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RATScreenerRMD.Rmd
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RATScreenerRMD.Rmd
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---
output:
html_document:
toc: true
toc_float: true
code_folding: hide
highlight: monochrome
css: custom.css
---
*This project was conducted as part of the University of Pennsylvania’s Master of Urban Spatial Analytics/Smart Cities Practicum in Spring 2023, which was taught by Michael Fichman and Matt Harris. In addition to Michael and Matt, we would like to thank Ren Massari, Peter Casey, and Kevin Wilson of The Lab at DC for their support and guidance on this project.*
**[View RATScreener Web App](https://henryfeinstein.github.io/musa-rats/site/)** | **[View RATScreener Github Repository](https://github.com/henryfeinstein/musa-rats)** | **[Return to MUSA 801 Projects page](https://pennmusa.github.io/MUSA_801.io/)**
```{r message=FALSE, warning=FALSE, include=FALSE}
#setup
# Rmarkdown global setting
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(cache = TRUE)
knitr::opts_chunk$set(fig.align = 'center')
#----------------------------------------------------------------------------------------------------------
# Import libraries
library(tidyverse)
library(tidycensus)
library(sf)
library(spdep)
library(caret)
library(ckanr)
library(FNN)
library(grid)
library(gridExtra)
library(ggcorrplot)# plot correlation plot
library(corrplot)
library(corrr) # another way to plot correlation plot
library(kableExtra)
library(jtools) # for regression model plots
library(ggstance) # to support jtools plots
library(ggpubr) # plotting R^2 value on ggplot point scatter
library(broom.mixed) # needed for effects plots
library(knitr)
library(rmarkdown)
library(RSocrata)
library(viridis)
library(ggplot2)
library(stargazer)
library(XML)
library(data.table)
library(ggpmisc)
library(patchwork)
library(spatstat)
library(raster)
library(classInt) # for KDE and ML risk class intervals
library(tableHTML)
library(exactextractr)
library(sp)
library(units)
library(lubridate)
library(pscl)
library(cvms)
library(yardstick)
library(plotROC)
library(gganimate)
library(gifski)
library(randomForest)
library(caret)
library(e1071) #SVM
library(pscl) # Zero-inflated
library(gbm) #Gradient Boosting
library(rpart) # Decision Tree
library(irr)
library(MLmetrics)
library(riem)
library(rjson)
#----------------------------------------------------------------------------------------------------------
# Temp
source("https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/functions.r")
root.dir = "https://github.com/henryfeinstein/musa-rats/blob/main/data/"
# Etc
options(scipen=999)
options(tigris_class = "sf")
#----------------------------------------------------------------------------------------------------------
# functions
st_c <- st_coordinates
st_coid <- st_centroid
plotTheme <- theme(
plot.title =element_text(size=12),
plot.subtitle = element_text(size=8),
plot.caption = element_text(size = 6),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 10),
# Set the entire chart region to blank
panel.background=element_blank(),
plot.background=element_blank(),
#panel.border=element_rect(colour="#F0F0F0"),
# Format the grid
panel.grid.major=element_line(colour="#D0D0D0",size=.2),
axis.ticks=element_blank())
mapThememin <- function(base_size = 10, title_size = 12, small_size = 8) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = title_size, colour = "black", hjust = 0.5),
plot.subtitle=element_text(size = base_size, colour = "black", hjust = 0.5, face="italic"),
plot.caption=element_text(size = small_size, colour = "black", hjust = 0.5),
axis.ticks = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
strip.text.x = element_text(size = small_size, face="italic"),
strip.text.y = element_text(size = small_size, face="italic"),
strip.background = element_rect(colour="transparent", fill="transparent"),
legend.title = element_text(size = small_size),
legend.text = element_text(size = small_size),
legend.key.size = unit(0.4, "cm"))
}
mapThememin2 <- function(base_size = 8, title_size = 10, small_size = 6) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = title_size, colour = "black", hjust = 0.5),
plot.subtitle=element_text(size = base_size, colour = "black", hjust = 0.5, face="italic"),
plot.caption=element_text(size = small_size, colour = "black", hjust = 0.5),
axis.ticks = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
strip.text.x = element_text(size = base_size),
strip.text.y = element_text(size = base_size),
strip.background = element_rect(colour="transparent", fill="transparent"),
legend.title = element_text(size = small_size),
legend.text = element_text(size = small_size),
legend.key.size = unit(0.25, "cm"))
}
corTheme <- function(base_size = 10, title_size = 12, small_size = 8){
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
text = element_text(size = 10),
panel.background = element_rect(fill = greyPalette5[1]),
axis.title.x = element_text(size = small_size),
axis.title.y = element_text(size = small_size),
plot.subtitle = element_text(hjust = 0.5, size = base_size),
plot.title = element_text(hjust = 0.5, size = title_size),
plot.caption=element_text(size = small_size, colour = "black", hjust = 0.5),
strip.background = element_rect(colour="transparent", fill="transparent"))
}
corTheme2 <- function(base_size = 10, title_size = 12, small_size = 8){
theme(axis.text = element_text(size = small_size),
text = element_text(size = 10),
panel.background = element_rect(fill = greyPalette5[1]),
axis.title.x = element_text(size = small_size),
axis.title.y = element_text(size = small_size),
plot.subtitle = element_text(hjust = 0.5, size = base_size, face="italic"),
plot.title = element_text(hjust = 0.5, size = title_size),
plot.caption=element_text(size = small_size, colour = "black", hjust = 0.5),
strip.background = element_rect(colour="transparent", fill="transparent"),
strip.text.x = element_text(size = small_size, face="italic"),
strip.text.y = element_text(size = small_size, face="italic"))
}
corTheme3 <- function(base_size = 9, title_size = 11, small_size = 7){
theme(axis.text = element_text(size = small_size),
text = element_text(size = 10),
panel.background = element_rect(fill = greyPalette5[1]),
axis.title.x = element_text(size = small_size),
axis.title.y = element_text(size = small_size),
plot.subtitle = element_text(hjust = 0.5, size = base_size, face="italic"),
plot.title = element_text(hjust = 0.5, size = title_size),
plot.caption=element_text(size = small_size, colour = "black", hjust = 0.5))
}
corTheme4 <- function(base_size = 9, title_size = 11, small_size = 7){
theme(axis.text = element_text(size = small_size),
text = element_text(size = 10),
panel.background = element_rect(fill = greyPalette5[1]),
axis.title.x = element_text(size = small_size),
