Food Protection Trends - July/August 2020 - 239

classes do not represent a threshold of acceptability or
significance, as they were arbitrarily selected to best represent
the data for visualization and comparison. Finally, although
the use of Jenks natural breaks is useful for visualizing
natural groupings inherent in the data, it is not suitable for
comparison across different datasets, since they are dataspecific classifications (11). Future research should be done
to determine which CP/C thresholds may point toward
significant food safety issues that require more attention from
public health officials.
Overall, spatial analysis using Moran's I shows that
first annual inspections of restaurants receiving CP/C
results are significantly clustered together, indicating that
inspection results are not independent from each other. The
spatial distribution of high CP/C rates is more prominent
outside the downtown core of Toronto, and hot spot
analysis confirms that these clusters are significant. The
descriptive findings show that spatial autocorrelation may

have some utility in food safety inspection analysis, despite
its limitations and assumptions. The regression analysis
shows where some neighborhood characteristics, such as
immigration, poverty, and English-speaking proficiency, may
point toward specific populations and locations where more
public health and food safety resources and outreach could
be provided to enhance food safety. Annual spatial analysis
of inspection results could inform public health and food
safety authorities about where to focus future education and
training efforts or interventions and could aid in identifying
unknown geographical biases influencing inspections.
ACKNOWLEDGMENTS
The authors thank Noel Damba from Ryerson University's
Geospatial Map & Data Centre for assistance with map projections and formatting, and Cornelius Ng for assistance with
GIS troubleshooting. We acknowledge the City of Toronto's
Open Data initiative as the source of data for these analyses.

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