IS424_Data_Mining/code/GIS_DataMining_G1/Datamining_qf.Rmd

```{r}
install.packages(c("sf", "raster"))

# Load packages
```

```{r}
install.packages(c("dplyr"))
```

```{r}
# Load necessary libraries
library(sf)
library(ggplot2)
library(rnaturalearth)
library(rnaturalearthdata)
# Read your CSV data
data <- read.csv("GIS_Purpose.csv")
```

```{r}
data_clean <- na.omit(data)

# Convert the cleaned data frame to an sf object, specifying the coordinates and CRS (Coordinate Reference System)
data_sf <- st_as_sf(data_clean, coords = c("lon", "lat"), crs = 4326)

# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Plot the world map with points from your data
my_plot <- ggplot(data = world) +
  geom_sf() + # This plots the world map as a base layer
  geom_sf(data = data_sf, aes(color = Severity), size = 0.4) + # This adds your points on top
  theme_minimal() +
  labs(title = "Spatial Distribution of Incidents with World Map Basemap") +
  theme(legend.position = "right") # Adjust legend position if needed

# Save the plot to a file
ggsave("my_spatial_plot.png", plot = my_plot, width = 10, height = 8, dpi = 300)
```

```{r}
library(lubridate)
library(ggplot2)
library(forecast)

# Check for NA values and remove them
data <- na.omit(data)

# Aggregate data by month
data$Month <- floor_date(data$Datetime, "month")
monthly_incidents <- aggregate(Index ~ Month, data, length)

# Make sure that there are no NA values
monthly_incidents <- na.omit(monthly_incidents)

# Assuming that you've verified the 'monthly_incidents' dataframe and it looks correct
# Create a time series object, checking the start and end values
start_year <- min(year(monthly_incidents$Month), na.rm = TRUE)
start_month <- min(month(monthly_incidents$Month), na.rm = TRUE)
end_year <- max(year(monthly_incidents$Month), na.rm = TRUE)
end_month <- max(month(monthly_incidents$Month), na.rm = TRUE)

# Check if start date is after end date
if (make_date(start_year, start_month) > make_date(end_year, end_month)) {
  stop("'start' cannot be after 'end'")
}

# Now create the time series object
ts_data <- ts(monthly_incidents$Index, frequency=12, start=c(start_year, start_month))

```

```{r}
plot(ts_data, main = "Monthly Incidents Time Series", xlab = "Time", ylab = "Number of Incidents", col = "blue")


```

```{r}
decomposed_data <- decompose(ts_data)
plot(decomposed_data)

```

```{r}
incidents_by_severity <- aggregate(Index ~ Severity, data = data, FUN = length)

# Visualize the number of incidents by Severity
ggplot(incidents_by_severity, aes(x = Severity, y = Index, fill = Severity)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "Severity", y = "Frequency", title = "Frequency of Incidents by Severity")
```

```{r}
# Assuming 'data' is your dataframe and 'Severity' is the column with the severity level
# First, count the frequency of each severity level
severity_counts <- table(data$Severity)

# Convert the names of the table (the severity levels) to numeric ranks
severity_ranks <- as.numeric(factor(names(severity_counts), 
                                    levels = c("Minor", "Moderate", "Severe", "Extreme")))

# Perform Spearman's rank correlation test between severity ranks and their frequencies
cor.test(severity_ranks, severity_counts, method = "spearman")

```

```{r}
# Assuming 'data' is your data frame and 'Category' is the column with incident types
category_counts <- table(data$Category)
top_categories <- sort(category_counts, decreasing = TRUE)[1:5]
```

```{r}
# Convert table to data frame for filtering
top_categories_df <- as.data.frame(top_categories)

# Filter your original data for only top categories
top_data <- data[data$Category %in% names(top_categories), ]

```

```{r}
library(dplyr)
library(ggplot2)
library(maps)

# Assuming 'data' is your data frame, 'Category' is the column with incident types, and 'lon', 'lat' are your longitude and latitude columns

# Calculate counts of incidents for each category at each location
top_data <- data %>%
  count(Category, lon, lat) %>%
  filter(Category %in% names(top_categories))

# Get world map data
world_map <- map_data("world")

# Create the plot
plot <- ggplot(data = world_map, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group), fill = "gray80", color = "white") +
  geom_point(data = top_data, aes(x = lon, y = lat, color = Category, size = n), alpha = 0.7) +
  scale_size(range = c(4, 16)) +  # Adjust the size range as needed
  scale_color_brewer(palette = "Dark2") +
  labs(title = "Top 5 Categories of Incidents on World Map",
       subtitle = "Size of point represents frequency of incidents",
       size = "Number of Incidents") +
  theme_minimal() +
  theme(legend.position = "bottom")

# Save the plot
ggsave("incident_map.png", plot = plot, width = 20, height = 10, dpi = 300)

```

```{r}
library(dplyr)
library(ggplot2)
library(maps)
library(scales)  # For more refined control over point sizes

# Assuming 'data' is your data frame, 'Category' is the column with incident types, and 'lon', 'lat' are your longitude and latitude columns

# Calculate counts of incidents for each category at each location
top_data <- data %>%
  count(Category, lon, lat) %>%
  filter(Category %in% names(top_categories)) %>%
  mutate(size = sqrt(n))  # Use square root scaling for point sizes

# Get world map data
world_map <- map_data("world")

# Create the plot with improved aesthetics
incident_map <- ggplot(data = world_map, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group), fill = "lightblue", color = "white") +  # Use a different fill color for water
  geom_point(data = top_data, aes(x = lon, y = lat, color = Category, size = size), alpha = 0.6) +
  scale_size_continuous(trans = "identity", range = c(1, 12)) +  # Use identity transformation and adjust the size range
  scale_color_brewer(palette = "Dark2", name = "Category") +
  labs(title = "Top 5 Categories of Incidents on World Map",
       subtitle = "Size of point represents frequency of incidents",
       size = "Frequency (sqrt scale)") +  # Updated legend title to reflect sqrt scaling
  coord_quickmap() +  # Use an equirectangular projection
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.key.size = unit(0.5, "cm"))  # Adjust legend key size for better appearance

# Save the plot using the new variable name
ggsave("incident_map_refined.png", plot = incident_map, width = 12, height = 8, dpi = 300)  # Adjusted dimensions for a better aspect ratio

```