axis.title.y.right = element_text(size = small_size),
plot.subtitle = element_text(hjust = 0.5, size = base_size, face="italic"),
plot.title = element_text(hjust = 0.5, size = title_size),
plot.caption=element_text(size = small_size, colour = "black", hjust = 0.5))
}
q5 <- function(variable) {as.factor(ntile(variable, 5))}
q <- function(variable) {as.factor(ntile(variable, 5))}
qBr <- function(df, variable, rnd) {
if (missing(rnd)) {
as.character(quantile(round(df[[variable]],0),
c(.01,.2,.4,.6,.8), na.rm=T))
} else if (rnd == FALSE | rnd == F) {
as.character(formatC(quantile(df[[variable]],
c(.01,.2,.4,.6,.8), na.rm=T),
digits = 3))
}
}
qBr2 <- function(df, variable, rnd) {
if (missing(rnd)) {
as.character(quantile(round(df[[variable]]*100,0)/100,
c(.01,.2,.4,.6,.8), na.rm=T))
} else if (rnd == FALSE | rnd == F) {
as.character(formatC(quantile(df[[variable]],
c(.01,.2,.4,.6,.8), na.rm=T),
digits = 3))
}
}
qBr3 <- function(df, variable, rnd) {
if (missing(rnd)) {
as.character(round(quantile(round(df[[variable]]*1000000,0),
c(.01,.2,.4,.6,.8), na.rm=T)),0)
} else if (rnd == FALSE | rnd == F) {
as.character(formatC(quantile(df[[variable]],
c(.01,.2,.4,.6,.8), na.rm=T),
digits = 3))
}
}
substrRight <- function(x, n){
substr(x, nchar(x)-n+1, nchar(x))
}
nn_function <- function(measureFrom,measureTo,k) {
measureFrom_Matrix <- as.matrix(measureFrom)
measureTo_Matrix <- as.matrix(measureTo)
nn <- get.knnx(measureTo, measureFrom, k)$nn.dist
output <- as.data.frame(nn) %>%
rownames_to_column(var = "thisPoint") %>%
gather(points, point_distance, V1:ncol(.)) %>%
arrange(as.numeric(thisPoint)) %>%
group_by(thisPoint) %>%
summarize(pointDistance = mean(point_distance)) %>%
arrange(as.numeric(thisPoint)) %>%
dplyr::select(-thisPoint) %>%
pull()
return(output)
}
myCrossValidate <- function(dataset, id, dependentVariable, indVariables) {
allPredictions <- data.frame()
cvID_list <- unique(dataset[[id]])
for (i in cvID_list) {
thisFold <- i
cat("This hold out fold is", thisFold, "\n")
fold.train <- filter(dataset, dataset[[id]] != thisFold) %>% as.data.frame() %>%
dplyr::select(id, geometry, indVariables, dependentVariable)
fold.test <- filter(dataset, dataset[[id]] == thisFold) %>% as.data.frame() %>%
dplyr::select(id, geometry, indVariables, dependentVariable)
regression <-
glm(countMVTheft ~ ., family = "poisson",
data = fold.train %>%
dplyr::select(-geometry, -id))
thisPrediction <-
mutate(fold.test, Prediction = predict(regression, fold.test, type = "response"))
allPredictions <-
rbind(allPredictions, thisPrediction)
}
return(st_sf(allPredictions))
}
#----------------------------------------------------------------------------------------------------------
#----------------------------------------------------------------------------------------------------------
# LoadAPI
census_api_key("4bbe4bead4e5817f6a6b79e62c5bea69e77f1887", overwrite = TRUE)
```
## Abstract
Rats thrive in cities. However, the feeling is not mutual according to city residents. In 2021, DC’s 311 Service received over 11,000 requests for rodent inspection and abatement. As inspection requests continue to increase after a pandemic-induced spike in rat populations, inspection departments are inundated with requests for treatment [1]. However, inspection and treatment require resources: personnel, time, and money. With over half of the inspection requests in DC not finding evidence of rats, the inspection and treatment process is costly to the city.
This project explores the spatial and time patterns related to vermin infestation in order to develop a predictive model for estimating the probability of rat detection in a given area of Washington, DC. The information provided by the tool will allow city health and vermin inspectors to prioritize exterior inspections of properties suspected of vermin infestation based on the actual likelihood of rodents being detected. We aim to create a proof-of-concept infestation forecast that will be used as the basis of an inspection optimization data system and web app, which will allow for more targeted and efficient inspections.
## 1. Introduction: Context & Use Case
Municipal rodent management is a critical part of local government operations. In many cities, rodent infestation is persistent and can have detrimental impacts on the public infrastructure, local economy, overall health and well-being of both residents and the environment [2]. Cities face the monumental challenge of conducting large-scale rodent management, responding to resident complaints, and educating the public on the cause of rodent infestations.
This project aims to develop a screening tool that prioritizes DC 311 requests for rodent inspection based on the likelihood of rat detection on a given block. The goal of this work is to more effectively distribute resources used for inspection and treatment with the hopes of freeing up resources for other rodent management needs. The ultimate goal is to aid in the abatement of DC’s rat infestation issues.
### 1.1: Understanding Urban Vermin Infestations
Norway rats, also known as brown rats, are the most common species found in U.S. cities. These rats are commonly associated with sanitation problems in cities. While this reputation is accurate, rats are also behind many other problems that occur in urban areas. Rodents can cause structural issues by burrowing in streets or buildings, causing property damage that can result in the loss of businesses and homes. Rats also cause power outages, internet blackouts, and fires by gnawing on gas lines or electrical wires. Finally, they also pose a risk to public health and well-being as they can contaminate food, carry diseases, and spread pathogens [1].
Cities, with their large and dense human populations, provide optimal habitats for rats. Colonies of rats can stretch across entire city blocks. Rats utilize human-made infrastructure by traveling via sewer systems or utility lines to reach neighboring buildings. Food is the most important resource that cities provide to rats. They often explore their territories for new food sources at night when there is less human activity. City residents know that residential and commercial trash cans and dumpsters are often not tightly secured, making them prime opportunities for rats to find food. Ultimately, human behaviors and food waste are a main driving force behind urban rat infestations [3].
### 1.2: DC's Current Rat Infestation Abatement Approach
The Distric of Columbia Department of Health (DC Health) is responsible for the city’s rodent control program. Currently, DC Health aims to protect public health by reducing rodent activity through a combination of proactive surveys, inspections, baiting, enforcement, community outreach and distribution of educational materials. This work is performed by the Rodent and Vector Control Division, but relies on interagency cooperation.
