MidtermCalgaryCombined.Rmd

---
title: "Calgary and Minneapolis Inundation Modeling"
author: "Kyle McCarthy, Tristan Grupp"
date: "3/30/2021"
output:
  html_document:
    keep_md: true
    toc: true
    theme: cosmo
    toc_float: true
    code_folding: hide
    number_sections: FALSE
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```



# Introduction 


  Knowledge of how and where waters move across the landscape following is necessary to  provide a wealth of information for resource managers and local planning organizations.Modeling is necessary to predict where flooding is expected to occur before it happens. Flooding events often cost municipalities thousands of dollars in damage, reduce community health and protect water quality.Planners, environmental scientists, and community members are able to utilize these models to fortify infrastructure in high risk flooding ares, predict nutrient and pollutant levels in streams, reduce fluctuations in various water bodies and improve stormwater infrastructure.

  This model will incorporate a variety of data sets to  including land use type, demographic data, tree canopy, soil type, and distance to water body.Inundation modeling helps those trying to fortify infrastructure, provide information on flow and flood frequency, predict nutrient and pollutant levels in surface water to understand the impact on regional water quality, and reduce elevation fluctuations in various water bodies.

  For this report, we utilize our model to predict flood inundation in Calgary and Minneapolis to test our model's validity. To accomplish this goal, we divide each city into grid cells and classify each city as "inundated" or "not inundated" based on past events or DEM flood inundation models. Utilizing the variables specified in the paragraph above, our model identifies the common characteristics associated with "inundated" areas or "not inundated" areas. We then split our data into a training set and test set. The training set identifies the common characteristics of the data, and the test set predicts weather a grid cell will be inundated or not inundated based on the training set characteristic. Based on how many grid cells the training set predicted correctly, we can evaluate the effectiveness of our model. 




```{r setup, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}
library(caret)
library(pscl)
library(plotROC)
library(pROC)
library(sf)
library(sp)
library(tidyverse)
library(knitr)
library(kableExtra)
library(FNN) 
library(lwgeom)
mapTheme <- function(base_size = 12) {
  theme(
    text = element_text( color = "black"),
    plot.title = element_text(size = 14,colour = "black"),
    plot.subtitle=element_text(face="italic"),
    plot.caption=element_text(hjust=0),
    axis.ticks = element_blank(),
    panel.background = element_blank(),axis.title = element_blank(),
    axis.text = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_rect(colour = "black", fill=NA, size=2)
  )
}
root.dir = "https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/DATA/"
source("https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/functions.r")
```

