A STEPWISE TOOL FOR SPATIAL  DELINEATION OF MARGINAL AREAS
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A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS

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cite as: Duque, J. C.; Royuela, V.; Norenña, M. (2010) A stepwise tool for spatial delimitation of poverty areas. Medellín (Colombia) as a case study. In Seminar of Spatial Econometrics in honor of ...

cite as: Duque, J. C.; Royuela, V.; Norenña, M. (2010) A stepwise tool for spatial delimitation of poverty areas. Medellín (Colombia) as a case study. In Seminar of Spatial Econometrics in honor of Doctor J.H.P. Paelinck, Oviedo, Spain. 22 October.

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    A STEPWISE TOOL FOR SPATIAL  DELINEATION OF MARGINAL AREAS A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS Presentation Transcript

    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY. Juan Carlos Duque (RISE) Vicente Royuela (AQR-IREA) Miguel Noreña (RISE) 2010
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY
      • Motivation
      • The interest for reducing poverty and inequality is present in all political agendas.
      • Inequalities may arise for individual reasons, for gender or ethnical reasons, … and also for spatial reasons.
      • The analysis of inequalities is changing from regional to urban (Weeks et al 2006), and extreme poverty can be found in cities associated to rich regions.
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY
      • Motivation
      • UN-Habitat: global population will move from 50% urban to 70% in the following two decades.
      • Millennium Development Goals: reducing slum areas in developing cities.
      • Quick development can be associated to the rise of disamenities , crime rates, etc.
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY
      • Motivation
      • Medellín (Colombia) is an example of these situations:
        • 50% of its inhabitants are at the lowest rates of the Human Development Index (Giraldo, 2005).
        • 90’s: pop growth 1.3%. 1995-2005: pop growth 2.6%
        • The homicide rate decreased from 150 o 33, but it increased again in 2009 to 95 homicides per 100.000 inh
        • 2008: 24% self identified as being in poverty; 39% below the poverty line;
        • 2009: 16% unemployment rate; 64% activity rate
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Objective Detect poverty in Medellín. As usual, the point is how to do it We present a stepwise procedure that efficiently delimits intra-urban slums. We aim supersede the use of administrative regions using analytical regions. We use the max-p region (available at Geogrouper ) to build analytical regions
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Case of study: Medellín 2007 data (Quality of Life Survey of Medellín). 184 questions on nine dimensions: housing, households, demography, education, social security, income and employment, participation, gender and family violence, and nutrition. The survey includes 21,861 households , representing 79,912 persons
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Case of study: Medellín
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY
      • Case of study: Medellín
      • The administrative units of analysis:
      • 16 Communes. Average size: 6.30±2.9 km 2
      • Surveyed households: 1,297±312 hous.
      • 234 neighbourhoods. Average size: 0.38±0.21 km 2
      • Surveyed households: 84±57 hous. (range 3-296)
              • 16% of the neighbourhoods have less than 30 surveyed households
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY
      • Problems associated administrative units
      • Statistical inference based on normative regions may be strongly affected by aggregation problems, such as:
      • The ecological fallacy (Robinson 1950)
      • The modifiable areal unit problem (Openshaw 1977a, 1977b, 1984; Openshaw and Taylor 1981; Arbia 1989)
      • Aggregation bias (Fotheringham and Wong 1991; Amrhein and Flowerdew 1992; Paelinck and Klaassen 1979; Paelinck 2000)
      • Small numbers problem, when working with rates (Diehr, 1984)
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY The alternative: using analytical regions Analytical homogeneous regions seek to divide the area of study into regions that are homogeneous in terms of a set of socioeconomic variables that are considered relevant to the phenomenon under study (Fischer, 1980; Duque et al. 2006). Analytical regions not only provide an alternative to deal with several statistical challenges, but also ensure that specific public policy actions implemented in a region will have an homogeneous impact throughout the region (Fischer, 1980).
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Before applying the Max-P, a list of aspects needs to be solved. 0. Choosing the relevant information 1. Small numbers problem 2. Too many variables 3. Selecting the neighbourhood structure 4. Selecting the minimum sample size
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Global Empirical Bayes This technique adjusts the raw rate in each area, ri , by taking into account information of the rest of the sample. The adjustment procedure operates in such a way that the raw rates that come from small populations are moved towards the overall mean, while the raw rates that come from large populations remain unchanged For each area i, the new estimated rate,  i, is calculated as follows: where…
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Neighbourhood structure Queen-based spatial contiguity matrix (W) Minimum population threshold We fix the requirement of at least one hundred surveyed households per regions (100)
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions
      • Max-p:
      • The Max-p is capable to create regions of any shape
      • The Max-p is the only partitioning algorithm that allows endogenizing the number of regions: the researcher only has to specify the minimum number of observations per region
      • The Max-P minimizes the lost of information by performing the minimum number of aggregations required to create feasible regions
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Analytical regions coming from Max-P: 139 regions (between 16 communes and 243 neigh.)
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions
      • The new regions:
      • Are homogeneous in terms of the relevant variables (minimizes the aggregation bias, Fotheringham and Wong, 1991).
      • Each region has at least 100 surveyed households: solves the small numbers problem, (Diehr, 1984), and ensures that the statistical secrecy it reduces the impact of geocoding inaccuracies.
      • The use of these spatial aggregation techniques is helpful for controlling problems of spurious spatial autocorrelation.
      • The number and shape of the regions is endogenously determined, which eliminates conflicts of subjectivity associated with the selection of the scale and aggregation of the data being analyzed .
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Delineation of marginal areas
      • Once we have the right units of analysis, we compute the Slum Index summing up to five dummy variables:
      • Walls are not of durable material (14%)
      • Absence of piped water (1%)
      • Toilet not connected to a sewer (4%)
      • Residents are not the owners (35%)
      • Three or more persons per room (17%)
      Raw data Spatially Smoothed data Analytical Regions
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Delineation of marginal areas We build the spatial clusters using two complementary measures: Local Moran’s I, and Getis Gi*: We identify statistically significant clusters using spatial permutation and we present two different significance level s: the usual 5% and the False Discovery Rate (FDR) (see Caldas de Castro and Singer, 2006)
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Raw data Spatially Smoothed data Analytical Regions Local Moran’s I, Getis Gi*: Raw data Spatially Smoothed data Analytical Regions
    • A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Concluding… Working with analytical regions helps to find where is poverty and then, where should we concentrate public efforts. Finding different clusters should lead policy makers to think on the different nature of poverty in these areas. Probably causes of poverty may differ in space, and consequently different policies should be applied. Our procedures can be easily applied in many other areas (remember.. Geogrouper!!)