DC’s business-as-usual approach relies on professional knowledge and ad-hoc decision-making on a daily basis. Residents can request inspections from DC Health and the Rodent and Vector Control Division by submitting reports through 311. When a request is received, DC Health inspectors are sent to the location of the call to inspect and treat any infestations. However, there are more inspection requests than inspectors can handle on a daily basis. There is currently no formal prioritization of inspection requests.This strategy allows for inefficiencies in terms of employee time and financial resources, limiting the benefits of the inspection services to the public.
### 1.3: Our Approach to Improving Rat Inspection Efficiency
Our project aims to disrupt the current inefficiencies in inspection services by developing an inspection optimization system, called RATScreener. The RATScreener system will be based on a predictive model that forecasts the likelihood of infestation based on a range of spatial, population, and built environment variables. This system will assign probabilities of infestation to specific blocks and provide an overview of hotspot areas in DC.
This approach will allow the inspection office to prioritize incoming requests and understand the probability of actual infestations. DC Health inspectors can then make more informed decisions, target inspections to areas of high infestation probability, and reduce strain on limited resources within the department.
### 1.4: RATScreener Overview
We present an overview of the modeling process behind RATScreener below. Our process began by collecting data and assigning variables to each block in DC. We then run our model and calculate predictions for each block, which are then categorized by priority based on the probability of rat detection. As new requests come in, RATScreener identifies the block in which the address is located and assigns a priority level based on the block’s risk of rat detection. Finally, a list of prioritized requests is presented to inspectors via the RATScreener app.
## 2. Exploratory Analysis
In the exploratory analysis phase of this project, we aim to identify the patterns of rat infestation across time and space. Our analysis attempts to understand the spatial process associated with infestation and the relationships between built environment, spatial, and population variables.
### 2.1: Processing Rat Inspection Outcomes as the Dependent Variable
The primary independent variable that the RATScreener model is trying to predict is whether a given 311 request for a rat inspection will lead to the discovery and treatment of an actual infestation. DC Health provided a dataset of all rat inspection requests placed through DC’s 311 Service between 2015 and 2018. The dataset includes the address the request was placed at, administrative information, and field notes from the resulting inspection.
A text analysis was performed on the field notes to assign each 311 request to a “rats found/no rats found” binary variable. The analysis detected words such as “baited,” “treatment,” and “treated” to indicate that rat activity was identified, and phrases such as “no evidence,” “no rat burrows,” or “no rat activity” as indications that no evidence of rats was found. The resulting binary variable is used as the independent variable for the remainder of the analysis.
As previously mentioned, rat colonies tend to be limited by barriers in the built environment such as roads and other impervious surfaces. Because of this, rat inspections typically cover an entire block, not just the address that the 311 request came from. In order to have the modeling process reflect this approach to treatment, the RATScreener tool predicts the likelihood of rat infestation at the city block level. After creating city block polygons using DC Open Data's [Street Centerlines shapefile](https://opendata.dc.gov/datasets/DCGIS::street-centerlines-2013/explore?location=38.894921%2C-77.025952%2C12.88), the binary variable described above was translated to a block-level binary variable of whether rats had been found anywhere on that block in the past. This process yielded 5,243 city blocks in DC which are used for the remainder of the analysis.
In addition to the 311 data shown above, DC Health provided us with a dataset of 100 inspections performed across the city at locations which were not connected to a 311 request. This secondary dataset is used later in the analysis for additional validation and to better understand how reliant the models are on patterns seen in 311 data specifically, rather than the underlying data of actual rat infestation.
### 2.2: Exploring Risk of Rat Infestation
```{r message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
# Load rat infestation dataset and spatialize
Rats <- read.csv("./data/rats_to_blocks.csv.gz", header = TRUE) %>%
na.omit() %>%
st_as_sf(.,coords=c("LONGITUDE","LATITUDE"),crs=4326) %>%
st_transform('ESRI:102685') %>%
mutate(month = month(ymd_hms(SERVICEORDERDATE)),
year = year(ymd_hms(SERVICEORDERDATE)),
serviceday = ymd(substr(SERVICEORDERDATE,1,10))) %>%
dplyr::select(P0010001, index_right, SERVICEORDERDATE, INSPECTIONDATE, SERVICENOTES, serviceday, WARD, week, year, month, calls, activity, geometry)
# load street centerlines from DC open data
centerlines <- st_read("./data/Street_Centerlines_2013/Street_Centerlines_2013.geojson") %>%
st_transform("ESRI:102685") %>%
filter(ROADTYPE == "Street")
# convert street centerlines to block polygons
blocks <- as.data.frame(st_collection_extract(st_polygonize(st_union(centerlines)))) %>%
dplyr::mutate(block_id = row_number()) %>%
st_as_sf()
boundary <- st_union(blocks)
# spatial join to assign each rat datapoint to a block polygon
rats_block_join <- st_join(Rats, blocks)
# count observations per block for mapping
block_dat <- left_join(blocks, rats_block_join %>%
st_drop_geometry() %>%
group_by(block_id) %>%
dplyr::summarize(inspection_count = n(),
rats_found_yn = ifelse(1 %in% activity, 1, 0),
rats_found_count = sum(activity))) %>%
dplyr::mutate(inspection_count = replace_na(inspection_count, 0),
rats_found_yn = replace_na(rats_found_yn, 0),
rats_found_count = replace_na(rats_found_count, 0),
area_acres = as.numeric(st_area(.)) / 43560) %>%
dplyr::mutate(found = case_when(rats_found_yn == "0" ~ "not_found",
rats_found_yn == "1" ~ "found"))
# count of inspections map
count_inspections <- ggplot() + geom_sf(data = block_dat, aes(fill = inspection_count), color = "transparent") +
scale_fill_gradient(low = "#e3b1b1", high = "#E91C1C", name = "Count") +
labs(title = "Count of Inspections by Block",
subtitle = "",
caption = "") +
mapThememin2()
# count of rats found per block map
count_rats <- ggplot() + geom_sf(data = block_dat, aes(fill = rats_found_count), color = "transparent") +
scale_fill_gradient(low = "#e3b1b1", high = "#E91C1C", name = "Count") +
labs(title = "Number of Rat Detection Instances by Block",
subtitle = "",
caption = "") +
mapThememin2()
# any rats found y/n per block map
rats_yn <- ggplot() +
geom_sf(data = block_dat, aes(fill = found), color = "transparent") +
scale_fill_manual(values = c("not_found" = "#e3b1b1", "found" = "#E91C1C"), name = "Outcome") +
labs(title = "Blocks by Binary Found/Not Found Variable",
subtitle = "",
caption = "") +
mapThememin2()
# rat ID rate per block map
rat_id <- ggplot() + geom_sf(data = block_dat, aes(fill = rats_found_count / inspection_count), color = "transparent") +
scale_fill_gradient(low = "#e3b1b1", high = "#E91C1C", name = "Detection Rate") +
labs(title = "Rat ID Rate (Count of Rats Found / Number of Inspections) \nby Block",
subtitle = "",
caption = "") +
mapThememin2()
grid.arrange(count_inspections, count_rats, rats_yn, rat_id, ncol = 2)
```
When viewing the distribution of 311 inspection requests and the outcomes of those requests, a clear pattern emerges. As the maps above show, inspections requests and rat infestations in 311 data are heavily concentrated in the center of DC, which is also the densest part of the city. This pattern is unsurprising given that human density drives rat population increase through the increased presence of food and garbage. One particularly variable to examine is what we call here the "Rat ID Rate," which gives the percentage of all 311 inspection requests that actually result in evidence of rats being identified for a given block. The patterns in Rat ID Rate generally follow the overall patterns in where rats are being seen, suggesting that 311 calls are in fact responding at least to some extent to the reality of rat distribution throughout the city.
