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Data-Driven Food Desert Metric to Understand Access to Grocery Stores Using Chi-Square Automatic Interaction Detector Decision Tree Analysis
Celeste Chavis, Istiak A Bhuyan. Transportation Research Record: Journal of the Transportation Research Board, 2022
Topic: Risk, prevention and care in population studies
A food desert is a geographic area lacking spatial and socioeconomic access to healthy foods. Disparities in food access and availability hinder public health and individual wellbeing. Access to healthy food has been evaluated by many agencies and researches yet they do not comparably demarcate food deserts or identify vulnerable populations in methodical terms. Existing literature suggests a link between food deserts, income level, and vehicle ownership. This study evaluates the existing methods and proposes a novel data-driven method to identify food deserts in Baltimore, Maryland. This study evaluates responses from 573 respondents for an in-depth analysis of individual grocery store choice and travel decisions. Chi-square automatic interaction detector (CHAID) decision trees are used to develop a user-generated food desert metric. Income level is found to be a key indicator for food desert demarcation, more than vehicle ownership. Network distance was applied to develop the prioritization matrix for a food desert. This research provides a replicable method for determining food-insecure areas in a locality by aggregating individual data to identify such areas. Such a metric can aid policymakers to make investment decisions and to direct resources to areas of need.
Key findings
- Chi-square automatic interaction detector (CHAID) decision trees are used to develop a user-generated food desert metric.
- Income level is found to be a key indicator for food desert demarcation, more than vehicle ownership.
- Network distance was applied to develop the prioritization matrix for a food desert.
- This research provides a replicable method for determining food-insecure areas in a locality by aggregating individual data to identify such areas.
- Such a metric can aid policymakers to make investment decisions and to direct resources to areas of need.
Why it matters
This study develops a data-driven food desert metric for Baltimore, not Alzheimer's disease, so it offers nothing on treating, curing or preventing Alzheimer's. It found income level was a key indicator for food desert demarcation.
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Study details
- Evidence strengthLow
How this study scored 30/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, so that was left out: it earned 29 of the 96 points we could judge it on, which is 30 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Transportation Research Record: Journal of the Transportation Research Board) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 6 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 2 / 6 | Cited 1.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Istiak Bhuyan: h-index 5 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Morgan State University: institution h-index 204 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 29 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 29 of the 96 points we could judge it on is 30 out of 100. |
| Total | 30 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Chavis, C., & Bhuyan, I. A. (2022). Data-Driven Food Desert Metric to Understand Access to Grocery Stores Using Chi-Square Automatic Interaction Detector Decision Tree Analysis. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981221089308
Vancouver
Chavis C, Bhuyan IA. Data-Driven Food Desert Metric to Understand Access to Grocery Stores Using Chi-Square Automatic Interaction Detector Decision Tree Analysis. Transportation Research Record: Journal of the Transportation Research Board. 2022. doi:10.1177/03611981221089308.
BibTeX
@article{chavis2022-8ca0501d,
author = {Chavis, Celeste and Bhuyan, Istiak A},
title = {{Data-Driven Food Desert Metric to Understand Access to Grocery Stores Using Chi-Square Automatic Interaction Detector Decision Tree Analysis}},
journal = {Transportation Research Record: Journal of the Transportation Research Board},
year = {2022},
doi = {10.1177/03611981221089308},
url = {https://doi.org/10.1177/03611981221089308}
}
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