## Exploratory Analysis 


This section includes our exploratory analysis. Variables explored included percent imperviousness, distance from streams, tree canopy cover, soil cover, percent open space, assessed property values, and infrastructure density. 

```{r ImportData, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}
sr <- "+proj=utm +zone=12 +ellps=GRS80 +datum=NAD83 +units=m +no_defs" 

Calgary <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/midTermProject_Data/CALGIS_CITYBOUND_LIMIT/CALGIS_CITYBOUND_LIMIT.shp")%>%
  #st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/midTermProject_Data/CALGIS_CITYBOUND_LIMIT/CALGIS_CITYBOUND_LIMIT.shp")%>% 
  st_transform(crs = sr)

imperviousness <- st_read("https://data.calgary.ca/resource/i9mt-qafb.geojson")%>%
  st_transform(crs = sr)

LandUse <- 
   st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/LandUse.geojson")%>%
 # st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/Land Use Districts.geojson")%>% 
  st_transform(crs = sr)%>% 
  filter(major == "Parks, Recreation and Public Education" | major == "Residential - Low Density")

PropVal <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/midTermProject_Data/PropVal.geojson")%>%
  #st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/PropVal.geojson")%>% 
  st_transform(crs = sr) 


treecanopy <- st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/calgarytreecover/calgarytreecover_repair.shp") %>%
  st_transform(crs = sr) %>%
  st_make_valid

inundation <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/inundation.shp")%>%
  # st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/midTermProject_Data/inundationpoly.shp")%>% 
  st_transform(crs = sr)%>% 
  mutate(Label = ifelse(gridcode == 1 | gridcode == 2, "No Flooding", "Flooding")) %>% 
  st_make_valid()%>% 
  st_intersection(Calgary)

imperviousness <- st_read("https://data.calgary.ca/resource/i9mt-qafb.geojson")%>%
  st_transform(crs = sr)


fishnet <- 
  st_make_grid(Calgary, cellsize = 500) %>%
  st_sf() %>%
  mutate(uniqueID = rownames(.))%>%
  st_transform(crs = sr) %>% st_make_valid() 



MinnBoundary <- st_read("https://opendata.arcgis.com/datasets/89f1a70c0cf24d7692e2d02fdf8f4e47_0.geojson")%>% 
  st_transform('ESRI:102293')

# Minneapolis is a smaller area, so a smaller Grid Cell size was used to make the cross validation comparable

fishnetM <- 
  st_make_grid(MinnBoundary, cellsize = 250) %>%
  st_sf() %>%
  mutate(uniqueID = rownames(.))%>%
  st_transform('ESRI:102293') %>% st_make_valid() 

minSales <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/New folder/minsale1.shp")%>% 
  st_transform('ESRI:102293')




ZonalWaterMin <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/New folder/ZonalWaterMin.dbf")

imperviousM <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/New folder/impervious.shp")%>% 
   st_transform('ESRI:102293')%>% 
  st_make_valid() 
  

openSpace <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/New folder/OpenSpace.shp")%>% 
   st_transform('ESRI:102293')%>% 
  st_make_valid() 

E911 <- st_read("https://opendata.arcgis.com/datasets/da0d8e48df10478bb2160977f988a113_0.geojson")%>%    st_transform('ESRI:102293')%>% 
  st_make_valid() 

FloodM <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/Flood/fldhaz.shp")%>% 
   st_transform('ESRI:102293')%>%
  st_intersection(MinnBoundary)

palette1 <- c("#f2e9e4","#c9ada7","#9a8c98","#4a4e69","#22223b")

palette2 <- c("#0294A1", "#B5F8FE")

palette3 <- c("#FFA866",  "#F17007", "#43C4DB", "#004CFF")
```
### Exploring Inundation


```{r Contours from DEM related to Fishnet, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE, fig.width = 10}
inundation_fishnet <- 
  inundation%>% 
  filter(gridcode == 1 | gridcode == 2)%>% 
  st_intersection(fishnet, inundation)%>% 
  mutate(Area = as.numeric(st_area(.)))%>%
  st_drop_geometry()%>% 
  group_by(uniqueID)%>% 
  summarise(InundationArea = sum(Area))%>% 
  left_join(fishnet, .)%>% 
  mutate(pctInundation = InundationArea / 250000)%>% 
  mutate_all(funs(replace_na(.,0)))%>%
  mutate(Inundated = ifelse(pctInundation > .15, 1, 0))%>%
  mutate(Label = ifelse(Inundated == 1, "Inundated", "Not Inundated"))


inundation_fishnet_minn <- 
  FloodM%>%
  st_intersection(fishnetM)%>% 
  mutate(Area = as.numeric(st_area(.)))%>%
  st_drop_geometry()%>% 
  group_by(uniqueID)%>%
  summarise(AreaIn = sum(Area))%>% 
  mutate(pctInundated = AreaIn / 62500 * 100)%>% 
  mutate(Inundated = ifelse(pctInundated > 20, 1, 0))%>% 
  left_join(fishnetM, .)%>% 
  mutate_all(funs(replace_na(.,0)))%>%
  mutate(Label = ifelse(Inundated == 1, "Inundated", "Not Inundated"))


grid.arrange(ncol = 2, 
             
  ggplot()+ 
  geom_sf(data = inundation_fishnet, aes(fill = Label))+ 
  scale_fill_manual(values = c("#0294A1", "#B5F8FE"))+ 
  labs(title = "Calgary Flood Inundation Map", 
       subtitle = "Inundated Grid Cells are > 50% inundated")+
  theme(legend.position = "bottom")+
  mapTheme(), 
  
  ggplot()+ 
  geom_sf(data = inundation_fishnet_minn, aes(fill = Label))+ 
  scale_fill_manual(values = c("#0294A1", "#B5F8FE"))+ 
  labs(title = "Minneapolis Flood Inundation Map", 
       subtitle = "Inundated Grid Cells are  > 50% inundated")+
  theme(legend.position = "bottom")+
  mapTheme()) 


  

 # inundation_fishnetw <- 
 #   inundation_fishnet %>% 
 #    st_centroid()%>% 
 #    st_make_valid()%>%
 #    st_join(contours)%>% 
 #    st_drop_geometry()%>% 
 #    left_join(fishnet, .)




# ggplot(inundation_fishnetw, aes(ContourMin))+ 
# geom_bar()+ 
#   labs(title = "Count of Inundated Gridcells by Elevation")+ 
#   xlab("Elevation")+ 
#   ylab("Count of Inundated Gridcells")
# 
# ggplot()+ 
#   geom_sf(data = contours, aes(fill = ContourMin))+ 
#   labs(title = "Calgary Flood Inundation Map")+
#   theme(legend.position = "bottom")+
#   mapTheme()
```

### Distance to Water

```{r ImportData, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE, fig.width = 12, fig.height = 12}
# Writing out fishnet as shapefile to perform zonal statistics as table on a euclidean distance raster from Calgary's water features 
# Reading zonal statistics Shapefile 

inundation_fishnet <- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/ZonalWater.dbf")%>%
  #st_read("C:/Users/Tristan/Downloads/CPLN-675-Midterm--main/Data/ZonalWater.dbf")%>% 
  dplyr::select(uniqueID, MEAN)%>%
  left_join(inundation_fishnet, .)%>% 
  na.omit() 

inundation_fishnet_minn <- 
  ZonalWaterMin%>% 
  dplyr:: select(uniqueID, MEAN)%>% 
  left_join(inundation_fishnet_minn, .)%>% 
  na.omit() 

grid.arrange(ncol = 2, 

ggplot() + 
  geom_sf(data = inundation_fishnet, aes(fill = q5(MEAN)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet, "MEAN"),
                    name = "Grid Cell Mean")+ 
  labs(title = "Calgary Distance From Stream (Meters)")+ 
  theme(legend.position = "bottom")+ 
  mapTheme(), 


ggplot()+ 
  geom_bar(data = inundation_fishnet, aes(x = Label, y = MEAN, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Distance From Strea")+ 
  xlab("Inundated Vs. Not Inundated")+ 
  labs(title = "Calgary Average Distance From Stream by Inundation")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none"), 

ggplot() + 
  geom_sf(data = Inundation_fishnet_minn, aes(fill = q5(MEAN)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet, "MEAN"),
                    name = "Grid Cell Mean")+ 
  labs(title = "Minneapolis Distance From Stream (Meters)")+ 
  theme(legend.position = "bottom")+ 
  mapTheme(), 

ggplot()+ 
  geom_bar(data = Inundation_fishnet_minn, aes(x = Label, y = MEAN, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Distance From Strea")+ 
  xlab("Inundated Vs. Not Inundated")+ 
  labs(title = "Minneapolis Average Distance From Stream by Inundation")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none")) 