### 2.3: Independent Variables
```{r load and join indepdent vars, message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
Data311 <-
rbind(st_read("./data/311_City_Service_Requests_in_2015.geojson") %>%
st_transform('ESRI:102685') %>%
filter(SERVICECODEDESCRIPTION == "Alley Cleaning" | SERVICECODEDESCRIPTION == "Curb and Gutter Repair" | SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" | SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" | SERVICECODEDESCRIPTION == "Dead Animal Collection" | SERVICECODEDESCRIPTION == "Illegal Dumping" | SERVICECODEDESCRIPTION == "Pothole" | SERVICECODEDESCRIPTION == "Sanitation Enforcement" | SERVICECODEDESCRIPTION == "Street Cleaning" | SERVICECODEDESCRIPTION == "Trash Collection - Missed" | SERVICECODEDESCRIPTION == "Yard Waste - Missed") %>%
dplyr::select(SERVICECODEDESCRIPTION, SERVICEORDERDATE, INSPECTIONDATE, LATITUDE, LONGITUDE, WARD) %>%
mutate(TYPE = case_when(SERVICECODEDESCRIPTION == "Alley Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Dead Animal Collection" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Illegal Dumping" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Sanitation Enforcement" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Street Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Trash Collection - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Yard Waste - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Curb and Gutter Repair" ~ "Infrastructure_issues",
SERVICECODEDESCRIPTION == "Pothole" ~ "Infrastructure_issues",
),
month = month(ymd_hms(SERVICEORDERDATE)),
serviceday = ymd(substr(SERVICEORDERDATE,1,10))
) %>%
filter(month >= 8),
st_read("./data/311_City_Service_Requests_in_2016.geojson") %>%
st_transform('ESRI:102685') %>%
filter(SERVICECODEDESCRIPTION == "Alley Cleaning" | SERVICECODEDESCRIPTION == "Curb and Gutter Repair" | SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" | SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" | SERVICECODEDESCRIPTION == "Dead Animal Collection" | SERVICECODEDESCRIPTION == "Illegal Dumping" | SERVICECODEDESCRIPTION == "Pothole" | SERVICECODEDESCRIPTION == "Sanitation Enforcement" | SERVICECODEDESCRIPTION == "Street Cleaning" | SERVICECODEDESCRIPTION == "Trash Collection - Missed" | SERVICECODEDESCRIPTION == "Yard Waste - Missed") %>%
dplyr::select(SERVICECODEDESCRIPTION, SERVICEORDERDATE, INSPECTIONDATE, LATITUDE, LONGITUDE, WARD) %>%
mutate(TYPE = case_when(SERVICECODEDESCRIPTION == "Alley Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Dead Animal Collection" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Illegal Dumping" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Sanitation Enforcement" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Street Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Trash Collection - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Yard Waste - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Curb and Gutter Repair" ~ "Infrastructure_issues",
SERVICECODEDESCRIPTION == "Pothole" ~ "Infrastructure_issues",
),
month = month(ymd_hms(SERVICEORDERDATE)),
serviceday = ymd(substr(SERVICEORDERDATE,1,10))
),
st_read("./data/311_City_Service_Requests_in_2017.geojson") %>%
st_transform('ESRI:102685') %>%
filter(SERVICECODEDESCRIPTION == "Alley Cleaning" | SERVICECODEDESCRIPTION == "Curb and Gutter Repair" | SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" | SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" | SERVICECODEDESCRIPTION == "Dead Animal Collection" | SERVICECODEDESCRIPTION == "Illegal Dumping" | SERVICECODEDESCRIPTION == "Pothole" | SERVICECODEDESCRIPTION == "Sanitation Enforcement" | SERVICECODEDESCRIPTION == "Street Cleaning" | SERVICECODEDESCRIPTION == "Trash Collection - Missed" | SERVICECODEDESCRIPTION == "Yard Waste - Missed") %>%
dplyr::select(SERVICECODEDESCRIPTION, SERVICEORDERDATE, INSPECTIONDATE, LATITUDE, LONGITUDE, WARD) %>%
mutate(TYPE = case_when(SERVICECODEDESCRIPTION == "Alley Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Dead Animal Collection" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Illegal Dumping" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Sanitation Enforcement" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Street Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Trash Collection - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Yard Waste - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Curb and Gutter Repair" ~ "Infrastructure_issues",
SERVICECODEDESCRIPTION == "Pothole" ~ "Infrastructure_issues",
),
month = month(ymd_hms(SERVICEORDERDATE)),
serviceday = ymd(substr(SERVICEORDERDATE,1,10))
),
st_read("./data/311_City_Service_Requests_in_2018.geojson") %>%
st_transform('ESRI:102685') %>%
filter(SERVICECODEDESCRIPTION == "Alley Cleaning" | SERVICECODEDESCRIPTION == "Curb and Gutter Repair" | SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" | SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" | SERVICECODEDESCRIPTION == "Dead Animal Collection" | SERVICECODEDESCRIPTION == "Illegal Dumping" | SERVICECODEDESCRIPTION == "Pothole" | SERVICECODEDESCRIPTION == "Sanitation Enforcement" | SERVICECODEDESCRIPTION == "Street Cleaning" | SERVICECODEDESCRIPTION == "Trash Collection - Missed" | SERVICECODEDESCRIPTION == "Yard Waste - Missed") %>%
