A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS

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.


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.


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


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

http://geogrouper.appspot.com/


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



The administrative units of analysis:

16 Communes. Average size: 6.30±2.9 km2

Surveyed households: 1,297±312 hous.

234 neighbourhoods. Average size: 0.38±0.21 km2

Surveyed households: 84±57 hous. (range 3-296)

16% of the neighbourhoods have less than 30 surveyed households


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)


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).


Building analytical regions

Before applying the Max-P, a list of aspects needs to be solved.

0. Choosing the relevant information1. Small numbers problem2. Too many variables3. Selecting the neighbourhood structure4. Selecting the minimum sample size



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…ˆ (1 )i i i iw r w i

i

w

n

1

1

n

i iin

ii

rn

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2

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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.



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)



Max-p:

1. The Max-p is capable to create regions of any shape

2. 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

3. The Max-P minimizes the lost of information by performing the minimum number of aggregations required to create feasible regions



Analytical regions coming from Max-P: 139 regions (between 16 communes and 243 neigh.)



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.


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%)

Slum Index % households

0 50.710%1 40.670%2 7.380%3 0.020%4 0.004%5 0.000%

Raw data Spatially Smoothed data Analytical Regions


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 levels: the usual 5% and the False Discovery Rate (FDR) (see Caldas de Castro and Singer, 2006)

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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!!)

A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS

Technology

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