```

### Percent Impervious

```{r Imperviousness, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE, fig.width = 12, fig.height = 12}
# The Intersection Here Takes a While -- est. 20 min 
Impervious <- st_read("C:/Users/Kyle McCarthy/Documents/GitHub/CPLN-675-Midterm-/Data/Impervious/Imperv_cal.geojson")%>%
  st_transform(crs = sr)

# The Intersection Here Takes a While -- est. 20 min 
inundation_fishnet <- 
  Impervious%>% 
  filter(gen_surface == "Gravel" | gen_surface == "Buildings" | gen_surface == "Pavement" | gen_surface == "Bridge" | gen_surface == "Roads (Pavement)" )%>% 
  st_make_valid()%>%
  st_intersection(fishnet)%>% 
  mutate(Area = as.numeric(st_area(.)))%>% 
  st_drop_geometry()%>% 
  group_by(uniqueID)%>% 
  summarise(Area = sum(Area))%>% 
  mutate(pctImpervious = Area / 250000)%>% 
  left_join(inundation_fishnet, .)%>% 
  mutate(Imperv = ifelse(pctImpervious > 0.50, "Pervious", "Impermeable"))%>% 
  mutate_all(funs(replace_na(.,0)))

inundation_fishnet<- 
  inundation_fishnet%>% 
  mutate(Imperv = ifelse(pctImpervious > 0.50, "Impervious", "Permeable"), 
         pctImpervious = pctImpervious * 100) 

Impervioustats <- 
  inundation_fishnet%>%
  dplyr::select(pctImpervious, Label)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  group_by(Label)%>% 
  summarise(pctImperv = mean(pctImpervious))


inundation_fishnet_minn <- 
  imperviousM %>% 
  st_intersection(inundation_fishnet_minn)%>% 
  mutate(Area = as.numeric(st_area(.)))%>% 
  st_drop_geometry()%>% 
  group_by(uniqueID)%>% 
  summarise(impervArea = sum(Area))%>% 
  left_join(inundation_fishnet_minn, .)%>% 
  mutate(pctImpervious = impervArea / 62500 * 100)%>% 
  mutate_all(funs(replace_na(.,0)))


ImpervioustatMinns <- 
  inundation_fishnet_minn %>%
  dplyr::select(pctImpervious, Label)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  group_by(Label)%>% 
  summarise(pctImperv = mean(pctImpervious))


grid.arrange(ncol = 2, 

# Group for Graph 
ggplot() + 
  geom_sf(data = inundation_fishnet, aes(fill = q5(pctImpervious)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet, "pctImpervious"),
                    name = "Percent Impervious")+ 
  labs(title = " Calgrary - Percent Impervious in each Grid Cell")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5))+mapTheme(), 


ggplot()+ 
  geom_bar(data = Impervioustats, aes(x = Label, y = pctImperv, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Percent Impervious")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Calgary Percent Impervious")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none"), 

ggplot() + 
  geom_sf(data = inundation_fishnet_minn , aes(fill = q5(pctImpervious)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet, "pctImpervious"),
                    name = "Percent Impervious")+ 
  labs(title = " Minneapolis - Percent Impervious in each Grid Cell")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5))+mapTheme(), 


ggplot()+ 
  geom_bar(data = ImpervioustatMinns, aes(x = Label, y = pctImperv, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Percent Impervious")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Minneapolis Percent Impervious")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none")) 



