:1) Meanmeasureofdevelopmentofallobjects
s Standarddeviationofsyntheticmeasure
m
wg Weightofkthvariable
k
r Coefficientofobject’sscalechangealongkthvariable
k
D Coefficientofobject’stranslationalongkthvariable
k
~
A Designationofvector
g Metrictensor
ij
gr Borderbetweenclasses
ij
odl Distanceofithobjectfromtheborderbetweenclassjthandclasskth
ijk
odl wzgl Percentagedistanceofithobjectfromtheborderbetweenclassjth
jk
i
andclasskth
pas Coefficientcontrollingwidthofborderbelt
szerklas Meanwidthofclass
pp Percentagereliabilityofithobjectmembershipofjthclass
i j
vii
viii ListofVariablesandNotation
~ ~
roz~~ Similarity between two objects represented by two vectors A and B
AB
calculatedaslengthoftheirvectorsdifference
~ ~
A UnitvectoralongvectorA
jA~j
~ ~
cjAA~~jjBB~~j Projectionofunitvector jAA~jontounitvector jBB~j
reg ValueatpointP ofregulargrid
k k
nreg ValueatpointP ofirregulargrid
i i
l klas Numberofclasses
Introduction
Methods used for regional development analysis are employed mainly to make
forecasts andcomparisons.Forecasting modelsof various types(e.g. econometric
models) are usually used for forecasting. Recently, vector-autoregressive models
(VAR)havebecomepopular.ThesemodelswereproposedbySimsin1980.Onthe
contrary,taxonomicmethods(thatareinthecenterofattentionasfarasthepresent
publication is concerned) are most often employed to make comparisons. Linear
ordering methods, including standard methods, are the most popular among taxo-
nomic methods. They are based on different distance and similarity measures,
which leads to the fact that they do not always provide reliable information.
When,forexample,oneconstruesthestandardforabaseyearandthencompares
itwithdataforotheryears,itmayturnoutthatthemeasuredeterminedwillhave
worsevaluesthanthestandardforarealobject(region,microregion)althoughthis
objectisbetterfromthestandard.Hence,onemustlookfornewmethodsemployed
inregionaldevelopmentanalysisorimprovehithertoexistingonesinsuchawayso
thatinformationobtainedreflectstherealitytoalargerextent.
Themainaimofthepresentpublicationistoworkoutmethodologicalbasisfor
regionaldevelopmentanalysisbasedonvectorcalculustogetherwithassumptions
aboutcomputersystemsupportingtheimplementationofthemethodsuggested.
Inthecontextofthepresentdiscussion,thefollowingthreestatementshavebeen
adoptedasresearchhypotheses:
1. Methods hitherto used for regional development analysis do not meet require-
mentsofobjectiveassessmentofregionsandmicroregionsbothforthepurpose
ofscienceandeconomicpractice.
2. Author’s system of regional development assessment (made with the use of
vector calculus) presented in the publication describes in detail the quality of
socio-economicandenvironmentalprocessesintheareaexaminedandhenceit
couldbeusefulinmakingeconomicandinvestmentdecisions.
3. Adaptationofregularandirregulargridsfromthesystemsofspatialinformationfor
thepurposeofspatialcorrelationanalysismaymakedecisionmakersawareofthe
influencethatparticulargroupsoffactorshaveonsocio-economicdevelopment.
1
2 Introduction
In order to verify the first mentioned research hypothesis, the analysis of
classical methods used for examining regional development has been carried out.
Thisanalysiswillallowtoindicatepremisesthatthesemethodsshouldmeet,which
willmakeitpossibletoachievetheaimofthepresentpublication.Inordertoverify
thesecondmentioned hypothesis, empiricalresearchhasbeenconductedwith the
use of both classical synthetic measures as well as taxonomic vector measure of
regional development proposed by the author. The empirical research relates to
Polishcountiesasacase-study.Thepresentedintheworkmeasurecanbeusedin
widevarietyofregionalstudies.
At the same time, the application of standards construed not on the basis of
maximalandminimalvaluesofvariablesbutonthebasisofquartilesisanovelty
here. Furthermore, in order to simplify the procedure for putting the approach
suggested into practice, assumptions about computer system were made and the
prototype ofthis system was developed.The system discussed has been equipped
with a module enabling to examine spatial relations among groups of variables,
whichwaspossiblethankstoauthor’sadaptationofregularandirregulargridsfrom
systemsofspatialinformationforthepurposeofspatialcorrelationanalysis.
The layout of the present publication was determined by the research purpose
andhypotheses.Thepublicationconsistsoffivechaptersprecededbyintroduction
andsucceededbyconclusion.
Inthefirstchapter,thenotionof‘‘regionaldevelopment’’wasanalyzedtogether
with factors determining this development. Moreover, the importance of regional
datamonitoringintheprocedureforregionaldevelopmentexaminationwashigh-
lighted.Finally,indicatorstakenintoaccountintheresearchwerediscussed.
In the second chapter, issues relating to methodology of regional development
analysis were raised paying special attention to classical methods. Furthermore,
reasonsbehindlookingfornewmethodsorimprovingtheexistingones(inorderto
maketheresearchapparatusmoreprecise)werepresented.
While discussing the issues raised in the first and second chapters, the author
employedelementaryanalysismethodwiththeuseofwhichthesubjectmatterof
researchwasdividedintoseveralpartsanddiscussedindividually.Causalanalysis
wasusedfordeterminingtherelationshipbetweenphenomenaunderexamination,
and deduction method – for discussing the questions important as far as the
fulfillment of the main aim of the present publication was concerned. Discussion
made in these chapters have deductive-logical and review character. The main
methodofjustificationislogicalargumentation.
Inthethirdchapter,methodicalbasisofregionaldevelopmentanalysis(withthe
useofvectorcalculusmethods)waspresented.Particularstagesofresearchproce-
dure based on the method suggested were described. Moreover, the author also
indicatedfeaturesofthemethodthankstowhichmeasures(obtainedwiththeuseof
this method) were more accurate than classical measures. The possibility of pre-
sentingtheresultsofanalysisin3Dspacewasdiscussedaswell.
The fourth chapter includes the description and results of research conducted
bymeansofthemethodsuggested.TheresearchwascarriedoutforNUTSIVlevel,
i.e.for314Polishcountiesandatthesametime,42diagnosticvariablesweretaken