Introduction to Poverty Mapping and Tools Methodology workshop for OSU IPSC Ola Ahlqvist, PhD. Assistant Professor Department of Geography

Department of GEOGRAPHY

Who is he?

Who are you & how do you see poverty? ●

U.S. Census Bureau and the Federal Poverty Level More?

From: CRP, 2008, The Real Bottom Line: The State of Poverty in Ohio 2008

So, let's look at some descriptive maps Open a web browser ● Got to http://geocommons.com/ ●

◦ Search for something you're interested in (could be the p-word, hunger, access) ◦ Some maps require you to create an account and login ◦ Demo - “health access”

Demonstrate the ability to share data that you can provide

◦ Possible to link spreadsheet with e.g. countries, states ◦ Geocode point locations and put on a map

Also some issues − − − −

Scale Different area sizes, numbers Classification Color choice

Outline ●

What is poverty?

◦ How to measure and why it matters ◦ The relevance of space

Elements of spatial analysis

◦ Distance, autocorrelation, scale, MAUP

Methods of Poverty Mapping ◦

Elements of Cartography

◦ Projection, classification,

Data and Tools ◦

The relevance of spatial analysis and mapping to combat poverty? ●

Space and distance matters

Descriptive - identification of poverty, knowing where problems exist

◦ Enables integration of multiple sources of data ◦ Ex. differences in geography, history, ethnicity, infrastructure, etc., make poverty highly variable over space (Davis, 2003)

Explanatory – better understanding of the processes surrounding poverty ◦ Enables analysis of spatial association between factors ◦ Ex. targeting of small geographic areas more cost efficient in reaching poor or bypassed areas (Hyman et al, 2005)

Distance – a key to spatial analysis Often a determinant of adjacency / interaction / neighborhood ● Easily calculated for a plane given two coordinates ●

d A, B

Pythagorean or Euclidean distance

d A, B =

( xA −

xB ) + ( y A − y B ) 2

2

x A − xB

But other “distances” also matter... Network distance, e.g. ‘hop count’ ● Travel time ● Percieved distance ●

C B

A

◦ A binary type of distance, two objects are either adjacent or not ◦ Distance often used to determine adjacency ◦ Intuitive for polygons and pixels, less so for points

?

And getting even trickier... ●

Interaction

◦ Tries to quantify the strength of some relationship between objects/places A ◦ Often some inverse distance 1 w A, B ∝ k d

B

Neighborhood

◦ Even less clear cut than interaction − −

could be a region maybe around some object could be a collection of objects considered to be neighbors of some object

◦ E.g. Where’s downtown? The Near Eastside? The Midwest?

Pitfalls in uses and abuses of poverty maps ●

Toblers “first law” of geography: “Everything is related to everything else, but near things are more related than distant things” (Waldo Tobler, 1970) ◦ This autocorrelation introduce redundancy in samples, n=50 is not really what your statistics textbook tells you ◦ Scale effects ◦ The Modifiable Area Unit Problem (MAUP)

◦ Reducing poverty to crude spatial and attribute scales ◦ Mistaking correlation for a causal relationship

Implications of spatial autocorrelation ●

Means that data are not independent observations ◦ For standard inferential statistics this creates problems

But…

◦ This is often exactly what we are interested in! ◦ We look for clusters in space, and their distribution…

Measures of autocorrelation give us information on spatial dependence

◦ As a descriptive technique it is very important!

Spatial autocorrelation ●

Can be

◦ Positive = Nearby points are similar i.e. clustering ◦ Negative = Nearby points a different ◦ “Zero” = no correlation in space

Cannot be “overcome”, so it is important to measure b/c

◦ Depending on the degree of autocorrelation, n may not be as big as you think ◦ Therefore, test results appear to be significant (null hypothesis is rejected). ◦ Type I error increases.

