DOI: 10.5061/dryad.n2z34tn5b
title: Data from: Energy efficient homes for rodent control across cityscapes

description: 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). 
description: # Energy Efficient Homes For Rodent Control Across Cityscapes
 [https://doi.org/10.5061/dryad.n2z34tn5b](https://doi.org/10.5061/dryad.n2z34tn5b) 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  **PhiladelphiaMainModel_Final_V5_NoRodentControl.nlogo** * 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 ## Code/Software 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.