1 Philadelphia Shooting Data Analysis

In order to visualize the spatial distribution of shootings in Philadelphia, PA, the City of Philadelphia Shooting Victim data as obtained from the Philadelphia Open Data project. This report includes a listing of city-wide shooting victims, including Police Officer-involved shootings, from 2015 - current day. In order to aggregate this data by neighborhood and or block, two GeoJSON files were read in for use respectively.

This data is available for public use at OpenPhillyData

str(philly_block)
Classes 'sf' and 'data.frame':  17555 obs. of  8 variables:
 $ geoid           : chr  "421010041013000" "421010041013001" "421010041013002" "421010041013003" ...
 $ cr_geoid        : int  1254 1254 1254 1254 1254 1254 1254 1254 1254 1254 ...
 $ name            : chr  "Greenwich" "Greenwich" "Greenwich" "Greenwich" ...
 $ original_id     : chr  NA NA NA NA ...
 $ pop100          : chr  "120" "139" "145" "0" ...
 $ hu100           : chr  "53" "50" "53" "0" ...
 $ state_place_fips: chr  "4260000" "4260000" "4260000" "4260000" ...
 $ geometry        :sfc_MULTIPOLYGON of length 17555; first list element: List of 1
  ..$ :List of 1
  .. ..$ : num [1:5, 1:2] -75.2 -75.2 -75.2 -75.2 -75.2 ...
  ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
 - attr(*, "sf_column")= chr "geometry"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA
  ..- attr(*, "names")= chr [1:7] "geoid" "cr_geoid" "name" "original_id" ...
str(philly_neighbor)
Classes 'sf' and 'data.frame':  158 obs. of  9 variables:
 $ name      : chr  "PENNYPACK_PARK" "OVERBROOK" "GERMANTOWN_SOUTHWEST" "EAST_PARKSIDE" ...
 $ listname  : chr  "Pennypack Park" "Overbrook" "Germantown, Southwest" "East Parkside" ...
 $ mapname   : chr  "Pennypack Park" "Overbrook" "Southwest Germantown" "East Parkside" ...
 $ shape_leng: num  87084 57005 14881 10886 13042 ...
 $ shape_area: num  60140756 76924995 14418666 4231000 6949968 ...
 $ cartodb_id: int  9 138 59 129 49 6 17 47 50 44 ...
 $ created_at: POSIXct, format: "2013-03-19 13:41:50" "2013-03-19 13:41:50" ...
 $ updated_at: POSIXct, format: "2013-03-19 13:41:50" "2013-03-19 13:41:50" ...
 $ geometry  :sfc_MULTIPOLYGON of length 158; first list element: List of 1
  ..$ :List of 1
  .. ..$ : num [1:1607, 1:2] -75.1 -75.1 -75.1 -75.1 -75.1 ...
  ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
 - attr(*, "sf_column")= chr "geometry"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA
  ..- attr(*, "names")= chr [1:8] "name" "listname" "mapname" "shape_leng" ...
str(philly_shooting_raw)
Classes 'sf' and 'data.frame':  15277 obs. of  22 variables:
 $ objectid         : int  12728033 12728034 12728035 12728036 12728037 12728038 12728039 12728040 12728041 12728042 ...
 $ year             : int  2016 2016 2018 2020 2018 2019 2019 2019 2022 2016 ...
 $ dc_key           : chr  "201615054780.0" "201615117555.0" "201815093657.0" "202015094989.0" ...
 $ code             : chr  "400" "300" "400" "400" ...
 $ date_            : POSIXct, format: "2016-06-06 20:00:00" "2016-12-03 19:00:00" ...
 $ time             : chr  "12:15:00" "05:43:00" "21:02:00" "17:12:00" ...
 $ race             : chr  "B" "B" "B" "B" ...
 $ sex              : chr  "M" "M" "M" "M" ...
 $ age              : chr  "19" "38" "31" "23" ...
 $ wound            : chr  "Hand" "Chest" "Multiple" "Hand" ...
 $ officer_involved : chr  "N" "N" "N" "N" ...
 $ offender_injured : chr  "N" "N" "N" "N" ...
 $ offender_deceased: chr  "N" "N" "N" "N" ...
 $ location         : chr  "4600 BLOCK Frankford Ave" "4600 BLOCK Frankford Ave" "4600 BLOCK Frankford Ave" "4600 BLOCK FRANKFORD AVE" ...
 $ latino           : int  0 0 0 0 0 1 1 0 0 0 ...
 $ point_x          : num  -75.1 -75.1 -75.1 -75.1 -75.1 ...
 $ point_y          : num  40 40 40 40 40 ...
 $ dist             : chr  "15" "15" "15" "15" ...
 $ inside           : int  0 0 0 0 0 0 0 0 0 0 ...
 $ outside          : int  1 1 1 1 1 1 1 1 1 1 ...
 $ fatal            : int  0 0 1 0 0 0 0 0 1 0 ...
 $ geometry         :sfc_POINT of length 15277; first list element:  'XY' num  -75.1 40
 - attr(*, "sf_column")= chr "geometry"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "names")= chr [1:21] "objectid" "year" "dc_key" "code" ...
 - attr(*, "na.action")= 'omit' Named int [1:278] 197 287 622 716 770 783 789 883 961 992 ...
  ..- attr(*, "names")= chr [1:278] "197" "287" "622" "716" ...
## Define a color palette
pal <- colorFactor(c("red", "gold"), domain = c(TRUE, FALSE))

