PAD 6833 - Testable Hypothesis: Understanding Sterilization Status in San Antonio Shelter Animals

Introduction

The City of San Antonio’s Animal Care Services collects detailed information about every animal that enters the shelter system. One of the most important questions they track is whether animals are already sterilized (spayed or neutered) at the time of intake. This matters because sterilization is one of the strongest tools we have for reducing overpopulation and decreasing the number of animals who end up on the street or in shelters.

To explore this issue, we can look at how an animal’s species, age group, and geographic location may affect its sterilization status. By doing so, we can better understand whether certain groups of animals or neighborhoods are less likely to have access to spay/neuter services, education, and whether targeted outreach programs could help.

Variables

In this data analysis, I will focus on two main types of variables:

• Dependent Variable (Outcome / X)
  • Sterilization Status at Intake (whether the animal was already sterilized when it arrived)
• Independent Variables (Predictors / Y)
  • Species (dogs versus cats)
  • Age Group (puppies/kittens, adults, or seniors)
  • Zip Code or Council District (the part of the city the animal came from)

Hypotheses

I will frame my study using two possible explanations, one assuming there is no effect, and one assuming there is.

• Null Hypothesis (no effect of Y on X): There is no relationship between an animal’s species, age group, or location and whether it is sterilized when it enters the shelter.

• Alternative Hypothesis (some effect of Y on X): An animal’s species, age group, or location is related to whether it is sterilized when it enters the shelter.

Modeling

To formally test my hypothesis, I can use a logistic regression model because the outcome (sterilized vs. not sterilized) is binary.

Why This Matters

This hypothesis is worth testing because sterilization patterns often reveal gaps in community resources or engagement / education efforts.

For example:

• If cats are consistently less likely to be sterilized than dogs, it might point to fewer community programs aimed at feral or stray cats.
• If younger animals are more often unsterilized, that could reflect accidental litters where owners did not yet spay or neuter the parents.
• If certain zip codes or council districts show higher rates of unsterilized animals, that may highlight geographic barriers to affordable veterinary care, lack of education on resources, or even mobility to resources.

Understanding these patterns can guide the City’s decisions. If we know where the biggest needs are, Animal Care Services can design more effective programs, like mobile spay/neuter clinics, neighborhood outreach, or partnerships with local vets or community groups.

Conclusion

By looking at sterilization status as the main outcome, and testing its relationship with species, age, and location, we can learn a great deal about how animal welfare practices vary across the city. The null hypothesis assumes that sterilization is equally likely across all groups, while the alternative hypothesis suggests meaningful differences. If the data support the alternative, this insight could lead to smarter, more targeted strategies to reduce intake numbers and improve animal well-being in San Antonio.