```

### Infrastructure Density

```{r Building Denisty, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE, fig.height = 12, fig.width = 12} 

inundation_fishnet <-st_read("C:/Users/Kyle McCarthy/Documents/GitHub/CPLN-675-Midterm-/Data/Buildings.geojson")%>%
  #st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/Buildings.geojson")%>% 
  st_centroid()%>% 
  st_transform(crs = sr)%>% 
  mutate(BuildingCount = 1) %>%
  dplyr::select(BuildingCount)%>%
  aggregate(., inundation_fishnet, sum)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  mutate(uniqueID = rownames(.))%>%
  st_drop_geometry()%>% 
  left_join(inundation_fishnet, .)

graph <- 
  inundation_fishnet%>% 
  na.omit()%>% 
  mutate(BuildingCount = as.integer(BuildingCount))
  

BuidlingStats<- 
  inundation_fishnet%>% 
  st_drop_geometry()%>%
  na.omit()%>%
  group_by(Label)%>% 
  summarise(BuildingSum = sum(BuildingCount), 
            BuildingCountAverage = mean(BuildingCount))


inundation_fishnet_minn <-E911%>%
  mutate(BuildingCount = 1) %>%
  dplyr::select(BuildingCount)%>%
  aggregate(., inundation_fishnet_minn, sum)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  mutate(uniqueID = rownames(.))%>%
  st_drop_geometry()%>% 
  left_join(inundation_fishnet_minn, .)

BuidlingStatsMinn<- 
  inundation_fishnet_minn%>% 
  st_drop_geometry()%>%
  na.omit()%>%
  group_by(Label)%>% 
  summarise(BuildingSum = sum(BuildingCount), 
            BuildingCountAverage = mean(BuildingCount))

grid.arrange(ncol = 2, 


ggplot() + 
  geom_sf(data = graph, aes(fill = q5(BuildingCount)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(graph, "BuildingCount"),
                    name = "Percent Impervious")+ 
  labs(title = " Calgrary - Number of Buildings in each Grid Cell")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5))+mapTheme(), 

ggplot()+ 
  geom_bar(data = BuidlingStats, aes(x = Label, y = BuildingCountAverage, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Building Count")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Calgary Building Count")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none"), 

ggplot() + 
  geom_sf(data = inundation_fishnet_minn, aes(fill = q5(BuildingCount)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet_minn, "BuildingCount"),
                    name = "Percent Impervious")+ 
  labs(title = " Minneapolis - Number of Buildings in each Grid Cell")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5))+mapTheme(), 

ggplot()+ 
  geom_bar(data = BuidlingStatsMinn, aes(x = Label, y = BuildingCountAverage, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Building Count")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Minneapolis Building Count")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none")) 

```

### Property Value

```{r assessedValue, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE, fig.height = 12, fig.width = 12} 
st_c <- st_coordinates

inundation_fishnet <- 
  PropVal %>% 
  mutate(assessed_value = as.numeric(assessed_value))%>%
  dplyr::select(assessed_value)%>%
  aggregate(., inundation_fishnet, mean)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  mutate(uniqueID = rownames(.))%>%
  st_drop_geometry()%>% 
  left_join(inundation_fishnet, .)%>%
  na.omit()

PropValStats <- 
  inundation_fishnet%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(assessed_value = sum(assessed_value))


inundation_fishnet_minn <- 
  minSales %>% 
  mutate(assessed_value = as.numeric(USER_ADJ_S))%>%
  dplyr::select(assessed_value)%>%
  aggregate(., inundation_fishnet_minn, mean)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  mutate(uniqueID = rownames(.))%>%
  st_drop_geometry()%>% 
  left_join(inundation_fishnet_minn, .)%>%
  na.omit()

PropValStatsMin <- 
  inundation_fishnet_minn%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(assessed_value = sum(assessed_value))

grid.arrange(ncol = 2, 

ggplot() + 
  geom_sf(data = inundation_fishnet , aes(fill = q5(assessed_value)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet , "assessed_value"),
                    name = "Average Property Value")+ 
  labs(title = " Calgrary - Average Property Value")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5))+mapTheme(), 

ggplot()+ 
  geom_bar(data = PropValStats, aes(x = Label, y = assessed_value, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Property Value")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Calgary Property Values")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none"), 

ggplot() + 
  geom_sf(data = inundation_fishnet_minn , aes(fill = q5(assessed_value)))+ 
   scale_fill_manual(values = palette1,
                    labels = qBr(inundation_fishnet , "assessed_value"),
                    name = "Average Property Value")+ 
  labs(title = " Calgrary - Average Property Value")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5))+mapTheme(), 

ggplot()+ 
  geom_bar(data = PropValStatsMin, aes(x = Label, y = assessed_value, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Property Value")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Calgary Property Values")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none")) 
```