dplyr::select(SERVICECODEDESCRIPTION, SERVICEORDERDATE, INSPECTIONDATE, LATITUDE, LONGITUDE, WARD) %>%
mutate(TYPE = case_when(SERVICECODEDESCRIPTION == "Alley Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - Nuisance Odor Complaints" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "DOEE - General Environmental Concerns" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Dead Animal Collection" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Illegal Dumping" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Sanitation Enforcement" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Street Cleaning" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Trash Collection - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Yard Waste - Missed" ~ "Trash_issues",
SERVICECODEDESCRIPTION == "Curb and Gutter Repair" ~ "Infrastructure_issues",
SERVICECODEDESCRIPTION == "Pothole" ~ "Infrastructure_issues",
),
month = month(ymd_hms(SERVICEORDERDATE)),
serviceday = ymd(substr(SERVICEORDERDATE,1,10))
) %>%
filter(month < 8)
)
## Ward
# Ward <- st_read("./data/Wards.geojson") %>%
# st_transform('ESRI:102685') %>%
# dplyr::select(ward_name = NAME,
# ward_pop_15 = POP_2011_2015)
## Census blocks
# Census_blocks <- st_read("./data/Census_Blocks__2010.geojson") %>%
# st_transform('ESRI:102685')
# Address Points
addresses <- read.csv("./data/Address_Points.csv", header = TRUE) %>%
st_as_sf(., coords = c("X", "Y"), crs = 4326, agr = "constant") %>%
st_transform('ESRI:102685')
## Community Garden Polygons
comm_gardens <- st_read("./data/Community_Garden_Areas.geojson") %>%
st_transform('ESRI:102685')
## CAMA Commercial
cama_comm <- read.csv("./data/Computer_Assisted_Mass_Appraisal_-_Commercial.csv", header = TRUE)
## CAMA Condominium
cama_condo <- read.csv("./data/Computer_Assisted_Mass_Appraisal_-_Condominium.csv", header = TRUE)
## CAMA Residential
cama_res <- read.csv("./data/Computer_Assisted_Mass_Appraisal_-_Residential.csv", header = TRUE)
## Construction Permits 2015
const_permits15 <- read.csv("./data/Construction_Permits_in_2015.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant")
## Construction Permits 2016
const_permits16 <- read.csv("./data/Construction_Permits_in_2016.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant")
## Joining construction permits
const_permits_all <- rbind(read.csv("./data/Construction_Permits_in_2015.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant") %>%
mutate(month = month(APPLICATIONDATE)) %>%
filter(month >= 8) %>%
dplyr::select(-month),
read.csv("./data/Construction_Permits_in_2016.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant"),
read.csv("./data/Construction_Permits_in_2017.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant"),
read.csv("./data/Construction_Permits_in_2018.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant")%>%
mutate(month = month(APPLICATIONDATE)) %>%
filter(month < 8) %>%
dplyr::select(-month)) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant") %>%
st_transform('ESRI:102685')
## Public Trash Cans
trash_cans <- read.csv("./data/Litter_Cans.csv", header = TRUE) %>%
st_as_sf(., coords = c("X", "Y"), crs = 4326, agr = "constant") %>%
st_transform('ESRI:102685')
## National Parks
nat_parks <- st_read("./data/National_Parks.geojson") %>%
st_transform('ESRI:102685')
## Parks and Rec Parks
dc_parks <- st_read("./data/Parks_and_Recreation_Areas.geojson") %>%
st_transform('ESRI:102685')
## Sidewalk Grates (Sewer)
sewer_grates <- read.csv("./data/Sidewalk_Grates_2019.csv", header = TRUE) %>%
st_as_sf(., coords = c("X", "Y"), crs = 4326, agr = "constant") %>%
st_transform('ESRI:102685')
## Storm Drains (Markers)
storm_drains <- read.csv("./data/Storm_Drain_Marker_Installations.csv", header = TRUE) %>%
st_as_sf(., coords = c("LONGITUDE", "LATITUDE"), crs = 4326, agr = "constant") %>%
st_transform('ESRI:102685')
## Urban Ag Area
urban_ag <- st_read("./data/Urban_Agriculture_Areas.geojson") %>%
st_transform('ESRI:102685')
## Zoning Map
zoning <- st_read("./data/Zoning_Regulations_of_2016.geojson") %>%
st_transform('ESRI:102685')
## Mean temperature per day
weather.Panel <-
riem_measures(station = "DCA", date_start = "2015-08-01", date_end = "2018-07-31") %>%
dplyr::select(valid, tmpf, p01i, sknt)%>%
replace(is.na(.), 0) %>%
mutate(serviceday = ymd(substr(valid,1,10))) %>%
mutate(week = week(serviceday),
dotw = wday(serviceday, label=TRUE)) %>%
group_by(serviceday) %>%
summarize(Temperature = max(tmpf),
Wind_Speed = max(sknt)) %>%
mutate(Temperature = ifelse(Temperature == 0, 42, Temperature))
# Join 311 Data, count per day
Data311_perday <-
st_join(Data311, blocks) %>%
st_drop_geometry() %>%
group_by(serviceday, TYPE, block_id) %>%
summarize(count = n()) %>%
na.omit()
Data311_perday <- spread(Data311_perday, key = TYPE, value = count)
Data311_perday[is.na(Data311_perday)] <- 0
## Intersection of blocks and feature points
storm_drains$block_id <- st_intersects(storm_drains, block_dat)
sewer_grates$block_id <- st_intersects(sewer_grates, block_dat)
trash_cans$block_id <- st_intersects(trash_cans, block_dat)
const_permits_all$block_id <- st_intersects(const_permits_all, block_dat)
storm_block <-
storm_drains %>%
st_drop_geometry() %>%
