Exploration using dataframes

Spatial snapshots through time

We will focus on the maximum temperature and see how it changes over time in 1993. First we will filter the data for Tmax for year 1993 and months 5 to 9. The the field julian has a unique number for each day. We will make a new column to represent the days starting from 1 and select certain days.

Tmax <- filter(NOAA_df_1990,
  proc == "Tmax" &
  month %in% 5:9 &
  year == 1993)
Tmax$t <- Tmax$julian - min(Tmax$julian) + 1
Tmax_days <- subset(Tmax, t %in% c(1, 15, 30))

Now we can use the spatial_snapshots function. For this function we need to give the data frame and the columns which are used for latitude, longitude, time and the column which has the quantity we want to plot. You can also give the main and legend title for the plot.

spatial_snapshots(Tmax_days, 
         lat_col = 'lat', 
         lon_col = 'lon', 
         t_col = 't', 
         z_col = 'z',  
         title = "Maximum Temperature for 3 days",
         legend_title = 'Temp')

Suppose we want to plot the maximum temperature for days 1 to 12.

Tmax_days <- subset(Tmax, t %in% c(1:12))

spatial_snapshots(Tmax_days, 
         lat_col = 'lat', 
         lon_col = 'lon', 
         t_col = 't', 
         z_col = 'z',  
         title = "Maximum Temperature for 12 days",
         legend_title = 'Temp')

Time series snapshots for different locations

We have looked at spatial snapshots over time. Now let’s look at temporal snapshots for given spatial locations. First let’s look at the location ids and sample 12 of them.

set.seed(148)                                          
Tmax_ID <- unique(Tmax$id)                               
ids <- sample(Tmax_ID, 12)
ids
#>  [1] 14926 93842 14848 93820  3919 94918 13871 94823 13988  3952 13971  3841

Then we use the temporal_snapshots function to plot the temporal snapshots at those locations. Similar to the previous function, we need to specify the dataframe, time column, value column, the id column and the id samples. You can specify the x and y labels and the title.

temporal_snapshots(Tmax, 
         t_col = 't', 
         z_col = 'z', 
         id_col = 'id', 
         id_sample = ids,  
         xlab = "Days", 
         ylab="Temperature",
         title = "Temperature Selected Days")

Spatial empirical means (averaged over time)

Suppose we want to explore the average value for the time period. That is, for every location we average the values over time. We can see how the average value changes for different latitude and longitude values. As our quantity of interest is Maximum Temperature, we get the Mean Maximum Temperature in these plots. We can also see that there is a relationship between Mean Maximum Temperature and the Latitude, but not the Longitude, which is expected.

sem <- spatial_means(Tmax, 
       lat_col = "lat", 
       lon_col = "lon", 
       t_col = "t", 
       z_col = "z" 
       )
autoplot(sem, ylab="Mean Max Temp")

#> TableGrob (2 x 2) "arrange": 3 grobs
#>   z     cells    name                 grob
#> 1 1 (2-2,1-1) arrange       gtable[layout]
#> 2 2 (2-2,2-2) arrange       gtable[layout]
#> 3 3 (1-1,1-2) arrange text[GRID.text.1423]

Temporal empirical means (averaged over space)

Similarly, we can plot the averages over space and see their fluctuations over time. In this case, we’re computing the average for each date over all the latitude and longitude values. We see a graph with the observed and the average values for each date.

tem <- temporal_means(Tmax,
                      t_col = 'date',
                      z_col = 'z',
                      id_col = 'id')

autoplot(tem,
         ylab = "Mean Maximum Temperature")

We finish the initial explorations with the Hovmoller plots.

Hovmoller plots

Hovmollet plots are generally used to reduce spatial dimensions. It is sometimes called a cross between a map and a graph. It is commmonly used to collapse one of the spatial dimensions. In our example we have the maximum temperature for different latitude, longitude and time values. We can average by latitude and see how the maximum temperature changes with time and longitude. Or we can average by longitude and explore the changes in maximum temperature with respect to latitude and time.

The function hovmoller takes similar arguments to the other functions with the exception of lat_or_lon and lat_or_lon_col arguments. The argument lat_or_lon specifies, which spatial component we will see in the plot. If we specify lat, then we will see a plot of the quantity of interest (Max Temp in our case) with latitude and time as the two axes. If we specify lon, then we will see longitude on the x-axis. The argument lat_or_lon_col is used to specify the column corresponding to the spatial variable.

To average by longitude, we need to specify lat_or_lon = ‘lat’. That is, if you want to see latitude on the x-axis, then specify lat.

hov <- hovmoller(lat_or_lon = "lat",
                 x = Tmax,
                 lat_or_lon_col = 'lat',
                 t_col = 't',
                 z_col = 'z')
autoplot(hov, legend_title = "Temperature")

The above graph gives valuable information. We see that at higher latitudes the maximum temperature is low compared to lower latitudes. There is another interesting bit of information. The dataset Tmax spans 150 days from May to September. This is from Spring to Autumn in the Northern Hemisphere. Generally, the maximum daily temperature would go up from May to July/August and then it would go down. This is confirmed by the parabolic pattern of dark red/orange squares we see over time.

