An R package to analyse potential superblocks. The following example demonstrates the main functionality.

With new text.

An example of the “Hansaviertel” of Münster, Germany. Note that this entire document is dynamically-generated from the four lines of code shown immediately below. All numerical values described in the text are then taken from the actual data as downloaded from Open Street Map (OSM) at the time the document was generated.

The first steps are to load the package and extract the necessary data:

library (superblock)
bbox <- c (7.63099, 51.95048, 7.66402, 51.96142)
hw_names <- c ("Bremer Straße", "Bremer Platz", "Wolbecker Straße", "Hansaring")
osmdat <- sb_osmdata_extract (bbox, hw_names)

The analyses described in more detail below produce the following summary statistics:

• Hansaviertel covers 23 Hectares
• 7.4% is public open space
• 15.7% is occupied by roads, of which:
  • 39.7% is used for parking cars, and
  • 27.7% is available for general public usage
• There are only enough car parking spaces for 8.6% of all residents, who thereby occupy 39.7% of the total open street space.

There are several large public parking facilities very close to Hansaviertel, and so cars could easily park there and free up this 39.7% of the street space for use by everybody. That would increase the total proportion of available public space by 97%. The average time needed to drive to one of these parking facilities and walk back home within Hansaviertel is 3.5 minutes. The results below show that times needed to drive around searching for parking spaces generally exceed that once around 90% of all parking spaces are full.

Moreover, this search for the 620 available parking spaces within Hansaviertel amounts to the 8.6% of residents who are able to park driving a total distance of around 186 km every evening to fill these spaces. Were they to park in the nearby parking facilities, this total distance would reduce to only 49 km.

By this measure, use of nearby public parking facilities would be 74% more efficient than this 39.7% of available street space being otherwise used by only 8.6% of all residents. Using nearby public parking facilities would also increase space available for general public usage within Hansaviertel by 97%.

Most of these results can easily be generated by calling the sb_summary() function:

sb_summary (osmdat)
#> • Total area = 22.8 hectares
#> • Proportion buildings: 36.6%
#> • Proportion open space: 7.36%
#> • Proportion roads: 15.7%, of which:
#>   • 32.5% is for car roads
#>   • 39.7% is for parked cars, and
#>   • 27.7% remains for everybody else.
#> • There are street parking spaces for 8.6% of all residents.
#> • So 8.6% of residents occupy 39.7% of all space
#> 
#> • Total proportion of public space: 11.7
#> • Total proportion of public space as superblock: 23.1
#> • Increase in public space as superblock: 97%

The additional statistics described above on distances driven and times taken to search for parking spaces are generated from functions described in the following section.

There are two additional functions to estimate how long people spend driving around searching for car parking spaces, and how far they actually drive.

The first function estimates the time taken on average for each resident of the superblock to find a car parking space, as a function of the proportion of available car parking spaces. Results are based on simulations using actual data for the specified superblock to estimate how long it takes on average to fill all of the remaining parking spaces.

The results are compared to equivalent times to drive to the nearest large parking facility (“garage” in the graph below), to park a car, and then walk back to the place of residence.

times <- sb_parking_times (osmdat)
plot (times)

The results shows how long it takes to find a parking space for an average attempt (“park(50%)”), for one out of every five attempts (“park(80%)”, or one working day per week), and for one out of every ten attempts (“park(90%)”, or one working day every two weeks). Above around 97-98% occupancy, which is the general state most nights, an “average” attempt may still take under 5 minutes, yet one out of four times will take 10 minutes or more, and one out of ten times it will take more than half an hour. The horizontal lines shows the time it would take on average for all residents of the area to drive directly to a nearby large parking facility (“garage”), park, and walk back. Beyond around 95% occupancy, it is always quicker to park in a garage and walk back.

The average times to find parking spaces shown above are only high in extreme cases (the 90% line), and when almost all parks are full. Nevertheless, most parking spaces are indeed completely full most nights of the week, suggesting the some people must experience these extremes most days. An additional function generates a more useful “overview” statistic by estimating the total distances driven by everybody while filling all parking spaces, assuming that 70% of parking spaces are initially full. This is equivalent to assuming that 30% of residents use their cars on a daily basis, with results for other initial proportions readily seen from the graph below.

d <- sb_parking_km_per_empty (osmdat)
plot (d$prop_full, d$d,
    type = "l", col = "red", lwd = 2,
    xlab = "Initial proportion filled",
    ylab = "Distance per empty car park until completely filled (in km)"
)

Because filling the final few spaces takes so long, as shown in the first graph above, and people have to drive so far to fill them, the overall distance driven to fill all spaces is actually quite long. The block has around 620 parking spaces in total, so with an initial proportion filled of 70%, people have to drive a total of around 1km times 0.3 × 620 = 186km. While this may seem extreme, consider that most of this distance is covered by the last few people driving to find parking spaces, who are forced to drive several kilometres. The longest street within the block is 1.3km, while one of the surrounding streets is 3.0km. Ten people driving along both of those streets will cover 43km, which is 23% of the total distance of 186 km covered by that same number of drivers.

The average distance needed to be driven to the public parking facility nearest to every building in Hansaviertel is only 0.3km, so the 186 people searching for parking spaces would have to drive only 49km in total. Thus, as summarised at the outset, the use of nearby parking facilities would be 74% more efficient than parking within Hansaviertel itself.

In terms of time, if the average driving speed while searching for a parking space is 20km/hr, then those 186 people each take an average of 1/20th of an hour, or only 3 minutes, to find a park, yet aggregated over all free parking spaces, that amounts to a total of 9.3 hours for everybody to fill all remaining car parks.