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"Geographical Analysis of Foreign Immigration and Spatial Patterns in Urban Areas. Density Estimation and Spatial Segregation" Third International Workshop on "Geographical Analysis, Urban Modeling, …

"Geographical Analysis of Foreign Immigration and Spatial Patterns in Urban Areas. Density Estimation and Spatial Segregation" Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

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  • 1. Geographical Analysis of Foreign Immigration and Spatial Patterns in Urban Areas. Density Estimation and Spatial Segregation Dr. Giuseppe Borruso Department of Geographical and Historical Sciences University of Trieste Email. [email_address] Ph. +39 040 558 7008 Fax. +39 040 558 7009 / 7005 GeogAnMod ‘08 The International Conference on Computer Science and its Applications ICCSA 2008 Perugia 30 June – 03 July 2008
  • 2. 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.
  • 3. 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.
  • 4. 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
  • 5.
    • 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
    Measures of segregation Segregation Index (Duncan & Duncan, 1955) Location Quotient (Cristaldi, 2002)
  • 6. 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)
  • 7. 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)
  • 8.
    • 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 ;
    • s i = the i th 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.
    Spatial indices of dissimilarity Kernel Density Estimation Diversity Index
  • 9. 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.
    • For each cell i minimum and maximum values are computed for the true probability of the two subgroups, px i and py i ,
    • 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
  • 10. 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
  • 11. 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).
  • 12. Zoning systems in a Municipality New districts Old districts Census blocks
  • 13. The study region
    • The municipality of Trieste
      • Census blocks
      • Residents’ address points
  • 14. 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
  • 15. 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
  • 16. Segregation index census blocks a); urban districts b) a) b)
  • 17. a) Albania b) China c) Romania d) Senegal Location Quotient for selected ethinc groups
  • 18. Kernel Density Estimator
    • Standardize value (comparison with other ethnic groups)
    • Areas with higher population density
    • Bandwidth = 300m
    • Grid cell = 50m
  • 19. a) Albania b) China c) Romania d) Senegal KDE On selected ethnic groups
  • 20. Segregation index S (O’ Sullivan e Wong, 2007) 73,13 43,84 73,13 50,15 900 72,94 49,46 74,39 54,56 600 76,60 54,83 77,12 58,31 450 81,46 62,53 80,31 64,14 300 81,38 77,43 86,83 75,66 150 Senegal Romania Cina Albania Kernel Bandwidth (m)
  • 21. KDE Max-min (bandwidth = 300m) a) Albania b) China c) Romania d) Senegal
  • 22. Diversity index (IDiv)
    • Area with higher density
    • (# of countries compared to % of foreign population)
    • bandwidth = 177m (nearest neighbour k=2)
    • Grid cell = 50m
  • 23. Diversity index (IDiv)
    • Area with higher density
    • (# of countries compared to % of foreign population)
    • bandwidth = 281m (nearest neighbour k=5)
    • Grid cell = 50m
  • 24. Superficie di densità della popolazione residente
  • 25. IDiv + KDE on residential population
    • Density surface of residential population (standardized, green shades)
    • Index of diversity of population (standardized, red shades)
    • Area with high population density and high diversity, but also areas with lower density and relevant diversity)
    • Need to affine the index:
      • Entropy
      • Qualitative indices
  • 26. 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.