The data was read in, and classifications updated to either logical or numeric as needed for improved analysis. Uneccessary columns (objectID, DC_Key, and Geometry) removed.

str(philly_shooting)
Classes 'sf' and 'data.frame':  15277 obs. of  20 variables:
 $ year             : int  2016 2016 2018 2020 2018 2019 2019 2019 2022 2016 ...
 $ code             : num  400 300 400 400 400 400 400 400 100 400 ...
 $ date_            : POSIXct, format: "2016-06-06 20:00:00" "2016-12-03 19:00:00" ...
 $ time             : chr  "12:15:00" "05:43:00" "21:02:00" "17:12:00" ...
 $ race             : chr  "B" "B" "B" "B" ...
 $ sex              : chr  "M" "M" "M" "M" ...
 $ age              : chr  "19" "38" "31" "23" ...
 $ wound            : chr  "Hand" "Chest" "Multiple" "Hand" ...
 $ officer_involved : chr  "N" "N" "N" "N" ...
 $ offender_injured : chr  "N" "N" "N" "N" ...
 $ offender_deceased: chr  "N" "N" "N" "N" ...
 $ location         : chr  "4600 BLOCK Frankford Ave" "4600 BLOCK Frankford Ave" "4600 BLOCK Frankford Ave" "4600 BLOCK FRANKFORD AVE" ...
 $ latino           : logi  FALSE FALSE FALSE FALSE FALSE TRUE ...
 $ point_x          : num  -75.1 -75.1 -75.1 -75.1 -75.1 ...
 $ point_y          : num  40 40 40 40 40 ...
 $ dist             : chr  "15" "15" "15" "15" ...
 $ inside           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ outside          : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ fatal            : logi  FALSE FALSE TRUE FALSE FALSE FALSE ...
 $ geometry         :sfc_POINT of length 15277; first list element:  'XY' num  -75.1 40
 - attr(*, "sf_column")= chr "geometry"
 - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
  ..- attr(*, "names")= chr [1:19] "year" "code" "date_" "time" ...
leaflet() %>%
  setView(lng=-75.15092, lat=40.00995, zoom = 11) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter, group = "Dark w/ Labels") %>% 
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels, group = "Dark w/o Labels") %>% 
  addProviderTiles(providers$Esri.WorldGrayCanvas, group = "Gray") %>% 
   addLayersControl(baseGroups = c("Dark w/ Labels", "Dark w/o Labels", "Gray"),
    options = layersControlOptions(collapsed = TRUE)) %>% 
  addControl(html = "<h5> Philadelphia Shooting Data<br>2015 - Current</h5>", position = "topleft", className = "map-title") %>% 
  addPolygons(data = philly_neighbor,
              color = 'magenta',
              fill= FALSE,
              weight = 1) %>% 
  addMiniMap() %>% 
  addCircleMarkers(data = philly_shooting,
                   radius = ~ifelse(fatal == TRUE, 5, 3),
                   color = ~pal(fatal),
                   fillOpacity = 0.5,
                   popup = ~popupTable(philly_shooting),
                   clusterOptions = markerClusterOptions(maxClusterRadius = 50)) %>% 
   addCircleMarkers(data = Crime_totals, 
                   color = "white",
                   weight = 2,
                   label = "Crime Totals by Classificaiton Code",
                   stroke = FALSE, 
                   fillOpacity = 0.95,
                   group = "code") %>% 
  addPopupImages(code_graph_img, 
                  width = 500,
                  height = 400,
                  tooltip = FALSE,
                  group = "code") 