group_by(block_id) %>%
summarize(count=n()) %>%
dplyr::filter(!grepl(':', block_id)) %>%
dplyr::filter(!grepl('integer', block_id)) %>%
na.omit()
block_dat$storm_drain <- storm_block$count[match(block_dat$block_id, storm_block$block_id)]
block_dat[is.na(block_dat[, "storm_drain"]), "storm_drain"] <- 0
sewer_block <-
sewer_grates %>%
st_drop_geometry() %>%
group_by(block_id) %>%
summarize(count=n()) %>%
dplyr::filter(!grepl(':', block_id)) %>%
dplyr::filter(!grepl('integer', block_id)) %>%
na.omit()
block_dat$sewer_grate <- sewer_block$count[match(block_dat$block_id, sewer_block$block_id)]
block_dat[is.na(block_dat[, "sewer_grate"]), "sewer_grate"] <- 0
trashcan_block <-
trash_cans %>%
st_drop_geometry() %>%
group_by(block_id) %>%
summarize(count=n()) %>%
dplyr::filter(!grepl(':', block_id)) %>%
dplyr::filter(!grepl('integer', block_id)) %>%
na.omit()
block_dat$trash_can <- trashcan_block$count[match(block_dat$block_id, trashcan_block$block_id)]
block_dat[is.na(block_dat[, "trash_can"]), "trash_can"] <- 0
const_block <-
const_permits_all %>%
st_drop_geometry() %>%
group_by(block_id) %>%
summarize(count=n()) %>%
dplyr::filter(!grepl(':', block_id)) %>%
dplyr::filter(!grepl('integer', block_id)) %>%
na.omit()
block_dat$const_permit <- const_block$count[match(block_dat$block_id, const_block$block_id)]
block_dat[is.na(block_dat[, "const_permit"]), "const_permit"] <- 0
#finding density (aka units) by block
addresses <- st_join(addresses, block_dat)
block_units <- addresses %>%
dplyr::select(geometry, ACTIVE_RES_UNIT_COUNT, block_id)
block_units <- block_units %>%
st_drop_geometry()%>%
group_by(block_id) %>%
summarize(unit_count = sum(ACTIVE_RES_UNIT_COUNT))
block_dat$res_unit_count <- block_units$unit_count[match(block_dat$block_id, block_units$block_id)]
block_dat[is.na(block_dat[, "res_unit_count"]), "res_unit_count"] <- 0
#finding zoning for each block
block_zone <- st_join(st_centroid(block_dat), zoning)
zoning_by_block <- block_zone %>%
st_drop_geometry() %>%
dplyr::select(block_id,ZONE_DISTRICT)
zoning_by_block$Residential_Zone <- ifelse(zoning_by_block$ZONE_DISTRICT == "Residential Zone", 1, 0)
zoning_by_block$Residential_Flat_Zone <- ifelse(zoning_by_block$ZONE_DISTRICT == "Residential Flat Zone", 1, 0)
zoning_by_block$Unzoned <- ifelse(zoning_by_block$ZONE_DISTRICT == "Unzoned", 1, 0)
zoning_by_block$Residential_Apt_Zone <- ifelse(zoning_by_block$ZONE_DISTRICT == "Residential Apartment Zone", 1, 0)
zoning_by_block$Mixed_Use <- ifelse(zoning_by_block$ZONE_DISTRICT== "Mixed-Use Zone", 1, 0)
zoning_by_block$Downtown_Zone <- ifelse(zoning_by_block$ZONE_DISTRICT== "Downtown Zone", 1, 0)
zoning_by_block$Prod_Dist_Repair <- ifelse(zoning_by_block$ZONE_DISTRICT== "Production, Distribution, and Repair Zone", 1, 0)
zoning_by_block$Special_Purpose <- ifelse(zoning_by_block$ZONE_DISTRICT== "Special Purpose Zone", 1, 0)
zoning_by_block$Neighborhood_Mixed_Use <- ifelse(zoning_by_block$ZONE_DISTRICT == "Neighborhood Mixed-Use Zone", 1, 0)
zoning_by_block <- zoning_by_block %>%
group_by(block_id) %>%
dplyr::select(-ZONE_DISTRICT)
zoning_by_block %>%
dplyr::select(-block_id) %>%
mutate(Zone= names(.)[which.max(c(Residential_Zone, Residential_Flat_Zone, Unzoned, Residential_Apt_Zone, Mixed_Use, Downtown_Zone, Prod_Dist_Repair, Special_Purpose, Neighborhood_Mixed_Use))])
zoning_by_block <- zoning_by_block %>%
group_by(block_id) %>%
rowwise() %>%
mutate(max = names(cur_data())[which.max(c_across(everything()))])
block_dat$zoning <- zoning_by_block$max[match(block_dat$block_id, zoning_by_block$block_id)]
# incorporating population density
pop_bg_17 <- get_acs(geography = "block group",
year = 2017,
state = "DC",
survey = "acs5",
variables = "B01001_001",
geometry = TRUE) %>%
st_transform("ESRI:102685") %>%
mutate(pop_dens = estimate / st_area(.))
# join to blocks by taking average pop density per block
block_bg_join <-
st_join(dplyr::select(blocks, block_id), dplyr::select(pop_bg_17, GEOID, pop_dens)) %>%
st_drop_geometry() %>%
group_by(block_id) %>%
summarize(pop_dens = mean(as.numeric(pop_dens)))
# join to block dataset
block_dat <- left_join(block_dat, block_bg_join)
```
Many different factors can contribute to the likelihood of a city block having a rat infestation. Most of these variables have to do with the nature of the built environment and settlement patterns that might encourage rat populations. Built environment variables we collected for initial testing included the locations of sewer grates and storm drains, active construction permits, trash cans, parks, and community gardens. Each of these physical factors presents an opportunity for a rat colony to burrow or navigate the urban environment.
On the settlement pattern side, we collected data on population density, zoning, and 311 complaints related to trash cleanup issues, illegal dumping, infrastructure repair, and other environmental concerns. Finally, we looked at weather and time-of-year variables to see if there was any relationship between the season and/or weather conditions when it comes to inspection requests.
Each of these variables was spatially aggregated into the city block dataset described above. The variables demonstrated a range of different relationships to the history of rat infestation identification in the primary dataset.
