Geographical Analysis of Foreign Immigration and Spatial Patterns in Urban Areas.

Density Estimation and Spatial Segregation

Dr. Giuseppe Borruso

Department of Geographical and Historical SciencesUniversity of TriesteEmail. giuseppe.borruso@econ.units.itPh. +39 040 558 7008Fax. +39 040 558 7009 / 7005

GeogAnMod ‘08The International Conference on Computer Science and its Applications

ICCSA 2008Perugia 30 June – 03 July 2008

Abstract

The paper is focused on the analysis of immigrant population with particular reference to their spatial distribution and the tendency to cluster in some parts of a city, with the risk of generating ethnic enclaves or ghettoes.

Methods used in the past to measure segregation and other characteristics of immigrants have long been aspatial, therefore not considering relationships between people within a city.

In this paper the attention is dedicated to methods to analyse the immigrant residential distribution spatially, with particular reference to density-based method.

The analysis is focused on the Municipality of Trieste (Italy) as a case study to test different methods for the analysis of immigration, and particularly to compare traditional indices, as Location Quotients and the Index of Segregation, to different, spatial ones, both based on Kernel Density Estimation functions, as the S index and the first version of an Index of Diversity.

Topics

Qualitative and quantitative methods for the analysis of immigrants at urban level;

Measures of segregation; Spatial indices of dissimilarity; The spatial distribution of migrant population in Trieste; Conclusions and discussion.

Qualitative and quantitative methods for the analysis of immigrants at urban level

The analysis on migrations can rely on a mixed combination of methods and tools, both quantitative and qualitative ones

diffusion of spatial analytical instruments and information systems

scholars involved in migration research should therefore rely also on qualitative methods in order to integrate their studies, with the difficult task of interpreting correctly what is happening over space

Researchers have focused their attention on different indicators in order to examine the characters of the spatial distribution of migrant groups, particularly in order to highlight the trends towards concentration rather than dispersion or homogeneity, or, still, the preferences for central rather than peripheral areas.

summary indices are useful to portray the level of segregation of a region and for comparing the results obtained for different regions, but they say little about some spatial aspects of segregation

Varies between 0 and 100 (or 0-1) Represent major or minor

dispersion or concetration of an ethnic group

with xi = # of residents of a national group in a sub-area i;

X number of residents in the study region (municipality);

yi the population in area i; Y the population of the study

region.

xi = number of residents of a national group in sub-area i;

X = number of residents in the study region (municipality),

yi = foreign population in area i Y = the overall foreign population QL = 1 => the group in the sub-

area present same characterisics of the distribution in the overall study region considered;

QL > 1 the group is over-represented in the sub-area

QL < 1 the group is under-represented in the sub-area

Y

y

X

xD ii

2

1

1005,0 Yy

XxD ii Y

Xy

xLQi

i

Measures of segregationSegregation

Index(Duncan & Duncan, 1955)

Location Quotient (Cristaldi,

2002)

Segregation index

Generally a-spatial The zoning system of the study

region affect the final result Higher disaggregation of data

=> higher value of segregation; Higher aggregation of data =>

lower value of segregaion Figures: segregation index on

foreign nationals from address point data aggregated to:

a) census blocks; b) urban districts

a)

b)

Segregation Index for selected ethnic groups (census blocks)

71,82

83,86

74,49

91,50

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

SI Albania SI China SI Romania SI Senegal

Seg

reg

atio

n In

dex

Segregation Index for selected ethnic groups (urban districts)

4,70

9,90

18,27

30,55

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

SI Albania SI China SI Romania SI Senegal

Seg

reg

atio

n In

dex

Location Quotient

Spatial index Represent ‘specialization’ or

representativeness of a phenomenon in a given place (area) with respect to the overall study region

Figure: foreign residents in Trieste census blocks > 5 % (mean municipal data)

The function creates a density surface from a distribution of points (events) in space, providing an estimate of events withn its searching function according to their distance from the point where the estimate is computed.

= estimate of intensity of the spatial distribution of events, measured in point s;

si = the ith event;k( ) = kernel function = bandwidth. Varying the bandwidth it is possible to

obtain smoother or sharper surfaces and analyze the phenomena at different scales.

Diversity Index (IDiv) for area i consider the number of countries in each sub-area (census block). The value is multiplied by the % of residents in the census block

Ni = # of countries in sub-area i; yi foreign population in sub-area

i and xi the overall population in sub-area i.

The index is computed by means of a KDE (from values assigned tocensus blocks’ centoids) in order to transform it into a density surface and visualize its variation in space.

n

i

issks

12

1

s

100*

ii

ii xyNIDiv

Spatial indices of dissimilarityKernel Density Estimation

Diversity Index

Segregation index S (O’ Sullivan e Wong, 2007) spatial modification of the Duncan’s index D comparing, at a very local level, the space deriving from the intersection of the extents

occupied by two sub-groups of an overall population and the total extent of the union of such areas

involves the computation of probability density functions by means of KDE for the different population sub-groups of interest.