If we want to see longitude on the x-axis (average by latitude), then we need to specify lat_or_lon = ‘lon’. but this plot is not so interesting.

hov <- hovmoller (lat_or_lon = "lon",
                  x = Tmax,
                  lat_or_lon_col = 'lon',
                  t_col = 't',
                  z_col = 'z')
autoplot(hov, legend_title = "Temperature")

Ridgeline plots

Ridgeline plots show the distribution of a numeric value for different groups. Let us look at the maximum temperature distribution for different latitude values.

ridgeline(Tmax, group_col = 'lat', z_col = 'z' )
#> Warning: `stat(x)` was deprecated in ggplot2 3.4.0.
#> ℹ Please use `after_stat(x)` instead.
#> ℹ The deprecated feature was likely used in the stxplore package.
#>   Please report the issue to the authors.
#> Picking joint bandwidth of 1.76

For higher latitudes, the maximum temperature distribution has a longer left tail. That is, during certain periods the maximum temperature is lower compared to lower latitudes.

Empirical covariances

To explore the empirical covariances, we first remove the trends and examine the residuals. The emp_spatial_cov function can remove either linear or quadratic trends in space and time. If you set quadratic_time = TRUE a quadratic trend is removed in time. Similarly if you set quadratic_space = TRUE, a quadratic component in space is removed. If these arguments are set to FALSE only a linear trend is removed.

The emp_spatial_cov function can plot latitude or longitude strips of empirical covariance. That is, you can divide the spatial domain into strips corresponding to either latitude or longitude and plot the associated covariance matrices for those strips.

Let’s see some longitudinal strips for this dataset after removing a quadratic time and a linear space trend. We will see how they change with latitude values.

esv <- emp_spatial_cov(Tmax,
                       lat_col = "lat",
                       lon_col = "lon",
                       t_col ="t",
                       z_col = "z",
                       num_strips = 4,
                       quadratic_space = FALSE,
                       quadratic_time = TRUE,
                       lat_or_lon_strips = "lon"
)

autoplot(esv)

Similarly, we can also plot latitude strips; the x axis will be longitude then.

esv <- emp_spatial_cov(Tmax,
                       lat_col = "lat",
                       lon_col = "lon",
                       t_col ="t",
                       z_col = "z",
                       num_strips = 4,
                       quadratic_space = FALSE,
                       quadratic_time = TRUE,
                       lat_or_lon_strips = "lat"
)
autoplot(esv)

We can also compute the lagged covariance. We do this by setting the lag parameter. Let’s see the lagged-1 empirical covariance for this data after removing a quadratic trend in time.

# longitudinal strips
esv1 <- emp_spatial_cov(Tmax,
                        lat_col = "lat",
                        lon_col = "lon",
                        t_col ="t",
                        z_col = "z",
                        num_strips = 4,
                        quadratic_space = FALSE,
                        quadratic_time = TRUE,
                        lat_or_lon_strips = "lon",
                        lag = 1
)
autoplot(esv1)

# latitude strips
esv2 <- emp_spatial_cov(Tmax,
                        lat_col = "lat",
                        lon_col = "lon",
                        t_col ="t",
                        z_col = "z",
                        num_strips = 4,
                        quadratic_space = FALSE,
                        quadratic_time = TRUE,
                        lat_or_lon_strips = "lat",
                        lag = 1
)
autoplot(esv2)

Space-time semi-variograms

To plot space-time semi-variograms we need to construct an object of STDF Class. We do this inside the function semivariogram. To create an STDF object, we need spatio-temporal data with a full spate-time grid. That is, we need a matrix of M x N size, where N is the number of spatial locations and M is the number of timestamps. Let’s have a look at these files.

# Location data
data(locs)
head(locs)
#>     id      lat       lon
#> 1 3804 39.35000 -81.43333
#> 2 3809 36.01667 -89.40000
#> 3 3810 35.73333 -81.38333
#> 4 3811 35.60000 -88.91666
#> 5 3812 35.43333 -82.48333
#> 6 3813 32.70000 -83.65000
dim(locs)
#> [1] 328   3

# Timestamp data
data(Times)
head(Times)
#>   julian year month day
#> 1 726834 1990     1   1
#> 2 726835 1990     1   2
#> 3 726836 1990     1   3
#> 4 726837 1990     1   4
#> 5 726838 1990     1   5
#> 6 726839 1990     1   6
dim(Times)
#> [1] 1461    4

# Spatio-temporal data
data(Tmax)
dim(Tmax) 
#> [1] 1461  328

For this dataset, NA values are stored as -9999. First we select part of the dataset by subsetting the time component. We select the appropriate rows in both Times and Tmax.

temp_part <- with(Times, paste(year, month, day, sep = "-"))
# Selecting the dates from 0992-07-01 to 1992-07-31
temp_part <- data.frame(date = as.Date(temp_part)[913:943])
Tmax2 <- Tmax[913:943, ]

