CUSP2024/GWR.Rmd at main · yiyansun/CUSP2024 · GitHub

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---
title: "Untitled"
author: "yiyan Sun"
date: "2024-02-22"
output: html_document
---

```{r}

library(spatstat)
library(here)
library(sp)
library(rgeos)
library(maptools)
library(tmap)
library(tmaptools)
library(sf)
library(geojson)
library(geojsonio)
library(tmaptools)
library(stringr)
library(terra)
library(spgwr)
library(raster)
library(ggplot2)

```


```{r}
sensor <- read.csv("D:/CASApre/DataDive2024/MonitoringSiteSpecies.csv")
airquality <- read.csv("D:/CASApre/DataDive2024/merged_air_quality.csv")
waterloo <- read.csv("D:/CASApre/DataDive2024/Waterloo_CE2.csv")
```

```{r}
LSOA <- st_read("D:/CASApre/DataDive2024/ESRI/LSOA_2011_London_gen_MHW.shp") %>% st_transform(27700)

borough <- st_read("D:/CASApre/DataDive2024/ESRI/London_Borough_Excluding_MHW.shp") %>% st_transform(27700)

```

```{r}
qtm(borough)
```


```{r}
speed <- rast("D:/CASApre/DataDive2024/hourly_speed_data.tif")

hourly_speed_data_1 <- speed[[1]]


crs(hourly_speed_data_1) <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs"

plot(hourly_speed_data_1)
```

```{r}
join <- st_join(sensor, borough, join = st_intersects)
```

```{r}
speed2024 <- raster("D:/CASApre/DataDive2024/2024010100_3600_50.gtiff")

plot(speed2024)
```

```{r}
```

```{r}
```


```{r}

merged_data <- merge(borough, hourly_speed_data_1, join="left")

# Now merged_data contains the LSOA data with hourly_speed_data_1 values as a new column


```

```{r}
plot(merged_data)
```

```{r}
```

```{r}
# Create a scatter plot of sensor data with coordinates
ggplot(sensor, aes(x = Longitude, y = Latitude)) +
  geom_point() +
  labs(x = "Longitude", y = "Latitude", title = "Sensor Data Visualization")

```
```{r}
# Convert sensor data to sf object
sensor_sf <- st_as_sf(sensor, coords = c("Longitude", "Latitude"), crs = 4326)

# Transform coordinates to EPSG 27700 (British National Grid)
sensor_sf <- st_transform(sensor_sf, crs = 27700)

# Create a scatter plot of sensor data with transformed coordinates
ggplot(sensor_sf) +
  geom_sf() +
  labs(x = "Longitude (British National Grid)", y = "Latitude (British National Grid)", title = "Sensor Data Visualization (British National Grid)")
```

```{r}
# Check CRS of sensor data and LSOA data
print(st_crs(sensor_sf))
print(st_crs(LSOA))

# Transform sensor data to match the CRS of LSOA data
sensor_sf <- st_transform(sensor_sf, crs = st_crs(LSOA))

# Perform spatial join
joined_data <- st_join(sensor_sf, LSOA, join = st_intersects)

# View the joined data
print(joined_data)
```


```{r}
tmap_mode("plot")
tm_shape(LSOA) +
  tm_polygons() +
  tm_shape(sensor_sf)
```

```{r}
```