### Percent Open Space

```{r LandUse, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE, fig.height = 12, fig.width = 12} 
inundation_fishnet <- 
  LandUse%>% 
  st_make_valid()%>%
  st_intersection(inundation_fishnet)%>% 
  mutate(landuArea = as.numeric(st_area(.)))%>% 
  st_drop_geometry()%>% 
  group_by(uniqueID)%>%
  summarise(landuArea = sum(landuArea))%>%
  left_join(inundation_fishnet, .)%>%
  mutate(pctLandCover = landuArea / 250000)

inundation_fishnet<-
   inundation_fishnet%>% 
  mutate(pctLandCover = pctLandCover * 100)

inundation_fishnet <-
  inundation_fishnet %>% 
  mutate_all(funs(replace_na(.,0)))

LandStats <- 
  inundation_fishnet %>%
  mutate_all(funs(replace_na(.,0)))%>% 
  group_by(Label)%>% 
  summarise(Rec_low = mean(pctLandCover))


inundation_fishnet_minn <- 
  openSpace %>% 
  st_intersection(inundation_fishnet_minn)%>% 
  mutate(Area = as.numeric(st_area(.)))%>% 
  st_drop_geometry()%>% 
  group_by(uniqueID)%>% 
  summarise(OpenArea = sum(Area))%>% 
  left_join(inundation_fishnet_minn, .)%>% 
  mutate(pctOpen = OpenArea / 62500 * 100)%>% 
  mutate_all(funs(replace_na(.,0)))


OpenStatMinns <- 
  inundation_fishnet_minn %>%
  dplyr::select(pctOpen, Label)%>% 
  mutate_all(funs(replace_na(.,0)))%>% 
  group_by(Label)%>% 
  summarise(pctOpen = mean(pctOpen))

grid.arrange(ncol = 2, 

ggplot() + 
  geom_sf(data = inundation_fishnet , aes(fill = pctLandCover))+ 
   scale_fill_viridis()+ 
  labs(title = "Minneapolis - Percent Open Space")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5), 
        legend.title = element_blank())+mapTheme(), 

ggplot()+ 
  geom_bar(data = LandStats, aes(x = Label, y = Rec_low, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Building Count")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Calgary Average Open Space by Inundation")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none"), 

ggplot() + 
  geom_sf(data = inundation_fishnet_minn , aes(fill = pctOpen))+ 
  scale_fill_viridis()+ 
  labs(title = "Minneapolis - Percent Open Space")+ 
  theme(legend.position = "bottom")+ 
  theme(plot.title = element_text(hjust = 0.5), 
        legend.title = element_blank())+mapTheme(), 

ggplot()+ 
  geom_bar(data = OpenStatMinns, aes(x = Label, y = pctOpen, fill = Label), stat="identity")+ 
  scale_fill_manual(values = palette2)+ 
  ylab("Average Building Count")+ 
  xlab("Inundated Grid Cells Vs. Not Inundated Grid Cells")+ 
  labs(title = "Minneapolis Average Open Space by Inundation")+ 
  theme(panel.background = element_rect(fill = "white", colour = "grey50"), 
        legend.position = "none")) 


```

### Tree Canopy 

```{r Tree Canopy Fishnet and Significance Check, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}

inundation_fishnet <-
  treecanopy %>%
  st_intersection(inundation_fishnet, treecanopy) %>%
  mutate(Area = as.numeric(st_area(.))) %>%
  st_drop_geometry()%>%
  group_by(uniqueID)%>%
  summarise(TreeCanopyArea = sum(Area)) %>%
  left_join(inundation_fishnet, .)%>%
  mutate(pctTreeCanopy = TreeCanopyArea / 250000)%>%
  mutate_all(funs(replace_na(.,0)))%>%
  mutate(Treed = ifelse(pctTreeCanopy > .10, 1, 0))%>%
  mutate(Label = ifelse(Treed == 1, "Treed", "Not Treed"))

TreeStats <- 
  inundation_fishnet%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Inundated)%>% 
  summarise(pctCanopy = mean(pctTreeCanopy))

ggplot()+
  geom_sf(data = inundation_fishnet, aes(fill=pctTreeCanopy))+
  scale_fill_gradient(low = "black", high = "green")+
  labs(title="Calgary Tree Canopy Percent of Fishnets")+
  theme(legend.position = "bottom")+
  mapTheme()

#Significance Quick Bar Chart Check

ggplot()+ 
  geom_bar(data = TreeStats, aes(x = Inundated, y = pctCanopy), stat="identity", fill = "blue")

```


```{r River Order and River Distances Significance Check, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}

# Preprocessing steps in ArcMap:
inundation_fishnet_rivsoil<- st_read("C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/soilCalgary/inundation_fishnet_riverorderdistance_soil.shp")%>% 
  st_transform(crs = sr)

#Significance Quick Bar Chart Check
#variable 1: RiverDist (distance to all rivers)
RiverDistStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(rivDist1 = mean(RiverDist))
ggplot()+ 
  geom_bar(data = RiverDistStats, aes(x = Label, y = rivDist1), stat="identity", fill = "blue")

#variable 2: RiverDist1 (distance to River Order of 1; Bow River)
RiverOrder1DistStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(rivDist1 = mean(RiverDist1))
ggplot()+ 
  geom_bar(data = RiverOrder1DistStats, aes(x = Label, y = rivDist1), stat="identity", fill = "blue")

#variable 3: RiverDist2 (distance to River Order of 2)
RiverOrder2DistStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(rivDist2 = mean(RiverDist2))
ggplot()+ 
  geom_bar(data = RiverOrder2DistStats, aes(x = Label, y = rivDist2), stat="identity", fill = "blue")

#variable 4: RiverDist3 (distance to River Order of 3)
RiverOrder3DistStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(rivDist3 = mean(RiverDist3))
ggplot()+ 
  geom_bar(data = RiverOrder3DistStats, aes(x = Label, y = rivDist3), stat="identity", fill = "blue")

#variable 5: TempDist (temporary/ephemeral streams)
RiverTempDistStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(rivTemp = mean(TempDist))
ggplot()+ 
  geom_bar(data = RiverTempDistStats, aes(x = Label, y = rivTemp), stat="identity", fill = "blue")

