3 Exercises I

\[ \newcommand{\Exp}{\mathrm{E}} \newcommand\given[1][]{\:#1\vert\:} \newcommand{\Cov}{\mathrm{Cov}} \newcommand{\Var}{\mathrm{Var}} \newcommand{\rank}{\mathrm{rank}} \newcommand{\bm}[1]{\boldsymbol{\mathbf{#1}}} \]

Required packages

pkgs <- c("sf", "mapview", "areal", "spdep", "spatialreg", "tmap", "viridisLite",
          "ggplot2", "ggthemes", "gridExtra") # note: load spdep first, then spatialreg
lapply(pkgs, require, character.only = TRUE)

Session info

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United Kingdom.utf8 
[2] LC_CTYPE=English_United Kingdom.utf8   
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.utf8    

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods  
[7] base     

other attached packages:
 [1] gridExtra_2.3     ggthemes_5.1.0    ggplot2_3.5.2    
 [4] viridisLite_0.4.2 tmap_4.1          spatialreg_1.3-6 
 [7] Matrix_1.7-3      spdep_1.3-13      spData_2.3.4     
[10] areal_0.1.8       mapview_2.11.2    sf_1.0-21        

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1        dplyr_1.1.4            
 [3] farver_2.1.2            fastmap_1.2.0          
 [5] leaflegend_1.2.1        leaflet_2.2.2          
 [7] TH.data_1.1-3           XML_3.99-0.18          
 [9] digest_0.6.37           lifecycle_1.0.4        
[11] LearnBayes_2.15.1       survival_3.8-3         
[13] terra_1.8-54            magrittr_2.0.3         
[15] compiler_4.5.1          rlang_1.1.6            
[17] tools_4.5.1             data.table_1.17.6      
[19] knitr_1.50              htmlwidgets_1.6.4      
[21] sp_2.2-0                classInt_0.4-11        
[23] RColorBrewer_1.1-3      multcomp_1.4-28        
[25] abind_1.4-8             KernSmooth_2.23-26     
[27] purrr_1.0.4             withr_3.0.2            
[29] leafsync_0.1.0          grid_4.5.1             
[31] stats4_4.5.1            cols4all_0.8           
[33] e1071_1.7-16            leafem_0.2.4           
[35] colorspace_2.1-1        spacesXYZ_1.6-0        
[37] scales_1.4.0            MASS_7.3-65            
[39] dichromat_2.0-0.1       cli_3.6.5              
[41] mvtnorm_1.3-3           rmarkdown_2.29         
[43] generics_0.1.4          rstudioapi_0.17.1      
[45] tmaptools_3.2           DBI_1.2.3              
[47] proxy_0.4-27            stringr_1.5.1          
[49] splines_4.5.1           stars_0.6-8            
[51] parallel_4.5.1          s2_1.1.9               
[53] base64enc_0.1-3         vctrs_0.6.5            
[55] boot_1.3-31             sandwich_3.1-1         
[57] jsonlite_2.0.0          crosstalk_1.2.1        
[59] units_0.8-7             maptiles_0.10.0        
[61] glue_1.8.0              lwgeom_0.2-14          
[63] codetools_0.2-20        leaflet.providers_2.0.0
[65] stringi_1.8.7           gtable_0.3.6           
[67] deldir_2.0-4            raster_3.6-32          
[69] tibble_3.3.0            logger_0.4.0           
[71] pillar_1.10.2           htmltools_0.5.8.1      
[73] satellite_1.0.5         R6_2.6.1               
[75] wk_0.9.4                microbenchmark_1.5.0   
[77] evaluate_1.0.4          lattice_0.22-7         
[79] png_0.1-8               class_7.3-23           
[81] Rcpp_1.0.14             coda_0.19-4.1          
[83] nlme_3.1-168            xfun_0.52              
[85] zoo_1.8-14              pkgconfig_2.0.3

Reload data from pervious session

load("_data/msoa2_spatial.RData")

3.1 General Exercises

3.1.1 1) Can you import the spatial administrative units of Germany (“Kreisgrenzen_2020_mit_Einwohnerzahl” in _data folder) and make a simple plot of the boundaries? {.unnumbered}

2) What is the Coordinate reference system of this German shape file?

