SWAMPEDMapping and Measuring Food Access in Cleveland

Md Rumi ShamminOberlin College (OC)

Paul BoehnleinWestern Reserve Land Conservancy (WRLC)

Mimi Stern (OC ‘16)

Isabella McKnight (OC ‘15)

http://www.aristotledesigngroup.com/

Prologue ...Healthy Food Access ↦ Urban Sustainability

Food Access ↔ Food Justice

Food Swamps ⇎ Food Desert

Study Area - Cleveland, Ohio

socialjusticefund.org

Food Swamp definedproximity, prevalence & affordability of unhealthy foods inundating more desirable food options

❏ Unhealthy - fast food, liquor stores ...❏ Healthy - supermarkets, grocery stores, farmer’s markets ...

www.neofoodweb.orgwksu.org www.cleveland.comwww.ideastream.org

Questions We Asked ...➢ Do some neighborhoods have overabundance of

undesirable food sources relative to the presence of desirable sources in Cleveland?

➢ Are neighborhoods in food swamps correlated with demographic characteristics such as race and income?

➢ What can we learn about food access, food justice and related policies from this analysis?

Methods We Used ...➢ Create a database of food sources➢ Create a database of homes➢ Organize demographic data for homes➢ Spatial analysis using GIS to measure proximity and access

○ Network analysis was used instead of linear distances

➢ Compare results across neighborhoods (block groups)➢ Explore policy implications of findings

Census Reporter

Proximity to Healthy Food ...Paired Samples Test

Paired Differences t df Sig.

(2-tailed

)Mean Std.

Deviation

Std. Error

Mean

95% Confidence Interval of the

Difference

Lower Upper

FIVE_CLOSEST_

AVERAGE_UNHEALTHY_FEET -

FIVE_CLOSEST_

AVERAGE_HEALTHY_FEET

-3197.19893 2034.51765 5.88880 -3208.74087 -3185.65699 -542.929 119362 .000

No Food within Walking Distance ...

Quarter Mile = 47%

Half Mile = 11.7%

1 Mile = 0.06%

Total # of homes = 119,363

http://getinmymouf.com/2014/07/10-reasons-not-to-live-in-the-burbs-if-you-enjoy-good-food/

Inundation Explained ...

Inundation = #unhealthy/#healthy

5 = Extreme = >84 = High = 6 - 83 = Moderate = 4 - 62 = Low = 2 - 41 = Very low = 0 - 2

http://www.canadianunderwriter.ca/insurance/report-documents-populations-businesses-in-california-tsunami-inundation-zone-1002169060/

1 2 3 4 5Inundation categories (1 = Very low; 5 = Extreme)

Inundation

Correlations

I PERCENT_AFRICAN_

AMERICAN_mean

PERCENT_

WHITE_mean

PERCENT_

HISPANIC_mean

PERCENT_

OTHER_mean

INUNDATION Pearson

Correlation

1 -.007 -.013 .312** -.088

Sig.

(2-tailed)

.886 .789 .000 .061

N 459 459 459 459 459

Food SWAMP Explained ...

Extreme poor, extreme inundation

High low-income, high inundation

Moderate not low-income, high inundation

Low low-income, low inundation

Very Low not low-income, very low inundation

Income Categories

5 = Poor (<$20,500)4 = Low income ($20,500 - $37,800)3 = Lower middle income ($37,800 - $79,300)2 = Upper middle income ($79,300 - $120,800)1 = High income (>$120,800)

SWAMPED

ANOVAR squared = 0.15

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 7.505 3.922 1.914 .056

PERBLACKBYWHITE .006 .010 .041 .619 .536

PERCENT_AFRICAN_AMERICAN

_mean

.049 .040 .311 1.221 .223

PERCENT_WHITE_mean -.011 .040 -.063 -.271 .786

PERCENT_HISPANIC_mean .192 .028 .440 6.819 .000

PERCENT_OTHER_mean .046 .067 .040 .685 .493

a. Dependent Variable: SWAMP

SWAMPED Population: by Race

SWAMPED Total% AA% W% H% O% # of Blocks

Very low 12.8% 9.6% 17.3% 11.0% 12.2% 59

Low 40.4% 39.2% 41.1% 34.6% 46.0% 180

Moderate 21.8% 22.9% 20.5% 21.1% 19.4% 99

High 20.4% 24.0% 16.4% 23.3% 16.7% 94

Extreme 4.6% 4.3% 4.6% 10.0% 5.7% 25

Moderate - Extreme 46.8% 51.2% 41.6% 54.4% 41.8% 218

Racial Profile of Cleveland

Racial Profile of Cleveland

Racial Profile of Cleveland

Racial Profile of Cleveland

Income & Poverty in Cleveland

Income & Poverty in Cleveland

Income & Poverty in Cleveland

Inundation

SWAMPED

Conclusions ...➢ In Cleveland, homes have unhealthy food sources

closer on average compared to healthy food sources.

➢ Higher percentages of African American and Hispanic populations are SWAMPED.

Conclusions ...➢ Low-income minority populations in urban areas are

often subject to housing and spatial segregation.

➢ According to the CDC, low-income and low-access characteristics are correlated with obesity, diabetes, some cancers, mental illness, heart disease, high cholesterol, and mortality rates.

Conclusions ...➢ Policy efforts need to be spatially calibrated to improve

access to healthy food and advance food justice.

Questions?student.unsw.edu.au