University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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                                    Foreigners 2004                  For./Residents 2004
REGIONS
                        M...
University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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University of Basilicata
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Spatial autocorrelation analysis for the evaluation of migration flows: the Italian case - Grazia Scardaccione, Francesco Scorza, Giuseppe Las Casas, Beniamino Murgante

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Spatial autocorrelation analysis for the
evaluation of migration flows:
the Italian case - Grazia Scardaccione, Francesco Scorza, Giuseppe Las Casas, Beniamino Murgante

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Spatial autocorrelation analysis for the evaluation of migration flows: the Italian case - Grazia Scardaccione, Francesco Scorza, Giuseppe Las Casas, Beniamino Murgante

  1. 1. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Spatial autocorrelation analysis for the evaluation of migration flows: the Italian case Grazia Scardaccione, Francesco Scorza, Giuseppe Las Casas, Beniamino Murgante Laboratory of Urban and Regional Systems Engineering (LISUT), University of Basilicata, Via dell’Ateneo Lucano 10, 85100 ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  2. 2. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering INDEX 1.  Migration analysis 2.  Migrants Distribution Analysis in Italy: Traditional Indexes 3.  Migrants Distribution Analysis in Italy: Spatial Analysis Techniques 4.  Conclusions ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  3. 3. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Migration analysis Interdisciplinary research field Relevant for the interpretation of socio economic dynamics (multi-scale interpretation) •  “migrations are forms of human capital” (Sjaastad, L. - 1962) •  “search for better economic conditions” (wealth maximization) (Mincer, J. - 1978) Italy: from origin to destination of migration flows ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  4. 4. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Migration analysis Structural aspects of the approach: •  Main statistical unit: the Municipality •  Data time series (from 1991 to 2007) •  “simple” data and elaborations •  High transferable approach ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  5. 5. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Traditional indexes Efficacy index of migration (Ie). Segregation measures: •  index of dissimilarity (D) •  and location quotient (LQ). To assess levels of territorial differentiation of a group (the foreigners) compared to resident population. To evaluate possible ghetto or ‘ethnic islands’ effect depending on social segregation connected with high concentration of a single immigrant group compared to local residents. ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  6. 6. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Efficacy index of migration I = Members (people who have moved their residence to specific municipalities), D = Deleted (people who have cancelled their residence from a specific municipality), (I-D) represents “net migration”. Values close to zero -> migration exchange produces not significant change in population; values close to 100 -> that the incoming flows are greater than outgoing ones; values close to -100 -> emigration flows are prevailing ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  7. 7. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Efficacy index of migration OUTCOMES: 1.  Heterogeneous behaviour of the system 2.  No relevant cluster identified 3.  Mountain municipalities have a marked tendency to generate migration confirming depopulation trends. ICCSA 2010 - Fukuoka JP Efficacy Index of Migrations calculated for migrants inIng. Francesco Scorza Italy in 2007 (our elaboration on ISTAT data).
  8. 8. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Location Quotient "Location Quotient" (LQ) provides an estimation of specialization degree of the each statistical unit to accept foreign population. xi represents the number of residents of a national group in area unit i (in our case the municipality), X the number of residents in the entire study area (in our case the Country), yi the foreign population in area unit i Y the foreign overall population in the study region. LQ = 1 -> the analyzed group holds in the area unit I the same characteristics of the whole study region; LQ > 1 -> the analyzed group is overrepresented in area unit i, LQ < 1 -> the analyzed group is underrepresented in area unit i, ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  9. 9. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Location Quotient OUTCOMES: 1.  Greater specialization is localized in central and north- eastern areas of the country ICCSA 2010 - Fukuoka JP Location Quotient calculated for resident immigrants in Italy in 2007 Ing. Francesco Scorza (our elaboration on ISTAT data).
  10. 10. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Dissimilarity Index Dissimilarity Index (Duncan and Duncan – 1955) provides an estimation of the segregation degree of two groups of population in the study area. It describes a spatial concentration of population groups. xi is the ratio between the number of residents in the area i and total population in the whole study area; Zi represents a ratio similar to x, for another group; k is the number of territorial parts in which we divide the study area. D varies between 0 and 100. Values close to 0 -> low dissimilarity. High values of D -> coexistence of the two groups in the same areas is quantitatively limited. ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  11. 11. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Dissimilarity Index OUTCOMES: 1.  The index of dissimilarity allowed to measure the heterogeneity of the structure of foreign population 2.  D allows a direct comparison of different areas, but it is not spatially embedded and it does not explain internal aspects of dissimilarity 3.  Segregation indices do not provide guidance on the spatial distribution of the phenomenon, in particular they do not allow to develop assessment of segregation degree within the study area ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  12. 12. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Spatial Analysis Techniques •  Moran Index (I), •  Moran scatter plots •  Local Indicator of Spatial Association (LISA). ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  13. 13. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Moran’s I statistic n n ∑ ∑ (x − x )(x − x )w i j ij n i=1j=1 I= S n 0 ∑ (x − x )2 i i=1 Xi
is
the
variable
observed
in
n
spatial
partitions
and