4) Below we will load some additional data on heat islands.

We will add some Data about London’s Urban Heat Island. It contains information about the mean temperature at midnight during the summer of 2011.

This is a tif file that we need to read in with stars and then transform into sf.

library(stars)

Loading required package: abind

# Read geo_tif with stars
urbclim <- read_stars("https://data.london.gov.uk/download/ae16d5af-5dce-49bc-b1e2-88bb41e8bfd0/f4e3a05d-fad7-4b56-8c42-ba2274f3bb3a/London_Tmin_midnight_2011.tif")

# urbclim <- read_stars("_data/London_Tmin_midnight_2011.tif")

# Transfer to sf
urbclim.spdf <- st_as_sf(urbclim)
names(urbclim.spdf)[1] <- "Tmin_midnight"

On which projection is the urbclim temperature data?
Can you please calculate the average night-time temperature for each MSOA using area weighted interpolation. Make sure that the objects are on the same projections / crs.
Create a map showing the temperature for each MSOA, and plot it next to the maps of non-EU immigrant residents (e.g. using grid.arrange()) .

::: {.content-hidden unless-format="html"}
$$
\newcommand{\Exp}{\mathrm{E}}
\newcommand\given[1][]{\:#1\vert\:}
\newcommand{\Cov}{\mathrm{Cov}}
\newcommand{\Var}{\mathrm{Var}}
\newcommand{\rank}{\mathrm{rank}}
\newcommand{\bm}[1]{\boldsymbol{\mathbf{#1}}}
$$
:::


# Exercises I

### Required packages {.unnumbered}

```{r, message = FALSE, warning = FALSE, results = 'hide'}
pkgs <- c("sf", "mapview", "areal", "spdep", "spatialreg", "tmap", "viridisLite",
          "ggplot2", "ggthemes", "gridExtra") # note: load spdep first, then spatialreg
lapply(pkgs, require, character.only = TRUE)

```

### Session info {.unnumbered}

```{r}
sessionInfo()

```

### Reload data from pervious session {.unnumbered}

```{r}
load("_data/msoa2_spatial.RData")
```



## General Exercises

### 1) Can you import the spatial administrative units of Germany ("Kreisgrenzen_2020_mit_Einwohnerzahl" in _data folder) and make a simple plot of the boundaries?   {.unnumbered}


### 2) What is the Coordinate reference system of this German shape file? {.unnumbered}



### 3) Using the MSOA data in London from above: Please draw a map showing the distribution of share of non-EU immigrants (per_nonEU).  {.unnumbered}


### 4) Below we will load some additional data on heat islands.   {.unnumbered}

We will add some Data about [London's Urban Heat Island](https://data.london.gov.uk/dataset/london-s-urban-heat-island---average-summer/). It contains information about the mean temperature at midnight during the summer of 2011.

This is a tif file that we need to read in with stars and then transform into sf.

```{r}
library(stars)

# Read geo_tif with stars
urbclim <- read_stars("https://data.london.gov.uk/download/ae16d5af-5dce-49bc-b1e2-88bb41e8bfd0/f4e3a05d-fad7-4b56-8c42-ba2274f3bb3a/London_Tmin_midnight_2011.tif")

# urbclim <- read_stars("_data/London_Tmin_midnight_2011.tif")

# Transfer to sf
urbclim.spdf <- st_as_sf(urbclim)
names(urbclim.spdf)[1] <- "Tmin_midnight"
```

a) On which projection is the urbclim temperature data?

b) Can you please calculate the average night-time temperature for each MSOA using area weighted interpolation. Make sure that the objects are on the same projections / crs.

c) Create a map showing the temperature for each MSOA, and plot it next to the maps of non-EU immigrant residents (e.g. using `grid.arrange()`) .





### 4) What's the correlation between the share of non-Eu residents and the termperature.   {.unnumbered}