Community Close-Up Data Analysis

Community Close-Up Data Analysis

Community Close-Up is an aptly named data analysis tool a community can use to determine how many meals its hungriest residents are missing and in which census tracts they reside. Demographic information provided for each census tract helps suggest and inform strategies for closing the meal gap at the community level.

Community Close-Up provides a unique viewpoint because both demand and supply for meals for the food insecure are considered.

• Missed Meals Demand: Food insecurity methodology (from Feeding America/University of Illinois) based upon 2010 poverty, unemployment and demographics adapted to Minnesota census tract level to estimate meals missed.
• Resource Supply: Data for relative utilization of government food assistance programs (such as Supplemental Nutrition Assistance Program; Women, Infants and Children, etc.) and nonprofit food providers gathered and allocated to census tracts based on Census 2010 demographic information.

Community Close-Up data analysis is no substitute for local knowledge. However, it can provide excellent insight about how and where to target efforts, and a vehicle to foster collaboration across hunger-fighting organizations. A food shelf, meal program, food bank or any other community service provider can use Community Close-Up analysis to:

• Quantify need in its service area (by using the analysis to determine the meal gap and food insecure population).
• Present the data to other people in the community to stimulate ideas about solutions (with census tract-level maps and data tables with detailed demographic data).
• Devise precise plans to close defined meal gaps (including multiple strategies such as food shelf/meal program/food bank expansion, SNAP and WIC outreach, working with the school district on school breakfast participation, mobile pantry programs and more).

It’s important to remember when using Community Close-Up:

• All data are estimates based primarily on 2010 sources.
• Relative comparison of data is based upon assigning all 1332 Census tracts into four segments: Twin Cities Metro Urban, Metro Suburban, Non-Metro Urban and Rural.  Classifications are all relative to other tracts in the segment.
• There may be discrepancies from current conditions for a variety of reasons (age of data, missing info from sources, etc.).
• Local knowledge is the key to interpret and then act upon the analysis.

Here is an example of how Community Close-Up data analysis is applied at the census-tract level for Rice County. Rice County is composed of 13 census tracts. Nine of them are classified as Non-Metro Urban: five comprising Northfield (I – M) and four comprising Faribault (A – D). The four other census tracts (E, F, G and H) are classified as Rural.

Looking at Faribault in more detail, it is possible, using Community Close-Up data analysis to estimate the food insecurity rate (the percentage and number of people who do not know where their next meal is coming from) in each census tract and from there, to estimate the number of missing meals in the census tracts comprising Faribault.

Equally helpful is demographic and economic information available for each census tract. Understanding the composition of the population can help in pinpointing precise strategies that will alleviate hunger issues.

What’s the point?

Armed with this data and invaluable local, situational knowledge, food shelves, food banks, meal programs and other community hunger resources can come together to determine the best ways to close their missing meal gaps.