exploring.md

Exploring OD2017 data

I create this separated document to be the main reference on the exploratory data analysis. The data set is at the trip level and I take advantage of the survey design and use the expansion factor (for the trips) in all the graphs and analysis here. The survey implies that there are 4.200666^{7} trips/day in the São Paulo Metro Region.

Key variables

The data set contains a lot of socioeconomic variables. Since our interest relies on trips, flows and mode choices, the analyses so far are at the trip level. In this context, the variables that contain a the modes used in each trip are the main ones (modo1, modo2, modo3, and modo4). There is also the coordinates for each residence (co_dom_x and co_dom_y) and workplaces (co_tr1_x and co_tr1_y), and obviously the origin and destination zones and coordinates (e.g. zona_o, co_o_x, co_o_y) and euclidean distance. As you can see in the graph below, the vast majority of trips are made using just one mode. I use the main mode used in the trip (modoprin) in the remaining graphs.

Recategorisation of modes

I tried two different recategorisations (see code for the choices).The first is based on the “vehicle” itself, and the other relies on the “role” of the individual while in the trip.

Now the A/B Streets classification (foot, bike, car, public transport and other):

Now the mode share by distance (using the first recategorisation). The average trips’ distance is 5305.0981626.

#> `summarise()` has grouped output by 'dist_bands'. You can override using the
#> `.groups` argument.

#> `summarise()` has grouped output by 'dist_bands'. You can override using the
#> `.groups` argument.

#> `summarise()` has grouped output by 'dist_bands'. You can override using the
#> `.groups` argument.

Number of trips / reason

#> # A tibble: 11 × 2
#>                          motivo         n
#>                       <dbl+lbl>     <dbl>
#>  1  1 [Trabalho Indústria]       2924869.
#>  2  2 [Trabalho Comércio]        3523074.
#>  3  3 [Trabalho Serviços]       12064328.
#>  4  4 [Escola/Educação]         14670928.
#>  5  5 [Compras]                  1929215.
#>  6  6 [Médico/Dentista/Saúde]    1827264.
#>  7  7 [Recreação/Visitas/Lazer]  1816626.
#>  8  9 [Procurar Emprego]          180089.
#>  9 10 [Assuntos Pessoais]        2496314.
#> 10 11 [Refeição]                  573954.
#> 11 NA                                  0

#> # A tibble: 3 × 2
#>   motivo_simples         n
#>   <chr>              <dbl>
#> 1 Escola         14670928.
#> 2 Outro           8823462.
#> 3 Trabalho       18512270.

Basic temporal analysis

Distributions of trips in the day

Basic geographic analysis (now with times )

Here I plot the main zones of origin and destination using the total number of trips – always using the survey’s expansion factor. It is interesting to note that there is no centrality pattern when we look at the main Zones of Destination. Maybe we should restrict the analysis to specific types of trips (e.g., commute strictly defined). I checked these results with the official Tables and they are the same in terms of the main zones of origin and destination.

#> Using year 2010
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Plotting the origin zones by time.

Plotting the destination zones by time.

Looking at the São Paulo City. First, the origins.

Now, the destinations for São Paulo City.

Distances by zone of residence

#> `summarise()` has grouped output by 'id_pess', 'zona'. You can override using
#> the `.groups` argument.
#> `summarise()` has grouped output by 'zona'. You can override using the
#> `.groups` argument.
#> `summarise()` has grouped output by 'id_pess', 'zona'. You can override using
#> the `.groups` argument.
#> `summarise()` has grouped output by 'zona'. You can override using the
#> `.groups` argument.

Looking at the population data here.

Comparing the population with the Census (2010) using census tracts.

Some people left São Paulo City, but is not significant relative to the total.

Active travels and trip patterns (Desire lines)

(I removed the previous visualizations since they were not saying anything about the data.)

Walking and cycling are highly concentrated in few OD pairs (>90% in 1,000 pairs for walking and >90% in 500 pairs for cycling). Car trips are more dispersed, we have >90% of trips in 10,000 pairs.

Bike trips:

Foot trips:

Car trips, still too many lines (10,000) to account for >92% of total car trips.

Using the first 5,000 OD pairs, we have >78% of all car trips.

Using the biggest 1,000 pairs, we have >45% of all car trips.

Main routes by mode (takes a long time here) – rewriting this part.

Plotting the routes

Load the routes I calculated.

something wrong here, check later…

Infrastructure data (São Paulo municipality only)

obs.: very strange behavior here: st_buffer + st_make_valid makes one single polygon very small. without st_buffer, R does not even plot – “C stack usage too close to …”. These operations seems to be necessary in the other geometries.

OD pairs that generate the most intensive usage of space (kms traveled in the routes) have more area allocated to the transport mode (both foot and car)

Very strong pattern here.

Some outliers in this graph, may be worth to check later…