is the variable average; Wij is the generic element of contiguity matrix; € is the sum of all matrix elements defined as contiguous according to the distance between points-event. In the case of spatial contiguity matrix, the sum is equal to the number of non-null links.
 ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  14. 14. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Moran’s I statistic The generalized matrix of W weight expresses the concept of contiguity W is usually symmetrical, representing the pattern of connections or ties and their intensity W is a dichotomic matrix of contiguity where wij = 1 if the i area touches the boundary of j area; and wij = 0 is otherwise. Index values may fall outside the range (-1, +1). Moreover, in case of no autocorrelation the value is not 0 but is -1/(n-1). So if: I < -1/(n-1) = Negative Autocorrelation, I = -1/(n-1) = No Autocorrelation, I > -1/(n-1) = Positive Autocorrelation. ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  15. 15.   Foreigners 2004 For./Residents 2004 REGIONS Moran’s I Z-score Moran’s I Z-score Italy 0,07 12,3 0,62 94,51 North-Western Italy 0,06 9,02 0,42 39,66 North-Eastern Italy 0,09 6,44 0,48 32,75 Central Italy 0,05 6,56 0,48 25,45 Southern Italy 0,13 11,13 0,41 29,53 Insular Italy 0,04 2,32 0,22 10,54 Piemonte 0,04 9,12 0,24 14,41 Valle d'Aosta 0,07 2,65 0,16 2,48 Lombardia 0,07 13,94 0,49 32,31 Trentino-Alto Adige 0,03 1,45 0,32 10,27 Veneto 0,06 2,08 0,47 19,21 Friuli-Venezia Giulia 0,03 1,13 0,39 9,68 Liguria -0,04 -2,5 0,42 10,42 Emilia-Romagna 0,03 1,24 0,41 12,46 Toscana 0,1 4,01 0,42 12,02 Umbria 0,07 1,95 0,28 4,56 Marche 0,14 4,14 0,27 7,41 Lazio 0,04 10,7 0,52 16,97 Abruzzo 0,19 5,84 0,33 9,76 Molise 0,05 1,16 0,15 3,13 Campania 0,12 8,68 0,37 14,7 Puglia 0,09 3,09 0,25 6,75 Basilicata 0,17 3,98 0,24 4,89 Calabria 0,02 0,99 0,18 6,2 Sicilia 0,01 0,67 0,24 8,25 Sardegna 0,17 6,9 0,19 6,28
  16. 16. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Moran Scatter plot GEODA allows to build Moran Scatter plot. The graph represents the distribution of the statistical unit of analysis. Moran Scatter plot shows the horizontal axis in the normalized variable x, and on the normalized ordinate spatial delay of that variable (Wx). In this representation the I° and III° quadrants represent areas with positive correlations (high-high, low-low) while the II° and IV° quadrants represent areas with negative correlation. Moran Scatter plot allow to generate spatial clusters of statistical units but it doesn’t provide information on the significance of spatial clusters. ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  17. 17. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Moran’s I statistic a) b) c) d) Moran Scatter plot for the variable Foreigners/Residents in 1999(a), 2002(b), 2004(c), 2007(d) (our elaboration with GeoDa on ISTAT data). ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  18. 18. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Moran’s I statistic a) c) ICCSA 2010 - Fukuoka JP Moran Scatter plot distribution a) in 1999 and b) in 2007 (our elaboration with GeoDa on ISTAT data) Ing. Francesco Scorza
  19. 19. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Local indicators of spatial association With:

 LISA
 allows
 for
 each
 statistical
 unit
 to
 assess
 the
 similarity
 of
 each
 observation
 with
that
of
its
surroundings.

 Five
scenarios
emerge:
 •  
Locations
with
high
values
of
the
phenomenon
and
high
level
of
similarity
with
 
 its
surroundings
(high
-
high),
defined
as
HOT
SPOTS;
 •  
Locations
with
low
values
of
the
phenomenon
and
high
level
of
similarity
with
 
 its
surroundings
(low
-
low),
defined
as
COLD
SPOTS;
 •  
Locations
with
high
values
of
the
phenomenon
and
low
level
of
similarity
with
 
 its
surroundings
(high
-
low),
defined
as
Potential
"Spatial
outliers";
 •  
Locations
with
low
values
of
the
phenomenon
and
low
level
of
similarity
with
its
 
 surroundings
(low
-
high),
defined
as
Potential
"Spatial
Outliers";
 • 
Location
devoid
of
significant
autocorrelations.
 
 ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  20. 20. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering LISA ICCSA 2010 - Fukuoka JP “LISA cluster map” 1999, 2002 (our elaboration with GeoDa on ISTAT data) Ing. Francesco Scorza
  21. 21. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering LISA ICCSA 2010 - Fukuoka JP “LISA cluster map” 2004, 2007 (our elaboration with GeoDa on ISTAT data) Ing. Francesco Scorza
  22. 22. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering LISA three agglomerations emerged: 1.  The first cluster included values for positive autocorrelation type high-high increasing over the years, geographically concentrated in north-eastern areas. Such areas are characterized by increasing levels of welfare and therefore they express strong attraction for foreigners linked with employment opportunities. 2.  The second cluster, always of high-high type affected the central part of the national territory and it could be explained with high levels of income and employment. (?) 3.  The third cluster, Low-Low type, included the towns of Southern Italy and islands, notoriously characterized by low incomes and few employment opportunities. The comparison of LISA cluster maps at different dates highlight the trend of the phenomenon. ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  23. 23. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Conclusions Migration phenomena is one of the key issues of political and social debates. Clustering as crutial step for effective policy development: Clusters could become target areas for specific policies Overcoming the traditional representation in macro regional aggregation uncertainty linked to the illegal component of the migration flows in Italy to the whole study. regional disparities of migration could be linked with the performance of each area: areas characterized by the same performance (high presence of foreigners or low presence of foreigners) tend to aggregate and to expand including neighbouring municipalities. ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza
  24. 24. University of Basilicata LISUT Faculty of Engineering Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali Department of Architecture, Planning and Transport Infrastructure Laboratory of Urban and Regional Systems Engineering Thanks for your attention Francesco Scorza francesco.scorza@unibas.it ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza

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