```

### Soil Analysis 

```{r Soil Significance Check, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}

# Preprocessing steps in ArcMap:

#Significance Quick Bar Chart Check
#Variable 1: MEAN_SMnum
SoilComplexStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(soilComplex = mean(MEAN_SMnum))
ggplot()+ 
  geom_bar(data = SoilComplexStats, aes(x = Label, y = soilComplex), stat="identity", fill = "blue")

#Variable 2: MEAN_SMn_1 (simplifed of MEAN_SMnum)
SoilSimpleStats  <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(soilSimple = mean(MEAN_SMn_1))
ggplot()+ 
  geom_bar(data = SoilSimpleStats, aes(x = Label, y = soilSimple), stat="identity", fill = "blue")

#Variable 3: SoilRiv
SoilRiverStats <- 
  inundation_fishnet_rivsoil%>% 
  na.omit()%>%
  st_drop_geometry()%>% 
  group_by(Label)%>% 
  summarise(soilSimple = mean(SoilRiv))
ggplot()+ 
  geom_bar(data = SoilRiverStats, aes(x = Label, y = soilSimple), stat="identity", fill = "blue")

inundation_fishnet_rivsoil_nogeometry <-
  inundation_fishnet_rivsoil %>%
  st_drop_geometry()

inundation_fishnet_merge <-
  merge(x=inundation_fishnet,y=inundation_fishnet_rivsoil_nogeometry, by.x='uniqueID', by.y='uniquID')

inundation_fishnet_merge <-
  inundation_fishnet_merge %>%
  st_make_valid()

inundation <-
  inundation %>%
  st_make_valid()

inundation_fishnet_merge2 <- 
  inundation%>% 
  filter(gridcode == 1 | gridcode == 2)%>% 
  st_intersection(inundation_fishnet_merge, inundation)%>% 
  mutate(Area = as.numeric(st_area(.)))%>%
  st_drop_geometry()%>% 
  group_by(uniqueID)%>% 
  summarise(InundationArea = sum(Area))%>% 
  left_join(fishnet, .)%>% 
  mutate(pctInundation = InundationArea / 250000)%>% 
  mutate_all(funs(replace_na(.,0)))%>%
  mutate(Inundated = ifelse(pctInundation > .15, 1, 0))%>%
  mutate(Label = ifelse(Inundated == 1, "Inundated", "Not Inundated"))

st_write(inundation_fishnet_merge2,
      "C:/Users/Tristan/Desktop/PennSpring2021/CPLN675/midterm/midTermProject_Data/inundation_fishnet_merge2.shp")

inundation_fishnet_merge2 <-
  inundation_fishnet_merge2 %>%
  st_drop_geometry()

undesired <- c('pctInundated', 'Inundated', 'InundationArea', 'Label')

inundation_fishnet_merge <- inundation_fishnet_merge %>%
  select(-one_of(undesired))

inundation_fishnet_merge <- subset(inundation_fishnet_merge, select = -c('pctImpervious', 'Inundated', 'InundationArea', 'Label'))

inundation_fishnet_merge <-
  merge(x=inundation_fishnet_merge,y=inundation_fishnet_merge2, by.x='uniqueID', by.y='uniqueID')

inundation_fishnet_merge<- st_read("C:/Users/Kyle McCarthy/Documents/CPLN 675/Midterm/fishnet2/inundation_fishnet_finalmerge.shp")

```
## Model 



```{r model, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}

inundation_Fish <- 
  inundation_fishnet_merge %>%
  dplyr::select (MEAN, pctImpr, BldngCn, assssd_, pctLndC, pctTrCn, SoilRiv, Inundatd)
  #dplyr::select(pctImpervious, landuArea, pctTreeCanopy, TempDist, MEAN_SMnum, SoilRiv, BuildingCount, assessed_value, MEAN, Inundated) 


inundation_Fish$Inundatd <- as.factor(inundation_Fish$Inundatd)
set.seed(3456)
trainIndex <- createDataPartition(inundation_Fish$Inundatd, p = .70,
                                  list = FALSE,
                                  times = 1)
preserveTrain <- inundation_Fish[ -trainIndex,]
preserveTest  <- inundation_Fish[-trainIndex,]
typeof(inundation_Fish) 
preserveModel <- glm(Inundatd ~ ., 
                    family="binomial"(link="logit"), data = preserveTrain %>%
                                                            as.data.frame() %>%
                                                            dplyr::select(-geometry))
summary(preserveModel)

```

```{r minn model, message=FALSE, warning=FALSE, include=TRUE, results='hide', cache=TRUE}
inundation_fishnet_minn1<- 
  inundation_fishnet_minn%>%
  dplyr::select("MEAN","pctImpervious", "BuildingCount", "assessed_value", "pctOpen", "Inundated" )

inundation_fishnet_minn1$Inundated <- as.factor(inundation_fishnet_minn1$Inundated)

set.seed(3456)
trainIndex <- createDataPartition(inundation_fishnet_minn1$Inundated, p = .70,
                                  list = FALSE,
                                  times = 1)
InundationTrain <- inundation_fishnet_minn1[ -trainIndex,]
InundationTest  <- inundation_fishnet_minn1[-trainIndex,]

InundationModel <- glm(Inundated ~ ., 
                    family="binomial"(link="logit"), data = InundationTrain %>%
                                                            as.data.frame() %>%
                                                            dplyr::select(-geometry))
summary(InundationModel)