Each reference cell i is therefore assigned a probability value for each subgroup, and for each of the subgroups the probability value in that cell contributes to the integration to unity.

i yx

i yx

ii

ii

pp

ppS

),max(

),min(1

For each cell i minimum and maximum values are computed for the true probability of the two subgroups, pxi and pyi, these are summed for all the i cells, obtaining minimum and maximum values under the two surfaces, and their ratio is

subtracted from unity to produce index S. The index S obtained is aspatial as well, as it can be obtained for a study region, but the intermediate values, as the

differences in maximum and minimum values, can be mapped, giving a view of the contribution of each cell to the overall segregation, with lower values indicating areas with some degree of ethnic mixing.

Different bandwidth values produce a decay of the index as bandwidth increases, thus reducing the segregation index overall the study region and still the differences of this behaviour in different regions or for different groups can be

analyzed to explore dynamics proper of territory or group

The spatial distribution of migrant population

in Trieste

The data and the study area Segregation Index Location Quotients Kernel Density Estimation The S index of segregation The Index of Diversity

Data

Spatial component: Areas (zoning systems) Points (addresses; fieldwork; areas centroids)

Attributes Characteristics of migrants (Anagraphical data: sex, age, gender,

country of origin, residence address; working activities.) Key issues related to data:

Availability Format Level of aggregation (results affected) Multidimensional data => difficult to analyze without suitable

instruments and a multicisiplinary approach (qualitative and quantitative methods integrated in the analysis).

Zoning systems in a Municipality

New districts Old districts Census blocks

The study region

The municipality of Trieste

Census blocks Residents’ address points

Indices of spatial distribution of population

Traditional indices -> elaboration/ visualization by GIS Segregation index Location quotient

New indices -> inmplemented thanks to possibilities allowe by GIS enviroment

Density estimators Diversity indices

Indices of spatial distribution of population

Ethnic group of more recent immigration in Trieste (data from Statistical Office, Municipality of Trieste, 2005)

Albania China Romania Senegal

Indices presented: Segregation Index D; Location Quotient LQ ; Kernel Density Estimation Segregation Index S (after O’ Sullivan and Wong) Index of Diversity

Spatial units considered: Urban districts Census blocks Address points

Segregation index census blocks a); urban districts b)

Segregation Index for selected ethnic groups (census blocks)

71,82

83,86

74,49

91,50

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

SI Albania SI China SI Romania SI Senegal

Seg

reg

atio

n In

dex

Segregation Index for selected ethnic groups (urban districts)

4,70

9,90

18,27

30,55

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

SI Albania SI China SI Romania SI Senegal

Seg

reg

atio

n In

dex

a) b)

a) Albania b) China

c) Romania d) SenegalLocation Quotient

for selected ethinc groups

Kernel Density Estimator

Standardize value(comparison with other ethnic groups)

Areas with higher population density

Bandwidth = 300m Grid cell = 50m

a) Albania b) China

c) Romania d) SenegalKDE

On selected ethnic groups

Segregation index S (O’ Sullivan e Wong, 2007)

Kernel Bandwidth (m) Albania Cina Romania Senegal

150 75,66 86,83 77,43 81,38

300 64,14 80,31 62,53 81,46

450 58,31 77,12 54,83 76,60

600 54,56 74,39 49,46 72,94

900 50,15 73,13 43,84 73,13

30,00

35,00

40,00

45,00

50,00

55,00

60,00

65,00

70,00

75,00

80,00

85,00

90,00

95,00

100,00

150 300 450 600 900

Kernel Bandwidth (m)

Seg

reg

atio

n I

nd

ex

Albania China Romania Senegal

KDE Max-min (bandwidth = 300m)

a) Albania b) China

c) Romania d) Senegal

Diversity index (IDiv)

Area with higher density

(# of countries compared to % of foreign population)

bandwidth = 177m(nearest neighbour k=2)

Grid cell = 50m

Diversity index (IDiv)

Area with higher density

(# of countries compared to % of foreign population)

bandwidth = 281m(nearest neighbour k=5)

Grid cell = 50m

Conclusions Indices for measuring segregation or diversity in the distribution of migrant groups at

urban level Considering the spatial aspects of such indices and the need to examine more in depth

the articulated structure and characteristics of the population. Problems still need to be addressed: Availability of disaggregated data, however, if the zoning system produces sufficiently

small areas some analytical methods reduce such problem. The choice of the bandwidth or distances of observation, although efforts in this

direction are under exam Other issues concern the multi-group analysis, therefore not limiting this to two

subgroups but to the overall variety of countries represented in a given study region. Qualitative, multivariate attribute of population data should be considered. Need to explore the opportunity to develop and implement entropy-based diversity

indices, as well as to examining the relations between economic activities, residential locations and segregation as emerging migration issues to analyse.

Methods based on density provide a good starting point for more in depth and local analysis by the researchers, that can focus their attention over a micro scale of analysis, going further than the administrative divisions of space and reducing the minimum distance of observation to examine locally the dynamics at urban scale.