Now we can plot the semi-variogram. Under the hood we use the function variogram in the package gstate to produce the semi-variogram. We include a bin size of 50km and up to 7 timelags. We can specify if we need to remove the linear trend of the latitude or longitude. First, let’s see what happens without removing the linear trend.

semiv <- semivariogram(locs,
        temp_part,
        Tmax2,
        latitude_linear = FALSE,
        longitude_linear = FALSE,
        missing_value = -9999,
        width = 50,
        cutoff = 1000,
        tlagmax = 7
)
autoplot(semiv)

Now let’s see what happens if we account for a linear trend in latitude.

semiv<- semivariogram(locs,
        temp_part,
        Tmax2,
        latitude_linear = TRUE,
        longitude_linear = FALSE,
        missing_value = -9999,
        width = 50,
        cutoff = 1000,
        tlagmax = 7)
autoplot(semiv)

What about longitude?

semiv <- semivariogram(locs,
        temp_part,
        Tmax2,
        latitude_linear = TRUE,
        longitude_linear = TRUE,
        missing_value = -9999,
        width = 50,
        cutoff = 1000,
        tlagmax = 7)
autoplot(semiv)

We see a big reduction in dark red squares when we include latitude, but not so much when we include both latitude and longitude. That means, there is a spatial component that we need to account for.

Empirical Orthogonal Functions (EOF)

Empirical orthogonal functions are the PCA equivalent to spatio-temporal data.

\[Z(x,y,t) = \sum_{k = 1}^N \lambda_k PC_k(t) \times EOF_k(x,y)\] Here the multiplication denotes the outer product. That is for each value of \(PC_k(t)\), \(EOF_k(x,y)\) gets multiplied by it and we get a 3D data cube. By adding \(N\) such data cubes we get \(Z\) back.

data(SSTlonlatshort)
data(SSTdatashort)
data(SSTlandmaskshort)
# Take first 396 months (33 years) and delete land
delete_rows <- which(SSTlandmaskshort  ==  1)
SSTdatashort   <- SSTdatashort[-delete_rows, 1:396]
eoff <- emp_orth_fun(SSTlonlatshort[-delete_rows,  ],
                     SSTdatashort)
autoplot(eoff)

That was the first EOF and PC time series. Let’s look at the second EOF and associated time series.

autoplot(eoff, EOF_num = 2)

EOF is used as a dimension reduction tool.

The matrix below shows the correlation between the first 3 PC time series. The off diagonal elements are very close to zero. Just like in PCA, the PC time series are uncorrelated.

pcs <- eoff$pcts %>% 
  select(t, EOF, nPC) %>% 
  pivot_wider(names_from = EOF, values_from = nPC) %>%
  select(-t)

cormat <- cor(pcs)[1:3, 1:3]
cormat
#>               X1            X2           X3
#> X1  1.000000e+00 -6.948138e-18 1.801647e-17
#> X2 -6.948138e-18  1.000000e+00 4.341978e-17
#> X3  1.801647e-17  4.341978e-17 1.000000e+00

Spatio-temporal Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is used to identify associations between two sets of variables. For example suppose \(X_1, X_2, X_3\) are variables from a psychological survey of a set of participants. Let \(Y_1, Y_2, Y_3, Y_4\) be a set of university grades of the same set of participants. CCA finds new variables that maximizes the correlation between X and Y sets of variables. For example CCA finds \(U_1 = a_1 X_1 + a_2 X_2 + a_3 X_3\) and \(V_1 = b_1 Y_1 + b_2 Y_2 + b_3 Y_3 + b_4 Y_4\) such that correlation between \(U_1\) and \(V_1\) is maximized. Similarly, it finds \(U_2\) and \(V_2\) and so on.

In the spatio-temporal setting we can either use two different datasets or we can use a lagged version of the same dataset. But, we cannot implement the CCA on raw data because for most datasets the unique time points \(T < n\) where \(n\) gives the number of spatial points. Because of this we carry out CCA on Empirical Orthogonal Functions of the data. Recall that EOFs find the temporal PC vectors and the spatial EOF maps.

\[Z(x,y,t) = \sum_{k = 1}^N \lambda_k PC_k(t) \times EOF_k(x,y)\] We take a number of temporal PCs and compute CCA on these. We can do it in two ways. If we’re interested in future predictions of the same data, we can use a lagged dataset. In this case, our first dataset will have \(PC_k(t)\) for \(1 \leq t \leq (T - \ell)\) for all \(k\) where \(l\) denotes the lag, and the second dataset will have \(PC_k(t)\) for \((\ell +1) \leq t \leq T\) for all \(k\). This analysis will give us the new variables. We will plot the first new temporal variable for the first dataset and the first new temporal variable for the second dataset.

data(SSTlonlatshort)
data(SSTdatashort)
cc1 <- cancor_eof(x = SSTlonlatshort,
           lag = 7,
           n_eof = 8,
           values_df = SSTdatashort)
#> Canonical Correlation Analysis is carried out using a lagged dataset.
autoplot(cc1)

We can also produce weighted maps. We can weight the EOF maps \(EOF_k(x,y)\) by CCA coefficients. The map above is such a weighted map.