```
### Confusion Matrix and ROC Curve 

```{r Model Validation, message=FALSE, warning=FALSE, include=TRUE, cache=TRUE, fig.height = 8, fig.width = 8} 
classProbs <- predict(preserveModel, preserveTest, type="response")

testProbs <- data.frame(obs = as.numeric(preserveTest$Inundatd),
                        pred = classProbs)

```

``````{r Model Validation Minn, Min message=FALSE, warning=FALSE, include=TRUE, cache=TRUE, fig.height = 8, fig.width = 8} 

classProbsM <- predict(InundationModel, InundationTest, type="response")

testProbsM <- data.frame(obs = as.numeric(InundationTest$Inundated),
                        pred = classProbsM)


```

```{r ROC, message=FALSE, warning=FALSE, include=TRUE, cache=TRUE, fig.height = 7, fig.width = 7}
testProbs$predClass  = ifelse(testProbs$pred > 0.5 , 1,0)
testProbs <- 
  testProbs %>% 
  mutate(obs = ifelse(obs == 2, 1, 0))
 x <- caret::confusionMatrix(reference = as.factor(testProbs$obs), 
                       data = as.factor(testProbs$predClass), 
                       positive = "1")


draw_confusion_matrix <- function(cm) {

  layout(matrix(c(1,1,2)))
  par(mar=c(2,2,2,2))
  plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
  title('CONFUSION MATRIX CALGARY', cex.main=2)

  # create the matrix 
  rect(150, 430, 240, 370, col='#3F97D0')
  text(195, 435, 'Not Inundated', cex=1.2)
  rect(250, 430, 340, 370, col='#F7AD50')
  text(295, 435, 'Inundated', cex=1.2)
  text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
  text(245, 450, 'Actual', cex=1.3, font=2)
  rect(150, 305, 240, 365, col='#F7AD50')
  rect(250, 305, 340, 365, col='#3F97D0')
  text(140, 400, 'Not Inundated', cex=1.2, srt=90)
  text(140, 335, 'Inundated', cex=1.2, srt=90)

  # add in the cm results 
  res <- as.numeric(cm$table)
  text(195, 400, res[1], cex=1.6, font=2, col='white')
  text(195, 335, res[2], cex=1.6, font=2, col='white')
  text(295, 400, res[3], cex=1.6, font=2, col='white')
  text(295, 335, res[4], cex=1.6, font=2, col='white')

  # add in the specifics 
  plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
  text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
  text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
  text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
  text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
  text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
  text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
  text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
  text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
  text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
  text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)

  # add in the accuracy information 
  text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
  text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
  text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
  text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}  

testProbsM$predClass  = ifelse(testProbsM$pred > 0.5 , 1,0)
testProbsM <- 
  testProbsM %>% 
  mutate(obs = ifelse(obs == 2, 1, 0))
 y <- caret::confusionMatrix(reference = as.factor(testProbsM$obs), 
                       data = as.factor(testProbsM$predClass), 
                       positive = "1")


draw_confusion_matrix2 <- function(cm) {

  layout(matrix(c(1,1,2)))
  par(mar=c(2,2,2,2))
  plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
  title('CONFUSION MATRIX CALGARY', cex.main=2)

  # create the matrix 
  rect(150, 430, 240, 370, col='#3F97D0')
  text(195, 435, 'Not Inundated', cex=1.2)
  rect(250, 430, 340, 370, col='#F7AD50')
  text(295, 435, 'Inundated', cex=1.2)
  text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
  text(245, 450, 'Actual', cex=1.3, font=2)
  rect(150, 305, 240, 365, col='#F7AD50')
  rect(250, 305, 340, 365, col='#3F97D0')
  text(140, 400, 'Not Inundated', cex=1.2, srt=90)
  text(140, 335, 'Inundated', cex=1.2, srt=90)

  # add in the cm results 
  res <- as.numeric(cm$table)
  text(195, 400, res[1], cex=1.6, font=2, col='white')
  text(195, 335, res[2], cex=1.6, font=2, col='white')
  text(295, 400, res[3], cex=1.6, font=2, col='white')
  text(295, 335, res[4], cex=1.6, font=2, col='white')

  # add in the specifics 
  plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
  text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
  text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
  text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
  text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
  text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
  text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
  text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
  text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
  text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
  text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)

  # add in the accuracy information 
  text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
  text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
  text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
  text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}  

par(mfrow=c(1,1))


draw_confusion_matrix(x)

draw_confusion_matrix2(y)



grid.arrange( ncol = 1, 
ggplot(testProbs, aes(d = obs, m = pred)) + 
  geom_roc(n.cuts = 50, labels = FALSE) + 
  style_roc(theme = theme_light) +
  ylab("True Positive Fraction")+
  xlab("True Negative Fraction")+
  labs(title = "Calgary ROC Curve")+ 
  geom_abline(slope = 1, intercept = 0, size = 1.5, color = 'red'), 

ggplot(testProbsM, aes(d = obs, m = pred)) + 
  geom_roc(n.cuts = 50, labels = FALSE) + 
  style_roc(theme = theme_light) +
  ylab("True Positive Fraction")+
  xlab("True Negative Fraction")+
  labs(title = "Minneapolis ROC Curve")+ 
  geom_abline(slope = 1, intercept = 0, size = 1.5, color = 'red')) 