```{r , message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
block_dat_nogeom <- block_dat %>%
st_drop_geometry()
block_dat_nogeom$pop_density <- ifelse(block_dat$pop_dens >= 0.0005250146, 'Above Avg', 'Below Avg')
block_dat_nogeom$rats_yn <- ifelse(block_dat_nogeom$rats_found_yn == 1, 'yes','no')
block_dat_nogeom$rats_yn_req <- ifelse(block_dat_nogeom$rats_found_yn == 1, 'yes', ifelse(block_dat_nogeom$rats_found_yn == 0 & block_dat_nogeom$inspection_count == 0 , 'no requests', 'no'))
block_dat_nogeom$dens_rats <- ifelse(block_dat_nogeom$pop_density == 'Above Avg' & block_dat_nogeom$rats_yn_req == 'yes', 'high density with rats', ifelse(block_dat_nogeom$pop_density == 'Above Avg' & block_dat_nogeom$rats_yn_req == 'no', 'high density with no rats', 'na'))
block_dat_nogeom$storm_drain_cat <- ifelse(block_dat_nogeom$storm_drain >= 1, 'storm drains', 'no storm drains')
block_dat_nogeom$sewer_grate_cat <- ifelse(block_dat_nogeom$sewer_grate >= 1, 'sewer grates', 'no sewer grates')
block_dat_nogeom$trash_can_cat <- ifelse(block_dat_nogeom$trash_can >= 1, 'trash cans', 'no trash cans')
block_dat_nogeom$const_permit_cat <- ifelse(block_dat_nogeom$const_permit >= 1, 'const permits', 'no const permits')
pop_dens_bars <- block_dat_nogeom %>%
dplyr::select(rats_yn, pop_density) %>%
dplyr::filter(block_dat_nogeom$pop_density == 'Above Avg' | block_dat_nogeom$pop_density == 'Below Avg') %>%
gather(Variable, value, -rats_yn) %>%
count(Variable, value, rats_yn) %>%
ggplot(., aes(value, n, fill = rats_yn)) +
geom_bar(position = "dodge", stat="identity") +
facet_wrap(~Variable, scales="free") +
scale_fill_manual(values = c("#E99191", "#E91C1C")) +
labs(x="Blocks with Rats", y="Value",
title = "Blocks with Rat ID by Population Density") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
zoning_bars <- block_dat_nogeom %>%
dplyr::select(rats_yn_req, zoning) %>%
dplyr::filter(block_dat_nogeom$rats_yn_req == 'yes' | block_dat_nogeom$rats_yn_req == 'no') %>%
gather(Variable, value, -rats_yn_req) %>%
count(Variable, value, rats_yn_req) %>%
ggplot(., aes(value, n, fill = rats_yn_req)) +
geom_bar(position = "dodge", stat="identity") +
facet_wrap(~Variable, scales="free") +
scale_fill_manual(values = c("#E99191", "#E91C1C")) +
labs(x="Blocks with Rats", y="Value",
title = "Blocks with Rat ID by Zoning") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
grid.arrange(pop_dens_bars, zoning_bars, ncol = 2)
```
Population density and zoning were two variables that stuck out as having particularly notable distributions related to the presence of rat infestation evidence. It is clear from the graph on the left that areas with an above-average population density, for instance, have a much higher rate of inspections leading to the treatment of rat infestation. The same is true for zoning, especially when blocks that recieved no requests at all were removed: the graph on the right shows this and demonstrates that certain zones such as Residential Apt. Zone, Residential Flat Zone, and Mixed Use are much more likely to yield evidence of rats upon an inspection.
```{r}
block_dat_nogeom %>%
dplyr::select(rats_yn_req, storm_drain_cat, sewer_grate_cat, trash_can_cat, const_permit_cat) %>%
dplyr::filter(block_dat_nogeom$rats_yn_req == 'yes' | block_dat_nogeom$rats_yn_req == 'no') %>%
gather(Variable, value, -rats_yn_req) %>%
count(Variable, value, rats_yn_req) %>%
ggplot(., aes(value, n, fill = rats_yn_req)) +
geom_bar(position = "dodge", stat="identity") +
facet_wrap(~Variable,ncol = 2, scales="free") +
scale_fill_manual(values = c("#E99191", "#E91C1C")) +
labs(x="Blocks with Rats", y="Value",
title = "Built Environment Variables, Removing Blocks Without Inspection Requests") +
theme(axis.text.x = element_text(angle = 0))
```
Variables related to the built environment also have an interesting relationship to the presence of rats. The four charts above show that, for blocks that have received at least one inspection request, the presence of factors such as active construction permits, sewer grates, and trash cans all contribute to a greater chance of finding evidence of rats.
### 2.4: Feature Engineering
Oftentimes, the pure presence-absence of a given independent variable in an area does not speak to the true relationship between it and the dependent variable. Feature engineering is a crucial and necessary step in the process of building machine learning models. It involves selecting and transforming relevant features from raw data to improve the performance and accuracy of the model. Without proper feature engineering, even the most sophisticated algorithms may fail to produce meaningful results. The quality and relevance of the features used directly impact the model’s ability to learn and make accurate predictions. Therefore, investing time and effort in feature engineering is essential for producing reliable and effective machine learning models.
#### 2.4.1: Calculating Rat Infestation Hotspots
In this project, much of the feature engineering revolved around creating new variables which speak to the spatial distribution of rat infestations in the past, as well as the relationship between other independent variables and rat infestations. One of the methods used to achieve this was to run a local Moran’s I hotspot analysis on rat infestation observations to identify the statistically-significant rat hotspots across the city based on the 311 data available to us.
```{r create fishnet and morans i, message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
# create fishnet
fishnet <-
st_make_grid(boundary,
cellsize = 500,
square = TRUE) %>%
.[boundary] %>%
st_sf() %>%
mutate(uniqueID = 1:n())
# assign each inspection a value of 1 and aggregate
inspection_net <-
dplyr::select(Rats) %>%
mutate(count_inspection = 1) %>%
aggregate(., fishnet, sum) %>%
mutate(count_inspection = replace_na(count_inspection, 0),
uniqueID = 1:n(),
cvID = sample(round(nrow(fishnet) / 24),
size=nrow(fishnet), replace = TRUE))
# join in rat observed variable
rat_net <-
Rats %>%
filter(activity == 1) %>%
st_join(fishnet, join=st_within) %>%
st_drop_geometry() %>%
group_by(uniqueID) %>%
summarize(rat_obs_count = n()) %>%
left_join(inspection_net, ., by = "uniqueID") %>%
ungroup() %>%
mutate(rat_obs_count = replace_na(rat_obs_count, 0))
