1.1Districts

Districts correspond to official census districts in cities; however, in those with lower population density, they are grouped together. In rural areas, one district is often equal to a municipality, but municipalities with low population are combined into larger units to preserve privacy of individuals in the dataset. Therefore, there are 2850 ‘districts’ compared to the 10494 official census districts on which they are based.

To access it:

districts_v1<-spod_get_zones("dist", ver=1)

Thedistricts_v1 object is of classsf consisting of polygons.

Data structure:

1.2Municipalities

Municipalities are made up of official municipalities in those of a certain size; however, they have also been aggregated in cases of lower population density. As a result, there are 2,205 municipalities compared to the 8,125 official municipalities on which they are based.

To access it:

municipalities_v1<-spod_get_zones("muni", ver=1)

The resultingmunicipalities_v1 object is typesf consisting of polygons.

Data structure:

The spatial data you get viaspanishoddata package is downloaded directly from the source, the geometries of polygons are automatically fixed if there are any invalid geometries. The zone identifiers are stored inid column. Apart from thatid column, the original zones files do not have any metadata. However, as seen above, using thespanishoddata package you get many additional columns that provide a semantic connection between official statistical zones used by the Spanish government and the zones you can get for the v2 data (for 2022 onward).

2.1. Origin-Destination data

The origin-destination data contain the number of trips and distance travelled betweendistricts ormunicipalities in Spain for every hour of every day between 2020-02-14 and 2021-05-09. Each flow also has attributes such as the trip purpose (composed of the type of activity (home/work_or_study/other) at both the origin and destination), province of residence of individuals making this trip, distance covered while making the trip. See the detailed attributes below in a table.Figure 2 shows an example of total flows in the province of Barcelona on Feb 14th, 2020.

Here are the variables you can find in both thedistrict andmunicipality level origin-destination data:

Getting the data

To access the data, use thespod_get() function. In this example we will use a short interval of dates:

dates<-c(start="2020-02-14", end="2020-02-17")od_dist<-spod_get(type="od", zones="dist", dates=dates)od_muni<-spod_get(type="od", zones="muni", dates=dates)

The data for the specified dates will be automatically downloaded and cached in theSPANISH_OD_DATA_DIR directory. Existing files will not be re-downloaded.

Working with the data

The resulting objectsod_dist andod_muni are of classtbl_duckdb_connection¹. Basically, you can treat these as regulardata.frames ortibbles. One important difference is that the data is not actually loaded into memory, because if you requested more dates, e.g. a whole month or a year, all that data would most likely not fit into your computer’s memory. Atbl_duckdb_connection is mapped to the downloaded CSV files that are cached on disk and the data is only loaded in small chunks as needed at the time of computation. You can manipulateod_dist andod_muni usingdplyr functions such asselect(),filter(),mutate(),group_by(),summarise(), etc. In the end of any sequence of commands you will need to addcollect() to execute the whole chain of data manipulations and load the results into memory in an Rdata.frame/tibble like so:

library(dplyr)od_mean_hourly_trips_over_the_4_days<-od_dist|>group_by(hour)|>summarise(    mean_hourly_trips=mean(n_trips, na.rm=TRUE),    .groups="drop")|>collect()od_mean_hourly_trips_over_the_4_days

# A tibble: 24 × 2   hour mean_hourly_trips       <int>             <dbl> 1        18              21.4 2        10              19.3 3         2              14.8 4        15              19.8 5        11              19.9 6        16              19.6 7        22              20.9 8         0              18.6 9        13              21.110        19              22.5# ℹ 14 more rows# ℹ Use `print(n = ...)` to see more rows

In this example above, we calculated mean hourly flows over the 4 days of the requested period. The full data for all 4 days was probably never loaded into memory all at once. Rather the available memory of the computer was used up to its maximum limit to make that calculation happen, without ever exceeding the available memory limit. If you were doing the same opearation on 100 or even more days, it would work in the same way and would be possible even with limited memory. This is done transparantly to the user with the help ofDuckDB (specifically, with{duckdb} R packageMühleisen and Raasveldt (2024)).

The same summary operation as provided in the example above can be done with the entire dataset for the full 18 month on a regular laptop with 8-16 GB memory. It will take a bit of time to complete, but it will be done. To speed things up, please also see thevignette on converting the data into formats that will increase the analsysis performance.

2.2. Population by trip count data

The population by trip count data shows the number of individuals in each district or municipality, categorized by the trips they make: 0, 1, 2, or more than 2.

Getting the data

To access it usespod_get() withtype set to “number_of_trips”, or just “nt”. We can also setdates to the maximum possible date range2020-02-14 to2021-05-09 to get all the data, as this data is relatively small (under 200 Mb).

dates<-c(start="2020-02-14", end="2021-05-09")nt_dist<-spod_get(type="number_of_trips", zones="dist", dates=dates)

Because this data is small, we can actually load it completely into memory:

nt_dist_tbl<-nt_dist|>dplyr::collect()