Moran’s I measure of spatial autocorrelation I=

n

∑ (y n

i= 1

i

− y

)

2

∑ ∑ × n

n

i= 1

j = 1 ij n

(

)(

w yi − y y j − y

∑ ∑ i= 1

n j= 1

)

wij

Estimates the extent to which pairs of neighbors are similar or different

◦ Uses the pairwise magnitude of deviation between neighboring locations (a measure of covariance) ◦ Depends on the definition of neighbors (distance/adjacency/interaction) ◦ Different versions can be calculated by using different sets of w values: − − − −

Inverse distance weighted k nearest neighbors All within a certain distance Combinations of these

Global vs. local autocorrelation ●

Doing an entire area give global measures of spatial association

◦ They say something about clustering as a summary of the entire area ◦ Usually we want to know where clusters are located

If we take each indiviual observation of neighboring devations, and map them…

A quick look at Ohio poverty

Scale effects Poverty Rates, Ohio Census Tracts, 2000

Poverty Rate 0% - 7.9% 8% - 11.9% 12% - 15.9% 16% - 19.9% 20% - 100%

Scale effects

From: Davis, 2003, Choosing a method for poverty mapping, FAO

Methods to measure poverty ●

Small area estimation

◦ Combining surveys with census data

Multivariate weighted basic-needs index

◦ Weighted linear combination, multivariate statistics

Combination of qualitative information and secondary data ◦ Focus group, interviews + data on indicators

Extrapolation of participatory approaches ◦ Based on local perception of poverty

Direct measurement of household-survey data ● Direct measurement of census data ●

From: Davis, 2003, Choosing a method for poverty mapping, FAO

Methods of poverty mapping - I ●

Multivariate Weighted basicneeds index ◦ Some combination of several factors ◦ A reverse example: Opportunity mapping − − − − − − −

Employment availability School quality Safety Access to health care Transportation … Average of Z-scores

The map overlay If all data is not contained in one attribute table ● 'Stack maps' idea from the 1930's ●

◦ Red-lining

1.Map co-registration 2.Geometric phase 3.Attribute phase

Explore the Overlay

Overlay â&#x20AC;&#x201C; the map co-registration

Overlay - the geometric phase

Overlay â&#x20AC;&#x201C; the attribute phase I â&#x2014;?

Many alternatives for evaluating attribute combinations

â&#x2014;Ś All depends on knowledge of the attributes

Overlay â&#x20AC;&#x201C; the attribute phase â&#x2014;?

Ex. factor evaluation and summary

The actual mapping Projection ● Classification ● Multi-variate exploration and mapping ● Poverty - a vague, ill-defined concept? ●

Direct projections - Two important classes Each projection creates specific distortions ● Decide what's important to preserve ●

◦ ◦ ◦ ◦

Area Distance Direction Shapes

Source: Goode’s World Atlas

Source: Dent (1999)

Classification Equal interval

Quantile

Standard Dev.

Optimal

0% - 20% 21% - 40% 41% - 60% 61% - 80% 81% - 100%

0% - 4% 5% - 6% 7% - 11% 12% - 20% 21% - 100%

< -0.50 Std. Dev. -0.50 - 0.50 Std. Dev. 0.50 - 1.5 Std. Dev. 1.5 - 2.5 Std. Dev. > 2.5 Std. Dev.

0% - 8% 9% - 17% 18% - 29% 30% - 48% 49% - 100%

Equal interval

Considers data distribution Easy to understand OK with ordinal data Legend easy to understand

Optimal.

0.05 0.14 0.26 0.38 0.50

Std. dev.

Quantile

Eq.Int.

Classification guidelines

-

0.13 0.25 0.37 0.49 0.59

Standard Deviation

Quantile 0.05 0.23 0.32 0.38 0.43

-

0.22 0.31 0.37 0.42 0.59

< -2.50 Std. Dev. -2.50 - -1.50 Std. Dev. -1.50 - -0.50 Std. Dev. -0.50 - 0.50 Std. Dev. 0.50 - 1.50 Std. Dev. 1.50 - 2.50 Std. Dev. > 2.50 Std. Dev.

Optimal 0.05 0.21 0.30 0.37 0.44

-

0.20 0.29 0.36 0.43 0.59

Multivariate analysis Poverty is a multifaceted issue â&#x2014;? One map/variable is rarely enough â&#x2014;?