```
### Mapped Confusion Matrix

```{r ROC2, message=FALSE, warning=FALSE, include=TRUE, cache=TRUE, fig.height = 9, fig.width = 16}   


preserveTest$uniqueID <- seq.int(nrow(preserveTest))
testProbs1 <- testProbs
testProbs$uniqueID <- seq.int(nrow(preserveTest))

try <- left_join(preserveTest, testProbs)
try <- 
  try%>% 
  mutate(result = "0")%>% 
  mutate(result = ifelse(obs == 0 & predClass == 0, "True Negative", result))%>% 
  mutate(result = ifelse(obs == 1 & predClass == 1, "True Positive", result))%>% 
  mutate(result = ifelse(obs == 0 & predClass == 1, "False Positive", result))%>% 
  mutate(result = ifelse(obs == 1 & predClass == 0, "False Negative", result))

InundationTest$uniqueID <- seq.int(nrow(InundationTest))

testProbsM$uniqueID <- seq.int(nrow(InundationTest))

In <- left_join(InundationTest, testProbs)
In<- 
  In%>% 
  mutate(result = "0")%>% 
  mutate(result = ifelse(obs == 0 & predClass == 0, "True Negative", result))%>% 
  mutate(result = ifelse(obs == 1 & predClass == 1, "True Positive", result))%>% 
  mutate(result = ifelse(obs == 0 & predClass == 1, "False Positive", result))%>% 
  mutate(result = ifelse(obs == 1 & predClass == 0, "False Negative", result))

grid.arrange(ncol = 2, 

ggplot() + 
  geom_sf(data = inundation_Fish, fill = "white")+ 
  geom_sf(data = try, aes(fill = result))+ 
  labs(title = "Calgary Mapped Coorelation Matrix")+
  scale_fill_manual(values = palette3, name = "My name", 
                  guide = guide_legend(reverse = TRUE))+ 
  theme(legend.position = "bottom", 
        legend.title = element_blank(), 
        plot.title = element_text(hjust = 0.5))+
  mapTheme() , 


ggplot() + 
  geom_sf(data = inundation_fishnet_minn, fill = "white")+ 
  geom_sf(data = In, aes(fill = result))+ 
  labs(title = "Minneapolis Mapped Coorelation Matrix")+
  scale_fill_manual(values = palette3, name = "My name", 
                  guide = guide_legend(reverse = TRUE))+ 
  theme(legend.position = "bottom", 
        legend.title = element_blank(), 
        plot.title = element_text(hjust = 0.5))+
  theme(plot.title = element_text(size = 36))+ 
  mapTheme() ) 
  

```

### All Mapped Predictions

```{r Cross Validation, message=FALSE, warning=FALSE, include=TRUE, cache = TRUE, fig.height = 8 fig.width = 8} 
ctrl <- trainControl(method = "cv", 
                     number = 100, 
                     savePredictions = TRUE, 
                     )
inundation_Fish1 <- 
  inundation_Fish %>% 
  na.omit() %>% 
  dplyr::select(MEAN, pctImpr, BldngCn, assssd_, pctLndC, pctTrCn, SoilRiv, Inundatd)

cvFit <- train(as.factor(Inundatd) ~ .,  data = inundation_Fish1 %>% 
                                                as.data.frame() %>%
                                                dplyr::select(-geometry), 
               method="glm", family="binomial",
               trControl = ctrl)
cvFit

inundation_fishnet_minn1 <- 
  inundation_fishnet_minn1 %>% 
  na.omit() %>% 
  dplyr::select("MEAN","pctImpervious", "BuildingCount", "assessed_value", "pctOpen", "Inundated" )

cvFit1 <- train(as.factor(Inundated) ~ .,  data = inundation_fishnet_minn1 %>% 
                                                as.data.frame() %>%
                                                dplyr::select(-geometry), 
               method="glm", family="binomial",
               trControl = ctrl)
cvFit1



```




```{r Mapping Predictions, message=FALSE, warning=FALSE, include=TRUE, cache = TRUE, fig.height = 8, fig.width = 16  } 

allPredictions <- 
  predict(cvFit, inundation_Fish, type="prob")[,2]

allPredictions1 <- 
  predict(cvFit1, inundation_fishnet_minn1, type="prob")[,2]
  
Inundation1 <- 
  cbind(inundation_Fish,allPredictions) %>%
  mutate(allPredictions = round(allPredictions * 100)) 

Inundation2 <- 
  cbind(inundation_fishnet_minn1,allPredictions1) %>%
  mutate(allPredictions = round(allPredictions1 * 100)) 



grid.arrange(ncol = 2, 
 ggplot() + 
    geom_sf(data=Inundation1, aes(fill=allPredictions))  +
   scale_fill_continuous(name = "Flood Probability %")+ 
   labs(title = "Predicted Probabilities of Flood Inundation Calgary")+ 
  mapTheme() , 
 
  ggplot() + 
    geom_sf(data=Inundation2, aes(fill=allPredictions))  +
   scale_fill_continuous(name = "Flood Probability %")+ 
   labs(title = "Predicted Probabilities of Flood Inundation Minneapolis")+ 
  mapTheme() ) 

 
 
```


## Conclusion