## make polygon to neighborhoods...
rat_net.nb <- poly2nb(as_Spatial(rat_net), queen=TRUE)
## ... and neighborhoods to list of weights
rat_net.weights <- nb2listw(rat_net.nb, style="W", zero.policy=TRUE)
local_morans <- localmoran(rat_net$rat_obs_count, rat_net.weights, zero.policy=TRUE) %>%
as.data.frame()
# join local Moran's I results to fishnet
rat_net.localMorans <-
cbind(local_morans, as.data.frame(rat_net)) %>%
st_sf() %>%
dplyr::select(Rat_Observation_Count = rat_obs_count,
Local_Morans_I = Ii,
P_Value = `Pr(z != E(Ii))`) %>%
mutate(Significant_Hotspots = ifelse(P_Value <= 0.001, 1, 0)) %>%
gather(Variable, Value, -geometry)
vars <- unique(rat_net.localMorans$Variable)
varList <- list()
for(i in vars){
varList[[i]] <-
ggplot() +
geom_sf(data = filter(rat_net.localMorans, Variable == i),
aes(fill = Value), colour=NA) +
scale_fill_gradient(low = "#e3b1b1", high = "#E91C1C") +
labs(title=i) +
mapThememin() + theme(legend.position="bottom")}
do.call(grid.arrange,c(varList, ncol = 4, top = "Local Morans I Statistics for Rat Infestation Hotspot Identification"))
```
The hotspots identified by the Moran’s I process can be seen on the right-most map here. Unsurprisingly, the primary hotspots are in the center of the city where the highest inspection and rat evidence identification clusters are found, as well as the highest population density. In order to make this data as useful as possible to the model, another engineered variable was calculated which assigned each block the distance to the nearest hotspot. This provides a sense of each block’s spatial relationship to most substantial rat populations:
```{r message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
# add distance to hot spot var to final fishnet
rat_net <- rat_net %>%
mutate(rat_obs.isSig =
ifelse(local_morans[,5] <= 0.001, 1, 0)) %>%
mutate(rat_obs.isSig.dist =
nn_function(st_coordinates(st_centroid(rat_net)),
st_coordinates(st_centroid(filter(rat_net,
rat_obs.isSig == 1))),
k = 1))
# add distance to hot spot var to block dataset
block_dat <-
block_dat %>%
mutate(hotspot_dist =
nn_function(st_coordinates(st_centroid(block_dat)),
st_coordinates(st_centroid(filter(rat_net,
rat_obs.isSig == 1))),
k = 1),
hotspot_dist_log = log(hotspot_dist))
block_dat <-
block_dat %>%
mutate(hotspot_dist_pop_dens = hotspot_dist / pop_dens,
hotspot_dist_log_pop_dens = hotspot_dist_log / pop_dens)
ggplot() +
geom_sf(data = block_dat, aes(fill=hotspot_dist), color = "transparent") +
scale_fill_gradient(low = "lightgray", high = "#E91C1C", name = "Distance (Feet)") +
labs(title = "Distance from Rat Infestation Hotspot by Block",
subtitle = "Hotspot : p-value ≤ 0.001",
caption = "") +
mapThememin()
```
#### 2.4.2: Nearest-Neighbor Calculations
```{r message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
# generating knn features at block level for nearest rat observations
block_dat <-
block_dat %>%
mutate(Transh_cans_nn3 = round(nn_function(st_c(st_coid(block_dat)), st_c(trash_cans), k = 3)),
Storm_drains_nn3 = round(nn_function(st_c(st_coid(block_dat)), st_c(storm_drains), k = 3)),
Sewer_grates_nn3 = round(nn_function(st_c(st_coid(block_dat)), st_c(sewer_grates), k = 3)),
area_acres_log = log(area_acres),
rats_nn3 = nn_function(st_coordinates(st_centroid(block_dat)), st_coordinates(filter(Rats, activity == 1)), k = 3),
rat_nn4 = nn_function(st_coordinates(st_centroid(block_dat)), st_coordinates(filter(Rats, activity == 1)), k = 4),
rat_nn5 = nn_function(st_coordinates(st_centroid(block_dat)), st_coordinates(filter(Rats, activity == 1)), k = 5),
rat_nn3_log = log(nn_function(st_coordinates(st_centroid(block_dat)), st_coordinates(filter(Rats, activity == 1)), k = 3)),
rat_nn4_log = log(nn_function(st_coordinates(st_centroid(block_dat)), st_coordinates(filter(Rats, activity == 1)), k = 4)),
rat_nn5_log = log(nn_function(st_coordinates(st_centroid(block_dat)), st_coordinates(filter(Rats, activity == 1)), k = 5)))
# create some pop density-sensitive variables
block_dat <-
block_dat %>%
mutate(rat_nn5_pop_dens = rat_nn5 / pop_dens)
block_dat.nn <-
dplyr::select(block_dat, ends_with("nn3")) %>%
gather(Variable, value, -geometry)
block_dat.nns <- unique(block_dat.nn$Variable)
mapList <- list()
for(i in block_dat.nns){
mapList[[i]] <-
ggplot() +
geom_sf(data = filter(block_dat.nn, Variable == i), aes(fill=value), color = "transparent") +
scale_fill_gradient(low = "lightgray", high = "#E91C1C") +
labs(title = i) +
mapThememin()
}
do.call(grid.arrange,c(mapList, ncol=2, top="Nearest Neighbor Potential Factors by Block"))
```
Our second feature engineering approach was to calculate the average nearest neighbor distance of rat observations to each block as a proxy for a smoother exposure relationship across space. To create average nearest neighbor features, the city blocks were first converted into centroid points. Then, the distance between each centroid point and the k-nearest risk factor points (in this case, observed evidence of rat infestation) is measured. We tried several different options for k and settled on k = 3 as the most effective representation of this data. The maps above show the spatial distribution of the k = 3 nearest neighbor variables calculated during this stage.
### 2.5: Correlation Analysis
```{r message=FALSE, warning=FALSE, out.width = '100%', results='hide', fig.keep='all'}
#correlation matrix
newVars <-
select_if(st_drop_geometry(block_dat), is.numeric) %>%
dplyr::select(-block_id, -rats_found_yn) %>%
na.omit()
ggcorrplot(
round(cor(newVars), 1),
p.mat = cor_pmat(newVars),
colors = c("#e3b1b1", "white", "#E91C1C"),
type="lower",
insig = "blank") +
labs(title = "Correlation across numeric variables") +
theme(axis.text.x=element_text(size=rel(0.75), angle=45)) +
theme(axis.text.y=element_text(size=rel(0.75)))
```
Before reaching the modeling stage, it is important to understand the relationship between independent variables in addition to the relationship between those variables and the dependent variable. The correlation plot above provides insight into how some of the explanatory variables collected in this portion of the process relate to one another. Unsurprisingly, the variables with the highest correlation are those which are measuring closely-related things, such as the k = 3 and k = 4 nearest neighbor variables. This chart was used to narrow down the variables included in the modeling process in an effort to eliminate redundancy.