Comparing multiple variables on maps ● ● ● ●

Traditionally a common view on multivariate data Small multiples Classification scheme Population density critical Optimal classification rarely appropriate for comparison

Optimal Jenks-Caspall classification Chester County, PA

Median income

Population density

Median income

19 - 774

0 - 37596

774 - 2147

37597 - 57949

2147 - 4864

57950 - 78533

4864 - 9400

78534 - 109023

9400 - 31802

109024 - 159471

Other multi-variate examples

● ●

Combinations of symbols, area, lines, are standard in ‘normal’ mapping Trickier when same object takes on several values

Source: Huber et al. 2007 and Atlas of Switzerland

Some tools and resources... ●

On the web – rich viewers with existing data ◦ www.gapminder.org ◦ www.communityresearchpartners.org

On the web – tools you can combine existing with your own data ◦ GeoCommons.com ◦ spotfire.tibco.com ◦ ArcGIS.com

GIS/mapping software

◦ ArcGIS - www.esri.com ◦ GeoDA – geodacenter.asu.edu

If we want to see more variables? ●

The general concept is data as points in a multidimensional ‘data-space’. 

There are as many dimensions to the space as there are variables

With more than 3 variables ●

visualizing the space is hard, but… 

A lot of work is being done in multivariate statistics using visualization methods to tackle the problem

http://www.unidata.ucar.edu

Some common exploratory tools Parallel coordinate plot ● Multidimensional scaling ● Scatter plot matrix ● Linked combinations of tools ●

Explore GeoDa

Poverty & Vagueness - The Sorites paradox ●

The paradox of the heap   

Is one grain of sand a heap? What if a second is added? And a third?

The logical inference:  

n grains and no heap… …then n+1 is no heap

Mapping vague phenomena

C h o ro p le th s h a d in g

P ro p o rtio n a l s y m b o ls

D o t d e n s ity

Poverty can be very heterogeneous Number of households

Poverty threshold

Households in poverty

Number of households

Poverty threshold

200

500

a

400

100

0

300

0.5

0.75 1.00

1.25 1.50

Poverty index

200

b

c

Number of households

Poverty threshold

200

100 100 0

0.5 0

Number of households

0.75 1.00

1.25 1.50

Poverty index

Poverty threshold

d

e

0

0.5

1.25 1.50

Poverty index

Number of households

200

0.75 1.00

Poverty threshold

200

100

f

100

0

0.5

0.75 1.00

1.25 1.50

0

Poverty index

0.5

0.75 1.00

1.25 1.50

Poverty index

Number of households

Poverty threshold

0 - 19 % 20 - 39% 40 - 59 % 60 - 79 % 80 - 100 %

200

100

0

0.5

0.75 1.00

1.25 1.50

Poverty index

Treating poverty as a vague concept

Potential visual metaphors for vagueness in dot maps?

References ●

● ● ● ● ●

Davis, B. (2003). Choosing a method for poverty mapping. Rome: Food and Agriculture Organization of the United Nations. Retrieved from ftp://ftp.fao.org/docrep/fao/005/y4597e/y4597e00.pdf Hyman, G., Larrea, C., & Farrow, A. (2005). Methods, results and policy implications of poverty and food security mapping assessments. Introduction to special issue Food Policy, 30(5/6), 453-460. Community Research Partners. (2008). The Real Bottom Line: The State of Poverty in Ohio 2008. Columbus, OH: Ohio Association of Community Action Agencies (OACAA). Retrieved from http://www.communityresearchpartners.org/uploads/publications//Real %20Bottom%20Line%206-24-08%20with%20Appendix.pdf Glasmeier, A., 2006. An Atlas of Poverty in America: One Nation, Pulling Apart, 1960-2003, Routledge. Reece, J., & Gambir, S. (2008). The Geography os Opportunity (p. 32). Columbus, OH: Kirwan Institute for the Study of Race and Ethnicity. Maantay, J., & Ziegler, J. (2006). GIS for the Urban Environment. Redlands, CA: ESRI Press. Dent, B. D. (2009). Cartography: Thematic Map Design (6th ed.). New York: McGraw-Hill Higher Education. Slocum, T. A., McMaster, R. B., Kessler, F. C., & Howard, H. H. (2009). Thematic cartography and geovisualization (3rd ed.). Pearson Prentice Hall.

Mapping Poverty Workshop
Mapping Poverty Workshop

IPSC, Mapping Poverty Workshop Presentation