---
title: "Bang: The GeoSpatial Impact of Philadelphia Gun Violence"
author: "Natalie LePera"
date: "West Chester University <br>STA 503: Data Visualization"
output:
  html_document: 
    toc: yes
    toc_depth: 4
    toc_float: yes
    number_sections: yes
    toc_collapsed: yes
    code_folding: hide
    code_download: yes
    smooth_scroll: true
    theme: readable
---

```{=html}
<style type="text/css">

div#TOC li {
    list-style:none;
    background-color:lightgray;
    background-image:none;
    background-repeat:none;
    background-position:0;
    font-family: Arial, Helvetica, sans-serif;
    color: #780c0c;
}

/* mouse over link */
div#TOC a:hover {
  color: red;
}

/* unvisited link */
div#TOC a:link {
  color: blue;
}



h1.title {
  font-size: 24px;
  color: Darkblue;
  text-align: center;
  font-family: Arial, Helvetica, sans-serif;
  font-variant-caps: normal;
}
h4.subtitle {
  font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkRed;
  text-align: center;
}
h4.author { 
    font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkRed;
  text-align: center;
}
h4.date { 
  font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
  text-align: center;
}
h1 {
    font-size: 24px;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: center;
}
h2 {
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: center;
}

h3 { 
    font-size: 15px;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: left;
}

h4 { /* Header 4 - and the author and data headers use this too  */
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: left;
}
h5 { 
    font-size: 15px;
    font-family: "Times New Roman", Times, serif;
    color: white;
    text-align: left;
}
/* unvisited link */
a:link {
  color: green;
}

/* visited link */
a:visited {
  color: green;
}

/* mouse over link */
a:hover {
  color: red;
}

/* selected link */
a:active {
  color: yellow;
}

</style>
```

```{r setup, include=FALSE}
# code chunk specifies whether the R code, warnings, and output 
# will be included in the output files.
options(repos = list(CRAN="http://cran.rstudio.com/"))
if (!require("tidyverse")) {
   install.packages("tidyverse")
   library(tidyverse)
}
if (!require("knitr")) {
   install.packages("knitr")
   library(knitr)
}
if (!require("sf")) {
   install.packages("sf")
   library(sf)
}
if (!require("cowplot")) {
   install.packages("cowplot")
   library(cowplot)
}
if (!require("latex2exp")) {
   install.packages("latex2exp")
   library(latex2exp)
}
if (!require("plotly")) {
   install.packages("plotly")
   library(plotly)
}
if (!require("gapminder")) {
   install.packages("gapminder")
   library(gapminder)
}
if (!require("png")) {
    install.packages("png")             # Install png package
    library("png")
}
if (!require("RCurl")) {
    install.packages("RCurl")           # Install RCurl package
    library("RCurl")
}
if (!require("colourpicker")) {
    install.packages("colourpicker")              
    library("colourpicker")
}
if (!require("gifski")) {
    install.packages("gifski")              
    library("gifski")
}
if (!require("magick")) {
    install.packages("magick")              
    library("magick")
}
if (!require("grDevices")) {
    install.packages("grDevices")              
    library("grDevices")
}
### ggplot and extensions
if (!require("ggplot2")) {
    install.packages("ggplot2")              
    library("ggplot2")
}
if (!require("gganimate")) {
    install.packages("gganimate")              
    library("gganimate")
}
if (!require("ggridges")) {
    install.packages("ggridges")              
    library("ggridges")
}
if (!require("graphics")) {
    install.packages("graphics")              
    library("graphics")
}
if (!require("openxlsx")) {
    install.packages("openxlsx")              
    library("openxlsx")
}

if (!require("lubridate")) {
    install.packages("lubridate")              
    library("lubridate")
}
if (!require("rjson")) {
    install.packages("rjson")              
    library("rjson")
}
if (!require("leaflet")) {
    install.packages("leaflet")              
    library("leaflet")
}
if (!require("leafpop")) {
    install.packages("leafpop")              
    library("leafpop")
}
if (!require("htmlwidgets")) {
    install.packages("htmlwidgets")              
    library("htmlwidgets")
}
knitr::opts_chunk$set(echo = TRUE,       
                      warning = FALSE,   
                      result = TRUE,   
                      message = FALSE,
                      comment = NA)
```
\


# Philadelphia Shooting Data Analysis

In order to visualize the spatial distribution of shootings in Philadelphia, PA, the City of Philadelphia Shooting Victim data as obtained from the Philadelphia Open Data project.  This report includes a listing of city-wide shooting victims, including Police Officer-involved shootings, from 2015 - current day.  In order to aggregate this data by neighborhood and or block, two GeoJSON files were read in for use respectively. 

