Uptake model

[Robinlovelace]

With something like:

# m = glm(formula = pcycle ~ distance + sqrt(distance) + gradient + circuity_cycling + road_speeds1,
#     family = quasibinomial)

[joeytalbot]

For this model, I suggest using the new development OAs we have identified to extract the proportion of people who walk/cycle, but using the LSOAs they sit within for the travel to work routing. Does that sound workable?

[Robinlovelace]

That sounds like a good plan, although there is OA-WPZ data available: https://www.nomisweb.co.uk/census/2011/wf02ew

[joeytalbot]

I think the amount of routing that would require is too much. Is there a simple way of looking up which LSOA each OA is in?

[Robinlovelace]

I think the amount of routing that would require is too much. Is there a simple way of looking up which LSOA each OA is in?

Good point, and with the LSOA - LSOA level analysis we can use the PCT routes as a quick proxy. I suggest:

sf::st_join(oa, lsoa)

To add LSOA variables to OA data.

[mem48]

There are oa to lsoa lookup tables on open geography portal

[Robinlovelace]

Another dataset we can use: https://data.london.gov.uk/dataset/property-build-period-lsoa

[joeytalbot]

This dataset looks amazing! @Robinlovelace there really are data on age of housing stock for LSOAs across the country, not just in London. For London itself there's also data comparing housing stock age to council tax band, and data on dwelling type and number of bedrooms.

[Robinlovelace]

Great data exploring @joeytalbot I plan to start a script that will read them in, who knows could eventually be an ACTON function!

[joeytalbot]

It certainly seems like it would make an appropriate function!

[Robinlovelace]

Heads-up @joeytalbot I've added a script that gets the data and deals with the pesky commas. Please take a look and work from that. Downing tools on this project for the day 🔨 .

[joeytalbot]

Ok, so with that data there are 84 LSOAs across England and Wales containing >90% homes built in the years 2000-09. These could go straight into a model (excluding 20% of them for validation purposes), or I could look at a wider selection of LSOAs (eg there are 326 with >60% of homes built 2000-09) and then pick out the OAs within them that relate to the new developments - although if the remaining homes are mostly built during the 1990s this is probably still fine.

The next step is to get OD data - what's the best source for nationwide OD data, without having to download each region separately?

[Robinlovelace]

MSOA data:

od_data = pct::get_od()
#> No region provided. Returning national OD data.
#> Parsed with column specification:
#> cols(
#>   `Area of residence` = col_character(),
#>   `Area of workplace` = col_character(),
#>   `All categories: Method of travel to work` = col_double(),
#>   `Work mainly at or from home` = col_double(),
#>   `Underground, metro, light rail, tram` = col_double(),
#>   Train = col_double(),
#>   `Bus, minibus or coach` = col_double(),
#>   Taxi = col_double(),
#>   `Motorcycle, scooter or moped` = col_double(),
#>   `Driving a car or van` = col_double(),
#>   `Passenger in a car or van` = col_double(),
#>   Bicycle = col_double(),
#>   `On foot` = col_double(),
#>   `Other method of travel to work` = col_double()
#> )
#> Parsed with column specification:
#> cols(
#>   MSOA11CD = col_character(),
#>   MSOA11NM = col_character(),
#>   BNGEAST = col_double(),
#>   BNGNORTH = col_double(),
#>   LONGITUDE = col_double(),
#>   LATITUDE = col_double()
#> )
od_data
#> # A tibble: 2,402,201 x 18
#>    geo_code1 geo_code2   all from_home light_rail train   bus  taxi motorbike
#>    <chr>     <chr>     <dbl>     <dbl>      <dbl> <dbl> <dbl> <dbl>     <dbl>
#>  1 E02000001 E02000001  1506         0         73    41    32     9         1
#>  2 E02000001 E02000014     2         0          2     0     0     0         0
#>  3 E02000001 E02000016     3         0          1     0     2     0         0
#>  4 E02000001 E02000025     1         0          0     1     0     0         0
#>  5 E02000001 E02000028     1         0          0     0     0     0         0
#>  6 E02000001 E02000051     1         0          1     0     0     0         0
#>  7 E02000001 E02000053     2         0          2     0     0     0         0
#>  8 E02000001 E02000057     1         0          1     0     0     0         0
#>  9 E02000001 E02000058     1         0          0     0     0     0         0
#> 10 E02000001 E02000059     1         0          0     0     0     1         0
#> # … with 2,402,191 more rows, and 9 more variables: car_driver <dbl>,
#> #   car_passenger <dbl>, bicycle <dbl>, foot <dbl>, other <dbl>,
#> #   geo_name1 <chr>, geo_name2 <chr>, la_1 <chr>, la_2 <chr>

^{Created on 2020-04-03 by the reprex package (v0.3.0)}

[Robinlovelace]

LSOA data:

library(sf)
u = "https://github.com/npct/pct-outputs-national/raw/master/commute/lsoa/l_all.Rds"
desire_lines_lsoa_national_sp = readRDS(url(u))
l = sf::st_as_sf(desire_lines_lsoa_national_sp)
plot(l[sample(nrow(l), size = 10000), ])

[Robinlovelace]

Output:

library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 7.0.0
u = "https://github.com/npct/pct-outputs-national/raw/master/commute/lsoa/l_all.Rds"
desire_lines_lsoa_national_sp = readRDS(url(u))
l = sf::st_as_sf(desire_lines_lsoa_national_sp)
#> Loading required package: sp
#> Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is
#> deprecated. It might return a CRS with a non-EPSG compliant axis order.
plot(l[sample(nrow(l), size = 10000), ])
#> Warning: plotting the first 10 out of 141 attributes; use max.plot = 141 to plot
#> all

^{Created on 2020-04-03 by the reprex package (v0.3.0)}