5 Growth Profiles

This chapter shows how to chart growth profiles.

In the previous chapter, we saw how to use chart_annual_growth_by() to examine relative changes in estimates of emissions and throughputs. We can also use chart_annual_growth_by() that have already been “normalized”, like growth profiles.

This chapter also introduces several new functions:

  • DB_growth_profiles()
  • DB_growth_profile_crosswalk()
  • DB_raw_growth_profiles()
  • chart_annual_quantities_by()

We’ll use the ones beginning with DB_ to fetch some growth profile data stored in DataBank. And, we’ll use chart_annual_quantities_by() to plot the raw (non-normalized) numbers behind a specific growth profile.

5.1 Normalized growth factors

Let’s start by pulling BY2011 growth factors from DataBank. DB_growth_profiles() takes care of normalizing them, making the assumption that BY2011 should be normalized to CY2011.

#
# `DB_growth_profiles()` automatically normalizes BY2011 profiles to CY2011. If
# you would like them normalized to a different year, just alter the value of
# `ref_year`, below.
#
# `DB_growth_profiles()` also does the work of linking profiles to categories,
# consistent with the configuration stored in DataBank for the given base year.
# 
BY2011_growth_profile_data <-
  BY(2011) %>%
  DB_growth_profiles(
    years = CY(1990:2030),
    verbose = TRUE)

With these in hand, we can chart the profile for BY2011 category #283 “Residential Combustion”. We use the same function, chart_annual_growth_by(), that we learned about in the previous chapter.

#
# Chart the relative growth in these growth factors. 
#
BY2011_growth_profile_data %>%
  filter_categories(
    "#283 Space Heating" = 283) %>%
  chart_annual_growth_by(
    color = cnty_abbr,
    verbose = TRUE,
    base_year = CY(2011),
    flag_years = CY(1993, 2011, 2030),
    title = "Residential NG Combustion: Space and Water Heating",
    subtitle = str_c(
      "This is the growth profile associated with category #283 in BY2011.",
      "There is a +9% change from CY2011 to CY2030.",
      sep = "\n"))

5.2 County-specific growth profiles

The above profile for natural gas usage doesn’t vary by county, but we can look at one that does. In DataBank, the growth profile associated with BY2011 category #761 “Sanitary Sewers” is a good example.

#
# Chart the *raw* growth profile data for profile #657 "Household Population".
# Profile #657 was used to project BY2011 category #761 "Sanitary Sewers".
# 
BY2011_growth_profile_data %>%
  filter_categories(
    "#761 Sanitary Sewers" = 761) %>%
  chart_annual_growth_by(
    color = cnty_abbr,
    base_year = CY(2011),
    title = "Household Population (Growth Profile #657)",
    subtitle = 'Used to project BY2011 category #761 "Sanitary Sewers".')

5.3 Raw growth profile data

What if we wanted to chart the un-normalized (“raw”) growth profile data?

  • First, we can pull the raw numbers, using DB_raw_growth_profiles() instead of DB_growth_profiles().
  • Then, we find the profile associated with a given BY2011 category ID, using DB_growth_profile_crosswalk().
  • Finally, we can use chart_annual_quantities(), rather than chart_annual_growth(), to plot the numbers. This ensures that the numbers aren’t normalized in the process of creating the chart.

5.3.1 Pulling raw growth profile data

Unlike DB_growth_profiles(), DB_raw_growth_profiles() does not normalize anything. It just assembles and returns whatever is contained in t0335, t0336, and t0337. Those numbers might be:

  • already normalized to CY2011; or
  • already normalized to a previous “base year” (like CY2008); or
  • in some natural unit, like “persons” or “acres”.

(It varies from profile to profile.)

#
# By default, `BY2011_raw_growth_profile_data()` returns nested results.
# Those are one row per profile, not one row per (profile, year, county). 
# 
# Here, we override that with `nested = FALSE`.
#
BY2011_raw_growth_profile_data <-
  BY(2011) %>%
  DB_raw_growth_profiles(
    nested = FALSE,
    years = CY(1990:2030),
    verbose = TRUE)

5.3.2 Finding the profile associated with a BY2011 category ID

Let’s look up the growth profile associated with BY2011 category #761 “Sanitary Sewers”.

#
# Figure out the ID of the growth profile that's been associated with BY2011
# category #761 "Sanitary Sewers".
# 
BY(2011) %>%
  DB_growth_profile_crosswalk(
    verbose = TRUE) %>%
  filter_categories(
    "#761 Sanitary Sewers" = 761)
cat_id cat_h0 gpf_id backcast forecast category
761 Area Source 657 TRUE TRUE #761 Sanitary Sewers

Above, we can see that the relevant growth profile ID is #657. Now we can look up the metadata associated with it: gpf_name (name), gpf_staff (staff), etc.

BY2011_raw_growth_profile_data %>%
  filter(
    gpf_id == 657) %>%
  distinct(
    gpf_id,
    gpf_name,
    gpf_staff,
    gpf_date)
gpf_id gpf_name gpf_staff gpf_date
657 Household Population Minh H Nguyen 2010-02-25

The name associated with this profile turns out to be “Household Population”. It was last updated, according to DataBank, on 2010-02-25.

5.3.3 Charting the un-normalized (“raw”) growth profile data

#
# Still charting `BY2011_raw_growth_profile_data`, but using
# `chart_annual_quantities_by()` instead of `chart_annual_growth_by()`.
#
# Now we can see the offsets, as well as the slopes, that are in the raw data.
#
# If we knew their units, we could label the y-axis ... but we don't.
# 
BY2011_raw_growth_profile_data %>%
  filter(
    gpf_id == 657) %>%
  chart_annual_quantities_by(
    color = cnty_abbr,
    flag_years = CY(2011),
    flag_labels = "{format(gf_qty, big.mark = ',', digits = 4, trim = TRUE)}",
    title = "Household Population (Growth Profile #657)",
    subtitle = 'Used to project BY2011 category #761 "Sanitary Sewers".')

5.3.4 A note on county fractions

Why are the above lines offset from each other, even at CY2011? Because we’re plotting the raw growth profile data! (Since this profile is labeled “Household Population”, we might presume that the units are in persons. Without going into further detail, these numbers do seem consistent with Bay Area county populations.)

The offsets are interesting — they’re not junk. See the Appendix to learn how to compute county fractions from raw growth profile data. (This is also a good way to double-check county fractions.)