This data is available for public use at <a href="https://opendataphilly.org/datasets/">OpenPhillyData</a>
```{r include = FALSE}
philly_shooting_raw  <- na.omit(st_read("https://pengdsci.github.io/STA553VIZ/w08/PhillyShootings.geojson"))
philly_neighbor  <- st_read("https://pengdsci.github.io/STA553VIZ/w08/Neighborhoods_Philadelphia.geojson")
philly_block  <- st_read("https://pengdsci.github.io/STA553VIZ/w08/PhillyNeighborhood-blocks.geojson")
```
```{r echo = TRUE}
str(philly_block)
str(philly_neighbor)
str(philly_shooting_raw)
```

```{r}


## Define a color palette
pal <- colorFactor(c("red", "gold"), domain = c(TRUE, FALSE))


```

```{r include = FALSE}
philly_shooting_raw$fatal <- as.logical(philly_shooting_raw$fatal)
philly_shooting_raw$latino <- as.logical(philly_shooting_raw$latino)
philly_shooting_raw$inside <- as.logical(philly_shooting_raw$inside)
philly_shooting_raw$outside <- as.logical(philly_shooting_raw$outside)
philly_shooting_raw$code <- as.numeric(philly_shooting_raw$code)
```

```{r include = FALSE}
philly_shooting <- subset(philly_shooting_raw, select = -c(objectid, dc_key, geometry))
philly_shooting %>% 
  rename(
    Shooting_Inside = inside,
    Shooting_Outside = outside,
    Police_district = dist,
    FBI_Crime_Code = code
  )
```


The data was read in, and classifications updated to either logical or numeric as needed for improved analysis.  Uneccessary columns (objectID, DC_Key, and Geometry) removed.


```{r echo = TRUE}
str(philly_shooting)
```

```{r include = FALSE}
philly_shooting$code_key <- cut(philly_shooting$code, breaks = c(0, 100, 200, 300, 400, 500, 600, 700, 800, 900),  labels = c("0-099: Additional Victim", "100-199: Homicide", "200-299: Rape", "300-399: Robbery", "400-499: Aggrivated Assault", "500-599: Burgarly", "600-699: Larceny", "700-799: Motor Vehicle Theft", "800-899: Arson"), include.lowest = TRUE)

code_graph <- ggplot(
  data = philly_shooting,
  aes(x = philly_shooting$code_key, color = philly_shooting$sex, fill = philly_shooting$sex))+
  geom_bar(position = "dodge") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  labs(title = "Philadelphia Shooting Related Crime Totals by FBI UCR Code Listing", subtitle = "2015 - Current data Obtained From OpenDataPhilly", fill = "Sex of Victims") +
  xlab("Shooting FBI Crime Code")+
  ylab("Total Shooting Related Crimes (2015 - Current)")+
  guides(color = "none")


Crime_totals = st_as_sf(data.frame(x = -75.4, y = 40.00995),
                coords = c("x", "y"),
                crs = 112)
code_graph_img = "https://nlepera.github.io/sta553/w08_interactive_maps/popup_code.jpg"

```

```{r fig.align='center', fig.height=6, fig.width=10, warning=FALSE, message = FALSE}
leaflet() %>%
  setView(lng=-75.15092, lat=40.00995, zoom = 11) %>% 
  addProviderTiles(providers$CartoDB.DarkMatter, group = "Dark w/ Labels") %>% 
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels, group = "Dark w/o Labels") %>% 
  addProviderTiles(providers$Esri.WorldGrayCanvas, group = "Gray") %>% 
   addLayersControl(baseGroups = c("Dark w/ Labels", "Dark w/o Labels", "Gray"),
    options = layersControlOptions(collapsed = TRUE)) %>% 
  addControl(html = "<h5> Philadelphia Shooting Data<br>2015 - Current</h5>", position = "topleft", className = "map-title") %>% 
  addPolygons(data = philly_neighbor,
              color = 'magenta',
              fill= FALSE,
              weight = 1) %>% 
  addMiniMap() %>% 
  addCircleMarkers(data = philly_shooting,
                   radius = ~ifelse(fatal == TRUE, 5, 3),
                   color = ~pal(fatal),
                   fillOpacity = 0.5,
                   popup = ~popupTable(philly_shooting),
                   clusterOptions = markerClusterOptions(maxClusterRadius = 50)) %>% 
   addCircleMarkers(data = Crime_totals, 
                   color = "white",
                   weight = 2,
                   label = "Crime Totals by Classificaiton Code",
                   stroke = FALSE, 
                   fillOpacity = 0.95,
                   group = "code") %>% 
  addPopupImages(code_graph_img, 
                  width = 500,
                  height = 400,
                  tooltip = FALSE,
                  group = "code") 
  
 

```

\