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Data from: Energy efficient homes for rodent control across cityscapes

Cite this dataset

Gadsden, Gabriel; Ferraro, Kristy; Harris, Nyeema (2024). Data from: Energy efficient homes for rodent control across cityscapes [Dataset]. Dryad.


Cities spend millions of dollars on rodent mitigation to reduce public health risks. Despite these efforts, infestations often remain high. Rodents thrive in the built environment in part due to reduced natural predators and the exploitation of garbage. Though sanitation and greenspace are important factors in rodent mitigation, more complex governance and action are needed. Urban rodents are dynamic and commensal in nature, so understanding the influence of prolific urban features, like building attributes, warrants scrutiny and additionally intersects mitigation strategies with stakeholders at a localized level. Here, we model how residential structures’ efficiency influences urban rodent populations. To do so, we created an agent-based model using characteristics of urban brown rats and their natural predator, red foxes, based on three distinct neighborhoods in Philadelphia, Pennsylvania. We varied whether retrofitting occurred and its duration as well as the percent of initial energy-efficient homes in each neighborhood. We found that initial housing conditions, retrofitting, and the duration of retrofitting all significantly reduced final rodent populations. However, retrofitting was most effective in reducing rodent populations in neighborhoods with extensive park access and low commercial activity. Additionally, across neighborhoods, single large efficiency initiatives showed greater potential for rodent reduction. Lastly, we show that the costs of large-scale retrofitting schemes are comparable to ten-year public health spending, demonstrating that retrofitting may have the potential to offset near-term costs. Our results showcase how system-view investments in integrated pest management can lead to sustained rodent pest mitigation and advance sustainable development goals, infrastructure innovation (Goal #9), reduced inequalities (Goal #10), and sustainable cities and communities (Goal #11). 

README: Energy Efficient Homes For Rodent Control Across Cityscapes

We have submitted our raw data as CSV files with the naming convention Philadelphia Main Model Final (Neighborhood) Year (1,5, or 10). We have also submitted the NetLogo code which is how our raw data was created. There are 5 NetLogo files. PhiladelphiaMainModel_Final_V5_NoRodentControl.nlogo, is the model which our analysis is derived. PhiladelphiaMainModel_Final_Rodent_Control.nlogo, is a complementary code with an extra function for those looking to use the code for exploration. The other NetLogo codes are bare editions for sensitivity analysis; the naming convention follows PhiladelphiaENV(Neighborhood)SensitivityAnalysisV3.nlogo. Finally we included 4 R scripts. The main analysis is FinalABMAnalysis4_24.R. The other complementary R codes are neighborhood specific sensitivity analysis codes with the naming convention, (Neighborhood)SensitivityAnalysisV(#).R.  

Description of the data and file structure

Philadelphia Main Model Final (Neighborhood) Year (1,5, or 10)

  • Run number: The current model run from 1 - 200
  • Neighborhood: Chestnut Hill, Cobbs Creek, or Olney 
  • Initial Conditions: Inefficient, 25%, 50%, 75%, or 100% 
  • Retrofit: False or True 
  • Step: Current place in the model run 
  • Count Foxes Current number of foxes in the model environment  
  • Count Rodents: Current number of rodents in the model environment  
  • Count Patches w/ P-Color 93: Current number of patches that are efficient 
  • Count Rodents w/ house = true: Current cumber of rodents within a residential building 
  • Count Rodents w/ P-Color 16: Current cumber of rodents within a commercial building 


  • Neighborhood: Select model location 
  • Time Run: Select how long you want the model to run for
  • Initial Condition: Alter starting conditions of the model 
  • Retrofit: Toggle if active retrofitting occurs during model runs 
  • Setup: Create a new template with variables including random placement of rodents and predators  
  • Go: Begin running model simulation


R and R Studio are required to run FinalABMAnalysis4_24.R. R, R Studio, and Java are required to run (Neighborhood)SensitivityAnalysisV(#).R. It is optimal to run the R code in R Studio. NetLogo is required to run all documents with suffix *.nlogo. Annotations are provided throughout R code for 1) library loading, 2) dataset loading and cleaning, 3) analyses, and 4) figure creation.


Hixon Center for Urban Sustainability

Yale Center for Environmental Justice

National Science Foundation, Award: DGE-1752134