Spatial autocorrelation analysis for the evaluation of migration flows: the Italian case -...

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

LISUT

Laboratorio di Ingegneria dei Sistemi Urbani e Territoriali

Laboratory of Urban and Regional Systems Engineering

University of Basilicata

Faculty of Engineering

Department of Architecture, Planning and Transport Infrastructure

ICCSA 2010 - Fukuoka JP Ing. Francesco Scorza

INDEX 1. Migration analysis 2. Migrants Distribution Analysis in Italy:

Traditional Indexes 3. Migrants Distribution Analysis in Italy: Spatial

Analysis Techniques 4. Conclusions

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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

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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

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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.

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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

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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.

Efficacy Index of Migrations calculated for migrants in Italy in 2007 (our elaboration on ISTAT data).

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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,

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Location Quotient

Location Quotient calculated for resident immigrants in Italy in 2007 (our elaboration on ISTAT data).

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OUTCOMES:

1. Greater specialization is localized in central and north-eastern areas of the country

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.

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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

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Spatial Analysis Techniques

• Moran Index (I), • Moran scatter plots • Local Indicator of Spatial Association (LISA).

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Moran’s I statistic

Xiisthevariableobservedinnspatialpartitionsandis 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.

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€

I =nS0

(xi − x )(x j − x )wijj = 1

n∑

i = 1

n∑

(xi − x )2

i = 1

n∑

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.

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REGIONS Foreigners 2004 For./Residents 2004

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

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.

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c) d)

a) b)

Moran Scatter plot for the variable Foreigners/Residents in 1999(a), 2002(b), 2004(c), 2007(d) (our elaboration with GeoDa on ISTAT data).

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c)

a)

Moran Scatter plot distribution a) in 1999 and b) in 2007 (our elaboration with GeoDa on ISTAT data)

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Local indicators of spatial association

With:

LISAallows foreachstatisticalunit toassess thesimilarityofeachobservationwiththatofitssurroundings.

Fivescenariosemerge:• Locationswithhighvaluesofthephenomenonandhighlevelofsimilaritywithitssurroundings(high-high),definedasHOTSPOTS;• Locationswithlowvaluesofthephenomenonandhighlevelofsimilaritywithitssurroundings(low-low),definedasCOLDSPOTS;• Locationswithhighvaluesofthephenomenonandlowlevelofsimilaritywithitssurroundings(high-low),definedasPotential"Spatialoutliers";• Locationswithlowvaluesofthephenomenonandlowlevelofsimilaritywithitssurroundings(low-high),definedasPotential"SpatialOutliers";• Locationdevoidofsignificantautocorrelations.

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LISA

“LISA cluster map” 1999, 2002 (our elaboration with GeoDa on ISTAT data)

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LISA

“LISA cluster map” 2004, 2007 (our elaboration with GeoDa on ISTAT data)

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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.

LISA

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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.

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Thanks for your attention

Francesco Scorza [email protected]

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Spatial autocorrelation analysis for the evaluation of migration flows: the Italian case -...

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